diff --git a/.gitignore b/.gitignore
index dcfc4b40f..c8450e952 100644
--- a/.gitignore
+++ b/.gitignore
@@ -134,3 +134,6 @@ Untitled*
 postgres_metadata
 postgres_superset
 node_modules
+
+# Local Claude Code workspace state (large; never committed)
+.claude/
diff --git a/datajunction-clients/python/datajunction/client.py b/datajunction-clients/python/datajunction/client.py
index ef9867264..f236ca422 100644
--- a/datajunction-clients/python/datajunction/client.py
+++ b/datajunction-clients/python/datajunction/client.py
@@ -207,31 +207,7 @@ def plan(
         include_temporal_filters: bool = False,
         lookback_window: str | None = None,
     ):
-        """
-        Returns a query execution plan for the given metrics and dimensions.
-
-        The plan shows:
-        - grain_groups: How metrics are grouped and their intermediate SQL
-        - metric_formulas: How each metric combines its components
-        - requested_dimensions: The dimensions being queried
-
-        This is useful for understanding how DJ decomposes metrics into
-        atomic aggregations and how multiple fact tables are joined together.
-
-        Args:
-            metrics: List of metric names to include
-            dimensions: List of dimensions to group by
-            filters: List of filter expressions
-            cube: Optional cube node name. When provided, the cube's stored
-                filters are automatically prepended to the query filters.
-            dialect: SQL dialect (e.g., 'spark', 'trino'). Defaults to engine dialect.
-            use_materialized: Whether to use materialized tables when available
-            include_temporal_filters: Whether to include temporal partition filters.
-                Only applies if the metrics and dimensions resolve to a cube with
-                temporal partitions.
-            lookback_window: Lookback window for temporal filters (e.g., '3 DAY',
-                '1 WEEK'). Only applicable when include_temporal_filters is True.
-        """
+        """Returns a query execution plan for the given metrics and dimensions."""
         params: dict = {
             "metrics": metrics,
             "dimensions": dimensions or [],
diff --git a/datajunction-clients/python/datajunction/deployment.py b/datajunction-clients/python/datajunction/deployment.py
index 596d23e96..68142ac7c 100644
--- a/datajunction-clients/python/datajunction/deployment.py
+++ b/datajunction-clients/python/datajunction/deployment.py
@@ -342,30 +342,7 @@ def build_codeowners(
         default_owner: str | None = None,
         exclude_dirs: list[str] | None = None,
     ) -> int:
-        """
-        Generate a CODEOWNERS file from the owners fields in DJ node YAML files.
-
-        Walks base_dir recursively, reads every *.yaml file (skipping dj.yaml),
-        and maps each file path to its owners list.  Files with no owners are
-        omitted.  Paths in the output are relative to base_dir and prefixed with
-        / so GitHub resolves them from the repo root.
-
-        If github_api_url is provided (and GITHUB_TOKEN / github_token_env is set),
-        email addresses in owners fields are resolved to GitHub usernames via the
-        search API.  Unresolvable emails are emitted as-is with a warning comment.
-
-        ``default_owner``, when set, emits a leading ``* <default_owner>`` rule so
-        unmatched files (and any excluded directories) fall through to it. Because
-        CODEOWNERS is last-match-wins, the per-file rules below it still take
-        precedence for the files they name.
-
-        ``exclude_dirs`` lists directories (relative to base_dir) whose nodes are
-        NOT given per-file owners — they fall through to ``default_owner`` instead.
-        This is for machine-generated trees (e.g. ``nodes/generated``) that have no
-        individual human owner and should be team-owned as a block.
-
-        Returns the number of per-file entries written (excludes the default rule).
-        """
+        """Generate a CODEOWNERS file from the owners fields in DJ node YAML files."""
         base = Path(base_dir).resolve()
         excluded_dirs = [(base / d).resolve() for d in (exclude_dirs or [])]
 
diff --git a/datajunction-query/uv.lock b/datajunction-query/uv.lock
index 085cb0ecd..a166f9f24 100644
--- a/datajunction-query/uv.lock
+++ b/datajunction-query/uv.lock
@@ -1,10 +1,11 @@
 version = 1
 revision = 3
-requires-python = ">=3.10, <4.0"
+requires-python = ">=3.11, <4.0"
 resolution-markers = [
-    "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*'",
-    "python_full_version < '3.11'",
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version == '3.12.*'",
+    "python_full_version < '3.12'",
 ]
 
 [[package]]
@@ -23,7 +24,6 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mako" },
     { name = "sqlalchemy" },
-    { name = "tomli", marker = "python_full_version < '3.11'" },
     { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/49/cc/aca263693b2ece99fa99a09b6d092acb89973eb2bb575faef1777e04f8b4/alembic-1.18.1.tar.gz", hash = "sha256:83ac6b81359596816fb3b893099841a0862f2117b2963258e965d70dc62fb866", size = 2044319, upload-time = "2026-01-14T18:53:14.907Z" }
@@ -54,7 +54,6 @@ name = "anyio"
 version = "4.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
@@ -76,23 +75,11 @@ wheels = [
 name = "astroid"
 version = "4.0.3"
 source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
-]
 sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/c17d0f83016532a1ad87d1de96837164c99d47a3b6bbba28bd597c25b37a/astroid-4.0.3.tar.gz", hash = "sha256:08d1de40d251cc3dc4a7a12726721d475ac189e4e583d596ece7422bc176bda3", size = 406224, upload-time = "2026-01-03T22:14:26.096Z" }
 wheels = [
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 ]
 
-[[package]]
-name = "backports-asyncio-runner"
-version = "1.2.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893, upload-time = "2025-07-02T02:27:15.685Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313, upload-time = "2025-07-02T02:27:14.263Z" },
-]
-
 [[package]]
 name = "boto3"
 version = "1.42.34"
@@ -127,10 +114,8 @@ version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "os_name == 'nt'" },
-    { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
-    { name = "tomli", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/42/18/94eaffda7b329535d91f00fe605ab1f1e5cd68b2074d03f255c7d250687d/build-1.4.0.tar.gz", hash = "sha256:f1b91b925aa322be454f8330c6fb48b465da993d1e7e7e6fa35027ec49f3c936", size = 50054, upload-time = "2026-01-08T16:41:47.696Z" }
 wheels = [
@@ -164,18 +149,6 @@ dependencies = [
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diff --git a/datajunction-server/datajunction_server/alembic/versions/2026_08_24_0000-rg0001reaggregate_add_reaggregate_to_noderevision.py b/datajunction-server/datajunction_server/alembic/versions/2026_08_24_0000-rg0001reaggregate_add_reaggregate_to_noderevision.py
new file mode 100644
index 000000000..8a04b3fd2
--- /dev/null
+++ b/datajunction-server/datajunction_server/alembic/versions/2026_08_24_0000-rg0001reaggregate_add_reaggregate_to_noderevision.py
@@ -0,0 +1,27 @@
+"""
+Add reaggregate column to noderevision
+
+Revision ID: rg0001reaggregate
+Revises: cm0003dropowner
+Create Date: 2026-08-24 00:00:00.000000+00:00
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "rg0001reaggregate"
+down_revision = "cm0003dropowner"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    op.add_column(
+        "noderevision",
+        sa.Column("reaggregate", sa.JSON(), nullable=True),
+    )
+
+
+def downgrade():
+    op.drop_column("noderevision", "reaggregate")
diff --git a/datajunction-server/datajunction_server/alembic/versions/2026_09_15_0000-fm0001params_add_params_to_frozen_measures.py b/datajunction-server/datajunction_server/alembic/versions/2026_09_15_0000-fm0001params_add_params_to_frozen_measures.py
new file mode 100644
index 000000000..c00473a55
--- /dev/null
+++ b/datajunction-server/datajunction_server/alembic/versions/2026_09_15_0000-fm0001params_add_params_to_frozen_measures.py
@@ -0,0 +1,29 @@
+"""
+Add params column to frozen_measures
+
+Adds a JSON column to persist tuning parameters (e.g., accuracy) for sketch-backed measures.
+
+Revision ID: fm0001params
+Revises: rg0001reaggregate
+Create Date: 2026-09-15 00:00:00.000000+00:00
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "fm0001params"
+down_revision = "rg0001reaggregate"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    op.add_column(
+        "frozen_measures",
+        sa.Column("params", sa.JSON(), nullable=True),
+    )
+
+
+def downgrade():
+    op.drop_column("frozen_measures", "params")
diff --git a/datajunction-server/datajunction_server/api/cubes.py b/datajunction-server/datajunction_server/api/cubes.py
index 58e92f6a2..55616b86f 100644
--- a/datajunction-server/datajunction_server/api/cubes.py
+++ b/datajunction-server/datajunction_server/api/cubes.py
@@ -15,6 +15,9 @@
     build_combiner_sql_from_preaggs,
 )
 from datajunction_server.construction.build_v3.cte import strip_role_suffix
+from datajunction_server.construction.build_v3.cube_matcher import (
+    validate_cube_reaggregate_materialization,
+)
 from datajunction_server.construction.dimensions import build_dimensions_from_cube_query
 from datajunction_server.database.materialization import Materialization
 from datajunction_server.database.node import Node
@@ -30,7 +33,6 @@
     AccessDenialMode,
     get_access_checker,
 )
-from datajunction_server.models.access import ResourceAction
 from datajunction_server.internal.materializations import (
     build_cube_materialization,
     stop_cube_materialization_workflows,
@@ -40,6 +42,7 @@
     get_single_cube_revision_metadata,
 )
 from datajunction_server.internal.views import CubeViewNames, _build_view_body
+from datajunction_server.models.access import ResourceAction
 from datajunction_server.models.cube import (
     CubeRevisionMetadata,
     DimensionValue,
@@ -59,11 +62,10 @@
 )
 from datajunction_server.models.dialect import Dialect
 from datajunction_server.models.materialization import (
-    DRUID_AGG_MAPPING,
-    DRUID_SKETCH_TYPES,
     Granularity,
     MaterializationJobTypeEnum,
     MaterializationStrategy,
+    get_druid_aggregator_spec,
 )
 from datajunction_server.models.metric import TranslatedSQL
 from datajunction_server.models.node_type import NodeNameVersion
@@ -175,32 +177,56 @@ def _build_metrics_spec(
             if internal_name:
                 component = component_by_name.get(internal_name)
 
-        druid_type = "longSum"  # Default fallback
-
-        if component:
-            # Use merge function for pre-aggregated data, fall back to aggregation
-            agg_func = component.merge or component.aggregation
-            if agg_func:
-                key = (col.type, agg_func.lower())
-                if key in DRUID_AGG_MAPPING:
-                    druid_type = DRUID_AGG_MAPPING[key]
-
-        metric_spec = {
+        metric_spec = (
+            get_druid_aggregator_spec(
+                column_name=col.name,
+                column_type=col.type,
+                aggregation=component.aggregation,
+                merge=component.merge,
+                params=component.params,
+            )
+            if component
+            else None
+        )
+        # Unmappable measures fall back to longSum here, since we're loading
+        # pre-aggregated data; the materialization config omits them instead.
+        metric_spec = metric_spec or {
             "fieldName": col.name,
             "name": col.name,
-            "type": druid_type,
+            "type": "longSum",
         }
 
-        # HLL sketches need additional configuration
-        if druid_type in DRUID_SKETCH_TYPES:
-            metric_spec["lgK"] = 12  # Log2 of K, controls precision (4-21)
-            metric_spec["tgtHllType"] = "HLL_4"  # HLL_4, HLL_6, or HLL_8
-
         metrics.append(metric_spec)
 
     return metrics
 
 
+async def _validate_cube_reaggregate_materialization(
+    session: AsyncSession,
+    cube: Node,
+) -> None:
+    """
+    Validate materialization safety using full metric decomposition.
+    """
+    if not cube.current:  # pragma: no cover
+        return
+
+    from datajunction_server.construction.build_v3.builder import setup_build_context
+
+    ctx = await setup_build_context(
+        session=session,
+        metrics=cube.current.cube_node_metrics,
+        dimensions=cube.current.cube_node_dimensions,
+        filters=cube.current.cube_filters or None,
+        dialect=Dialect.SPARK,
+        use_materialized=False,
+    )
+    validate_cube_reaggregate_materialization(
+        cube.current,
+        decomposed_metrics=ctx.decomposed_metrics,
+    )
+
+
 @router.get("/cubes", name="Get all Cubes")
 async def get_all_cubes(
     *,
@@ -288,6 +314,7 @@ async def cube_materialization_info(
             message=f"Cube node `{name}` does not exist.",
             http_status_code=404,
         )
+    await _validate_cube_reaggregate_materialization(session, node)
     temporal_partitions = node.current.temporal_partition_columns()  # type: ignore
     if len(temporal_partitions) != 1:
         raise DJInvalidInputException(
@@ -520,6 +547,7 @@ async def materialize_cube(
             message=f"Cube '{name}' has no current revision",
             http_status_code=HTTPStatus.NOT_FOUND,
         )
+    await _validate_cube_reaggregate_materialization(session, node)
 
     cube_tps = cube_revision.temporal_partition_columns()
 
diff --git a/datajunction-server/datajunction_server/api/graphql/scalars/metricmetadata.py b/datajunction-server/datajunction_server/api/graphql/scalars/metricmetadata.py
index 536f54abd..802cb6ce1 100644
--- a/datajunction-server/datajunction_server/api/graphql/scalars/metricmetadata.py
+++ b/datajunction-server/datajunction_server/api/graphql/scalars/metricmetadata.py
@@ -1,6 +1,7 @@
 """Metric metadata scalars"""
 
 import strawberry
+from strawberry.scalars import JSON
 
 from datajunction_server.models.cube_materialization import (
     Aggregability as Aggregability_,
@@ -15,9 +16,15 @@
     MetricComponent as MetricComponent_,
 )
 from datajunction_server.models.node import MetricDirection as MetricDirection_
+from datajunction_server.models.reaggregate import (
+    DimensionReaggregateRule as DimensionReaggregateRule_,
+    ReaggregateSpec as ReaggregateSpec_,
+    ReaggregationFunction as ReaggregationFunction_,
+)
 
 MetricDirection = strawberry.enum(MetricDirection_)
 Aggregability = strawberry.enum(Aggregability_)
+ReaggregationFunction = strawberry.enum(ReaggregationFunction_)
 
 
 @strawberry.type
@@ -32,12 +39,48 @@ class Unit:
     abbreviation: str | None
 
 
+@strawberry.experimental.pydantic.type(
+    model=DimensionReaggregateRule_,
+    all_fields=True,
+)
+class DimensionReaggregateRule: ...
+
+
+@strawberry.experimental.pydantic.type(model=ReaggregateSpec_)
+class ReaggregateSpec:
+    """
+    Metric reaggregation declaration.
+
+    Fields are listed explicitly rather than via `all_fields` because `params`
+    is an open dict, which has no automatic GraphQL mapping.
+    """
+
+    fn: strawberry.auto
+    weight: strawberry.auto
+    rules: strawberry.auto
+    params: JSON | None = None
+
+
 @strawberry.experimental.pydantic.type(model=AggregationRule_, all_fields=True)
 class AggregationRule: ...
 
 
-@strawberry.experimental.pydantic.type(model=MetricComponent_, all_fields=True)
-class MetricComponent: ...
+@strawberry.experimental.pydantic.type(model=MetricComponent_)
+class MetricComponent:
+    """
+    A single measure with accumulate/merge phases.
+
+    Fields are listed explicitly rather than via `all_fields` because `params`
+    is an open dict, which has no automatic GraphQL mapping.
+    """
+
+    name: strawberry.auto
+    expression: strawberry.auto
+    aggregation: strawberry.auto
+    merge: strawberry.auto
+    rule: strawberry.auto
+    grain_alias: strawberry.auto
+    params: JSON | None = None
 
 
 @strawberry.experimental.pydantic.type(model=DecomposedMetric_, all_fields=True)
diff --git a/datajunction-server/datajunction_server/api/graphql/scalars/node.py b/datajunction-server/datajunction_server/api/graphql/scalars/node.py
index e92b7ffd3..24be9d652 100644
--- a/datajunction-server/datajunction_server/api/graphql/scalars/node.py
+++ b/datajunction-server/datajunction_server/api/graphql/scalars/node.py
@@ -31,7 +31,9 @@
 )
 from datajunction_server.api.graphql.scalars.metricmetadata import (
     DecomposedMetric,
+    DimensionReaggregateRule,
     MetricMetadata,
+    ReaggregateSpec,
 )
 from datajunction_server.api.graphql.scalars.user import User
 from datajunction_server.api.graphql.utils import extract_fields
@@ -45,6 +47,7 @@
 from datajunction_server.models.node import NodeMode as NodeMode_
 from datajunction_server.models.node import NodeStatus as NodeStatus_
 from datajunction_server.models.node import NodeType as NodeType_
+from datajunction_server.models.reaggregate import parse_reaggregate_spec
 from datajunction_server.sql.parsing.backends.antlr4 import ast, parse
 
 NodeType = strawberry.enum(NodeType_)
@@ -410,6 +413,29 @@ def materializations(
     # Only metrics will have these fields
     required_dimensions: list[Column] | None = None
 
+    @strawberry.field
+    def reaggregate(self, root: DBNodeRevision) -> ReaggregateSpec | None:
+        """
+        Metric reaggregation declaration.
+        """
+        if root.type != NodeType.METRIC:
+            return None
+        spec = parse_reaggregate_spec(root.reaggregate)
+        if not spec:
+            return None
+        return ReaggregateSpec(
+            fn=spec.fn,  # type: ignore
+            weight=spec.weight,
+            params=spec.params,
+            rules=[
+                DimensionReaggregateRule(
+                    dimension=rule.dimension,
+                    fn=rule.fn,  # type: ignore
+                )
+                for rule in spec.rules
+            ],
+        )
+
     @strawberry.field
     def primary_key(self, root: DBNodeRevision) -> list[str]:
         """
diff --git a/datajunction-server/datajunction_server/api/graphql/schema.graphql b/datajunction-server/datajunction_server/api/graphql/schema.graphql
index 6d442f339..e4f8b97e0 100644
--- a/datajunction-server/datajunction_server/api/graphql/schema.graphql
+++ b/datajunction-server/datajunction_server/api/graphql/schema.graphql
@@ -7,6 +7,7 @@ enum Aggregability {
 type AggregationRule {
   type: Aggregability!
   level: [String!]
+  reaggregate: DimensionReaggregateRule
 }
 
 type Attribute {
@@ -149,6 +150,11 @@ type DimensionLink {
   defaultValue: String
 }
 
+type DimensionReaggregateRule {
+  dimension: String!
+  fn: ReaggregationFunction!
+}
+
 type Engine {
   name: String!
   version: String!
@@ -296,6 +302,7 @@ type MetricComponent {
   merge: String
   rule: AggregationRule!
   grainAlias: String
+  params: JSON
 }
 
 enum MetricDirection {
@@ -397,6 +404,7 @@ type NodeRevision {
   dimensionLinks: [DimensionLink!]!
   availability: AvailabilityState
   materializations: [MaterializationConfig!]
+  reaggregate: ReaggregateSpec
   primaryKey: [String!]!
   metricMetadata: MetricMetadata
   isDerivedMetric: Boolean!
@@ -716,6 +724,25 @@ type Query {
   listNamespaces: [Namespace!]!
 }
 
+type ReaggregateSpec {
+  fn: ReaggregationFunction
+  weight: String
+  rules: [DimensionReaggregateRule!]!
+  params: JSON
+}
+
+enum ReaggregationFunction {
+  AUTO
+  NONE
+  SUM
+  AVG
+  WEIGHTED_AVG
+  LAST_VALUE
+  FIRST_VALUE
+  MIN
+  MAX
+}
+
 type SemanticEntity {
   name: String!
 
diff --git a/datajunction-server/datajunction_server/api/sql.py b/datajunction-server/datajunction_server/api/sql.py
index 3e1fca938..a5561f7cf 100644
--- a/datajunction-server/datajunction_server/api/sql.py
+++ b/datajunction-server/datajunction_server/api/sql.py
@@ -218,36 +218,7 @@ async def get_measures_sql_v3(
     session: AsyncSession = Depends(get_session),
     current_user: User = Depends(get_current_user),
 ) -> MeasuresSQLResponse:
-    """
-    Generate pre-aggregated measures SQL for the requested metrics.
-
-    Measures SQL represents the first stage of metric computation - it decomposes
-    each metric into its atomic aggregation components (e.g., SUM(amount), COUNT(*))
-    and produces SQL that computes these components at the requested dimensional grain.
-
-    Metrics are separated into grain groups, which represent sets of metrics that can be
-    computed together at a common grain. Each grain group produces its own SQL query, which
-    can be materialized independently to produce intermediate tables that are then queried
-    to compute final metric values.
-
-    Returns:
-        One or more `GrainGroupSQL` objects, each containing:
-        - SQL query computing metric components at the specified grain
-        - Column metadata with semantic types
-        - Component details for downstream re-aggregation
-
-    Args:
-        cube: Optional cube node name. When provided, the cube's stored filters are
-            automatically prepended to the query filters.
-        use_materialized: If True (default), use materialized tables when available.
-            Set to False when generating SQL for materialization refresh to avoid
-            circular references.
-        include_temporal_filters: If True, checks if metrics+dimensions resolve to
-            a cube with temporal partitions, and applies partition filters if so.
-        lookback_window: Lookback window for temporal filters when applicable.
-
-    See also: `/sql/metrics/v3/` for the final combined query with metric expressions.
-    """
+    """Generate pre-aggregated measures SQL for the requested metrics."""
     merged_filters = list(filters)
     cube_node = None
     if cube:
@@ -440,38 +411,7 @@ async def get_combined_measures_sql_v3(
     session: AsyncSession = Depends(get_session),
     current_user: User = Depends(get_current_user),
 ) -> CombinedMeasuresSQLResponse:
-    """
-    Generate combined pre-aggregated measures SQL for the requested metrics.
-
-    This endpoint combines multiple grain groups into a single SQL query using
-    FULL OUTER JOIN on shared dimensions. Dimension columns are wrapped with
-    COALESCE to handle NULLs from non-matching rows.
-
-    This is useful for:
-    - Druid cube materialization where a single combined table is needed
-    - Simplifying downstream queries that need data from multiple fact tables
-    - Pre-computing joined aggregations for dashboards
-
-    The combined SQL contains:
-    - CTEs for each grain group's pre-aggregated data
-    - FULL OUTER JOIN between grain groups on shared dimensions
-    - COALESCE on dimension columns to handle NULL values
-    - All measure columns from all grain groups
-
-    Args:
-        metrics: List of metric names to include
-        dimensions: List of dimensions to group by (the grain)
-        filters: Optional filters to apply
-        use_preagg_tables: If False (default), compute from scratch using source tables.
-            If True, read from pre-aggregation tables.
-
-    Returns:
-        Combined SQL query with column metadata and grain information.
-
-    See also:
-        - `/sql/measures/v3/` for individual grain group queries
-        - `/sql/metrics/v3/` for final metric computations with combiner expressions
-    """
+    """Generate combined pre-aggregated measures SQL for the requested metrics."""
     _t0 = time.monotonic()
     if use_preagg_tables:
         # Generate SQL that reads from pre-agg tables (deterministic names)
@@ -610,40 +550,7 @@ async def get_metrics_sql_v3(
     session: AsyncSession = Depends(get_session),
     current_user: User = Depends(get_current_user),
 ) -> V3TranslatedSQL:
-    """
-    Generate final metrics SQL with fully computed metric expressions for the
-    requested metrics, dimensions, and filters using the specified dialect.
-
-    Metrics SQL is the second (and final) stage of metric computation - it takes
-    the pre-aggregated components from Measures SQL and applies combiner expressions
-    to produce the actual metric values requested.
-
-    - Metric components are re-aggregated as needed to match the requested
-    dimensional grain.
-
-    - Derived metrics (defined as expressions over other metrics)
-    (e.g., `conversion_rate = order_count / visitor_count`) are computed by
-    substituting component references with their re-aggregated expressions.
-
-    - When metrics come from different fact tables, their
-    grain groups are FULL OUTER JOINed on the common dimensions, with COALESCE
-    for dimension columns to handle NULLs from non-matching rows.
-
-    - Dimension references in metric expressions are resolved to their
-    final column aliases.
-
-    Args:
-        metrics: List of metric names to include
-        dimensions: List of dimensions to group by (the grain)
-        filters: Optional filters to apply
-        cube: Optional cube node name. When provided, the cube's stored filters are
-            prepended and the cube is passed directly to avoid a redundant lookup.
-        dialect: SQL dialect for the generated query. If not specified, auto-resolves
-            based on cube availability (uses Druid if cube exists, else metric's catalog).
-        use_materialized: If True (default), use materialized tables when available.
-            Set to False when generating SQL for materialization refresh to avoid
-            circular references.
-    """
+    """Generate final metrics SQL with fully computed metric expressions."""
     # Shared metrics-SQL core (cube pinning, cube_filters prepend, dialect
     # auto-resolve, build_metrics_sql, and the build-latency metrics + [SQL] log).
     # Also used by the semantic-layer endpoint.
diff --git a/datajunction-server/datajunction_server/construction/build_v3/builder.py b/datajunction-server/datajunction_server/construction/build_v3/builder.py
index 9b1b5fa7a..6abff4a47 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/builder.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/builder.py
@@ -21,6 +21,7 @@
 )
 from datajunction_server.construction.build_v3.decomposition import (
     decompose_and_group_metrics,
+    missing_reaggregate_dimensions,
 )
 from datajunction_server.construction.build_v3.dimensions import parse_dimension_ref
 from datajunction_server.construction.build_v3.filters import (
@@ -57,6 +58,7 @@
 from datajunction_server.errors import DJError, DJInvalidInputException, ErrorCode
 from datajunction_server.instrumentation import events
 from datajunction_server.models.dialect import Dialect
+from datajunction_server.models.materialization import MaterializationTarget
 from datajunction_server.models.partition import PartitionType
 from datajunction_server.sql.parsing import ast
 from datajunction_server.sql.parsing.backends.antlr4 import parse
@@ -250,6 +252,7 @@ async def setup_build_context(
     include_temporal_filters: bool = False,
     lookback_window: str | None = None,
     matched_cube: NodeRevision | None = None,
+    materialization_target: MaterializationTarget | None = None,
 ) -> BuildContext:
     """
     Create and initialize a BuildContext with all setup done.
@@ -320,6 +323,14 @@ async def setup_build_context(
 
     # Add dimensions referenced in metric expressions (e.g., LAG ORDER BY)
     add_dimensions_from_metric_expressions(ctx, ctx.decomposed_metrics)
+    output_dimensions_after_expression_scan = list(ctx.dimensions)
+    internal_reaggregate_dimensions = missing_reaggregate_dimensions(
+        ctx.decomposed_metrics.values(),
+        output_dimensions_after_expression_scan,
+    )
+    for dimension in internal_reaggregate_dimensions:
+        if dimension not in ctx.dimensions:
+            ctx.dimensions.append(dimension)
 
     # A second load_nodes pass is needed when either:
     # 1. metric expressions introduced dimension nodes not yet in ctx.nodes, OR
@@ -333,8 +344,15 @@ async def setup_build_context(
     }
     missing_dim_nodes = dim_roots_after - ctx.nodes.keys()
     internally_added_roots = dim_roots_after - dim_roots_before_load
-    if missing_dim_nodes or internally_added_roots:
-        await load_nodes(ctx)
+    try:
+        if (
+            missing_dim_nodes
+            or internally_added_roots
+            or internal_reaggregate_dimensions
+        ):
+            await load_nodes(ctx)
+    finally:
+        ctx.dimensions = output_dimensions_after_expression_scan
 
     # Classify filters into dimension filters (WHERE) and metric filters (HAVING)
     # This MUST happen AFTER all nodes are loaded so we can correctly identify
@@ -355,6 +373,7 @@ async def build_measures_sql(
     lookback_window: str | None = None,
     query_parameters: dict[str, Any] | None = None,
     matched_cube: NodeRevision | None = None,
+    materialization_target: MaterializationTarget | None = None,
 ) -> GeneratedMeasuresSQL:
     """
     Build measures SQL for a set of metrics, dimensions, and filters.
@@ -395,6 +414,7 @@ async def build_measures_sql(
         include_temporal_filters=include_temporal_filters,
         lookback_window=lookback_window,
         matched_cube=matched_cube,
+        materialization_target=materialization_target,
     )
 
     # Build grain groups from context
diff --git a/datajunction-server/datajunction_server/construction/build_v3/combiners.py b/datajunction-server/datajunction_server/construction/build_v3/combiners.py
index c39857745..7c5d710cf 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/combiners.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/combiners.py
@@ -23,6 +23,9 @@
 from datajunction_server.construction.build_v3.cte import (
     process_metric_combiner_expression,
 )
+from datajunction_server.construction.build_v3.decomposition import (
+    build_merge_call,
+)
 from datajunction_server.construction.build_v3.preagg_matcher import (
     get_temporal_partitions,
 )
@@ -600,6 +603,7 @@ async def build_combiner_sql_from_preaggs(
             measure_identity_token(
                 compute_expression_hash(m.expression),
                 m.aggregation,
+                m.params,
             )
             for m in gg.components
             if m.expression
@@ -861,20 +865,19 @@ def _build_grain_group_from_preagg_table(
 
             # Find the component to get the merge function
             merge_func = None
+            merge_args: list[str] = []
             for comp in original_gg.components:
                 if (  # pragma: no branch
                     comp.name == col.name
                     or original_gg.component_aliases.get(comp.name) == col.name
                 ):
                     merge_func = comp.merge
+                    merge_args = comp.merge_args
                     break
 
             if merge_func:
                 # Apply re-aggregation
-                agg_expr = ast.Function(
-                    name=ast.Name(merge_func),
-                    args=[col_ref],
-                )
+                agg_expr = build_merge_call(merge_func, merge_args, col_ref)
                 aliased = ast.Alias(child=agg_expr, alias=ast.Name(col.name))
                 select_items.append(aliased)
             else:
diff --git a/datajunction-server/datajunction_server/construction/build_v3/cube_matcher.py b/datajunction-server/datajunction_server/construction/build_v3/cube_matcher.py
index 07fead205..f0955bd04 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/cube_matcher.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/cube_matcher.py
@@ -12,9 +12,13 @@
 
 from sqlalchemy import and_, select
 from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy.orm import joinedload, load_only, noload, selectinload
+from sqlalchemy.orm import aliased, joinedload, load_only, noload, selectinload
 
-from datajunction_server.construction.build_v3.decomposition import is_derived_metric
+from datajunction_server.construction.build_v3.decomposition import (
+    _reaggregate_dimension_requested,
+    is_derived_metric,
+    missing_reaggregate_dimensions,
+)
 from datajunction_server.construction.build_v3.dimensions import parse_dimension_ref
 from datajunction_server.construction.build_v3.filters import (
     parse_and_resolve_filters,
@@ -36,14 +40,19 @@
 )
 from datajunction_server.database.catalog import Catalog
 from datajunction_server.database.column import Column
-from datajunction_server.database.node import Node, NodeRevision
+from datajunction_server.database.node import Node, NodeRelationship, NodeRevision
 from datajunction_server.database.partition import Partition
 from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.instrumentation.provider import timed
 from datajunction_server.models.decompose import Aggregability
 from datajunction_server.models.dialect import Dialect
 from datajunction_server.models.node_type import NodeType
+from datajunction_server.models.reaggregate import (
+    ReaggregationFunction,
+    dimension_reaggregate_rules,
+)
 from datajunction_server.naming import amenable_name
+from datajunction_server.sql.decompose import MetricComponentExtractor
 from datajunction_server.sql.parsing import ast
 
 logger = logging.getLogger(__name__)
@@ -53,6 +62,327 @@
 # parsed: coverage can't be proven, so cube matching must fail SAFE (reject the
 # cube and fall back) rather than fail open and emit invalid SQL.
 _FILTER_COVERAGE_UNKNOWN = object()
+ReaggregateRequirement = tuple[str, str, ReaggregationFunction]
+
+
+def _split_dimension_ref(ref: str) -> tuple[str, str | None]:
+    """
+    Return a dimension ref without role suffix and the role suffix, if present.
+    """
+    if "[" not in ref:
+        return ref, None
+    base, role = ref.rsplit("[", 1)
+    return base, role.rstrip("]")
+
+
+def _cube_dimension_covers_reaggregate_dimension(
+    protected_dimension: str,
+    cube_dimension: str,
+) -> bool:
+    """
+    Return whether a cube dimension materializes a protected dimension.
+    """
+    if _reaggregate_dimension_requested(protected_dimension, [cube_dimension]):
+        return True
+
+    protected_base, protected_role = _split_dimension_ref(protected_dimension)
+    cube_base, cube_role = _split_dimension_ref(cube_dimension)
+    if protected_role != cube_role:
+        return False
+
+    # Deployment validation allows a metric to author the protected dimension as
+    # a bare parent column (the same form required_dimensions accepts). Cubes
+    # expose dimensions as fully-qualified element names, so compare the short
+    # column name for that narrow compatibility case.
+    if "." not in protected_base:
+        return protected_base == cube_base.rsplit(".", 1)[-1]
+    return False
+
+
+def _missing_reaggregate_requirements(
+    requirements: list[ReaggregateRequirement],
+    cube_dims: set[str],
+) -> list[ReaggregateRequirement]:
+    """
+    Reaggregate requirements not covered by a cube's materialized dimensions.
+    """
+    return [
+        requirement
+        for requirement in requirements
+        if not any(
+            _cube_dimension_covers_reaggregate_dimension(
+                requirement[1],
+                cube_dim,
+            )
+            for cube_dim in cube_dims
+        )
+    ]
+
+
+async def _metric_graph_has_reaggregate(
+    session: AsyncSession,
+    metrics: list[str],
+) -> bool:
+    """
+    Cheaply test whether requested metrics or metric ancestors use reaggregate.
+    """
+    seen: set[str] = set()
+    pending = set(metrics)
+    ParentNode = aliased(Node)
+
+    while pending:
+        batch = sorted(pending - seen)
+        if not batch:
+            break
+        seen.update(batch)
+        result = await session.execute(
+            select(
+                Node.name,
+                NodeRevision.reaggregate,
+                ParentNode.name.label("parent_name"),
+            )
+            .select_from(Node)
+            .join(
+                NodeRevision,
+                and_(
+                    Node.id == NodeRevision.node_id,
+                    Node.current_version == NodeRevision.version,
+                ),
+            )
+            .outerjoin(NodeRelationship, NodeRelationship.child_id == NodeRevision.id)
+            .outerjoin(
+                ParentNode,
+                and_(
+                    NodeRelationship.parent_id == ParentNode.id,
+                    ParentNode.type == NodeType.METRIC,
+                ),
+            )
+            .where(Node.name.in_(batch))
+            .where(Node.type == NodeType.METRIC),
+        )
+        for _, reaggregate, parent_name in result.all():
+            if reaggregate:
+                return True
+            if parent_name and parent_name not in seen:
+                pending.add(parent_name)
+
+    return False
+
+
+async def _reaggregate_requirements_for_metrics_if_needed(
+    session: AsyncSession,
+    metrics: list[str],
+    dimensions: list[str],
+) -> list[ReaggregateRequirement]:
+    """
+    Run full metric decomposition only when reaggregate is possible.
+    """
+    if not await _metric_graph_has_reaggregate(session, metrics):
+        return []
+    return await _reaggregate_requirements_for_metrics(session, metrics, dimensions)
+
+
+def _reaggregate_requirements_for_cube_metrics(
+    cube: NodeRevision,
+    metrics: list[str],
+    requested_dimensions: list[str],
+) -> list[ReaggregateRequirement]:
+    """
+    Reaggregation requirements that are absent from the output grain.
+    """
+    requirements: list[ReaggregateRequirement] = []
+    metric_names = set(metrics)
+    for metric_revision in cube.metric_node_revisions():
+        if (
+            not metric_revision
+            or metric_revision.name not in metric_names
+            or not metric_revision.reaggregate
+        ):
+            continue
+        for rule in dimension_reaggregate_rules(metric_revision.reaggregate):
+            if not _reaggregate_dimension_requested(
+                rule.dimension,
+                requested_dimensions,
+            ):
+                requirements.append(
+                    (
+                        metric_revision.name,
+                        rule.dimension,
+                        rule.fn,
+                    ),
+                )
+    return requirements
+
+
+async def _reaggregate_requirements_for_metrics(
+    session: AsyncSession,
+    metrics: list[str],
+    requested_dimensions: list[str],
+) -> list[ReaggregateRequirement]:
+    """
+    Reaggregation requirements from full metric decomposition.
+
+    Direct cube metadata only sees the metrics declared on the cube. Decomposition
+    also catches derived metrics whose base components carry reaggregate rules.
+    """
+    requirements: list[ReaggregateRequirement] = []
+    for metric_name in metrics:
+        extractor = await MetricComponentExtractor.from_node_name(
+            metric_name,
+            session,
+        )
+        components, _ = await extractor.extract(session)
+        for component in components:
+            reaggregate = component.rule.reaggregate
+            if (
+                reaggregate
+                and not _reaggregate_dimension_requested(
+                    reaggregate.dimension,
+                    requested_dimensions,
+                )
+                and (
+                    metric_name,
+                    reaggregate.dimension,
+                    reaggregate.fn,
+                )
+                not in requirements
+            ):
+                requirements.append(
+                    (
+                        metric_name,
+                        reaggregate.dimension,
+                        reaggregate.fn,
+                    ),
+                )
+    return requirements
+
+
+def _reaggregate_requirements_for_decomposed_metrics(
+    decomposed_metrics: dict[str, DecomposedMetricInfo],
+    metrics: list[str],
+    requested_dimensions: list[str],
+) -> list[ReaggregateRequirement]:
+    """
+    Reaggregation requirements from already-decomposed planner context.
+    """
+    requirements: list[ReaggregateRequirement] = []
+    metric_names = set(metrics)
+    for metric_name, decomposed in decomposed_metrics.items():
+        if metric_name not in metric_names:
+            continue
+        for component in decomposed.components:
+            reaggregate = component.rule.reaggregate
+            if reaggregate and not _reaggregate_dimension_requested(
+                reaggregate.dimension,
+                requested_dimensions,
+            ):
+                requirement = (
+                    metric_name,
+                    reaggregate.dimension,
+                    reaggregate.fn,
+                )
+                if requirement not in requirements:
+                    requirements.append(requirement)
+    return requirements
+
+
+def _reaggregate_dimensions_for_cube_metrics(
+    cube: NodeRevision,
+    metrics: list[str],
+    requested_dimensions: list[str],
+) -> list[str]:
+    """
+    Protected dimensions a cube must materialize for semi-additive rollups.
+    """
+    required: list[str] = []
+    for _, dimension, _ in _reaggregate_requirements_for_cube_metrics(
+        cube,
+        metrics,
+        requested_dimensions,
+    ):
+        if dimension not in required:
+            required.append(dimension)
+    return required
+
+
+def _format_reaggregate_requirements(
+    requirements: list[ReaggregateRequirement],
+) -> list[str]:
+    """Human-readable semi-additive requirements for error messages."""
+    return [
+        f"{metric} -> {dimension} ({function.value})"
+        for metric, dimension, function in requirements
+    ]
+
+
+def validate_cube_covers_reaggregate_dimensions(
+    cube: NodeRevision,
+    metrics: list[str],
+    dimensions: list[str],
+    *,
+    decomposed_metrics: dict[str, DecomposedMetricInfo] | None = None,
+    additional_requirements: list[ReaggregateRequirement] | None = None,
+    usage: str = "Cube",
+    http_status_code: int = 422,
+) -> None:
+    """
+    Fail loud when a materialized cube cannot safely serve semi-additive metrics.
+
+    V1 materialized cube usage is only safe for semi-additive metrics when the
+    cube itself retains the protected dimension. Otherwise querying the cube can
+    silently merge across that dimension with the wrong Druid aggregator.
+    """
+    requirements = _reaggregate_requirements_for_cube_metrics(
+        cube,
+        metrics,
+        dimensions,
+    )
+    if decomposed_metrics is not None:
+        for requirement in _reaggregate_requirements_for_decomposed_metrics(
+            decomposed_metrics,
+            metrics,
+            dimensions,
+        ):
+            if requirement not in requirements:
+                requirements.append(requirement)
+    for requirement in additional_requirements or []:
+        if requirement not in requirements:
+            requirements.append(requirement)
+
+    cube_dims = set(cube.cube_dimensions())
+    missing_requirements = _missing_reaggregate_requirements(requirements, cube_dims)
+    if not missing_requirements:
+        return
+
+    raise DJInvalidInputException(
+        f"{usage} `{cube.name}` cannot safely use materialized semi-additive "
+        "metric(s) because it does not materialize protected dimension(s) "
+        f"{_format_reaggregate_requirements(missing_requirements)}. "
+        "Add the protected dimension to the cube, or run the query with "
+        "use_materialized=false.",
+        http_status_code=http_status_code,
+    )
+
+
+def validate_cube_reaggregate_materialization(
+    cube: NodeRevision,
+    decomposed_metrics: dict[str, DecomposedMetricInfo] | None = None,
+) -> None:
+    """
+    Guard Druid cube materialization from baking in semi-additive misaggregation.
+
+    V1 does not materialize collapsed semi-additive metrics unless the cube grain
+    includes the protected dimension. This avoids generating a Druid metricsSpec
+    that sums first/last/min/max collapse inputs across the protected dimension.
+    """
+    validate_cube_covers_reaggregate_dimensions(
+        cube,
+        cube.cube_node_metrics,
+        cube.cube_node_dimensions,
+        decomposed_metrics=decomposed_metrics,
+        usage="Cube",
+        http_status_code=400,
+    )
 
 
 async def _required_filter_dimensions(
@@ -190,7 +520,15 @@ async def find_matching_cube(
 
     result = await session.execute(statement)
     candidate_cubes = result.unique().scalars().all()
-
+    reaggregate_requirements = (
+        await _reaggregate_requirements_for_metrics_if_needed(
+            session,
+            metrics,
+            dimensions,
+        )
+        if candidate_cubes
+        else []
+    )
     # Find the best matching cube (smallest grain that covers all dimensions)
     best_match: NodeRevision | None = None
     best_grain_size = float("inf")
@@ -214,12 +552,32 @@ async def find_matching_cube(
         # Druid SQL referencing a missing column).
         cube_dims = set(cube_rev.cube_dimensions())
 
-        if not required_dims.issubset(cube_dims):
+        missing_required_dims = required_dims - cube_dims
+        cube_reaggregate_requirements = _reaggregate_requirements_for_cube_metrics(
+            cube_rev,
+            metrics,
+            dimensions,
+        )
+        for requirement in reaggregate_requirements:
+            if requirement not in cube_reaggregate_requirements:
+                cube_reaggregate_requirements.append(requirement)
+        missing_reaggregate_requirements = _missing_reaggregate_requirements(
+            cube_reaggregate_requirements,
+            cube_dims,
+        )
+        if missing_required_dims:
             logger.debug(
                 f"[BuildV3] Cube {cube_rev.name} dims {cube_dims} "
                 f"don't cover required {required_dims}",
             )
             continue
+        if missing_reaggregate_requirements:
+            logger.debug(
+                f"[BuildV3] Cube {cube_rev.name} dims {cube_dims} "
+                f"don't cover reaggregate requirements "
+                f"{_format_reaggregate_requirements(missing_reaggregate_requirements)}",
+            )
+            continue
 
         # Found a match - prefer smallest grain (less roll-up work)
         if len(cube_dims) < best_grain_size:
@@ -244,6 +602,7 @@ async def validate_pinned_cube_covers_filters(
     cube: NodeRevision,
     dimensions: list[str],
     filters: list[str] | None,
+    metrics: list[str] | None = None,
 ) -> None:
     """
     Ensure an explicitly pinned cube covers every filtered dimension.
@@ -278,6 +637,18 @@ async def validate_pinned_cube_covers_filters(
             http_status_code=422,
         )
 
+    validate_cube_covers_reaggregate_dimensions(
+        cube,
+        metrics or cube.cube_node_metrics,
+        dimensions,
+        additional_requirements=await _reaggregate_requirements_for_metrics_if_needed(
+            session,
+            metrics or cube.cube_node_metrics,
+            dimensions,
+        ),
+        usage="Pinned cube",
+    )
+
 
 async def resolve_dialect_and_engine_for_metrics(
     session: AsyncSession,
@@ -465,6 +836,13 @@ def build_sql_from_cube_impl(
     Returns:
         GeneratedSQL with the query and column metadata.
     """
+    validate_cube_covers_reaggregate_dimensions(
+        cube,
+        ctx.metrics,
+        ctx.dimensions,
+        decomposed_metrics=ctx.decomposed_metrics,
+    )
+
     # Build synthetic GrainGroupSQL for cube table
     # This applies all filters in the cube CTE's WHERE clause
     synthetic_grain_group = build_synthetic_grain_group(
@@ -640,13 +1018,27 @@ def build_synthetic_grain_group(
     # (filter-only dimensions were added by add_dimensions_from_filters() in setup_build_context)
     dimension_aliases: dict[str, str] = {}
 
-    # Add all dimensions (requested + filter-only). We need all dimensions
+    output_dimensions = [
+        dim for dim in ctx.dimensions if dim not in ctx.filter_dimensions
+    ]
+    internal_reaggregate_dimensions = missing_reaggregate_dimensions(
+        decomposed_metrics.values(),
+        output_dimensions,
+    )
+
+    # Add all dimensions (requested + filter-only + internal semi-additive
+    # protected dimensions). We need all dimensions
     # in the cube SELECT for proper filter resolution.
     # dim_short_names holds the alias (short name) used everywhere outside the CTE.
     # dim_physical_names holds the actual column name in the Druid table (may differ).
     dim_short_names = []
     dim_physical_names = []
-    for dim_ref in ctx.dimensions:
+    dimension_to_alias: dict[str, str] = {}
+    dimension_refs = list(ctx.dimensions)
+    for dim_ref in internal_reaggregate_dimensions:
+        if dim_ref not in dimension_refs:
+            dimension_refs.append(dim_ref)
+    for dim_ref in dimension_refs:
         parsed_dim = parse_dimension_ref(dim_ref)
         short_name = parsed_dim.column_name
         if parsed_dim.role:
@@ -662,6 +1054,7 @@ def build_synthetic_grain_group(
         # the WHERE is applied directly on the cube table, so we must reference
         # the physical column (e.g. common_DOT_..._DOT_dateint) not the alias.
         dimension_aliases[dim_ref] = physical_name
+        dimension_to_alias[dim_ref] = short_name
         grain_group_columns.append(
             ColumnMetadata(
                 name=short_name,
@@ -735,6 +1128,17 @@ def build_synthetic_grain_group(
         if metric_node and not is_derived_metric(ctx, metric_node):
             base_metrics.append(metric_name)
 
+    reaggregate_dimension_aliases: dict[str, str] = {}
+    internal_reaggregate_dimension_set = set(internal_reaggregate_dimensions)
+    for comp in all_components:
+        if (
+            comp.rule.reaggregate
+            and comp.rule.reaggregate.dimension in internal_reaggregate_dimension_set
+        ):
+            reaggregate_dimension_aliases[comp.name] = dimension_to_alias[
+                comp.rule.reaggregate.dimension
+            ]
+
     # Create the synthetic GrainGroupSQL
     # Note: We use a placeholder parent_name since the cube combines multiple parents
     return GrainGroupSQL(
@@ -745,6 +1149,7 @@ def build_synthetic_grain_group(
         metrics=base_metrics,  # Only base metrics, not derived
         parent_name=cube.name,  # Use cube name as parent
         component_aliases=component_aliases,
+        reaggregate_dimension_aliases=reaggregate_dimension_aliases,
         is_merged=False,
         components=all_components,
         dialect=ctx.dialect,
diff --git a/datajunction-server/datajunction_server/construction/build_v3/decomposition.py b/datajunction-server/datajunction_server/construction/build_v3/decomposition.py
index 8a3c1b56f..e5eb36ffe 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/decomposition.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/decomposition.py
@@ -9,6 +9,8 @@
 
 from __future__ import annotations
 
+from collections import defaultdict
+from collections.abc import Iterable
 from typing import cast
 
 from sqlalchemy.ext.asyncio import AsyncSession
@@ -22,10 +24,13 @@
 from datajunction_server.database.node import Node
 from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.decompose import Aggregability, MetricComponent
+from datajunction_server.models.dialect import Dialect
+from datajunction_server.models.materialization import MaterializationTarget
 from datajunction_server.models.node_type import NodeType
 from datajunction_server.sql.decompose import MetricComponentExtractor
 from datajunction_server.sql.parsing import ast
 from datajunction_server.sql.parsing.backends.antlr4 import parse
+from datajunction_server.utils import SEPARATOR
 
 
 async def decompose_and_group_metrics(
@@ -73,6 +78,7 @@ async def decompose_and_group_metrics(
                         base_metric,
                         nodes_cache=ctx.nodes,
                         parent_map=ctx.parent_map,
+                        dialect=ctx.dialect,
                     )
                     all_decomposed[base_metric.name] = decomposed
 
@@ -92,6 +98,7 @@ async def decompose_and_group_metrics(
                     metric_node,
                     nodes_cache=ctx.nodes,
                     parent_map=ctx.parent_map,
+                    dialect=ctx.dialect,
                 )
                 all_decomposed[metric_name] = derived_decomposed
         else:
@@ -115,6 +122,7 @@ async def decompose_and_group_metrics(
                 metric_node,
                 nodes_cache=ctx.nodes,
                 parent_map=ctx.parent_map,
+                dialect=ctx.dialect,
             )
             all_decomposed[metric_node.name] = decomposed
 
@@ -140,6 +148,7 @@ async def decompose_metric(
     *,
     nodes_cache: dict[str, Node] | None = None,
     parent_map: dict[str, list[str]] | None = None,
+    dialect: Dialect = Dialect.SPARK,
 ) -> DecomposedMetricInfo:
     """
     Decompose a metric into its constituent components.
@@ -156,6 +165,10 @@ async def decompose_metric(
             parent_map, avoids database queries by using cached data.
         parent_map: Optional dict of child_name -> list of parent_names.
             Required if nodes_cache is provided.
+        dialect: Dialect the combiner is rendered for. ``decompose_and_group_metrics``
+            passes the build's resolved dialect, which for a materialized cube is
+            the one taken from its availability catalog -- Druid, in practice.
+            Defaults to Spark for direct callers.
 
     Returns:
         DecomposedMetricInfo with components, combiner expression, and aggregability
@@ -166,7 +179,7 @@ async def decompose_metric(
         )
 
     # Use the MetricComponentExtractor with optional cache
-    extractor = MetricComponentExtractor(metric_node.current.id)
+    extractor = MetricComponentExtractor(metric_node.current.id, dialect=dialect)
     components, derived_ast = await extractor.extract(
         session,
         nodes_cache=nodes_cache,
@@ -200,7 +213,10 @@ async def decompose_metric(
     )
 
 
-def build_component_expression(component: MetricComponent) -> ast.Expression:
+def build_component_expression(
+    component: MetricComponent,
+    materialization_target: MaterializationTarget | None = None,
+) -> ast.Expression:
     """
     Build the accumulate expression AST for a metric component.
 
@@ -210,11 +226,18 @@ def build_component_expression(component: MetricComponent) -> ast.Expression:
     Note: Templates may be pre-expanded (e.g., "SUM(POWER(match_score, 2))")
     by the decomposition phase, so we detect this by checking for parentheses
     without template placeholders.
+
+    When ``materialization_target`` is one the component declares a ``serialize``
+    conversion for, the accumulated expression is wrapped in it. That happens
+    only while writing a measures table: a query-time build passes no target, so
+    the unwrapped expression stands.
     """
     if not component.aggregation:  # pragma: no cover
         # No aggregation - just return the expression as a column
         return ast.Column(name=ast.Name(component.expression))
 
+    accumulated: ast.Expression
+
     # Check if it's an unexpanded template with {}
     if "{" in component.aggregation:  # pragma: no cover
         # Template like "SUM(POWER({}, 2))" - expand it
@@ -224,25 +247,67 @@ def build_component_expression(component: MetricComponent) -> ast.Expression:
         if isinstance(expr_ast, ast.Alias):
             expr_ast = expr_ast.child
         expr_ast.clear_parent()
-        return cast(ast.Expression, expr_ast)
+        accumulated = cast(ast.Expression, expr_ast)
 
     # Check if it's a pre-expanded template (contains parentheses, like "SUM(POWER(x, 2))")
     # vs a simple function name (like "SUM")
-    if "(" in component.aggregation:
+    elif "(" in component.aggregation:
         # Pre-expanded template - parse it directly as a complete expression
         expr_ast = parse(f"SELECT {component.aggregation}").select.projection[0]
         if isinstance(expr_ast, ast.Alias):
             expr_ast = expr_ast.child  # pragma: no cover
         expr_ast.clear_parent()
-        return cast(ast.Expression, expr_ast)
+        accumulated = cast(ast.Expression, expr_ast)
     else:
         # Simple function name like "SUM" - build SUM(expression)
         arg_expr = parse(f"SELECT {component.expression}").select.projection[0]
-        func = ast.Function(
+        accumulated = ast.Function(
             name=ast.Name(component.aggregation),
             args=[cast(ast.Expression, arg_expr)],
         )
-        return func
+
+    # All accumulate shapes must receive the target conversion.
+    return _apply_serialize(accumulated, component, materialization_target)
+
+
+def _apply_serialize(
+    expr: ast.Expression,
+    component: MetricComponent,
+    materialization_target: MaterializationTarget | None,
+) -> ast.Expression:
+    """
+    Wrap an accumulated expression in the component's serialize conversion.
+
+    Returns the expression untouched unless the component declares a conversion
+    and names this target -- so every existing component, and every query-time
+    build, is unaffected.
+    """
+    if not component.serializes_for(materialization_target):
+        return expr
+    wrapped = parse(
+        f"SELECT {component.serialize.replace('{}', str(expr))}",
+    ).select.projection[0]
+    wrapped.clear_parent()
+    return cast(ast.Expression, wrapped)
+
+
+def build_merge_call(
+    merge: str,
+    merge_args: list[str],
+    arg: ast.Expression,
+) -> ast.Function:
+    """
+    Build a component's Phase 2 merge call, including any fixed tuning arguments.
+
+    Components without `merge_args` -- every non-sketch aggregation -- produce
+    the same single-argument call as before.
+    """
+    extra: list[ast.Expression] = []
+    for literal in merge_args:
+        parsed = parse(f"SELECT {literal}").select.projection[0]
+        parsed.clear_parent()
+        extra.append(cast(ast.Expression, parsed))
+    return ast.Function(name=ast.Name(merge), args=[arg, *extra])
 
 
 def get_base_metrics_for_derived(ctx: BuildContext, metric_node: Node) -> list[Node]:
@@ -260,12 +325,9 @@ def collect_bases(node: Node):
             return
         visited.add(node.name)
 
-        # Recurse through metric parents. A node with any metric parent is itself
-        # derived; a node with no metric parent is a base metric -- regardless of
-        # whether its data source is a fact/transform or a dimension node (a base
-        # metric can be defined directly on a dimension node). Classifying by
-        # "has a metric parent" avoids mistaking a dimension-sourced base metric
-        # for a bare required-dimension reference, which would drop its grain group.
+        # Recurse through metric parents. A node with any metric parent is derived;
+        # a node with no metric parent is a base metric. Checking for metric parents 
+        # avoids dropping base metrics that are defined directly on dimension nodes.
         has_metric_parent = False
         for parent_name in ctx.parent_map.get(node.name, []):
             parent = ctx.nodes.get(parent_name)
@@ -322,6 +384,76 @@ def get_native_grain(node: Node) -> list[str]:
     return pk_columns
 
 
+def _dimension_ref_base(ref: str) -> str:
+    """Return a dimension ref without its role suffix."""
+    return ref.split("[", 1)[0]
+
+
+def _dimension_ref_role(ref: str) -> str | None:
+    """Return the role suffix for a dimension ref, if any."""
+    if "[" not in ref:
+        return None
+    return ref.rsplit("[", 1)[1].rstrip("]")
+
+
+def _dimension_ref_column(ref: str) -> str:
+    """Return the column part of a fully qualified or local dimension ref."""
+    return _dimension_ref_base(ref).rsplit(SEPARATOR, 1)[-1]
+
+
+def _reaggregate_dimension_requested(
+    protected_dimension: str,
+    requested_dimensions: list[str],
+) -> bool:
+    """
+    Whether the protected dimension is already in the requested output grain.
+
+    Role-qualified dimensions only count when the role matches exactly. Coarser
+    dimensions on the same node do not count.
+    """
+    protected_base = _dimension_ref_base(protected_dimension)
+    protected_role = _dimension_ref_role(protected_dimension)
+    protected_col = _dimension_ref_column(protected_dimension)
+    for requested in requested_dimensions:
+        requested_base = _dimension_ref_base(requested)
+        requested_role = _dimension_ref_role(requested)
+        if requested == protected_dimension:
+            return True
+        if requested_base == protected_base and requested_role == protected_role:
+            return True
+        if (
+            protected_role is None
+            and requested_role is None
+            and requested == protected_col
+        ):
+            return True
+    return False
+
+
+def missing_reaggregate_dimensions(
+    decomposed_metrics: Iterable[DecomposedMetricInfo],
+    requested_dimensions: list[str],
+) -> list[str]:
+    """
+    Protected dimensions needed as internal grain but absent from output grain.
+    """
+    missing: list[str] = []
+    for decomposed in decomposed_metrics:
+        for component in decomposed.components:
+            if not component.rule.reaggregate:
+                continue
+            dimension = component.rule.reaggregate.dimension
+            if (
+                not _reaggregate_dimension_requested(
+                    dimension,
+                    requested_dimensions,
+                )
+                and dimension not in missing
+            ):
+                missing.append(dimension)
+    return missing
+
+
 def analyze_grain_groups(
     metric_group: MetricGroup,
     requested_dimensions: list[str],
@@ -341,7 +473,7 @@ def analyze_grain_groups(
 
     Args:
         metric_group: MetricGroup with decomposed metrics
-        requested_dimensions: Dimensions requested by user (column names only)
+        requested_dimensions: Dimension refs requested by the user
 
     Returns:
         List of GrainGroups, one per unique grain
@@ -349,11 +481,15 @@ def analyze_grain_groups(
     parent_node = metric_group.parent_node
 
     # Group components by their effective grain
-    # Key: (aggregability, tuple of additional grain columns)
+    # Key: (aggregability, tuple of additional grain columns, tuple of internal dimensions)
     grain_buckets: dict[
-        tuple[Aggregability, tuple[str, ...]],
+        tuple[Aggregability, tuple[str, ...], tuple[str, ...]],
         list[tuple[Node, MetricComponent]],
     ] = {}
+    reaggregate_component_dimensions: dict[
+        tuple[Aggregability, tuple[str, ...], tuple[str, ...]],
+        dict[str, str],
+    ] = {}
 
     # Track non-decomposable metrics (those with no components)
     non_decomposable: list[DecomposedMetricInfo] = []
@@ -369,29 +505,50 @@ def analyze_grain_groups(
             agg_type = component.rule.type
 
             # Explicitly type the key to satisfy mypy
-            key: tuple[Aggregability, tuple[str, ...]]
+            key: tuple[Aggregability, tuple[str, ...], tuple[str, ...]]
+            reaggregate_dimension = (
+                component.rule.reaggregate.dimension
+                if (
+                    component.rule.reaggregate
+                    and not _reaggregate_dimension_requested(
+                        component.rule.reaggregate.dimension,
+                        requested_dimensions,
+                    )
+                )
+                else None
+            )
+            internal_dimensions = (
+                (reaggregate_dimension,) if reaggregate_dimension else ()
+            )
             if agg_type == Aggregability.FULL:
-                # FULL: no additional grain columns needed
-                key = (Aggregability.FULL, ())
+                # FULL: no additional grain columns needed, unless a semi-additive
+                # component must keep its protected dimension as internal grain.
+                key = (Aggregability.FULL, (), internal_dimensions)
             elif agg_type == Aggregability.LIMITED:
                 # LIMITED: add level columns to grain
                 level_cols = tuple(sorted(component.rule.level or []))
-                key = (Aggregability.LIMITED, level_cols)
+                key = (Aggregability.LIMITED, level_cols, internal_dimensions)
             else:  # NONE
                 # NONE: use native grain (PK columns)
                 native_grain = get_native_grain(parent_node)
                 key = (
                     Aggregability.NONE,
                     tuple(sorted(native_grain)),
+                    internal_dimensions,
                 )  # pragma: no cover
 
             if key not in grain_buckets:
                 grain_buckets[key] = []
             grain_buckets[key].append((decomposed.metric_node, component))
+            if reaggregate_dimension:
+                reaggregate_component_dimensions.setdefault(key, {})[component.name] = (
+                    reaggregate_dimension
+                )
 
     # Convert buckets to GrainGroup objects
     grain_groups = []
-    for (agg_type, grain_cols), components in grain_buckets.items():
+    for bucket_key, components in grain_buckets.items():
+        agg_type, grain_cols, _internal_dimensions = bucket_key
         # Map each grain expression to its SQL alias.
         # LIMITED: alias comes from component.grain_alias (_make_component decides:
         #   plain column maps to the bare name, complex expr maps to component.name).
@@ -412,6 +569,10 @@ def analyze_grain_groups(
                 grain_columns=list(grain_cols),
                 components=components,
                 grain_col_aliases=grain_col_aliases,
+                reaggregate_component_dimensions=reaggregate_component_dimensions.get(
+                    bucket_key,
+                    {},
+                ),
             ),
         )
 
@@ -470,9 +631,9 @@ def merge_grain_groups(grain_groups: list[GrainGroup]) -> list[GrainGroup]:
     Returns:
         List of grain groups with compatible groups merged
     """
-    from collections import defaultdict
-
-    # Group by parent node name
+    # Group by parent node name first, then by the internal grain needed for
+    # semi-additive collapse. LIMITED/NONE groups must not be merged into a
+    # semi-additive FULL group, as extra grain columns would corrupt MAX_BY/MIN_BY.
     by_parent: dict[str, list[GrainGroup]] = defaultdict(list)
     for gg in grain_groups:
         by_parent[gg.parent_node.name].append(gg)
@@ -483,6 +644,11 @@ def merge_grain_groups(grain_groups: list[GrainGroup]) -> list[GrainGroup]:
         if len(parent_groups) == 1:
             # Only one group for this parent - no merge needed
             merged_groups.append(parent_groups[0])
+        elif any(group.reaggregate_component_dimensions for group in parent_groups):
+            # Keep semi-additive groups isolated. Merging a protected-dimension
+            # group with another grain can add rows inside the protected bucket,
+            # corrupting the already-aggregated value for MAX_BY/MIN_BY.
+            merged_groups.extend(parent_groups)
         else:
             # Multiple groups for same parent - merge them
             merged = _merge_parent_grain_groups(parent_groups)
@@ -520,17 +686,19 @@ def _merge_parent_grain_groups(groups: list[GrainGroup]) -> GrainGroup:
     # Collect all components and track their original aggregabilities
     all_components: list[tuple[Node, MetricComponent]] = []
     component_aggregabilities: dict[str, Aggregability] = {}
+    reaggregate_component_dimensions: dict[str, str] = {}
 
     for gg in groups:
         for metric_node, component in gg.components:
             all_components.append((metric_node, component))
             # Track original aggregability for each component
             component_aggregabilities[component.name] = gg.aggregability
+        reaggregate_component_dimensions.update(
+            gg.reaggregate_component_dimensions,
+        )
 
-    # Carry over non-decomposable metrics from every contributing group.
-    # Without this, merging a NONE group into a FULL/LIMITED neighbor
-    # silently drops the non-decomposable metric expressions and the
-    # response loses those metrics entirely.
+    # Carry over non-decomposable metrics from every contributing group to prevent them
+    # from being silently dropped when merging a NONE group into a FULL/LIMITED neighbor.
     all_non_decomposable: list[DecomposedMetricInfo] = []
     for gg in groups:
         all_non_decomposable.extend(gg.non_decomposable_metrics)
@@ -559,5 +727,6 @@ def _merge_parent_grain_groups(groups: list[GrainGroup]) -> GrainGroup:
         is_merged=True,
         component_aggregabilities=component_aggregabilities,
         grain_col_aliases=merged_grain_col_aliases,
+        reaggregate_component_dimensions=reaggregate_component_dimensions,
         non_decomposable_metrics=all_non_decomposable,
     )
diff --git a/datajunction-server/datajunction_server/construction/build_v3/loaders.py b/datajunction-server/datajunction_server/construction/build_v3/loaders.py
index 934bf069b..9c744c900 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/loaders.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/loaders.py
@@ -87,6 +87,7 @@ async def batch_load_nodes_with_dependencies(
                     NodeRevision.name,
                     NodeRevision.query,
                     NodeRevision.schema_,
+                    NodeRevision.reaggregate,
                     NodeRevision.table,
                 ),
                 # NOTE: don't noload Column.attributes — Columns are identity-
diff --git a/datajunction-server/datajunction_server/construction/build_v3/measures.py b/datajunction-server/datajunction_server/construction/build_v3/measures.py
index f6912654c..50ae7695c 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/measures.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/measures.py
@@ -28,6 +28,7 @@
 from datajunction_server.construction.build_v3.decomposition import (
     analyze_grain_groups,
     build_component_expression,
+    build_merge_call,
     merge_grain_groups,
 )
 from datajunction_server.construction.build_v3.dimensions import (
@@ -76,6 +77,7 @@
 from datajunction_server.internal.scan_estimation import calculate_scan_estimate
 from datajunction_server.models.decompose import Aggregability, MetricComponent
 from datajunction_server.models.node_type import NodeType
+from datajunction_server.models.materialization import MaterializationTarget
 from datajunction_server.sql.functions import function_registry
 from datajunction_server.sql.parsing import ast
 from datajunction_server.sql.parsing import types as ct
@@ -155,10 +157,51 @@ def _parse_type_string(type_str: str | None) -> ct.ColumnType | None:
     return _TYPE_STRING_MAP.get(normalized)
 
 
+def _multi_argument_accumulate_types(
+    aggregation: str,
+    parent_node: Node | None,
+) -> list[ct.ColumnType] | None:
+    """
+    Argument types of an accumulate whose outermost call takes more than one.
+
+    Type inference feeds a component exactly one input type, which is right for
+    every aggregation that takes one argument -- including the templated ones,
+    whose outermost call is still a single-argument ``SUM``. A sketch breaks
+    that assumption: ``nflx_tdigest(latency_ms, CAST(200.0 AS DOUBLE))`` needs
+    two, so calling ``infer_type`` with one raises ``TypeError``, and the caller
+    quietly falls back to the metric's own type. That is how a struct-valued
+    sketch column comes to be recorded as ``double``.
+
+    Returns None whenever this does not apply or an argument cannot be typed,
+    so the single-argument path is left exactly as it was.
+    """
+    if parent_node is None:
+        return None
+    call = parse(f"SELECT {aggregation}").select.projection[0]
+    if not isinstance(call, ast.Function) or len(call.args) < 2:
+        return None
+
+    arg_types: list[ct.ColumnType] = []
+    for arg in call.args:
+        if isinstance(arg, ast.Column):
+            # Only the columns need the parent to resolve; literals and casts
+            # carry their own type without being bound to a table.
+            resolved = _parse_type_string(
+                get_column_type(parent_node, str(arg.alias_or_name.name)),
+            )
+        else:
+            resolved = arg.type
+        if resolved is None:
+            return None
+        arg_types.append(resolved)
+    return arg_types
+
+
 def infer_component_type(
     component: MetricComponent,
     metric_type: str,
     parent_node: Node | None = None,
+    materialization_target: MaterializationTarget | None = None,
 ) -> str:
     """
     Infer the SQL type of a metric component based on its aggregation function.
@@ -175,6 +218,14 @@ def infer_component_type(
     Returns:
         The inferred SQL type string
     """
+    # A serialized column stores the converted type, and the family declares it.
+    # Checked first: inference below cannot see the conversion, and for a
+    # multi-argument sketch accumulate it falls back to `metric_type` entirely,
+    # so leaving this to inference records a type that makes the Druid
+    # aggregator lookup miss and drop the measure with no error.
+    if component.serializes_for(materialization_target) and component.serialize_type:
+        return component.serialize_type
+
     if not component.aggregation:
         return metric_type  # pragma: no cover
 
@@ -199,17 +250,22 @@ def infer_component_type(
         col_type_str = get_column_type(parent_node, component.expression)
         input_type = _parse_type_string(col_type_str)
 
+    multi_arg_types = _multi_argument_accumulate_types(agg_str, parent_node)
+
     try:
-        if input_type:
+        if multi_arg_types is not None:
+            result_type = func_class.infer_type(*multi_arg_types)
+        elif input_type:
             result_type = func_class.infer_type(input_type)
         else:  # pragma: no cover
             # Fallback: try with a generic ColumnType
             result_type = func_class.infer_type(ct.ColumnType("unknown", "unknown"))
-        return str(result_type)
     except (TypeError, NotImplementedError, AttributeError):
         # Function may require more specific types - fall back to metric type
         return metric_type
 
+    return str(result_type)
+
 
 def _get_filter_column_name_for_dimension(
     resolved_dim: ResolvedDimension,
@@ -1078,6 +1134,8 @@ def build_select_ast(
     grain_columns: list[str] | None = None,
     grain_col_aliases: dict[str, str] | None = None,
     grain_col_specs: list[tuple[ast.Expression, str]] | None = None,
+    output_dimension_refs: set[str] | None = None,
+    internal_dimension_aliases: dict[str, str] | None = None,
     filters: list[str] | None = None,
     skip_aggregation: bool = False,
 ) -> tuple[ast.Query, list[str]]:
@@ -1096,6 +1154,9 @@ def build_select_ast(
                            the SQL alias to use in the generated SELECT.  When provided,
                            the alias is taken from this dict (keyed by the raw expression
                            string from ``rule.level``).
+        output_dimension_refs: Optional set of dimension refs to expose as user-facing
+                               dimensions. Other resolved dimensions are join/group-only.
+        internal_dimension_aliases: Dimension refs to project as private grain columns.
         filters: Optional list of filter strings to apply as WHERE clause.
                  Filter strings can reference dimensions (e.g., "v3.product.category = 'Electronics'")
                  or local columns (e.g., "status = 'active'").
@@ -1119,16 +1180,29 @@ def build_select_ast(
     spark_hints = _collect_spark_hints(resolved_dimensions, dim_aliases)
 
     # Add dimension columns to projection
-    # Filter-only dimensions are excluded from projection but included in GROUP BY
+    # Filter-only dimensions are excluded from projection but included in GROUP BY.
+    # Internal dimensions are projected under private aliases so the metrics layer
+    # can collapse semi-additive measures without exposing those dimensions.
+    internal_dimension_aliases = internal_dimension_aliases or {}
     for resolved_dim in resolved_dimensions:
         clean_alias = ctx.alias_registry.register(resolved_dim.original_ref)
         if resolved_dim.original_ref in ctx.filter_dimensions:
             continue
+        if (
+            output_dimension_refs is not None
+            and resolved_dim.original_ref not in output_dimension_refs
+            and resolved_dim.original_ref not in internal_dimension_aliases
+        ):
+            continue
+        output_alias = internal_dimension_aliases.get(
+            resolved_dim.original_ref,
+            clean_alias,
+        )
         col_expr = build_dimension_col_expr(
             resolved_dim,
             main_alias,
             dim_aliases,
-            clean_alias,
+            output_alias,
             ctx=ctx,
             resolved_dimensions=resolved_dimensions,
             parent_node_name=parent_node.name,
@@ -1690,6 +1764,7 @@ def build_grain_group_from_preagg(
     select_items: list[ast.Aliasable | ast.Expression | ast.Column] = []
     columns: list[ColumnMetadata] = []
     component_aliases: dict[str, str] = {}
+    reaggregate_dimension_aliases: dict[str, str] = {}
     metrics_covered: set[str] = set()
     unique_components: list[MetricComponent] = []
     seen_components: set[str] = set()
@@ -1787,6 +1862,14 @@ def build_grain_group_from_preagg(
             ),
         )
 
+    for (
+        component_name,
+        dimension_ref,
+    ) in grain_group.reaggregate_component_dimensions.items():
+        reaggregate_dimension_aliases[component_name] = ctx.alias_registry.register(
+            dimension_ref,
+        )
+
     # Add measure columns with re-aggregation (or grain columns if no merge func)
     for metric_node, component in grain_group.components:
         metrics_covered.add(metric_node.name)
@@ -1819,9 +1902,10 @@ def build_grain_group_from_preagg(
         # If no merge function, output column directly (e.g., grain column for LIMITED)
         # Otherwise, apply the merge function for re-aggregation
         if component.merge:
-            agg_expr = ast.Function(
-                name=ast.Name(component.merge),
-                args=[_preagg_column(scan_name, scan_alias)],
+            agg_expr = build_merge_call(
+                component.merge,
+                component.merge_args,
+                _preagg_column(scan_name, scan_alias),
             )
             aliased = ast.Alias(child=agg_expr, alias=ast.Name(output_alias))
             select_items.append(aliased)
@@ -1947,6 +2031,7 @@ def build_grain_group_from_preagg(
         metrics=list(metrics_covered),
         parent_name=parent_node.name,
         component_aliases=component_aliases,
+        reaggregate_dimension_aliases=reaggregate_dimension_aliases,
         is_merged=grain_group.is_merged,
         component_aggregabilities=grain_group.component_aggregabilities,
         components=unique_components,
@@ -1960,6 +2045,7 @@ def build_grain_group_sql(
     grain_group: GrainGroup,
     resolved_dimensions: list[ResolvedDimension],
     components_per_metric: dict[str, int],
+    output_dimension_refs: set[str] | None = None,
 ) -> GrainGroupSQL:
     """
     Build SQL for a single grain group.
@@ -2012,6 +2098,21 @@ def build_grain_group_sql(
     # Track mapping from component name to actual SQL alias
     # This is needed for metrics SQL to correctly reference component columns
     component_aliases: dict[str, str] = {}
+    reaggregate_dimension_aliases: dict[str, str] = {}
+    internal_dimension_aliases: dict[str, str] = {}
+
+    if output_dimension_refs:
+        for resolved_dim in resolved_dimensions:
+            if resolved_dim.original_ref in output_dimension_refs:
+                ctx.alias_registry.register(resolved_dim.original_ref)
+
+    for (
+        component_name,
+        dimension_ref,
+    ) in grain_group.reaggregate_component_dimensions.items():
+        dimension_alias = ctx.alias_registry.register(dimension_ref)
+        reaggregate_dimension_aliases[component_name] = dimension_alias
+        internal_dimension_aliases[dimension_ref] = dimension_alias
 
     for metric_node, component in grain_group.components:
         metrics_covered.add(metric_node.name)
@@ -2057,7 +2158,10 @@ def build_grain_group_sql(
                 # FULL: apply aggregation at finest grain, will be re-aggregated in final SELECT
                 # Always use component.name for consistency - no special case for single-component
                 component_alias = component.name
-                expr_ast = build_component_expression(component)
+                expr_ast = build_component_expression(
+                    component,
+                    ctx.materialization_target,
+                )
                 component_expressions.append((component_alias, expr_ast))
                 component_metadata.append(
                     (component_alias, component, metric_node),
@@ -2076,7 +2180,7 @@ def build_grain_group_sql(
         # Always use component.name for consistency - no special case for single-component
         component_alias = component.name
 
-        expr_ast = build_component_expression(component)
+        expr_ast = build_component_expression(component, ctx.materialization_target)
         component_expressions.append((component_alias, expr_ast))
         component_metadata.append((component_alias, component, metric_node))
 
@@ -2180,6 +2284,8 @@ def build_grain_group_sql(
             resolved_dimensions=effective_resolved_dimensions,
             parent_node=parent_node,
             grain_columns=pass_through_columns,
+            output_dimension_refs=output_dimension_refs,
+            internal_dimension_aliases=internal_dimension_aliases,
             filters=ctx.dimension_filters,  # Use dimension_filters only (not metric_filters)
             skip_aggregation=True,  # Don't add GROUP BY
         )
@@ -2203,6 +2309,8 @@ def build_grain_group_sql(
             grain_columns=effective_grain_columns,
             grain_col_aliases=grain_group.grain_col_aliases or None,
             grain_col_specs=grain_col_specs,  # already parsed above
+            output_dimension_refs=output_dimension_refs,
+            internal_dimension_aliases=internal_dimension_aliases,
             filters=ctx.dimension_filters,  # Use dimension_filters only (not metric_filters)
             skip_aggregation=skip_agg,
         )
@@ -2211,7 +2319,12 @@ def build_grain_group_sql(
     columns_metadata = []
 
     # Add dimension columns (skip filter-only dimensions as they're not in projection)
+    effective_output_dimension_refs = output_dimension_refs or {
+        resolved_dim.original_ref for resolved_dim in resolved_dimensions
+    }
     for resolved_dim in resolved_dimensions:
+        if resolved_dim.original_ref not in effective_output_dimension_refs:
+            continue
         # Skip filter-only dimensions from column metadata
         if resolved_dim.original_ref in ctx.filter_dimensions:
             continue
@@ -2268,6 +2381,30 @@ def build_grain_group_sql(
             ),
         )
 
+    for dimension_ref, dimension_alias in internal_dimension_aliases.items():
+        internal_resolved_dim = next(
+            (dim for dim in resolved_dimensions if dim.original_ref == dimension_ref),
+            None,
+        )
+        if not internal_resolved_dim:
+            continue  # pragma: no cover
+        dim_node = ctx.nodes.get(internal_resolved_dim.node_name)
+        col_type = (
+            get_column_type(parent_node, internal_resolved_dim.column_name)
+            if internal_resolved_dim.is_local
+            else get_column_type(dim_node, internal_resolved_dim.column_name)
+            if dim_node
+            else "string"
+        )
+        columns_metadata.append(
+            ColumnMetadata(
+                name=dimension_alias,
+                semantic_name=dimension_ref,
+                type=col_type,
+                semantic_type="dimension",
+            ),
+        )
+
     # Add metric component columns
     # All decomposed metrics are now treated as components - no special case for single-component
     for comp_alias, component, metric_node in component_metadata:
@@ -2288,7 +2425,12 @@ def build_grain_group_sql(
                 ColumnMetadata(
                     name=ctx.alias_registry.get_alias(comp_alias) or comp_alias,
                     semantic_name=f"{metric_node.name}:{component.name}",
-                    type=infer_component_type(component, metric_type, parent_node),
+                    type=infer_component_type(
+                        component,
+                        metric_type,
+                        parent_node,
+                        ctx.materialization_target,
+                    ),
                     semantic_type="metric_component",
                 ),
             )
@@ -2313,6 +2455,8 @@ def build_grain_group_sql(
     if grain_group.aggregability != Aggregability.NONE:
         # FULL/LIMITED: dimensions are part of the grain
         for resolved_dim in resolved_dimensions:
+            if resolved_dim.original_ref not in effective_output_dimension_refs:
+                continue
             # Skip filter-only dimensions from grain
             if resolved_dim.original_ref in ctx.filter_dimensions:
                 continue
@@ -2327,6 +2471,9 @@ def build_grain_group_sql(
     for gc_alias in effective_grain_aliases:
         if gc_alias not in full_grain:  # pragma: no branch
             full_grain.append(gc_alias)
+    for dimension_alias in internal_dimension_aliases.values():
+        if dimension_alias not in full_grain:
+            full_grain.append(dimension_alias)
 
     # Sort for deterministic output
     full_grain.sort()
@@ -2339,6 +2486,7 @@ def build_grain_group_sql(
         metrics=list(metrics_covered),
         parent_name=grain_group.parent_node.name,
         component_aliases=component_aliases,
+        reaggregate_dimension_aliases=reaggregate_dimension_aliases,
         is_merged=grain_group.is_merged,
         component_aggregabilities=grain_group.component_aggregabilities,
         components=unique_components,
@@ -2375,18 +2523,15 @@ def process_metric_group(
         components_per_metric[decomposed.metric_node.name] = len(decomposed.components)
 
     # Analyze grain groups - split by aggregability
-    # Extract just the column names from dimensions for grain analysis
-    dim_column_names = [parse_dimension_ref(d).column_name for d in ctx.dimensions]
-    grain_groups = analyze_grain_groups(metric_group, dim_column_names)
+    output_dimensions = list(ctx.dimensions)
+    output_dimension_refs = set(output_dimensions)
+    grain_groups = analyze_grain_groups(metric_group, output_dimensions)
 
     # Merge compatible grain groups from same parent into single CTEs
     # This optimization reduces duplicate JOINs by outputting raw values
     # at finest grain, with aggregations applied in final SELECT
     grain_groups = merge_grain_groups(grain_groups)
 
-    # Resolve dimensions (find join paths) - shared across grain groups
-    resolved_dimensions = resolve_dimensions(ctx, parent_node)
-
     # Build SQL for each grain group. Fan-out risk is flagged inside
     # build_grain_group_sql, where the full set of emitted join paths is known.
     grain_group_sqls: list[GrainGroupSQL] = []
@@ -2394,13 +2539,26 @@ def process_metric_group(
         # Reset alias registry for each grain group to avoid conflicts
         ctx.alias_registry = AliasRegistry()
         ctx._table_alias_counter = 0
-
-        grain_group_sql = build_grain_group_sql(
-            ctx,
-            grain_group,
-            resolved_dimensions,
-            components_per_metric,
-        )
+        original_dimensions = ctx.dimensions
+        internal_dimensions = [
+            dimension_ref
+            for dimension_ref in dict.fromkeys(
+                grain_group.reaggregate_component_dimensions.values(),
+            )
+            if dimension_ref not in output_dimension_refs
+        ]
+        ctx.dimensions = output_dimensions + internal_dimensions
+        try:
+            resolved_dimensions = resolve_dimensions(ctx, parent_node)
+            grain_group_sql = build_grain_group_sql(
+                ctx,
+                grain_group,
+                resolved_dimensions,
+                components_per_metric,
+                output_dimension_refs=output_dimension_refs,
+            )
+        finally:
+            ctx.dimensions = original_dimensions
         grain_group_sqls.append(grain_group_sql)
     return grain_group_sqls
 
@@ -2514,6 +2672,33 @@ def find_parent_for_window_metric(
         else:
             return None, base_metrics  # pragma: no cover
 
+    def find_base_metrics_for_window_parent(
+        metric_name: str,
+        visited: set[str],
+    ) -> set[str]:
+        """
+        Recursively find grain-group metrics needed to compute a window parent.
+
+        Window expressions may reference a derived metric directly, but the
+        window grain group still needs the underlying base metric components.
+        """
+        if metric_name in visited:
+            return set()  # pragma: no cover
+        visited.add(metric_name)
+
+        for gg in existing_grain_groups:
+            if metric_name in gg.metrics:
+                return {metric_name}
+
+        grain_group_metrics: set[str] = set()
+        for parent_name in ctx.parent_map.get(metric_name, []):
+            parent_node = ctx.nodes.get(parent_name)
+            if parent_node and parent_node.type.value == "metric":  # pragma: no branch
+                grain_group_metrics.update(
+                    find_base_metrics_for_window_parent(parent_name, visited),
+                )
+        return grain_group_metrics
+
     # Group window metrics by (ORDER BY grain, parent fact)
     # This ensures window metrics from different facts are processed separately
     # Key: (frozenset of grain cols, parent_name or "cross_fact")
@@ -2525,13 +2710,18 @@ def find_parent_for_window_metric(
         parent_name, base_metrics = find_parent_for_window_metric(metric_name)
         # Use "cross_fact" as a marker for cross-fact window metrics
         parent_key = parent_name if parent_name else "cross_fact"
+        component_metrics = set()
+        for base_metric in base_metrics:
+            component_metrics.update(
+                find_base_metrics_for_window_parent(base_metric, set()),
+            )
 
         group_key = (grain_key, parent_key)
         if group_key not in grain_parent_to_metrics:
             grain_parent_to_metrics[group_key] = []
             grain_parent_to_base_metrics[group_key] = set()
         grain_parent_to_metrics[group_key].append(metric_name)
-        grain_parent_to_base_metrics[group_key].update(base_metrics)
+        grain_parent_to_base_metrics[group_key].update(component_metrics)
 
     additional_grain_groups: list[GrainGroupSQL] = []
 
diff --git a/datajunction-server/datajunction_server/construction/build_v3/metrics.py b/datajunction-server/datajunction_server/construction/build_v3/metrics.py
index fb8cb3b28..31bc8eb8c 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/metrics.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/metrics.py
@@ -50,13 +50,159 @@
 )
 from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.decompose import Aggregability
+from datajunction_server.models.dialect import Dialect
 from datajunction_server.models.node_type import NodeType
+from datajunction_server.models.reaggregate import ReaggregationFunction
 from datajunction_server.sql.decompose import wrap_divisions_in_nullif
 from datajunction_server.sql.parsing import ast
+from datajunction_server.sql.parsing.backends.antlr4 import parse
 
 logger = logging.getLogger(__name__)
 
 
+def _build_reaggregate_collapse_expression(
+    collapse_function: ReaggregationFunction,
+    dialect: Dialect,
+    value_ref: ast.Expression,
+    protected_dim_ref: ast.Expression,
+) -> ast.Function:
+    """
+    Build the final aggregation for a semi-additive metric collapse.
+    """
+    if collapse_function == ReaggregationFunction.LAST_VALUE:
+        function_name = "LATEST_BY" if dialect == Dialect.DRUID else "MAX_BY"
+        return ast.Function(
+            ast.Name(function_name),
+            args=[value_ref, protected_dim_ref],
+        )
+    if collapse_function == ReaggregationFunction.FIRST_VALUE:
+        function_name = "EARLIEST_BY" if dialect == Dialect.DRUID else "MIN_BY"
+        return ast.Function(
+            ast.Name(function_name),
+            args=[value_ref, protected_dim_ref],
+        )
+    if collapse_function == ReaggregationFunction.MIN:
+        return ast.Function(ast.Name("MIN"), args=[value_ref])
+    if collapse_function == ReaggregationFunction.MAX:
+        return ast.Function(ast.Name("MAX"), args=[value_ref])
+
+    raise DJInvalidInputException(
+        f"Unsupported semi-additive collapse function: {collapse_function}",
+    )
+
+
+def _references_component(expr: ast.Node, component_name: str) -> bool:
+    """
+    Return whether an expression references a decomposed metric component.
+    """
+    for col in expr.find_all(ast.Column):
+        if col.name and col.name.name == component_name:
+            return True
+    return False
+
+
+def _replace_reaggregate_merge_expression(
+    combiner_ast: ast.Expression,
+    component_name: str,
+    merge_function: str,
+    collapse_expr: ast.Expression,
+) -> ast.Expression:
+    """
+    Replace the component's merge aggregate in a combiner with a collapse expr.
+
+    Semi-additive metrics still need the full decomposed combiner expression.
+    For example, ``SUM(balance) / 100`` should become
+    ``MAX_BY(balance_sum, date_id) / 100``, not just ``MAX_BY(...)``.
+    """
+    expr_ast = (
+        cast(ast.Expression, combiner_ast.child)
+        if isinstance(combiner_ast, ast.Alias)
+        else combiner_ast
+    )
+    target_function = merge_function.upper()
+
+    for func in list(expr_ast.find_all(ast.Function)):
+        if func.name.name.upper() == target_function and _references_component(
+            func,
+            component_name,
+        ):
+            if func is expr_ast:
+                return collapse_expr
+            if func.parent:
+                func.parent.replace(from_=func, to=collapse_expr)
+                return expr_ast
+
+    raise DJInvalidInputException(
+        "Unsupported semi-additive metric shape: could not find the component "
+        "merge expression in the metric combiner.",
+    )
+
+
+def _dimension_ref_base(ref: str) -> str:
+    """Return a dimension ref without its role suffix."""
+    return ref.split("[", 1)[0]
+
+
+def _dimension_ref_role(ref: str) -> str | None:
+    """Return the role suffix for a dimension ref, if any."""
+    if "[" not in ref:
+        return None
+    return ref.rsplit("[", 1)[1].rstrip("]")
+
+
+def _source_dimension_alias(ctx: BuildContext, dimension_ref: str) -> str | None:
+    """
+    Return the registered source column alias for a dimension ref.
+    """
+    alias = ctx.alias_registry.get_alias(dimension_ref)
+    if alias or _dimension_ref_role(dimension_ref) is not None:
+        return alias
+    return ctx.alias_registry.get_alias(_dimension_ref_base(dimension_ref))
+
+
+def _metric_refs_from_query(ctx: BuildContext, metric_name: str) -> set[str]:
+    """
+    Return metric refs used by a metric's query expression.
+    """
+    metric_node = ctx.nodes.get(metric_name)
+    if not metric_node:
+        return set()  # pragma: no cover
+
+    if not metric_node.current or not metric_node.current.query:
+        return set()  # pragma: no cover
+
+    query = parse(metric_node.current.query)
+    refs: set[str] = set()
+    for col in query.select.projection[0].find_all(ast.Column):
+        full_name = get_column_full_name(col)
+        if not full_name and hasattr(col, "identifier"):
+            full_name = col.identifier()
+        node = ctx.nodes.get(full_name) if full_name else None
+        if node and node.type == NodeType.METRIC:
+            refs.add(full_name)
+    return refs
+
+
+def _metric_parent_refs(ctx: BuildContext, metric_name: str) -> list[str]:
+    """
+    Return metric parents for a metric, using the parsed query as a fallback.
+    """
+    refs: list[str] = []
+    seen: set[str] = set()
+    for parent_name in ctx.parent_map.get(metric_name, []):
+        parent_node = ctx.nodes.get(parent_name)
+        if parent_node and parent_node.type == NodeType.METRIC:
+            refs.append(parent_name)
+            seen.add(parent_name)
+
+    for parent_name in sorted(_metric_refs_from_query(ctx, metric_name)):
+        if parent_name not in seen:
+            refs.append(parent_name)
+            seen.add(parent_name)
+
+    return refs
+
+
 def classify_filters(
     filters: list[str],
     ctx: BuildContext,
@@ -207,6 +353,31 @@ def get_comp_aggregability(comp_name: str) -> Aggregability:
         comp = decomposed.components[0]
         orig_agg = get_comp_aggregability(comp.name)
 
+        if comp.rule.reaggregate and comp.name in gg.reaggregate_dimension_aliases:
+            _, col_name = comp_mappings[comp.name]
+            value_ref = make_column_ref(col_name, cte_alias)
+            protected_dim_ref = make_column_ref(
+                gg.reaggregate_dimension_aliases[comp.name],
+                cte_alias,
+            )
+            if not comp.merge:  # pragma: no cover
+                raise DJInvalidInputException(
+                    "Unsupported semi-additive metric shape: missing merge function.",
+                )
+            collapse_expr = _build_reaggregate_collapse_expression(
+                comp.rule.reaggregate.fn,
+                gg.dialect,
+                value_ref,
+                protected_dim_ref,
+            )
+            combiner_ast = parse(f"SELECT {decomposed.combiner}").select.projection[0]
+            return _replace_reaggregate_merge_expression(
+                cast(ast.Expression, combiner_ast),
+                comp.name,
+                comp.merge,
+                collapse_expr,
+            )
+
         if orig_agg == Aggregability.LIMITED:
             _, col_name = comp_mappings[comp.name]
             col_ref = make_column_ref(col_name, cte_alias)
@@ -413,6 +584,41 @@ def collect_and_build_ctes(
     return all_cte_asts, cte_aliases
 
 
+def _validate_reaggregate_base_group_join_safety(
+    grain_groups: list[GrainGroupSQL],
+) -> None:
+    """
+    Fail closed for final joins that can duplicate non-idempotent aggregates.
+
+    Semi-additive groups retain the protected dimension as private grain, so a
+    final join by only the requested output dimensions can fan out other CTEs.
+    LIMITED groups that were pre-aggregated to one row per output dimension use
+    MAX passthrough in the final select and are safe. Reaggregate groups are
+    idempotent under duplicate protected-dimension rows for their collapse.
+    Other separate groups must be rejected unless they were merged into the
+    protected grain earlier.
+    """
+    if len(grain_groups) <= 1 or not any(
+        gg.reaggregate_dimension_aliases for gg in grain_groups
+    ):
+        return
+
+    unsafe_groups = [
+        gg.parent_name
+        for gg in grain_groups
+        if not gg.reaggregate_dimension_aliases
+        and not (gg.aggregability == Aggregability.LIMITED and gg.is_pre_aggregated)
+    ]
+    if not unsafe_groups:
+        return
+
+    raise DJInvalidInputException(
+        "Semi-additive live SQL with multiple base grain groups is not "
+        "supported when another group can be fanned out before final "
+        f"aggregation. Unsafe parents: {sorted(set(unsafe_groups))}.",
+    )
+
+
 def get_dimension_types(
     grain_groups: list[GrainGroupSQL],
 ) -> dict[str, str]:
@@ -1230,11 +1436,7 @@ def build_window_agg_cte_from_grain_group(
     # Find the base metrics that the window metrics reference
     base_metrics_needed: set[str] = set()
     for window_metric_name in window_grain_group.window_metrics_served:
-        parent_names = ctx.parent_map.get(window_metric_name, [])
-        for parent_name in parent_names:
-            parent_node = ctx.nodes.get(parent_name)
-            if parent_node and parent_node.type == NodeType.METRIC:  # pragma: no branch
-                base_metrics_needed.add(parent_name)
+        base_metrics_needed.update(_metric_parent_refs(ctx, window_metric_name))
 
     def is_derived_metric(metric_name: str) -> bool:
         """Check if a metric is derived (has metric parents, no direct components)."""
@@ -1242,11 +1444,8 @@ def is_derived_metric(metric_name: str) -> bool:
         if not decomposed or not isinstance(decomposed, DecomposedMetricInfo):
             return False  # pragma: no cover
         # Derived metrics have parent metrics but no direct components in grain groups
-        parent_metrics = ctx.parent_map.get(metric_name, [])
-        has_metric_parents = any(
-            ctx.nodes.get(p) and ctx.nodes.get(p).type == NodeType.METRIC  # type: ignore
-            for p in parent_metrics
-        )
+        parent_metrics = _metric_parent_refs(ctx, metric_name)
+        has_metric_parents = bool(parent_metrics)
         return has_metric_parents and not decomposed.components
 
     def get_metric_aggregation_expr(
@@ -1284,7 +1483,7 @@ def get_metric_aggregation_expr(
 
                 # Try to find matching metric
                 parent_metric_name = None
-                for parent_name in ctx.parent_map.get(metric_name, []):
+                for parent_name in _metric_parent_refs(ctx, metric_name):
                     parent_short = get_short_name(parent_name)
                     if parent_short == short_col_name or col_name.endswith(parent_name):
                         parent_metric_name = parent_name
@@ -1300,21 +1499,11 @@ def get_metric_aggregation_expr(
                         # Swap the column node with the parent's expression
                         col_node.swap(parent_expr)
         else:
-            # Base metric: replace component references with CTE column refs
-            # Use base_grain_group's component_aliases since that's the source CTE
-            for col_node in combiner_ast.find_all(ast.Column):
-                col_full_name = (
-                    col_node.identifier() if hasattr(col_node, "identifier") else ""
-                )
-                # Check if this matches a component alias
-                for (
-                    comp_name,
-                    comp_alias,
-                ) in base_grain_group.component_aliases.items():  # pragma: no branch
-                    if col_full_name == comp_name or col_full_name.endswith(comp_alias):
-                        col_node.name = ast.Name(comp_alias)
-                        col_node._table = ast.Table(ast.Name(source_cte_alias))
-                        break
+            combiner_ast, _ = build_base_metric_expression(
+                decomposed,
+                source_cte_alias,
+                base_grain_group,
+            )
 
         return combiner_ast
 
@@ -1326,13 +1515,13 @@ def get_metric_aggregation_expr(
 
         # Get aggregation expression (handles both base and derived metrics)
         combiner_ast = get_metric_aggregation_expr(base_metric_name, set())
-        if not combiner_ast:
+        if combiner_ast is None:
             continue  # pragma: no cover
 
         short_name = get_short_name(base_metric_name)
-        aliased = combiner_ast.set_alias(ast.Name(short_name))  # type: ignore
-        aliased.set_as(True)
-        projection.append(aliased)
+        metric_alias = ast.Alias(child=combiner_ast, alias=ast.Name(short_name))
+        metric_alias.set_as(True)
+        projection.append(metric_alias)
 
     # Build FROM clause
     from_clause = ast.From(
@@ -1394,62 +1583,107 @@ def build_window_agg_cte_from_base_metrics(
     # Find the base metrics that the window metrics reference
     base_metrics_needed: set[str] = set()
     for window_metric_name in window_grain_group.window_metrics_served:
-        parent_names = ctx.parent_map.get(window_metric_name, [])
-        for parent_name in parent_names:
-            parent_node = ctx.nodes.get(parent_name)
-            if parent_node and parent_node.type == NodeType.METRIC:  # pragma: no branch
-                base_metrics_needed.add(parent_name)
-
-    # Build reaggregation expressions for each base metric
-    # Since we're selecting from base_metrics, we reference the metric columns directly
+        base_metrics_needed.update(_metric_parent_refs(ctx, window_metric_name))
+
+    window_dimension_aliases = {
+        col.name
+        for col in window_grain_group.columns
+        if col.semantic_type == "dimension"
+    }
+
+    def build_base_metric_reaggregation_expr(
+        metric_name: str,
+        decomposed: DecomposedMetricInfo,
+    ) -> ast.Expression:
+        """
+        Build a reaggregation expression for a base metric from base_metrics.
+        """
+        short_name = get_short_name(metric_name)
+        metric_ref = make_column_ref(short_name, base_metrics_cte_alias)
+
+        if len(decomposed.components) == 1:
+            comp = decomposed.components[0]
+            if comp.rule.reaggregate:
+                protected_dim_alias = _source_dimension_alias(
+                    ctx,
+                    comp.rule.reaggregate.dimension,
+                )
+                if (
+                    protected_dim_alias
+                    and protected_dim_alias not in window_dimension_aliases
+                ):
+                    return _build_reaggregate_collapse_expression(
+                        comp.rule.reaggregate.fn,
+                        ctx.dialect,
+                        metric_ref,
+                        make_column_ref(protected_dim_alias, base_metrics_cte_alias),
+                    )
+
+        if decomposed.aggregability == Aggregability.NONE:  # pragma: no cover
+            # NONE: non-additive (like AVG), need to recompute.
+            # For AVG, we need the raw sum and count, but those are in components.
+            # For now, just use the column directly (window function will handle it).
+            return metric_ref
+
+        # base_metrics exposes metric columns, not raw component/grain columns.
+        # Reaggregate leaf metrics from those projected metric values.
+        return ast.Function(ast.Name("SUM"), args=[metric_ref])
+
+    def build_metric_reaggregation_expr(
+        metric_name: str,
+        visited: set[str],
+    ) -> ast.Expression | None:
+        """
+        Build a reaggregation expression for base or derived metrics.
+        """
+        if metric_name in visited:
+            return None  # pragma: no cover
+        visited.add(metric_name)
+
+        parent_refs = _metric_parent_refs(ctx, metric_name)
+        if not parent_refs:
+            decomposed = decomposed_metrics.get(metric_name)
+            if not decomposed:
+                return None  # pragma: no cover
+            return build_base_metric_reaggregation_expr(metric_name, decomposed)
+
+        metric_node = ctx.nodes.get(metric_name)
+        if not metric_node:
+            return None  # pragma: no cover
+        if not metric_node.current or not metric_node.current.query:
+            return None  # pragma: no cover
+        original_query = parse(metric_node.current.query)
+        expr_ast = deepcopy(original_query.select.projection[0])
+        if isinstance(expr_ast, ast.Alias):
+            expr_ast = expr_ast.child  # pragma: no cover
+
+        for col_node in list(expr_ast.find_all(ast.Column)):
+            parent_name = get_column_full_name(col_node)
+            if not parent_name and hasattr(col_node, "identifier"):
+                parent_name = col_node.identifier()
+            if not parent_name or parent_name not in parent_refs:
+                continue
+
+            parent_expr = build_metric_reaggregation_expr(
+                parent_name,
+                visited.copy(),
+            )
+            if parent_expr is not None:
+                col_node.swap(parent_expr)
+
+        wrap_divisions_in_nullif(cast(ast.Expression, expr_ast))
+        return expr_ast  # type: ignore
+
+    # Build reaggregation expressions for each parent metric.
     for base_metric_name in sorted(base_metrics_needed):
-        decomposed = decomposed_metrics.get(base_metric_name)
-        if not decomposed:
+        agg_expr = build_metric_reaggregation_expr(base_metric_name, set())
+        if agg_expr is None:
             continue  # pragma: no cover
 
         short_name = get_short_name(base_metric_name)
-
-        # For non-additive metrics (COUNT DISTINCT), we need COUNT(DISTINCT grain_col)
-        # For additive metrics, we can SUM/AVG the pre-computed column
-        if decomposed.aggregability == Aggregability.LIMITED:
-            # LIMITED: needs COUNT DISTINCT at this grain
-            # Find the grain column for COUNT DISTINCT
-            if decomposed.components and decomposed.components[0].rule.level:
-                grain_col = decomposed.components[0].rule.level[0]
-                # The grain column is in base_metrics as a dimension column
-                # Actually, for COUNT DISTINCT, the raw grain column should be in
-                # the base grain group, not base_metrics. We need to reference
-                # the original grain column from base_metrics.
-                # For now, use the metric column - this works because base_metrics
-                # computes COUNT DISTINCT at the fine grain, and we re-compute at coarser grain
-                col_ref = make_column_ref(grain_col, base_metrics_cte_alias)
-                agg_expr = ast.Function(
-                    ast.Name("COUNT"),
-                    args=[col_ref],
-                    quantifier=ast.SetQuantifier.Distinct,
-                )
-            else:  # pragma: no cover
-                # Fallback: SUM the metric column
-                col_ref = make_column_ref(short_name, base_metrics_cte_alias)
-                agg_expr = ast.Function(ast.Name("SUM"), args=[col_ref])
-        elif decomposed.aggregability == Aggregability.FULL:  # pragma: no cover
-            # FULL: additive metric, use SUM
-            col_ref = make_column_ref(short_name, base_metrics_cte_alias)
-            agg_expr = ast.Function(ast.Name("SUM"), args=[col_ref])
-        elif decomposed.aggregability == Aggregability.NONE:  # pragma: no cover
-            # NONE: non-additive (like AVG), need to recompute
-            # For AVG, we need the raw sum and count, but those are in components
-            # For now, just use the column directly (window function will handle it)
-            col_ref = make_column_ref(short_name, base_metrics_cte_alias)
-            agg_expr = col_ref  # type: ignore
-        else:  # pragma: no cover
-            # Default: SUM
-            col_ref = make_column_ref(short_name, base_metrics_cte_alias)
-            agg_expr = ast.Function(ast.Name("SUM"), args=[col_ref])
-
-        aliased = agg_expr.set_alias(ast.Name(short_name))  # type: ignore
-        aliased.set_as(True)
-        projection.append(aliased)
+        metric_alias = ast.Alias(child=agg_expr, alias=ast.Name(short_name))
+        metric_alias.set_as(True)
+        projection.append(metric_alias)
 
     # Build FROM clause - just base_metrics
     from_clause = ast.From(
@@ -1760,6 +1994,7 @@ def generate_metrics_sql(
         base_grain_groups,
         skip_pre_agg=will_have_base_metrics_cte,
     )
+    _validate_reaggregate_base_group_join_safety(base_grain_groups)
 
     # Build dimension info and projection
     # Filter out filter-only dimensions (they're needed for WHERE but not output)
diff --git a/datajunction-server/datajunction_server/construction/build_v3/preagg_matcher.py b/datajunction-server/datajunction_server/construction/build_v3/preagg_matcher.py
index 575a22901..4cdcdd311 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/preagg_matcher.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/preagg_matcher.py
@@ -19,6 +19,8 @@
 from datajunction_server.database.preaggregation import (
     PreAggregation,
     compute_expression_hash,
+    get_measure_identities,
+    measure_identity_token,
 )
 from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.decompose import Aggregability, MetricComponent
@@ -42,10 +44,9 @@
 
 def get_required_measure_identities(
     grain_group: GrainGroup,
-) -> set[tuple[str, str]]:
+) -> set[str]:
     """
-    Identity of each measure a grain group needs: (expression hash, Phase-1
-    aggregation).
+    Identity token of each measure a grain group needs.
 
     Matching is name-independent, so ``expr_hash`` stands in for identity -- but
     it covers the expression alone, making ``SUM(x)`` and ``MAX(x)`` hash alike
@@ -55,16 +56,40 @@ def get_required_measure_identities(
 
     Phase-1 ``aggregation`` rather than ``merge``, because merge is not unique --
     COUNT accumulates with COUNT but merges with SUM.
+
+    Built through ``measure_identity_token`` rather than assembled here, so the
+    definition of identity lives in one place -- it also folds in sketch tuning
+    parameters, which a locally-built tuple would silently omit.
     """
     return {
-        (
+        measure_identity_token(
             compute_expression_hash(component.expression),
-            component.normalized_aggregation,
+            component.aggregation,
+            component.params,
         )
         for _, component in grain_group.components
     }
 
 
+def required_reaggregate_grain(
+    ctx: BuildContext,
+    node_rev_id: int,
+    grain_group: GrainGroup,
+) -> set[str]:
+    """
+    Protected dimensions a pre-agg must retain for final semi-additive collapse.
+
+    ``reaggregate_component_dimensions`` is populated only when the requested
+    output grain omitted the protected dimension. In that case a pre-agg at the
+    output grain would have already collapsed away the key needed by MAX_BY /
+    MIN_BY / min / max in the metrics layer, so it is not substitutable.
+    """
+    return {
+        canonical_dimension_ref(ctx, node_rev_id, dimension_ref)
+        for dimension_ref in grain_group.reaggregate_component_dimensions.values()
+    }
+
+
 def canonical_dimension_ref(
     ctx: BuildContext,
     node_rev_id: int,
@@ -256,6 +281,12 @@ def find_matching_preagg(
     if not required_measures:
         return None
 
+    reaggregate_grain = required_reaggregate_grain(
+        ctx,
+        node_rev_id,
+        grain_group,
+    )
+
     requested_grain_set = {
         canonical_dimension_ref(ctx, node_rev_id, rdim.original_ref)
         for rdim in resolved_dimensions
@@ -282,6 +313,14 @@ def find_matching_preagg(
             for ref in (preagg.grain_columns or [])
         }
 
+        if not reaggregate_grain.issubset(preagg_grain_set):
+            logger.debug(
+                f"[BuildV3] Pre-agg {preagg.id} grain {preagg_grain_set} "
+                f"doesn't retain semi-additive protected grain "
+                f"{reaggregate_grain}",
+            )
+            continue
+
         # Check grain compatibility. For additive measures the pre-agg may be at
         # the same or a finer grain (roll-up allowed → subset match). For
         # non-additive measures the pre-agg grain must exactly match the request.
@@ -315,13 +354,9 @@ def find_matching_preagg(
                 continue
             join_back = coverage
 
-        # Coverage check on (expression hash, Phase-1 aggregation) -- see
-        # get_required_measure_identities for why the aggregation is part of it.
-        preagg_measures = {
-            (measure.expr_hash, measure.normalized_aggregation)
-            for measure in preagg.measures
-            if measure.expr_hash
-        }
+        # Coverage check on measure identity -- see
+        # get_required_measure_identities for what that comprises.
+        preagg_measures = get_measure_identities(preagg.measures)
         if not required_measures.issubset(preagg_measures):
             logger.debug(
                 f"[BuildV3] Pre-agg {preagg.id} measures {preagg_measures} "
@@ -373,13 +408,21 @@ def get_preagg_measure_column(
     Returns:
         Column name in the pre-agg, or None if not found
     """
-    target = (
+    target = measure_identity_token(
         compute_expression_hash(component.expression),
-        component.normalized_aggregation,
+        component.aggregation,
+        component.params,
     )
 
     for measure in preagg.measures:
-        if (measure.expr_hash, measure.normalized_aggregation) == target:
+        if (
+            measure_identity_token(
+                measure.expr_hash,
+                measure.aggregation,
+                measure.params,
+            )
+            == target
+        ):
             # Externally-registered pre-aggs bind the measure to a physical
             # column name that may differ from the DJ component name.
             return measure.source_column or measure.name
diff --git a/datajunction-server/datajunction_server/construction/build_v3/types.py b/datajunction-server/datajunction_server/construction/build_v3/types.py
index faac2e34c..2d3f6523c 100644
--- a/datajunction-server/datajunction_server/construction/build_v3/types.py
+++ b/datajunction-server/datajunction_server/construction/build_v3/types.py
@@ -13,6 +13,7 @@
 from datajunction_server.errors import DJInvalidInputException, DJWarning
 from datajunction_server.models.decompose import Aggregability, MetricComponent
 from datajunction_server.models.dialect import Dialect
+from datajunction_server.models.materialization import MaterializationTarget
 from datajunction_server.models.node_type import NodeType
 from datajunction_server.sql.parsing import ast
 from datajunction_server.sql.parsing.ast import to_sql
@@ -43,6 +44,9 @@ class BuildContext:
     dimensions: list[str]
     filters: list[str] = field(default_factory=list)
     dialect: Dialect = Dialect.SPARK
+    # Set only when this build is producing a measures table for materialization.
+    # None for query-time builds, which never serialize.
+    materialization_target: MaterializationTarget | None = None
     alias_registry: AliasRegistry = field(default_factory=AliasRegistry)
 
     # Filter classification (populated early in setup)
@@ -316,6 +320,10 @@ class GrainGroupSQL:
     # Used by metrics SQL to correctly reference component columns
     component_aliases: dict[str, str] = field(default_factory=dict)
 
+    # Active semi-additive components that need collapse in the metrics layer.
+    # Maps component.name -> protected dimension column alias emitted by this CTE.
+    reaggregate_dimension_aliases: dict[str, str] = field(default_factory=dict)
+
     # Merge tracking: when True, aggregations happen in final SELECT, not in CTE
     is_merged: bool = False
 
@@ -743,6 +751,10 @@ class GrainGroup:
     # between decompose.py component identifiers and measures.py SQL aliases.
     grain_col_aliases: dict[str, str] = field(default_factory=dict)
 
+    # Active semi-additive components that require an internal protected dimension
+    # in this grain group. Maps component.name -> protected dimension ref.
+    reaggregate_component_dimensions: dict[str, str] = field(default_factory=dict)
+
     # Non-decomposable metrics that couldn't be broken into components
     # These need their raw metric expression applied in the final SELECT
     non_decomposable_metrics: list[DecomposedMetricInfo] = field(default_factory=list)
diff --git a/datajunction-server/datajunction_server/database/measure.py b/datajunction-server/datajunction_server/database/measure.py
index bfdb81c11..32d0bc079 100644
--- a/datajunction-server/datajunction_server/database/measure.py
+++ b/datajunction-server/datajunction_server/database/measure.py
@@ -119,6 +119,12 @@ class FrozenMeasure(Base):
     # How to aggregate the resolved expression (e.g., SUM, COUNT, AVG)
     aggregation: Mapped[str]
 
+    # Tuning parameters for `aggregation`, for sketch-backed measures whose
+    # function name alone doesn't pin down the accumulate (a t-digest still needs
+    # a compression). Part of the measure's identity: a sketch frozen at one
+    # accuracy must not be reused for a metric asking for another.
+    params: Mapped[dict | None] = mapped_column(JSON, nullable=True)
+
     # Additivity or rollup rule - tells the planner if this measure can be summed,
     # needs special handling, or is non-additive.
     rule: Mapped[MeasureAggregationRule] = mapped_column(MeasureAggregationRuleType)
diff --git a/datajunction-server/datajunction_server/database/node.py b/datajunction-server/datajunction_server/database/node.py
index d93acc753..942bc2556 100644
--- a/datajunction-server/datajunction_server/database/node.py
+++ b/datajunction-server/datajunction_server/database/node.py
@@ -88,6 +88,7 @@
 from datajunction_server.models.custom_metadata import CustomMetadataFilter
 from datajunction_server.models.node_type import NodeType
 from datajunction_server.models.partition import PartitionType
+from datajunction_server.models.reaggregate import parse_reaggregate_spec
 from datajunction_server.models.unit import (
     AtomicUnit,
     CompoundUnit,
@@ -678,6 +679,7 @@ async def to_spec(self, session: AsyncSession) -> NodeSpec:
                 )
             extra_kwargs.update(
                 required_dimensions=required_dimensions_spec,
+                reaggregate=parse_reaggregate_spec(self.current.reaggregate),
                 direction=self.current.metric_metadata.direction
                 if self.current.metric_metadata
                 else None,
@@ -1721,6 +1723,14 @@ class NodeRevision(
         uselist=False,
     )
 
+    # Declares how a metric should roll up across dimensions.
+    # Stored as JSON here and validated at the API/deployment boundaries.
+    reaggregate: Mapped[dict[str, Any] | None] = mapped_column(
+        JSON,
+        nullable=True,
+        default=None,
+    )
+
     # Filters that are always applied when generating SQL for a cube node
     cube_filters: Mapped[list[str] | None] = mapped_column(
         JSON,
@@ -2067,6 +2077,12 @@ def extra_validation(self) -> None:
                 "bound dimensions which are only for metrics.",
             )
 
+        if self.type != NodeType.METRIC and self.reaggregate:
+            raise DJInvalidInputException(
+                f"Node {self.name} of type {self.type} cannot have "
+                "reaggregate settings which are only for metrics.",
+            )
+
         if self.type == NodeType.METRIC:
             self.check_metric()
 
diff --git a/datajunction-server/datajunction_server/database/preaggregation.py b/datajunction-server/datajunction_server/database/preaggregation.py
index 2572ff418..ce37033ad 100644
--- a/datajunction-server/datajunction_server/database/preaggregation.py
+++ b/datajunction-server/datajunction_server/database/preaggregation.py
@@ -6,6 +6,7 @@
 import json
 from datetime import UTC, datetime
 from functools import partial
+from typing import Any
 
 import sqlalchemy as sa
 from sqlalchemy import (
@@ -80,16 +81,64 @@ def compute_grain_group_hash(
     return hashlib.md5(content.encode()).hexdigest()
 
 
-def measure_identity_token(expr_hash: str, aggregation: str | None) -> str:
+def canonical_params(params: dict[str, Any] | None) -> str:
+    """
+    Stable string form of a component's tuning parameters, for identity.
+
+    Keys are sorted and numeric values normalized, so ``{"compression": 200}``
+    and ``{"compression": 200.0}`` cannot yield two identities for one sketch.
+    """
+    if not params:
+        return ""
+
+    def norm(value: Any) -> Any:
+        # 200 and 200.0 are the same compression; don't let the literal's
+        # spelling fork the identity. (bools are not floats, so they pass
+        # through untouched.)
+        if isinstance(value, float) and value.is_integer():
+            return int(value)
+        return value
+
+    # `default=str` so an exotic value can't raise from inside pre-agg matching.
+    # Params arrive as parsed YAML/JSON, so this should be unreachable; an opaque
+    # TypeError deep in the matcher is a bad way to find out otherwise.
+    return json.dumps(
+        {key: norm(params[key]) for key in sorted(params)},
+        sort_keys=True,
+        separators=(",", ":"),
+        default=str,
+    )
+
+
+def measure_identity_token(
+    expr_hash: str,
+    aggregation: str | None,
+    params: dict[str, Any] | None = None,
+) -> str:
     """
     Canonical token identifying one measure: expression hash plus Phase-1
-    aggregation.
+    aggregation, plus tuning parameters when the aggregation takes them.
 
     The hash alone is not an identity -- ``SUM(x)`` and ``MAX(x)`` hash alike --
     and a partial is reusable only by a metric that accumulates the same way.
     Everything comparing or hashing measures goes through this.
+
+    Parameters extend that reasoning to sketches: a t-digest accumulated at
+    ``compression=100`` is not interchangeable with one at ``compression=1000``,
+    though both are ``nflx_tdigest_agg`` over the same expression. The segment is
+    appended **only** when params are present, so tokens for the overwhelming
+    majority of components -- plain ``SUM``, ``COUNT``, ``MAX`` -- are byte-identical
+    to what this returned before params existed. Stored ``preagg_hash`` values are
+    built from these tokens, so that stability is a compatibility requirement, not
+    a nicety.
+
+    Note the quantile *fractions* a metric asks for are deliberately not here. One
+    sketch column serves p50, p95 and p99; folding fractions in would fragment it
+    into three identical columns.
     """
-    return f"{expr_hash}:{(aggregation or '').strip().upper()}"
+    base = f"{expr_hash}:{(aggregation or '').strip().upper()}"
+    suffix = canonical_params(params)
+    return f"{base}:{suffix}" if suffix else base
 
 
 def get_measure_identities(measures: list[PreAggMeasure]) -> set[str]:
@@ -103,7 +152,7 @@ def get_measure_identities(measures: list[PreAggMeasure]) -> set[str]:
         Set of identity tokens
     """
     return {
-        measure_identity_token(m.expr_hash, m.aggregation)
+        measure_identity_token(m.expr_hash, m.aggregation, m.params)
         for m in measures
         if m.expr_hash
     }
@@ -173,36 +222,7 @@ def compute_preagg_hash(
 
 
 class PreAggregation(Base):
-    """
-    First-class pre-aggregation entity that can be shared across cubes.
-
-    A pre-aggregation represents a materialized grouping of measures at a specific grain,
-    enabling efficient metric calculations by pre-computing aggregations.
-
-    Pre-aggregations are ALWAYS created by DJ (via /preaggs/plan endpoint) from
-    metrics + dimensions. Users never manually construct them - this ensures
-    consistency between DJ-managed (Flow A) and user-managed (Flow B) materialization.
-
-    Key concepts:
-    - `node_revision`: The specific node revision this pre-agg is based on
-    - `grain_columns`: Fully qualified dimension references that define the aggregation level
-    - `measures`: Full MetricComponent info for matching and re-aggregation
-    - `sql`: The generated SQL for materializing this pre-agg
-    - `grain_group_hash`: Hash of (node_revision_id + sorted(grain_columns)) for grouping
-
-    Measure format (MetricComponent):
-    - name: Column name in materialized table
-    - expression: The raw SQL expression
-    - expr_hash: Hash of expression for identity matching
-    - aggregation: Phase 1 function (e.g., "SUM")
-    - merge: Phase 2 re-aggregation function
-    - rule: Aggregation rules (type, level)
-
-    Availability tracking:
-    - Materialization status is tracked via AvailabilityState
-    - Flow A: DJ's query service posts availability after materialization
-    - Flow B: User's query service posts to /preaggs/{id}/availability/
-    """
+    """First-class pre-aggregation entity that can be shared across cubes."""
 
     __tablename__ = "pre_aggregation"
 
@@ -441,33 +461,7 @@ async def find_matching(
         grain_columns: list[str],
         measure_identities: set[str],
     ) -> PreAggregation | None:
-        """
-        Find the row this exact declaration already occupies, if any.
-
-        This is the upsert's identity check -- "is this the same declaration I am
-        about to write?" -- so it matches the uniqueness key exactly: same
-        revision and grain (via ``grain_group_hash``) and the SAME set of measure
-        identities, not merely a covering one.
-
-        Covering was wrong here, in two compounding ways. Callers replace the
-        matched row's contents wholesale, so a narrow declaration could match a
-        wider pre-agg and silently strip measures off it, breaking routing for
-        whatever metric depended on the dropped ones. And ``preagg_hash`` is
-        UNIQUE over exactly ``(node_revision_id, grain_columns,
-        measure_identities)`` and frozen at insert, so a covering match could
-        hand back a row whose stored hash no longer described its own contents.
-        A declaration that genuinely differs now gets its own row instead.
-
-        Identity tokens rather than bare expression hashes, because the latter
-        made SUM- and MAX-backed pre-aggs look like one row, so registering
-        either silently overwrote the other.
-
-        Contrast ``find_latest_for_node``, which asks the other question -- "what
-        did this declaration look like before?" -- and does want covering.
-
-        Returns:
-            Matching PreAggregation if found, None otherwise
-        """
+        """Find the row this exact declaration already occupies, if any."""
         grain_group_hash = compute_grain_group_hash(node_revision_id, grain_columns)
         candidates = await cls.get_by_grain_group_hash(session, grain_group_hash)
 
diff --git a/datajunction-server/datajunction_server/internal/client.py b/datajunction-server/datajunction_server/internal/client.py
index 7964b4cd2..37c9d24ed 100644
--- a/datajunction-server/datajunction_server/internal/client.py
+++ b/datajunction-server/datajunction_server/internal/client.py
@@ -192,6 +192,11 @@ async def python_client_create_node(
                 "required_dimensions": [  # type: ignore
                     col.name for col in node.current.required_dimensions
                 ],
+                **(
+                    {"reaggregate": node.current.reaggregate}  # type: ignore
+                    if node.current.reaggregate
+                    else {}
+                ),
                 **(
                     {
                         "direction": (  # type: ignore
diff --git a/datajunction-server/datajunction_server/internal/cube_materializations.py b/datajunction-server/datajunction_server/internal/cube_materializations.py
index f5531ff9d..aa529c30b 100644
--- a/datajunction-server/datajunction_server/internal/cube_materializations.py
+++ b/datajunction-server/datajunction_server/internal/cube_materializations.py
@@ -24,7 +24,10 @@
     UpsertCubeMaterialization,
 )
 from datajunction_server.models.dialect import Dialect
-from datajunction_server.models.materialization import MaterializationStrategy
+from datajunction_server.models.materialization import (
+    MaterializationStrategy,
+    MaterializationTarget,
+)
 from datajunction_server.models.node_type import NodeNameVersion
 from datajunction_server.models.partition import Granularity
 from datajunction_server.models.query import ColumnMetadata
@@ -371,6 +374,11 @@ async def build_cube_materialization(
         filters=(current_revision.cube_filters or []) + extra_filters,
         dialect=Dialect.SPARK,
         use_materialized=True,
+        # Measures SQL runs in Spark for every target, so the dialect above
+        # cannot say where the output is headed. Druid is the only cube target
+        # (`UpsertCubeMaterialization.job` is Literal["druid_cube"]), and this is
+        # the one caller that knows it.
+        materialization_target=MaterializationTarget.DRUID,
     )
     measures_queries = sorted(
         [
diff --git a/datajunction-server/datajunction_server/internal/deployment/orchestrator.py b/datajunction-server/datajunction_server/internal/deployment/orchestrator.py
index adbb15ee3..365c712f3 100644
--- a/datajunction-server/datajunction_server/internal/deployment/orchestrator.py
+++ b/datajunction-server/datajunction_server/internal/deployment/orchestrator.py
@@ -122,6 +122,7 @@
     NodeStatus,
     NodeType,
 )
+from datajunction_server.models.reaggregate import dump_reaggregate_spec
 from datajunction_server.models.unit import (
     AtomicUnit,
     CompoundUnit,
@@ -5367,6 +5368,9 @@ async def _create_node_revision(
                     dependency_nodes,
                 )
                 new_revision.required_dimensions = matched_columns
+            new_revision.reaggregate = dump_reaggregate_spec(
+                metric_spec.rendered_reaggregate,
+            )
         return new_revision
 
     def _resolve_metric_unit(
diff --git a/datajunction-server/datajunction_server/internal/deployment/validation.py b/datajunction-server/datajunction_server/internal/deployment/validation.py
index d6d65f3bf..92c7369cd 100644
--- a/datajunction-server/datajunction_server/internal/deployment/validation.py
+++ b/datajunction-server/datajunction_server/internal/deployment/validation.py
@@ -29,6 +29,9 @@
 )
 from datajunction_server.models.dimensionlink import JoinType
 from datajunction_server.models.node import NodeStatus, NodeType
+from datajunction_server.models.reaggregate import (
+    unsupported_dimension_reaggregate_functions,
+)
 from datajunction_server.sql.dag import get_dimensions
 from datajunction_server.sql.parsing.ast import fast_parse_mode
 from datajunction_server.sql.parsing.backends.antlr4 import ast, parse_rule
@@ -153,7 +156,7 @@ async def validate(self, node_specs: list[NodeSpec]) -> list[NodeValidationResul
                 specs_needing_parse.append(spec)
                 spec_indices.append(i)
 
-        # Pre-fetch dimension nodes for required_dimensions validation
+        # Pre-fetch dimension nodes for metric dimension-reference validation
         await self._prefetch_required_dimension_nodes(specs_needing_parse)
 
         # Pre-fetch dimension sets for cross-fact derived metric validation
@@ -520,6 +523,10 @@ def validate_query_node(
             if req_dim_error is not None:
                 errors.append(req_dim_error)
 
+            reaggregate_error = self._check_reaggregate_dimension(spec)
+            if reaggregate_error is not None:
+                errors.append(reaggregate_error)
+
             cross_fact_error = self._check_cross_fact_dimensions(spec)
             if cross_fact_error is not None:
                 errors.append(cross_fact_error)  # pragma: no cover
@@ -655,9 +662,9 @@ async def _prefetch_required_dimension_nodes(
         specs: list[NodeSpec],
     ) -> None:
         """
-        Collect all dimension node names referenced via required_dimensions across the
-        batch, fetch any not already in dependency_nodes in a single DB query, and
-        store the combined map in self._all_dim_nodes.
+        Collect all dimension node names referenced via metric-only dimension
+        declarations across the batch, fetch any not already in dependency_nodes in a
+        single DB query, and store the combined map in self._all_dim_nodes.
         """
         req_dim_node_names: set[str] = set()
         for spec in specs:
@@ -667,6 +674,12 @@ async def _prefetch_required_dimension_nodes(
                 if SEPARATOR in req_dim:
                     dim_node_name = req_dim.rsplit(SEPARATOR, 1)[0]
                     req_dim_node_names.add(dim_node_name)
+            reaggregate = getattr(spec, "rendered_reaggregate", None)
+            if reaggregate:
+                for rule in reaggregate.rules:
+                    if SEPARATOR in rule.dimension:
+                        dim_node_name = rule.dimension.rsplit(SEPARATOR, 1)[0]
+                        req_dim_node_names.add(dim_node_name)
 
         self._all_dim_nodes = dict(self.context.dependency_nodes)
 
@@ -780,6 +793,54 @@ def _check_required_dimensions(self, spec: NodeSpec) -> DJError | None:
             debug={"invalid_required_dimensions": list(invalid)},
         )
 
+    def _check_reaggregate_dimension(self, spec: NodeSpec) -> DJError | None:
+        """
+        Validate that reaggregate rule dimensions resolve to real columns.
+        """
+        reaggregate = getattr(spec, "rendered_reaggregate", None)
+        if not reaggregate or not reaggregate.rules:
+            return None
+
+        invalid_functions = unsupported_dimension_reaggregate_functions(reaggregate)
+        if invalid_functions:
+            return DJError(
+                code=ErrorCode.INVALID_ARGUMENTS_TO_FUNCTION,
+                message=(
+                    "Node definition contains unsupported dimension "
+                    "reaggregate function(s)."
+                ),
+                debug={
+                    "invalid_reaggregate_functions": invalid_functions,
+                },
+            )
+
+        dep_names = self.context.node_graph.get(spec.rendered_name, [])
+        parent_columns = [
+            col
+            for dep_name in dep_names
+            for dep_node in [self.context.dependency_nodes.get(dep_name)]
+            if dep_node and dep_node.current
+            for col in dep_node.current.columns
+        ]
+
+        invalid, _ = _resolve_required_dimensions(
+            [rule.dimension for rule in reaggregate.rules],
+            parent_columns,
+            self._all_dim_nodes,
+        )
+
+        if not invalid:
+            return None
+
+        return DJError(
+            code=ErrorCode.INVALID_COLUMN,
+            message=(
+                "Node definition contains references to columns as "
+                "reaggregate dimensions that are not on parent nodes."
+            ),
+            debug={"invalid_reaggregate_dimensions": list(invalid)},
+        )
+
     async def _prefetch_metric_dimensions(
         self,
         specs: list[NodeSpec],
diff --git a/datajunction-server/datajunction_server/internal/materializations.py b/datajunction-server/datajunction_server/internal/materializations.py
index eafc57440..0535a215e 100644
--- a/datajunction-server/datajunction_server/internal/materializations.py
+++ b/datajunction-server/datajunction_server/internal/materializations.py
@@ -475,36 +475,6 @@ async def reconcile_declared_materializations(
 
     Returns what each declared block resolved to, and the materializations the
     blocks together superseded.
-
-    A cube may declare more than one block -- typically an `incremental_time` build
-    for freshness beside a periodic `full` rebuild that corrects late-arriving data
-    -- and each is reconciled the same way. They cannot collide: the row name is
-    derived from job, strategy and partition, the job is fixed for a cube and the
-    strategies are unique by validation, so every declared block owns a row of its
-    own.
-
-    Mirrors what `POST /nodes/{name}/materialization/` does -- build the config, then
-    update the row of the same name in place rather than inserting a second one,
-    since `(name, node_revision_id)` is unique -- with one deliberate difference. The
-    endpoint decides "unchanged" on `config` alone, but `schedule` and `strategy` are
-    columns rather than config keys, so by that test a schedule-only edit compares
-    equal and is silently dropped. Rescheduling is the whole point of a declared
-    block, so all three are compared here.
-
-    The declared blocks describe *the* materializations for their cube, so every
-    other active row on the revision is deactivated. The rule is "any active row
-    whose name no block built" rather than "any row of the same job type", because a
-    cube's materializations are all writing one Druid datasource and a full rebuild
-    replaces that datasource wholesale -- so a legacy `druid_measures_cube` row is
-    just as much a competing writer as a second `druid_cube` row, and matching on job
-    type would leave it running. The name is what carries the difference: it is
-    derived from job, strategy and partition, so declaring a strategy the cube was
-    not already materialized with builds a differently named row, and without this
-    the cube ends up with two live workflows deleting each other's data.
-
-    A cube planner row is the one thing left alone. It writes a datasource of its own
-    and DJ cannot rebuild it from a declared block, so superseding it would stop a
-    workflow nothing here can replace.
     """
     # Snapshotted before the builds, each of which sets the new materialization's
     # backref and so appends it to this very collection -- searching afterwards would
@@ -584,27 +554,7 @@ async def swap_cube_materializations(
 ) -> CubeMaterializationSwap | None:
     """
     Rebuild a cube's materializations against a new revision and retire the old ones.
-
-    Materializations belong to a single `NodeRevision` and availability is scoped to
-    the revision encoded in the materialized table name, so without this a new cube
-    revision -- including a metadata-only one -- has neither: the cube silently falls
-    back to live queries while the superseded revision's workflow keeps posting
-    availability for a table built from the old definition. Every new revision
-    therefore swaps, no matter how insignificant the change was.
-
-    Rebuilt rather than copied: a stored config embeds the cube version plus combiner
-    SQL and a Druid spec derived from the old definition, so the new revision goes
-    back through `create_new_materialization`. The old materialization is the default
-    source of the user's intent because it is often the only record of it -- a cube
-    materialized through the UI has no YAML to read it from. A cube that does declare
-    `materialization:` passes it as `declared`, which wins, so a push that edits both
-    a metric and the schedule rebuilds with the new schedule rather than the old.
-
-    `previous_table_usable` is the caller's answer to whether the superseded
-    revision's materialized table is still valid (`is_non_trivial_cube_change`
-    inverted), recorded on the history event so an operator can tell whether the
-    rebuild can adopt the existing data or needs a fresh build and backfill.
-
+    
     Touches only DJ-side state, and returns the query service work still owed --
     `None` when the cube had nothing materialized and there is no work at all. The
     caller commits and then hands the result to `apply_cube_materialization_swap`, so
@@ -650,18 +600,8 @@ async def swap_cube_materializations(
         try:
             upsert = _upsert_from_materialization(materialization)
             if declared and isinstance(upsert, UpsertCubeMaterialization):
-                # The declared block wins over the recovered intent: a cube that
-                # declares `materialization:` has its config in the repo, so a
-                # rebuild triggered by the same deploy must build what the YAML now
-                # says. Only cube materializations can be declared; anything else
-                # keeps what was recovered.
-                #
-                # Which block, when the cube declares several: the one naming the
-                # strategy this row was built with, since that is what identifies a
-                # declared entry. A row whose strategy nothing declares falls to the
-                # first block, which is what a cube declaring exactly one has always
-                # done -- and is the only sensible answer, since a rebuild has to
-                # produce something for a row that is being retired either way.
+                # The declared block wins over the recovered intent.
+                # If there are several, match by strategy, otherwise use the first.
                 block = next(
                     (
                         candidate
diff --git a/datajunction-server/datajunction_server/internal/namespaces.py b/datajunction-server/datajunction_server/internal/namespaces.py
index d3eb5f10b..32bae088f 100644
--- a/datajunction-server/datajunction_server/internal/namespaces.py
+++ b/datajunction-server/datajunction_server/internal/namespaces.py
@@ -1229,6 +1229,7 @@ def _metric_project_config(node: Node, namespace_requested: str) -> dict:
         "query": node.current.query,
         "tags": [tag.name for tag in node.tags],
         "required_dimensions": [dim.name for dim in node.current.required_dimensions],
+        "reaggregate": node.current.reaggregate,
         "direction": (
             node.current.metric_metadata.direction.name.lower()
             if node.current.metric_metadata and node.current.metric_metadata.direction
@@ -1642,6 +1643,14 @@ async def get_node_specs_for_export(
                     )
                     for required_dim in metric_spec.required_dimensions
                 ]
+            if metric_spec.reaggregate:
+                for rule in metric_spec.reaggregate.rules:
+                    rule.dimension = _inject_prefix_for_cube_ref(
+                        rule.dimension,
+                        namespace,
+                        parent_namespace,
+                        namespace_suffixes,
+                    )
         if node_spec.node_type in (
             NodeType.SOURCE,
             NodeType.TRANSFORM,
diff --git a/datajunction-server/datajunction_server/internal/nodes.py b/datajunction-server/datajunction_server/internal/nodes.py
index 9f81e399b..13432e3e3 100644
--- a/datajunction-server/datajunction_server/internal/nodes.py
+++ b/datajunction-server/datajunction_server/internal/nodes.py
@@ -65,6 +65,7 @@
 )
 from datajunction_server.internal.access.authorization.context import AuthContext
 from datajunction_server.internal.caching.interface import Cache
+from datajunction_server.internal.custom_metadata import validate_custom_metadata
 from datajunction_server.internal.history import ActivityType, EntityType
 from datajunction_server.internal.materializations import (
     apply_cube_materialization_swap,
@@ -95,6 +96,12 @@
     UpsertCubeMaterialization,
     principal_refs,
 )
+from datajunction_server.models.decompose import (
+    AggregationRule as DecomposeAggregationRule,
+)
+from datajunction_server.models.decompose import (
+    MetricComponent,
+)
 from datajunction_server.models.deployment import (
     ChangeTier,
     CubeSpec,
@@ -127,6 +134,7 @@
 )
 from datajunction_server.models.node_type import NodeType
 from datajunction_server.models.query import QueryCreate
+from datajunction_server.models.reaggregate import dump_reaggregate_spec
 from datajunction_server.models.table_metadata import TableMetadata, TableOwner
 from datajunction_server.service_clients import QueryServiceClient
 from datajunction_server.sql.dag import (
@@ -140,7 +148,6 @@
 from datajunction_server.sql.parsing.ast import CompileContext
 from datajunction_server.sql.parsing.backends.antlr4 import parse, parse_rule
 from datajunction_server.typing import UTCDatetime
-from datajunction_server.internal.custom_metadata import validate_custom_metadata
 from datajunction_server.utils import (
     SEPARATOR,
     Version,
@@ -585,6 +592,11 @@ async def create_node_revision(
         query=data.query,
         mode=data.mode,
         required_dimensions=data.required_dimensions or [],
+        reaggregate=(
+            dump_reaggregate_spec(data.reaggregate)
+            if node_type == NodeType.METRIC
+            else None
+        ),
         created_by_id=current_user.id,
         custom_metadata=data.custom_metadata,
     )
@@ -886,12 +898,15 @@ async def _derive_frozen_measures_impl(
             session=session,
             name=measure.name,
         )
+        if frozen_measure:
+            _raise_if_frozen_measure_conflicts(frozen_measure, measure)
         if not frozen_measure and measure.aggregation:
             frozen_measure = FrozenMeasure(
                 name=measure.name,
                 upstream_revision_id=upstream_revision_id,
                 expression=measure.expression,
                 aggregation=measure.aggregation,
+                params=measure.params,
                 rule=measure.rule,
                 used_by_node_revisions=[],
             )
@@ -1019,14 +1034,51 @@ async def derive_frozen_measures_bulk(
                     upstream_revision_id=upstream_revision_id,
                     expression=measure.expression,
                     aggregation=measure.aggregation,
-                    rule=measure.rule,
+                    params=measure.params,
+                    rule=_frozen_measure_rule(measure.rule),
                     used_by_node_revisions=[],
                 )
                 session.add(frozen_measure)
                 fm_by_name[measure.name] = frozen_measure
+            else:
+                _raise_if_frozen_measure_conflicts(frozen_measure, measure)
             frozen_measure.used_by_node_revisions.append(rev)
 
 
+def _aggregation_rule_identity(rule: DecomposeAggregationRule) -> dict[str, Any]:
+    """Return the stable JSON shape used for frozen-measure rule comparison."""
+    return rule.model_dump(mode="json", exclude_none=True, exclude={"reaggregate"})
+
+
+def _frozen_measure_rule(rule: DecomposeAggregationRule) -> DecomposeAggregationRule:
+    """
+    Return the metric-independent rule persisted on a shared frozen measure.
+    """
+    return rule.model_copy(update={"reaggregate": None})
+
+
+def _raise_if_frozen_measure_conflicts(
+    frozen_measure: FrozenMeasure,
+    measure: MetricComponent,
+) -> None:
+    """
+    Prevent component-name collisions from reusing a different frozen measure.
+    """
+    if (
+        frozen_measure.expression == measure.expression
+        and frozen_measure.aggregation == measure.aggregation
+        and (frozen_measure.params or None) == (measure.params or None)
+        and _aggregation_rule_identity(frozen_measure.rule)
+        == _aggregation_rule_identity(measure.rule)
+    ):
+        return
+
+    raise DJInvalidInputException(
+        f"Frozen measure `{measure.name}` already exists with a different "
+        "expression, aggregation, aggregation rule, or tuning parameters.",
+    )
+
+
 async def save_node(
     session: AsyncSession,
     node_revision: NodeRevision,
@@ -1128,6 +1180,7 @@ async def copy_to_new_node(
         table=old_revision.table,
         required_dimensions=list(old_revision.required_dimensions),
         metric_metadata=old_revision.metric_metadata,
+        reaggregate=old_revision.reaggregate,
         cube_elements=list(old_revision.cube_elements),
         cube_filters=old_revision.cube_filters,
         status=old_revision.status,
@@ -1866,6 +1919,7 @@ async def cube_metric_component_identities(
             FrozenMeasure.name,
             FrozenMeasure.expression,
             FrozenMeasure.aggregation,
+            FrozenMeasure.params,
         )
         .select_from(NodeRevisionFrozenMeasure)
         .join(
@@ -1881,8 +1935,12 @@ async def cube_metric_component_identities(
     rows = (await session.execute(statement)).all()
     return {
         f"{metric_name}:{component_name}:"
-        + measure_identity_token(compute_expression_hash(expression), aggregation)
-        for metric_name, component_name, expression, aggregation in rows
+        + measure_identity_token(
+            compute_expression_hash(expression),
+            aggregation,
+            params,
+        )
+        for metric_name, component_name, expression, aggregation, params in rows
     }
 
 
@@ -2392,14 +2450,7 @@ async def _propagate_update_downstream(
                 cache.delete(upstream_cache_key)
 
         if downstream.type == NodeType.CUBE:
-            # Any tier rebuilds, and the churn is deliberate. Narrowing this by
-            # comparing the upstream's resolved columns was rejected: a query edit
-            # can move a filter, a join or a CASE threshold while leaving every
-            # column and type identical, and each changes every row the cube serves.
-            # Nothing short of reading the SQL tells those apart, so a changed query
-            # makes anything built from it suspect. Only NONE is skipped, the one
-            # case where DJ knows nothing material happened.
-            #
+            # Any tier rebuilds except NONE.
             # A rebuild can fail, and one cube's failure must not cost the remaining
             # downstreams theirs.
             if change_tier is not ChangeTier.NONE:
@@ -2505,6 +2556,7 @@ def copy_existing_node_revision(old_revision: NodeRevision, current_user: User):
         status=old_revision.status,
         required_dimensions=list(old_revision.required_dimensions),
         metric_metadata=old_revision.metric_metadata,
+        reaggregate=old_revision.reaggregate,
         dimension_links=[
             DimensionLink(
                 dimension_id=link.dimension_id,
@@ -2727,8 +2779,20 @@ async def create_new_revision_from_existing(
         and {col.name for col in old_revision.required_dimensions}
         != set(data.required_dimensions)
     )
+    reaggregate_was_set = bool(
+        data and "reaggregate" in data.model_fields_set,
+    )
+    reaggregate_changes = (
+        reaggregate_was_set
+        and old_revision.reaggregate
+        != dump_reaggregate_spec(data.reaggregate if data else None)
+    )
     major_changes = (
-        query_changes or column_changes or pk_changes or required_dim_changes
+        query_changes
+        or column_changes
+        or pk_changes
+        or required_dim_changes
+        or reaggregate_changes
     )
 
     # If nothing has changed, do not create the new node revision
@@ -2778,6 +2842,13 @@ async def create_new_revision_from_existing(
             if data and data.metric_metadata
             else old_revision.metric_metadata
         ),
+        reaggregate=(
+            dump_reaggregate_spec(data.reaggregate)
+            if data and data.reaggregate is not None
+            else None
+            if reaggregate_was_set
+            else old_revision.reaggregate
+        ),
         dimension_links=[
             DimensionLink(
                 dimension_id=link.dimension_id,
@@ -4256,17 +4327,6 @@ async def revalidate_node(
     ]
     # A tier rather than a version, so propagation can hand the same value to
     # `bump_version` for downstream cubes.
-    #
-    # Any column change is major. An addition looks harmless -- nothing could
-    # already reference a column that did not exist -- but the query produced it,
-    # and a query edit can move a filter or a join while leaving the rest of the
-    # projection identical. Demoting additions to MINOR buys nothing anyway: the
-    # cube rebuild below skips only NONE, so a minor bump rebuilds all the same.
-    #
-    # `order_fixed` earns no tier. DJ filling in a missing projection index is its
-    # own bookkeeping, not a change to the node, so a revision would describe
-    # nothing and any tier above NONE would rebuild every cube below. It is applied
-    # to the current revision in place instead, below.
     change_tier = fold_change_tiers(
         [
             ChangeTier.MAJOR
diff --git a/datajunction-server/datajunction_server/internal/preaggregations.py b/datajunction-server/datajunction_server/internal/preaggregations.py
index 956053fe6..e1347e9d1 100644
--- a/datajunction-server/datajunction_server/internal/preaggregations.py
+++ b/datajunction-server/datajunction_server/internal/preaggregations.py
@@ -28,6 +28,7 @@
     compute_grain_group_hash,
     compute_preagg_hash,
     get_measure_identities,
+    measure_identity_token,
 )
 from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.decompose import PreAggMeasure
@@ -195,7 +196,7 @@ async def register_external_preaggregations(
     #    identity -- (expression hash, Phase-1 aggregation) -- to the declared
     #    column. Keying on the hash alone would collapse SUM(x) and MAX(x),
     #    silently discarding one metric's declared column.
-    measure_identity_to_column: dict[tuple[str, str], str] = {}
+    measure_identity_to_column: dict[str, str] = {}
     for metric_name, physical_column in measure_columns.items():
         node = await Node.get_by_name(
             session,
@@ -224,9 +225,10 @@ async def register_external_preaggregations(
         # is_measure guarantees exactly one component.
         component = components[0]
         measure_identity_to_column[
-            (
+            measure_identity_token(
                 compute_expression_hash(component.expression),
-                component.normalized_aggregation,
+                component.aggregation,
+                component.params,
             )
         ] = physical_column
 
@@ -311,7 +313,11 @@ async def register_external_preaggregations(
         grain_measures: list[PreAggMeasure] = []
         for component in grain_group.components:
             expr_hash = compute_expression_hash(component.expression)
-            identity = (expr_hash, component.normalized_aggregation)
+            identity = measure_identity_token(
+                expr_hash,
+                component.aggregation,
+                component.params,
+            )
             if identity not in measure_identity_to_column:
                 raise DJInvalidInputException(
                     message=(
diff --git a/datajunction-server/datajunction_server/internal/sql.py b/datajunction-server/datajunction_server/internal/sql.py
index 26689d09e..97644851e 100644
--- a/datajunction-server/datajunction_server/internal/sql.py
+++ b/datajunction-server/datajunction_server/internal/sql.py
@@ -242,6 +242,7 @@ async def generate_metrics_sql(
                 matched_cube,
                 dimensions,
                 merged_filters,
+                metrics,
             )
 
     # Auto-resolve dialect if not explicitly provided.
diff --git a/datajunction-server/datajunction_server/internal/validation.py b/datajunction-server/datajunction_server/internal/validation.py
index 6eb1af905..55df63941 100644
--- a/datajunction-server/datajunction_server/internal/validation.py
+++ b/datajunction-server/datajunction_server/internal/validation.py
@@ -23,6 +23,10 @@
 from datajunction_server.models.base import labelize
 from datajunction_server.models.node import NodeRevisionBase, NodeStatus
 from datajunction_server.models.node_type import NodeType
+from datajunction_server.models.reaggregate import (
+    parse_reaggregate_spec,
+    unsupported_dimension_reaggregate_functions,
+)
 from datajunction_server.sql.parsing import ast
 from datajunction_server.sql.parsing.backends.antlr4 import SqlSyntaxError, parse
 from datajunction_server.sql.parsing.backends.exceptions import DJParseException
@@ -357,11 +361,34 @@ async def validate_node_data(
             parent_columns,
         )
         node_validator.required_dimensions = matched_bound_columns
+        reaggregate_spec = parse_reaggregate_spec(validated_node.reaggregate)
+        invalid_reaggregate_dimensions: set[str] = set()
+        invalid_reaggregate_functions: list[str] = []
+        if reaggregate_spec and reaggregate_spec.rules:
+            (
+                invalid_reaggregate_dimensions,
+                _,
+            ) = await find_required_dimensions(
+                session,
+                [rule.dimension for rule in reaggregate_spec.rules],
+                parent_columns,
+            )
+            invalid_reaggregate_functions = unsupported_dimension_reaggregate_functions(
+                reaggregate_spec,
+            )
     except MissingGreenlet:
         invalid_required_dimensions = set()
+        invalid_reaggregate_dimensions = set()
+        invalid_reaggregate_functions = []
         node_validator.required_dimensions = []
 
-    if missing_parents_map or type_inference_failures or invalid_required_dimensions:
+    if (
+        missing_parents_map
+        or type_inference_failures
+        or invalid_required_dimensions
+        or invalid_reaggregate_dimensions
+        or invalid_reaggregate_functions
+    ):
         # update status
         node_validator.status = NodeStatus.INVALID
         # build errors
@@ -410,10 +437,46 @@ async def validate_node_data(
             if invalid_required_dimensions
             else []
         )
+        invalid_reaggregate_dimensions_error = (
+            [
+                DJError(
+                    code=ErrorCode.INVALID_COLUMN,
+                    message=(
+                        "Node definition contains references to columns as "
+                        "reaggregate dimensions that are not on parent nodes."
+                    ),
+                    debug={
+                        "invalid_reaggregate_dimensions": list(
+                            invalid_reaggregate_dimensions,
+                        ),
+                    },
+                ),
+            ]
+            if invalid_reaggregate_dimensions
+            else []
+        )
+        invalid_reaggregate_functions_error = (
+            [
+                DJError(
+                    code=ErrorCode.INVALID_ARGUMENTS_TO_FUNCTION,
+                    message=(
+                        "Node definition contains unsupported dimension "
+                        "reaggregate function(s)."
+                    ),
+                    debug={
+                        "invalid_reaggregate_functions": invalid_reaggregate_functions,
+                    },
+                ),
+            ]
+            if invalid_reaggregate_functions
+            else []
+        )
         errors = (
             missing_parents_error
             + type_inference_error
             + invalid_required_dimensions_error
+            + invalid_reaggregate_dimensions_error
+            + invalid_reaggregate_functions_error
         )
         node_validator.errors.extend(errors)
 
@@ -705,10 +768,30 @@ async def validate_node_data_v2(
         parent_columns,
     )
     node_validator.required_dimensions = matched_bound_columns
+    reaggregate_spec = parse_reaggregate_spec(validated_node.reaggregate)
+    invalid_reaggregate_dimensions: set[str] = set()
+    invalid_reaggregate_functions: list[str] = []
+    if reaggregate_spec and reaggregate_spec.rules:
+        (
+            invalid_reaggregate_dimensions,
+            _,
+        ) = await find_required_dimensions(
+            session,
+            [rule.dimension for rule in reaggregate_spec.rules],
+            parent_columns,
+        )
+        invalid_reaggregate_functions = unsupported_dimension_reaggregate_functions(
+            reaggregate_spec,
+        )
 
     # --- Step 12: final error assembly for missing parents + invalid required
     #              dims (matches legacy code shapes).
-    if node_validator.missing_parents_map or invalid_required_dimensions:
+    if (
+        node_validator.missing_parents_map
+        or invalid_required_dimensions
+        or invalid_reaggregate_dimensions
+        or invalid_reaggregate_functions
+    ):
         node_validator.status = NodeStatus.INVALID
         if node_validator.missing_parents_map:
             node_validator.errors.append(
@@ -740,6 +823,34 @@ async def validate_node_data_v2(
                     },
                 ),
             )
+        if invalid_reaggregate_dimensions:
+            node_validator.errors.append(
+                DJError(
+                    code=ErrorCode.INVALID_COLUMN,
+                    message=(
+                        "Node definition contains references to columns as "
+                        "reaggregate dimensions that are not on parent nodes."
+                    ),
+                    debug={
+                        "invalid_reaggregate_dimensions": list(
+                            invalid_reaggregate_dimensions,
+                        ),
+                    },
+                ),
+            )
+        if invalid_reaggregate_functions:
+            node_validator.errors.append(
+                DJError(
+                    code=ErrorCode.INVALID_ARGUMENTS_TO_FUNCTION,
+                    message=(
+                        "Node definition contains unsupported dimension "
+                        "reaggregate function(s)."
+                    ),
+                    debug={
+                        "invalid_reaggregate_functions": invalid_reaggregate_functions,
+                    },
+                ),
+            )
 
     return node_validator
 
diff --git a/datajunction-server/datajunction_server/models/cube_materialization.py b/datajunction-server/datajunction_server/models/cube_materialization.py
index 1e12ebb40..23abfbd2b 100644
--- a/datajunction-server/datajunction_server/models/cube_materialization.py
+++ b/datajunction-server/datajunction_server/models/cube_materialization.py
@@ -16,11 +16,11 @@
 )
 from datajunction_server.models.materialization import (
     DEFAULT_CUBE_RETENTION,
-    DRUID_AGG_MAPPING,
     CoverageSpec,
     MaterializationJobTypeEnum,
     MaterializationStrategy,
     SparkSpec,
+    get_druid_aggregator_spec,
 )
 from datajunction_server.models.node_type import NodeNameVersion
 from datajunction_server.models.partition import Granularity
@@ -385,22 +385,19 @@ def metrics_spec(self) -> list[dict[str, Any]]:
         Returns the Druid metrics spec for ingestion
         """
         column_mapping = {col.name: col.type for col in self.columns}  # type: ignore
-        return [
-            {
-                "fieldName": measure.name,
-                "name": measure.name,
-                "type": DRUID_AGG_MAPPING[
-                    (column_mapping[measure.name], measure.aggregation.lower())
-                ],
-            }
-            for measure in self.measures
-            if measure.aggregation
-            and (
-                column_mapping.get(measure.name),
-                measure.aggregation.lower(),
+        specs = (
+            get_druid_aggregator_spec(
+                column_name=measure.name,
+                column_type=column_mapping.get(measure.name),
+                aggregation=measure.aggregation,
+                merge=measure.merge,
+                params=measure.params,
             )
-            in DRUID_AGG_MAPPING
-        ]
+            for measure in self.measures
+        )
+        # Unmappable measures are omitted, as they always have been here -- the
+        # cube API substitutes longSum instead. See get_druid_aggregator_spec.
+        return [spec for spec in specs if spec is not None]
 
     @computed_field  # type: ignore[misc]
     @property
diff --git a/datajunction-server/datajunction_server/models/decompose.py b/datajunction-server/datajunction_server/models/decompose.py
index 5e5655838..f8966434d 100644
--- a/datajunction-server/datajunction_server/models/decompose.py
+++ b/datajunction-server/datajunction_server/models/decompose.py
@@ -9,9 +9,13 @@
 - DecomposedMetric: A metric broken into components + combiner expression
 """
 
-from pydantic import BaseModel
+from typing import Any
+
+from pydantic import BaseModel, Field
 
 from datajunction_server.enum import StrEnum
+from datajunction_server.models.materialization import MaterializationTarget
+from datajunction_server.models.reaggregate import DimensionReaggregateRule
 
 
 class Aggregability(StrEnum):
@@ -54,6 +58,7 @@ class AggregationRule(BaseModel):
 
     type: Aggregability = Aggregability.NONE
     level: list[str] | None = None
+    reaggregate: DimensionReaggregateRule | None = None
 
 
 class MetricComponent(BaseModel):
@@ -84,6 +89,10 @@ class MetricComponent(BaseModel):
                      {} placeholder ("SUM(POWER({}, 2))").
         merge: The function name for combining pre-aggregated values (Phase 2).
         rule: Aggregation rules defining how/when the component can be aggregated.
+
+    The sketch-backed fields added alongside these -- params, merge_args,
+    serialize, serialize_targets, serialize_type -- are documented inline
+    below, where the reasoning sits next to the declaration.
     """
 
     name: str
@@ -96,6 +105,47 @@ class MetricComponent(BaseModel):
     # ("order_id"); for complex expressions it is component.name so the identifier stays
     # valid and consistent with what decompose.py computed.
     grain_alias: str | None = None
+    # Tuning parameters for `aggregation`/`merge`, for sketch-backed components
+    # whose function name alone doesn't pin down the accumulate (a t-digest still
+    # needs a compression, a KLL a `k`). Part of measure identity -- see
+    # `measure_identity_token` -- because a sketch built at one accuracy must not
+    # satisfy a query asking for another.
+    params: dict[str, Any] | None = None
+    # Fixed arguments appended after the column in the Phase 2 merge call, as SQL
+    # literals: `nflx_tdigest_agg(col, 200.0)` is merge="nflx_tdigest_agg" plus
+    # merge_args=["200.0"]. `merge` stays a bare function name because two things
+    # match on it -- the Druid aggregator lookup key and the semi-additive rewrite
+    # in `_replace_reaggregate_merge_expression` -- so the tuning cannot be folded
+    # into it as a template. Rendered from `params` by the decomposition; `params`
+    # remains the identity record, this is the emission form.
+    merge_args: list[str] = Field(default_factory=list)
+
+    # Conversion applied when writing this component to a materialized table for
+    # a target listed in `serialize_targets` -- see `ComponentDef.serialize`. The
+    # value is a template expanded against the accumulated expression.
+    serialize: str | None = None
+    # A list rather than a set: this model is dumped straight to JSON in cube
+    # materialization configs, and `json.dumps` cannot encode a set. StrEnum
+    # members are fine -- they subclass `str`.
+    serialize_targets: list[MaterializationTarget] = Field(default_factory=list)
+    # Column type `serialize` produces. See `ComponentDef.serialize_type`.
+    serialize_type: str | None = None
+
+    def serializes_for(self, materialization_target: Any | None) -> bool:
+        """
+        Whether this component converts its accumulated value for `target`.
+
+        Two things key off this and they must agree: the SQL that writes the
+        column, and the type recorded for it. If they disagree the measures
+        table holds one representation while the catalog claims another, and the
+        Druid aggregator lookup -- which keys on the column type -- silently
+        finds nothing and drops the measure.
+        """
+        return bool(
+            self.serialize
+            and materialization_target is not None
+            and materialization_target in self.serialize_targets,
+        )
 
     @property
     def normalized_aggregation(self) -> str:
diff --git a/datajunction-server/datajunction_server/models/deployment.py b/datajunction-server/datajunction_server/models/deployment.py
index 1336b6aa7..67ac67fbe 100644
--- a/datajunction-server/datajunction_server/models/deployment.py
+++ b/datajunction-server/datajunction_server/models/deployment.py
@@ -40,6 +40,7 @@
     NodeType,
 )
 from datajunction_server.models.partition import Granularity, PartitionType
+from datajunction_server.models.reaggregate import ReaggregateSpec
 from datajunction_server.models.unit import (
     Unit,
     legacy_unit_to_structured,
@@ -200,37 +201,10 @@ class HierarchySpec(NamespacedSpec):
 class PreAggSpec(NamespacedSpec):
     """
     Specification for an externally-built pre-aggregation table adopted at deploy
-    time (equivalent to POST /preaggs/register). ``name`` is a stable handle used
-    for reconciliation and availability callbacks. Metric/dimension references may
-    use ``${prefix}`` or be fully qualified; they are rendered against the
-    deployment namespace.
-
-    Every metric and every dimension is declared together with the physical
-    column of the external table that holds it, as a map::
-
-        metrics:
-          ${prefix}paid_members: paid_members_sum
-        dimensions:
-          ${prefix}country_dim.country_iso: country
-          ${prefix}date_dim.utc_date: utc_date
-
-    Both maps require a value for every key -- including a dimension whose
-    physical column happens to match its DJ column name, which is written out
-    rather than left empty. An optional value would make the map not really a
-    mapping, a trailing colon is easy to write by accident, and spelling the
-    physical name out documents the table in the file that declares it.
-
-    What goes under ``metrics`` are the measures the table stores. A derived
-    metric (a ratio of two others, say) is not listed and cannot be: it has no
-    column of its own. It is covered anyway, because both registration and
-    query-time matching work on decomposed measure identities rather than metric
-    names, so any metric that decomposes into the stored measures resolves to
-    this table.
-
-    The earlier four-field form -- ``metrics``/``dimensions`` as lists alongside
-    separate ``measure_columns``/``dimension_columns`` maps -- is no longer
-    accepted, and a spec still using it is rejected with a message describing
-    what to write instead.
+    time. ``name`` is a stable handle used for reconciliation.
+    
+    Every metric and dimension is declared together with its physical column
+    in the external table.
     """
 
     # Metric/dimension reference -> the physical column of the external table
@@ -345,16 +319,7 @@ class PartitionSpec(BaseModel):
 
 
 class MaterializationSpec(BaseModel):
-    """
-    Declarative materialization config for a cube.
-
-    Deliberately carries only what the author decides: when to build, how, how far
-    back to look, how much of history to serve, how long the result is kept, and the
-    Druid, Spark and platform settings their own site needs. The backend that runs it
-    (and everything it derives -- the measures queries, combiner SQL, the rest of the
-    Druid spec, output tables) is DJ's choice, so no `job` field is exposed here and
-    the block stays portable if that choice changes.
-    """
+    """Declarative materialization config for a cube."""
 
     schedule: str
     strategy: MaterializationStrategy = MaterializationStrategy.INCREMENTAL_TIME
@@ -1022,6 +987,7 @@ class MetricSpec(NodeSpec):
     # Excluded from serialization so it's never exported.
     columns: list[ColumnSpec] | None = Field(default=None, exclude=True)
     required_dimensions: list[str] | None = None  # Field(default_factory=list)
+    reaggregate: ReaggregateSpec | None = None
     direction: MetricDirection | None = None
     unit_enum: MetricUnit | None = Field(default=None, exclude=True)
     # Structured unit form at the metric level — peer of `unit_enum`.
@@ -1038,6 +1004,7 @@ class MetricSpec(NodeSpec):
         "columns": ChangeTier.NONE,
         # Required dimensions constrain which queries the metric can answer.
         "required_dimensions": ChangeTier.MAJOR,
+        "reaggregate": ChangeTier.MAJOR,
         # Everything below is presentation metadata on the metric's single output
         # column -- the same set the PATCH path already treats as minor via
         # `metric_metadata` in `create_new_revision_from_existing`.
@@ -1179,6 +1146,29 @@ def diff(self, other: "NodeSpec") -> list[str]:
             changed.remove("required_dimensions")
         return changed
 
+    @property
+    def rendered_reaggregate(self) -> ReaggregateSpec | None:
+        """
+        Reaggregate spec with `${prefix}` resolved to this spec's namespace.
+        """
+        if not self.reaggregate:
+            return None
+        rules = [
+            rule.model_copy(
+                update={
+                    "dimension": render_prefixes(rule.dimension, self.namespace)
+                    if "${prefix}" in rule.dimension
+                    else rule.dimension,
+                },
+            )
+            for rule in self.reaggregate.rules
+        ]
+        return self.reaggregate.model_copy(
+            update={
+                "rules": rules,
+            },
+        )
+
     def model_dump(self, **kwargs):  # pragma: no cover
         base = super().model_dump(**kwargs)
         base["unit"] = self.unit
@@ -1218,6 +1208,7 @@ def __eq__(self, other: object) -> bool:
                 other.canonical_required_dimensions,
                 preserve_order=False,
             )
+            and self.rendered_reaggregate == other.rendered_reaggregate
             and eq_or_fallback(self.direction, other.direction, MetricDirection.NEUTRAL)
             and self._normalized_unit() == other._normalized_unit()
             and self.significant_digits == other.significant_digits
@@ -1241,20 +1232,11 @@ class CubeSpec(NodeSpec):
     filters: list[str] | None = None
     columns: list[ColumnSpec] | None = None
     # Tri-state. A spec materializes the cube on the schedule it names; the `none`
-    # sentinel tears down whatever the cube has materialized; absent -- and null,
-    # which is what serializing an absent field produces -- means the cube's
-    # materialization is not managed here and whatever exists is left alone. Only a
-    # value can carry intent through serialization, so removal is spelled
-    # `materialization: none` rather than inferred from a key being present.
+    # sentinel tears down whatever the cube has materialized; absent means the cube's
+    # materialization is not managed here and whatever exists is left alone.
     #
-    # A list declares more than one, which a cube legitimately needs: an
-    # `incremental_time` build for freshness alongside a periodic `full` rebuild
-    # that corrects late-arriving data, out-of-order events and dimension
-    # backfills. `strategy` is what tells two entries apart -- `job` is not
-    # authorable (see `MaterializationSpec`) and everything else is a knob rather
-    # than an identity -- so two entries sharing one are rejected below. The scalar
-    # form stays valid and means exactly what it always did; nothing has to be
-    # rewritten as a one-element list.
+    # A list declares more than one (e.g. incremental + full). `strategy` is what tells
+    # two entries apart, so two entries sharing one are rejected below.
     materialization: (
         MaterializationSpec | list[MaterializationSpec] | MaterializationAction | None
     ) = None
@@ -1271,18 +1253,8 @@ class CubeSpec(NodeSpec):
         # Only user-authored partition config is compared here (see __eq__);
         # everything else about cube columns is auto-derived.
         "columns": ChangeTier.MAJOR,
-        # Materialization is not part of a cube's definition. Configuring one
-        # through `POST /nodes/{name}/materialization/` has never created a node
-        # revision, and a YAML-declared schedule has to behave the same way, or
-        # the same edit would cut a version through one door and not the other.
-        # Because `materialization` is also excluded from `__eq__`, a
-        # materialization-only edit leaves the cube in the deployment's skip list
-        # and never reaches the classifier at all; NONE records that intent rather
-        # than describing a reachable code path. Nothing is lost by the exclusion:
-        # reconciling the declared block against the persisted config is a separate
-        # concern that runs over every declared cube whether or not the node
-        # changed, which is also what catches a materialization changed outside
-        # YAML.
+        # Materialization is not part of a cube's definition, so changing it
+        # does not mint a new node revision.
         "materialization": ChangeTier.NONE,
     }
 
diff --git a/datajunction-server/datajunction_server/models/materialization.py b/datajunction-server/datajunction_server/models/materialization.py
index 421e6269c..031e82c2b 100644
--- a/datajunction-server/datajunction_server/models/materialization.py
+++ b/datajunction-server/datajunction_server/models/materialization.py
@@ -32,6 +32,22 @@
 if TYPE_CHECKING:
     from datajunction_server.database.node import NodeRevision
 
+
+class MaterializationTarget(StrEnum):
+    """
+    Where a measures table is being written.
+
+    Distinct from ``Dialect``: measures SQL executes in Spark for a Druid cube
+    and an Iceberg pre-agg alike, so the dialect cannot tell the two apart. Only
+    the target can, and some components must be written differently depending on
+    it -- a sketch whose in-engine representation is not the one the destination
+    reads needs converting on the way out.
+    """
+
+    DRUID = "druid"
+    ICEBERG = "iceberg"
+
+
 DRUID_AGG_MAPPING = {
     ("bigint", "sum"): "longSum",
     ("int", "sum"): "longSum",
@@ -54,8 +70,124 @@
     ("binary", "hll_sketch_agg"): "HLLSketchMerge",
 }
 
-# Aggregation types that need special handling (extra config parameters)
-DRUID_SKETCH_TYPES = {"HLLSketchMerge"}
+# Aggregator types that carry extra config in the Druid metricsSpec, mapped to
+# their default configuration. A sketch aggregator is not fully specified by its
+# type: an HLL needs a precision, a t-digest a compression, a KLL a `k`. Defaults
+# apply when a metric declares no `reaggregate.params`.
+DRUID_SKETCH_CONFIG: dict[str, dict[str, Any]] = {
+    "HLLSketchMerge": {
+        "lgK": 12,  # Log2 of K, controls precision (4-21)
+        "tgtHllType": "HLL_4",  # HLL_4, HLL_6, or HLL_8
+    },
+}
+
+
+def register_druid_aggregator(
+    column_type: str,
+    merge_func: str,
+    aggregator: str,
+    default_config: dict[str, Any] | None = None,
+) -> None:
+    """
+    Register a Druid ingestion aggregator for a (column type, merge function) pair.
+
+    Args:
+        column_type: Measures-table column type, e.g. "binary"
+        merge_func: Phase-2 merge function name, e.g. "nflx_tdigest_agg"
+        aggregator: Druid aggregator type, e.g. "tDigestSketch"
+        default_config: Extra metricsSpec keys and their defaults, e.g.
+            ``{"compression": 200}``. Marks the aggregator as parameterized.
+
+    Raises:
+        DJInvalidInputException: If the pair is registered to a different aggregator,
+            or the aggregator has different defaults. Re-registering identical defaults
+            is a no-op.
+    """
+    key = (column_type, merge_func.lower())
+    existing = DRUID_AGG_MAPPING.get(key)
+    if existing is not None and existing != aggregator:
+        raise DJInvalidInputException(
+            message=(
+                f"Druid aggregator for {key} is already registered as "
+                f"`{existing}`; refusing to replace it with `{aggregator}`."
+            ),
+        )
+    new_config = dict(default_config) if default_config else None
+    existing_config = DRUID_SKETCH_CONFIG.get(aggregator)
+    if (
+        existing_config is not None
+        and new_config is not None
+        and existing_config != new_config
+    ):
+        raise DJInvalidInputException(
+            message=(
+                f"Druid aggregator `{aggregator}` is already registered with "
+                f"different defaults ({existing_config}); refusing to replace "
+                f"them with {new_config}."
+            ),
+        )
+    DRUID_AGG_MAPPING[key] = aggregator
+    if new_config:
+        DRUID_SKETCH_CONFIG[aggregator] = new_config
+
+
+def get_druid_aggregator_spec(
+    column_name: str,
+    column_type: str | None,
+    aggregation: str | None,
+    merge: str | None,
+    params: dict[str, Any] | None = None,
+) -> dict[str, Any] | None:
+    """
+    Build a Druid metricsSpec entry for a measure column, or None if unmappable.
+
+    The `merge` function takes precedence over `aggregation` since ingestion
+    operates on pre-aggregated partials.
+
+    Returns None for unmappable measures, allowing callers to determine fallback behavior.
+
+    Raises:
+        DJInvalidInputException: If `params` contains a key not declared by the aggregator.
+    """
+    agg_func = merge or aggregation
+    if not agg_func or column_type is None:
+        return None
+
+    aggregator = DRUID_AGG_MAPPING.get((column_type, agg_func.lower()))
+    if aggregator is None:
+        return None
+
+    family_config = DRUID_SKETCH_CONFIG.get(aggregator)
+    if params:
+        # Restrict `params` to declared tuning knobs to prevent overwriting
+        # structural keys (like `type` or `fieldName`) and catch typos.
+        allowed = set(family_config or ())
+        unknown = sorted(set(params) - allowed)
+        if unknown:
+            raise DJInvalidInputException(
+                message=(
+                    f"Druid aggregator `{aggregator}` does not accept "
+                    f"{', '.join(f'`{key}`' for key in unknown)}. "
+                    + (
+                        f"Supported: {', '.join(sorted(allowed))}."
+                        if allowed
+                        else "It takes no parameters."
+                    )
+                ),
+            )
+
+    spec: dict[str, Any] = {
+        "fieldName": column_name,
+        "name": column_name,
+        "type": aggregator,
+    }
+    if family_config:
+        # Apply metric-specific params over family defaults.
+        spec.update(family_config)
+        if params:
+            spec.update(params)
+    return spec
+
 
 # How long ingested cube data is kept in Druid. Without an explicit rule a datasource
 # inherits the cluster default, which is unrelated to the span the cube holds, so an
@@ -458,16 +590,18 @@ def metrics_spec(self) -> dict:
             for measure in measure_group.measures
             if (measure.type.lower(), measure.agg.lower()) not in DRUID_AGG_MAPPING
         ]
-        return {
-            measure.name: {
-                "fieldName": measure.field_name,
-                "name": measure.field_name,
-                "type": DRUID_AGG_MAPPING[(measure.type.lower(), measure.agg.lower())],
-            }
+        # The V2 `Measure` model lacks a merge phase, so `agg` is used for lookup.
+        specs = {
+            measure.name: get_druid_aggregator_spec(
+                column_name=measure.field_name,
+                column_type=measure.type.lower(),
+                aggregation=measure.agg,
+                merge=None,
+            )
             for measure_group in self.measures.values()  # type: ignore
             for measure in measure_group.measures
-            if (measure.type.lower(), measure.agg.lower()) in DRUID_AGG_MAPPING
         }
+        return {name: spec for name, spec in specs.items() if spec is not None}
 
     def build_druid_spec(self, node_revision: "NodeRevision"):
         """
diff --git a/datajunction-server/datajunction_server/models/metric.py b/datajunction-server/datajunction_server/models/metric.py
index 2ec7e50b6..43813ccec 100644
--- a/datajunction-server/datajunction_server/models/metric.py
+++ b/datajunction-server/datajunction_server/models/metric.py
@@ -16,6 +16,7 @@
     MetricMetadataOutput,
 )
 from datajunction_server.models.query import ColumnMetadata, V3ColumnMetadata
+from datajunction_server.models.reaggregate import ReaggregateSpec
 from datajunction_server.models.sql import ScanEstimate, TranspiledSQL
 from datajunction_server.models.unit import unit_to_dict
 from datajunction_server.sql.decompose import MetricComponentExtractor
@@ -50,6 +51,7 @@ class Metric(BaseModel):
     # going forward. `None` when no unit is set, regardless of input shape.
     unit: dict | None = None
     required_dimensions: list[str]
+    reaggregate: ReaggregateSpec | None = None
 
     # Whether the metric is a single aggregation call (a "measure") that can map
     # 1:1 to a column in an externally-built pre-aggregation table. Derived/ratio
@@ -104,6 +106,7 @@ async def parse_node(
                 node.current.columns[0].unit if node.current.columns else None,
             ),
             required_dimensions=[dim.name for dim in node.current.required_dimensions],
+            reaggregate=node.current.reaggregate,
             is_measure=node.current.is_measure,
             incompatible_druid_functions=incompatible_druid_functions,
             measures=measures,
diff --git a/datajunction-server/datajunction_server/models/node.py b/datajunction-server/datajunction_server/models/node.py
index e60ff6b67..13da677a4 100644
--- a/datajunction-server/datajunction_server/models/node.py
+++ b/datajunction-server/datajunction_server/models/node.py
@@ -33,6 +33,7 @@
 from datajunction_server.models.materialization import MaterializationConfigOutput
 from datajunction_server.models.node_type import NodeNameOutput, NodeType
 from datajunction_server.models.partition import PartitionOutput
+from datajunction_server.models.reaggregate import ReaggregateSpec
 from datajunction_server.models.tag import TagMinimum, TagOutput
 from datajunction_server.models.unit import (
     AtomicUnit,
@@ -878,6 +879,7 @@ class MetricNodeFields(BaseModel):
     """
 
     required_dimensions: list[str] | None = None
+    reaggregate: ReaggregateSpec | None = None
     metric_metadata: MetricMetadataInput | None = None
 
 
@@ -1033,6 +1035,7 @@ class NodeRevisionOutput(BaseModel):
     materializations: list[MaterializationConfigOutput]
     parents: list[NodeNameOutput]
     metric_metadata: MetricMetadataOutput | None = None
+    reaggregate: ReaggregateSpec | None = None
     dimension_links: list[LinkDimensionOutput] | None = None
     custom_metadata: dict | None = None
 
@@ -1065,6 +1068,7 @@ class NodeOutput(GenericNodeOutputModel):
     materializations: list[MaterializationConfigOutput]
     parents: list[NodeNameOutput]
     metric_metadata: MetricMetadataOutput | None = None
+    reaggregate: ReaggregateSpec | None = None
     dimension_links: list[LinkDimensionOutput] = Field(default_factory=list)
     created_at: UTCDatetime
     created_by: UserNameOnly | None = None
diff --git a/datajunction-server/datajunction_server/models/reaggregate.py b/datajunction-server/datajunction_server/models/reaggregate.py
new file mode 100644
index 000000000..004b98944
--- /dev/null
+++ b/datajunction-server/datajunction_server/models/reaggregate.py
@@ -0,0 +1,153 @@
+"""Models for metric reaggregation declarations."""
+
+from typing import Any
+
+from pydantic import BaseModel, Field, model_validator
+
+from datajunction_server.enum import StrEnum
+
+
+class ReaggregationFunction(StrEnum):
+    """
+    Supported functions for metric reaggregation.
+    """
+
+    AUTO = "auto"
+    NONE = "none"
+    SUM = "sum"
+    AVG = "avg"
+    WEIGHTED_AVG = "weighted_avg"
+    LAST_VALUE = "last_value"
+    FIRST_VALUE = "first_value"
+    MIN = "min"
+    MAX = "max"
+    # Quantile sketch family. Unlike the functions above it does not describe a
+    # rollup arithmetic; it selects how a quantile metric is accumulated, merged
+    # and read back, which is what makes percentiles pre-aggregatable at all.
+    TDIGEST = "tdigest"
+
+
+DIMENSION_REAGGREGATE_FUNCTIONS = frozenset(
+    {
+        ReaggregationFunction.LAST_VALUE,
+        ReaggregationFunction.FIRST_VALUE,
+        ReaggregationFunction.MIN,
+        ReaggregationFunction.MAX,
+    },
+)
+
+
+PARAMETERIZED_REAGGREGATE_FUNCTIONS: frozenset[ReaggregationFunction] = frozenset(
+    {
+        # compression: centroids retained, trading sketch size for tail accuracy.
+        ReaggregationFunction.TDIGEST,
+    },
+)
+
+
+def is_parameterized_reaggregate_function(
+    function: ReaggregationFunction | None,
+) -> bool:
+    """
+    Return whether a function accepts tuning parameters in `params`.
+    """
+    return function in PARAMETERIZED_REAGGREGATE_FUNCTIONS
+
+
+def is_supported_dimension_reaggregate_function(
+    function: ReaggregationFunction,
+) -> bool:
+    """
+    Return whether a function can collapse across a protected dimension.
+    """
+    return function in DIMENSION_REAGGREGATE_FUNCTIONS
+
+
+def unsupported_dimension_reaggregate_functions(
+    spec: "ReaggregateSpec | dict | None",
+) -> list[str]:
+    """
+    Unsupported dimension-specific collapse functions in a reaggregate spec.
+    """
+    reaggregate = parse_reaggregate_spec(spec)
+    if not reaggregate:
+        return []
+    return sorted(
+        {
+            rule.fn.value
+            for rule in reaggregate.rules
+            if not is_supported_dimension_reaggregate_function(rule.fn)
+        },
+    )
+
+
+class DimensionReaggregateRule(BaseModel):
+    """
+    Dimension-specific metric reaggregation rule.
+    """
+
+    dimension: str
+    fn: ReaggregationFunction
+
+
+class ReaggregateSpec(BaseModel):
+    """
+    Declaration for how a metric rolls up from its accumulation grain.
+    """
+
+    fn: ReaggregationFunction | None = None
+    weight: str | None = None
+    rules: list[DimensionReaggregateRule] = Field(default_factory=list)
+
+    # Tuning parameters for `fn` (e.g., compression for t-digest).
+    # Rejected for functions that do not accept parameters.
+    params: dict[str, Any] | None = None
+
+    @model_validator(mode="after")
+    def check_params_supported(self) -> "ReaggregateSpec":
+        """
+        Reject `params` for unparameterized functions.
+        """
+        if self.params and not is_parameterized_reaggregate_function(self.fn):
+            named = f"`{self.fn.value}`" if self.fn else "no reaggregation function"
+            raise ValueError(
+                f"Reaggregation function {named} does not accept parameters, "
+                f"but got: {', '.join(sorted(self.params))}.",
+            )
+        return self
+
+
+def dump_reaggregate_spec(
+    spec: ReaggregateSpec | dict | None,
+) -> dict | None:
+    """
+    Return a JSON-serializable reaggregate spec dictionary.
+    """
+    if spec is None:
+        return None
+    if isinstance(spec, ReaggregateSpec):
+        return spec.model_dump(mode="json")
+    return ReaggregateSpec.model_validate(spec).model_dump(mode="json")
+
+
+def parse_reaggregate_spec(
+    spec: ReaggregateSpec | dict | None,
+) -> ReaggregateSpec | None:
+    """
+    Return a validated reaggregate spec model.
+    """
+    if spec is None:
+        return None
+    if isinstance(spec, ReaggregateSpec):
+        return spec
+    return ReaggregateSpec.model_validate(spec)
+
+
+def dimension_reaggregate_rules(
+    spec: ReaggregateSpec | dict | None,
+) -> list[DimensionReaggregateRule]:
+    """
+    Return the dimension-specific reaggregation rules from a spec.
+    """
+    reaggregate = parse_reaggregate_spec(spec)
+    return reaggregate.rules if reaggregate else []
diff --git a/datajunction-server/datajunction_server/semantic_fingerprints/v1.py b/datajunction-server/datajunction_server/semantic_fingerprints/v1.py
index 08cc05e83..d40f99c16 100644
--- a/datajunction-server/datajunction_server/semantic_fingerprints/v1.py
+++ b/datajunction-server/datajunction_server/semantic_fingerprints/v1.py
@@ -45,7 +45,7 @@
         "primary_key",
         "query",
     ),
-    MetricSpec: ("query", "required_dimensions"),
+    MetricSpec: ("query", "required_dimensions", "reaggregate"),
     CubeSpec: ("metrics", "dimensions", "filters", "columns"),
 }
 
diff --git a/datajunction-server/datajunction_server/sql/decompose.py b/datajunction-server/datajunction_server/sql/decompose.py
index 3003ad291..cde8397ed 100644
--- a/datajunction-server/datajunction_server/sql/decompose.py
+++ b/datajunction-server/datajunction_server/sql/decompose.py
@@ -3,19 +3,29 @@
 import hashlib
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import cast
+from typing import Any, cast
 
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import aliased
 
 from datajunction_server.database.node import Node, NodeRelationship, NodeRevision
+from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.decompose import (
     Aggregability,
     AggregationRule,
     MetricComponent,
 )
+from datajunction_server.models.dialect import Dialect
+from datajunction_server.models.materialization import MaterializationTarget
 from datajunction_server.models.node_type import NodeType
+from datajunction_server.models.reaggregate import (
+    ReaggregateSpec,
+    ReaggregationFunction,
+    dimension_reaggregate_rules,
+    is_supported_dimension_reaggregate_function,
+    parse_reaggregate_spec,
+)
 from datajunction_server.naming import amenable_col_names
 from datajunction_server.sql import functions as dj_functions
 from datajunction_server.sql.parsing.backends.antlr4 import ast, parse
@@ -95,7 +105,29 @@ class ComponentDef:
     suffix: str
     accumulate: str
     merge: str
+    # Fixed arguments appended after the column in the merge call, as SQL literals.
+    # `nflx_tdigest_agg(col, 200.0)` is merge="nflx_tdigest_agg" with
+    # merge_args=("200.0",). Kept separate from `merge` rather than templated into
+    # it because the Druid aggregator mapping and the semi-additive rewrite both
+    # match on the bare function name. Omitting a required tuning argument is not
+    # an error the engine reports: `nflx_tdigest_agg` has a one-argument overload
+    # that silently falls back to a near-useless compression and collapses the
+    # digest to a single centroid at the mean.
+    merge_args: tuple[str, ...] = ()
     arg_index: int | None = 0  # Which arg to use, or None for multi-arg templates
+    # Conversion applied to the accumulated value when writing a measures table
+    # for a target that cannot read the in-engine representation. A template, as
+    # `accumulate` is: "nflx_tdigest_sketch({})". Applied only at materialization
+    # time and only for the targets in `serialize_targets`, so query-time SQL and
+    # every other destination keep the unwrapped value.
+    serialize: str | None = None
+    # Targets that need `serialize`. Empty means it never applies.
+    serialize_targets: tuple[MaterializationTarget, ...] = ()
+    # Column type the conversion produces, e.g. "binary". Declared rather than
+    # inferred: a sketch accumulate takes more arguments than type inference
+    # feeds it, so inference falls back to the metric type and would silently
+    # record the wrong type for the column.
+    serialize_type: str | None = None
 
 
 class AggDecomposition(ABC):
@@ -115,9 +147,30 @@ class AggDecomposition(ABC):
     def components(self) -> list[ComponentDef]:
         """Define the components needed for this aggregation."""
 
+    def __init__(self, params: dict[str, Any] | None = None):
+        """
+        Args:
+            params: Tuning parameters from the metric's ``reaggregate.params``.
+                Empty for the registry-by-function decompositions, which take
+                none; a sketch family reads its accuracy setting from here.
+        """
+        self.params = params or {}
+
     @abstractmethod
-    def combine(self, components: list[MetricComponent]) -> ast.Expression:
-        """Build the combiner expression from merged metric components."""
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ) -> ast.Expression:
+        """
+        Build the combiner expression from merged metric components.
+
+        Returns Spark-canonical SQL; translation to other dialects happens in
+        the transpilation layer, so re-spelling function names, array literals
+        or index bases here applies them twice. Branch on `dialect` only for an
+        engine needing a structurally different expression.
+        """
 
 
 # =============================================================================
@@ -127,6 +180,30 @@ def combine(self, components: list[MetricComponent]) -> ast.Expression:
 DECOMPOSITION_REGISTRY: dict[type, type[AggDecomposition] | None] = {}
 
 
+# Decompositions selected by the metric's declared reaggregation family rather
+# than by its aggregation function. A family entry wins over the by-function
+# registry, which is what lets `APPROX_PERCENTILE(x, 0.95)` keep its spelling and
+# still decompose, but only for metrics that opted in with `reaggregate.fn`.
+FAMILY_DECOMPOSITION_REGISTRY: dict[ReaggregationFunction, type[AggDecomposition]] = {}
+
+
+def decomposes_family(fn: ReaggregationFunction):
+    """
+    Register a decomposition for a reaggregation family.
+
+    Exported for downstream deployments, which supply the engine-specific
+    functions a family needs. OSS registers none, so a metric declaring an
+    unregistered family falls through to the by-function registry and keeps the
+    aggregability it has today.
+    """
+
+    def decorator(decomp_class: type[AggDecomposition]):
+        FAMILY_DECOMPOSITION_REGISTRY[fn] = decomp_class
+        return decomp_class
+
+    return decorator
+
+
 def decomposes(func_class: type):
     """Decorator to register a decomposition class for a function."""
 
@@ -155,7 +232,12 @@ class SumDecomposition(AggDecomposition):
     def components(self) -> list[ComponentDef]:
         return [ComponentDef("_sum", "SUM", "SUM")]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("SUM", components[0].name)
 
 
@@ -167,7 +249,12 @@ class MaxDecomposition(AggDecomposition):
     def components(self) -> list[ComponentDef]:
         return [ComponentDef("_max", "MAX", "MAX")]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("MAX", components[0].name)
 
 
@@ -179,7 +266,12 @@ class MinDecomposition(AggDecomposition):
     def components(self) -> list[ComponentDef]:
         return [ComponentDef("_min", "MIN", "MIN")]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("MIN", components[0].name)
 
 
@@ -191,7 +283,12 @@ class AnyValueDecomposition(AggDecomposition):
     def components(self) -> list[ComponentDef]:
         return [ComponentDef("_any_value", "ANY_VALUE", "ANY_VALUE")]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("ANY_VALUE", components[0].name)
 
 
@@ -208,7 +305,12 @@ class CountDecomposition(AggDecomposition):
     def components(self) -> list[ComponentDef]:
         return [ComponentDef("_count", "COUNT", "SUM")]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("SUM", components[0].name)
 
 
@@ -220,7 +322,12 @@ class CountIfDecomposition(AggDecomposition):
     def components(self) -> list[ComponentDef]:
         return [ComponentDef("_count_if", "COUNT_IF", "SUM")]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("SUM", components[0].name)
 
 
@@ -240,7 +347,12 @@ def components(self) -> list[ComponentDef]:
             ComponentDef("_count", "COUNT", "SUM"),
         ]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return ast.BinaryOp(
             op=ast.BinaryOpKind.Divide,
             left=make_func("SUM", components[0].name),
@@ -272,7 +384,12 @@ def components(self) -> list[ComponentDef]:
             ),
         ]
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func(
             "hll_sketch_estimate",
             make_func("hll_union_agg", components[0].name),
@@ -375,7 +492,12 @@ def _make_var_samp(self, components: list[MetricComponent]) -> ast.Expression:
 class VarPopDecomposition(VarianceDecompositionBase):
     """Population variance: E[X²] - E[X]²"""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return self._make_var_pop(components)
 
 
@@ -383,7 +505,12 @@ def combine(self, components: list[MetricComponent]):
 class VarSampDecomposition(VarianceDecompositionBase):
     """Sample variance with Bessel's correction."""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return self._make_var_samp(components)
 
 
@@ -391,7 +518,12 @@ def combine(self, components: list[MetricComponent]):
 class VarianceDecomposition(VarianceDecompositionBase):
     """VARIANCE (alias for VAR_SAMP in Spark)."""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return self._make_var_samp(components)  # pragma: no cover
 
 
@@ -404,7 +536,12 @@ def combine(self, components: list[MetricComponent]):
 class StddevPopDecomposition(VarianceDecompositionBase):
     """Population standard deviation: sqrt(VAR_POP)"""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("SQRT", self._make_var_pop(components))
 
 
@@ -412,7 +549,12 @@ def combine(self, components: list[MetricComponent]):
 class StddevSampDecomposition(VarianceDecompositionBase):
     """Sample standard deviation: sqrt(VAR_SAMP)"""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("SQRT", self._make_var_samp(components))
 
 
@@ -420,7 +562,12 @@ def combine(self, components: list[MetricComponent]):
 class StddevDecomposition(VarianceDecompositionBase):
     """STDDEV (alias for STDDEV_SAMP in Spark)."""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return make_func("SQRT", self._make_var_samp(components))  # pragma: no cover
 
 
@@ -525,7 +672,12 @@ def _make_covar_samp(self, components: list[MetricComponent]) -> ast.Expression:
 class CovarPopDecomposition(CovarianceDecompositionBase):
     """Population covariance: E[XY] - E[X]*E[Y]"""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return self._make_covar_pop(components)
 
 
@@ -533,7 +685,12 @@ def combine(self, components: list[MetricComponent]):
 class CovarSampDecomposition(CovarianceDecompositionBase):
     """Sample covariance with Bessel's correction."""
 
-    def combine(self, components: list[MetricComponent]):
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         return self._make_covar_samp(components)
 
 
@@ -567,7 +724,12 @@ def components(self) -> list[ComponentDef]:
             ComponentDef("_count", "COUNT({0})", "SUM", arg_index=0),
         ]
 
-    def combine(self, components: list[MetricComponent]) -> ast.Expression:
+    def combine(
+        self,
+        components: list[MetricComponent],
+        func: ast.Function,
+        dialect: Dialect = Dialect.SPARK,
+    ) -> ast.Expression:
         """
         Build CORR: COVAR(X,Y) / (STDDEV(X) * STDDEV(Y))
 
@@ -637,8 +799,27 @@ def combine(self, components: list[MetricComponent]) -> ast.Expression:
 # =============================================================================
 
 
-def get_decomposition(func_class: type) -> AggDecomposition | None:
-    """Get decomposition instance for a function class, or None if not decomposable."""
+def get_decomposition(
+    func_class: type,
+    reaggregate: ReaggregateSpec | None = None,
+) -> AggDecomposition | None:
+    """
+    Get the decomposition for an aggregation, or None if not decomposable.
+
+    A metric that declares a reaggregation family gets that family's
+    decomposition, which is how an opt-in sketch overrides the default handling
+    of its aggregation function. Everything else resolves by function class as
+    before.
+
+    A declared family with nothing registered for it falls through rather than
+    raising: OSS ships no family implementations, so the metric simply keeps the
+    aggregability it would have had.
+    """
+    if reaggregate is not None and reaggregate.fn is not None:
+        family_class = FAMILY_DECOMPOSITION_REGISTRY.get(reaggregate.fn)
+        if family_class is not None:
+            return family_class(params=reaggregate.params)
+
     decomp_class = DECOMPOSITION_REGISTRY.get(func_class)
     if decomp_class is None:
         return None
@@ -746,6 +927,7 @@ class BaseMetricData:
 
     name: str
     query: str
+    reaggregate: ReaggregateSpec | dict | None = None
 
 
 @dataclass
@@ -771,14 +953,30 @@ class MetricComponentExtractor:
     For derived metrics: collects components from base metrics and substitutes references.
     """
 
-    def __init__(self, node_revision_id: int):
+    def __init__(
+        self,
+        node_revision_id: int,
+        dialect: Dialect = Dialect.SPARK,
+    ):
         """
         Extract metric components from a specific metric revision.
 
         Args:
             node_revision_id: ID of the metric node revision
+            dialect: Dialect the combiner will be rendered for. Every dialect
+                gets a combiner -- whether one exists at all is a property of
+                the aggregation function, not the engine -- but its shape can
+                differ: a sketch family whose engines expose different function
+                shapes needs the target, as Druid fuses a t-digest's merge and
+                combine where Spark and Trino keep them separate.
+
+                Defaults to Spark, which is right for the callers that render
+                for display or for frozen measures. Only the build_v3 path,
+                which knows the cube's actual target engine, passes anything
+                else.
         """
         self._node_revision_id = node_revision_id
+        self._dialect = dialect
 
     @classmethod
     async def from_node_name(  # pragma: no cover
@@ -919,7 +1117,10 @@ async def extract(
 
             if is_parent_derived and parent_revision_id:
                 # Recursively extract the derived metric (inline expansion)
-                parent_extractor = MetricComponentExtractor(parent_revision_id)
+                parent_extractor = MetricComponentExtractor(
+                    parent_revision_id,
+                    dialect=self._dialect,
+                )
                 base_components, derived_ast = await parent_extractor.extract(
                     session,
                     nodes_cache=nodes_cache,
@@ -930,7 +1131,10 @@ async def extract(
             else:
                 # True base metric - decompose aggregations
                 base_ast = parse(base_metric.query)
-                base_components, derived_ast = self._extract_base(base_ast)
+                base_components, derived_ast = self._extract_base(
+                    base_ast,
+                    parse_reaggregate_spec(base_metric.reaggregate),
+                )
 
             for comp in base_components:
                 if comp.name not in components_tracker:
@@ -984,6 +1188,12 @@ def _build_metric_data_from_cache(
         ]
 
         if metric_parents:
+            if metric_node.current.reaggregate:
+                raise DJInvalidInputException(
+                    "Semi-additive metrics must be base metrics in V1. "
+                    f"Derived metric `{metric_node.name}` declares reaggregate.",
+                )
+
             # Derived metric - base metrics are the parent metrics
             return MetricData(
                 query=metric_node.current.query,
@@ -992,6 +1202,7 @@ def _build_metric_data_from_cache(
                     BaseMetricData(
                         name=parent_name,
                         query=nodes_cache[parent_name].current.query,
+                        reaggregate=nodes_cache[parent_name].current.reaggregate,
                     )
                     for parent_name in metric_parents
                     if nodes_cache[parent_name].current
@@ -1007,6 +1218,7 @@ def _build_metric_data_from_cache(
                     BaseMetricData(
                         name=metric_node.name,
                         query=metric_node.current.query,
+                        reaggregate=metric_node.current.reaggregate,
                     ),
                 ],
             )
@@ -1023,6 +1235,7 @@ async def _load_metric_data(self, session: AsyncSession) -> MetricData:
             select(
                 Node.name.label("parent_name"),
                 NodeRevision.query.label("parent_query"),
+                NodeRevision.reaggregate.label("parent_reaggregate"),
             )
             .select_from(NodeRelationship)
             .join(Node, NodeRelationship.parent_id == Node.id)
@@ -1043,6 +1256,7 @@ async def _load_metric_data(self, session: AsyncSession) -> MetricData:
         this_metric_stmt = (
             select(
                 NodeRevision.query,
+                NodeRevision.reaggregate,
                 Node.name,
             )
             .join(Node, NodeRevision.node_id == Node.id)
@@ -1055,12 +1269,22 @@ async def _load_metric_data(self, session: AsyncSession) -> MetricData:
         this_row = this_result.one()
 
         if parent_rows:
+            if this_row.reaggregate:
+                raise DJInvalidInputException(
+                    "Semi-additive metrics must be base metrics in V1. "
+                    f"Derived metric `{this_row.name}` declares reaggregate.",
+                )
+
             # Derived metric - base metrics are the parents
             return MetricData(
                 query=this_row.query,
                 is_derived=True,
                 base_metrics=[
-                    BaseMetricData(name=row.parent_name, query=row.parent_query)
+                    BaseMetricData(
+                        name=row.parent_name,
+                        query=row.parent_query,
+                        reaggregate=row.parent_reaggregate,
+                    )
                     for row in parent_rows
                 ],
             )
@@ -1069,12 +1293,19 @@ async def _load_metric_data(self, session: AsyncSession) -> MetricData:
             return MetricData(
                 query=this_row.query,
                 is_derived=False,
-                base_metrics=[BaseMetricData(name=this_row.name, query=this_row.query)],
+                base_metrics=[
+                    BaseMetricData(
+                        name=this_row.name,
+                        query=this_row.query,
+                        reaggregate=this_row.reaggregate,
+                    ),
+                ],
             )
 
     def _extract_base(
         self,
         query_ast: ast.Query,
+        reaggregate: ReaggregateSpec | None = None,
     ) -> tuple[list[MetricComponent], ast.Query]:
         """
         Extract components from a base metric by decomposing aggregations.
@@ -1105,11 +1336,24 @@ def _extract_base(
 
             # If any aggregation is non-decomposable, abort decomposition
             # entirely — the metric is non-decomposable as a whole.
-            if any(get_decomposition(dj_fn) is None for _, dj_fn in agg_funcs):
+            #
+            # The spec has to be passed here, not just to `_decompose` below:
+            # a family-gated metric decomposes precisely because it declared a
+            # family, and its aggregation function has no entry of its own. Ask
+            # without the spec and every such metric aborts at this gate and
+            # never reaches the family registry at all.
+            if any(
+                get_decomposition(dj_fn, reaggregate) is None for _, dj_fn in agg_funcs
+            ):
+                if dimension_reaggregate_rules(reaggregate):
+                    self._raise_unsupported_reaggregate_shape(
+                        "dimension-specific reaggregation requires a "
+                        "decomposable aggregation",
+                    )
                 return [], query_ast
 
             for func, dj_function in agg_funcs:
-                result = self._decompose(func, dj_function, query_ast)
+                result = self._decompose(func, dj_function, query_ast, reaggregate)
                 if result:  # pragma: no branch
                     # Apply combiner to AST
                     func.parent.replace(from_=func, to=result.combiner)  # type: ignore
@@ -1128,8 +1372,78 @@ def _extract_base(
             for proj in query_ast.select.projection:
                 wrap_divisions_in_nullif(cast(ast.Expression, proj))
 
+        if reaggregate is not None and dimension_reaggregate_rules(reaggregate):
+            self._attach_reaggregate_spec(components, reaggregate)
+
+        if reaggregate is not None and reaggregate.params:
+            self._attach_reaggregate_params(components, reaggregate)
+
         return components, query_ast
 
+    def _attach_reaggregate_params(
+        self,
+        components: list[MetricComponent],
+        reaggregate: ReaggregateSpec,
+    ) -> None:
+        """
+        Propagate tuning parameters from the reaggregate spec to aggregating components.
+        """
+        configurable = [
+            component
+            for component in components
+            if component.aggregation is not None and component.merge is not None
+        ]
+        if not configurable:
+            self._raise_unsupported_reaggregate_shape(
+                "parameterized reaggregation requires an aggregating component",
+            )
+        for component in configurable:
+            component.params = dict(reaggregate.params or {})
+
+    def _attach_reaggregate_spec(
+        self,
+        components: list[MetricComponent],
+        reaggregate: ReaggregateSpec,
+    ) -> None:
+        """
+        Attach a dimension-specific reaggregation rule to one ordinary measure.
+        """
+        dimension_rules = dimension_reaggregate_rules(reaggregate)
+        if len(dimension_rules) != 1:
+            self._raise_unsupported_reaggregate_shape(
+                "dimension-specific reaggregation requires exactly one rule",
+            )
+
+        if not is_supported_dimension_reaggregate_function(dimension_rules[0].fn):
+            self._raise_unsupported_reaggregate_shape(
+                f"unsupported dimension reaggregation function "
+                f"`{dimension_rules[0].fn.value}`",
+            )
+
+        if len(components) != 1:
+            self._raise_unsupported_reaggregate_shape(
+                "dimension-specific reaggregation requires exactly one component",
+            )
+
+        component = components[0]
+        if (
+            component.rule.type != Aggregability.FULL
+            or component.aggregation is None
+            or component.merge is None
+        ):
+            self._raise_unsupported_reaggregate_shape(
+                "dimension-specific reaggregation requires one non-distinct "
+                "fully-aggregatable component",
+            )
+
+        component.rule.reaggregate = dimension_rules[0]
+
+    @staticmethod
+    def _raise_unsupported_reaggregate_shape(reason: str) -> None:
+        raise DJInvalidInputException(
+            f"Unsupported reaggregate metric shape: {reason}.",
+        )
+
     def _substitute_metric_references(
         self,
         query_ast: ast.Query,
@@ -1179,9 +1493,10 @@ def _decompose(
         func: ast.Function,
         dj_function: type,
         query_ast: ast.Query,
+        reaggregate: ReaggregateSpec | None = None,
     ) -> DecompositionResult | None:
         """Decompose an aggregation function using the registry."""
-        decomposition = get_decomposition(dj_function)
+        decomposition = get_decomposition(dj_function, reaggregate)
 
         if decomposition is None:  # pragma: no cover
             # Defensive: ``_extract_base`` filters non-decomposable
@@ -1208,7 +1523,7 @@ def _decompose(
                 quantifier=ast.SetQuantifier.Distinct,
             )
         else:
-            combiner_ast = decomposition.combine(components)
+            combiner_ast = decomposition.combine(components, func, self._dialect)
             # Decomposed AVG / variance / stddev / covariance all build
             # SUM(...) / SUM(count)-style combiners where the denominator
             # can legitimately be 0.  Wrap to produce NULL rather than
@@ -1288,11 +1603,15 @@ def _make_component(
             expression=expression,
             aggregation=None if is_distinct else accumulate_expr,
             merge=None if is_distinct else comp_def.merge,
+            merge_args=[] if is_distinct else list(comp_def.merge_args),
             rule=AggregationRule(
                 type=Aggregability.LIMITED if is_distinct else Aggregability.FULL,
                 level=[str(a) for a in func.args] if is_distinct else None,
             ),
             grain_alias=grain_alias,
+            serialize=comp_def.serialize,
+            serialize_targets=list(comp_def.serialize_targets),
+            serialize_type=comp_def.serialize_type,
         )
 
     def _expand_template(self, template: str, args: list) -> str:
diff --git a/datajunction-server/datajunction_server/sql/functions.py b/datajunction-server/datajunction_server/sql/functions.py
index b194d46e4..87c6891ae 100644
--- a/datajunction-server/datajunction_server/sql/functions.py
+++ b/datajunction-server/datajunction_server/sql/functions.py
@@ -552,16 +552,16 @@ class ApproxPercentile(Function):
 def infer_type(
     col: ct.NumberType,
     percentage: ct.ListType,
-    accuracy: ct.NumberType | None,
-) -> ct.DoubleType:
+    accuracy: ct.NumberType | None = None,
+) -> ct.ListType:
     return ct.ListType(element_type=col.type)  # type: ignore
 
 
 @ApproxPercentile.register
 def infer_type(
     col: ct.NumberType,
-    percentage: ct.FloatType,
-    accuracy: ct.NumberType | None,
+    percentage: ct.FloatingBase,
+    accuracy: ct.NumberType | None = None,
 ) -> ct.NumberType:
     return col.type  # type: ignore
 
diff --git a/datajunction-server/datajunction_server/sql/parsing/ast.py b/datajunction-server/datajunction_server/sql/parsing/ast.py
index 324bb2abb..8bd013d3f 100644
--- a/datajunction-server/datajunction_server/sql/parsing/ast.py
+++ b/datajunction-server/datajunction_server/sql/parsing/ast.py
@@ -1604,29 +1604,6 @@ async def add_ref_column(
         """
         Add column referenced from this table. Returns True if the table has the column
         and False otherwise.
-
-        This function handles the following cases:
-
-        Regular columns. For example:
-        (1) non-aliased columns
-          `SELECT country_id AS country1 FROM countries` should match the `country_id`
-          column in the table `countries`
-        (2) aliased columns
-          `SELECT C.country_id AS country1 FROM countries C` should match the `country_id`
-          column in the table `countries` with the column namespace/table alias `C`
-
-        Struct columns. For example:
-        (1) non-aliased struct columns
-          `countries` has column `identifiers` with type:
-                STRUCT<country_name STR, country_code STR>
-          `SELECT identifiers.country_name AS name FROM countries` should match the
-          `identifier` -> `country_name` column in the table `countries`
-        (2) aliased struct columns
-          `countries` has column `identifiers` with type:
-                STRUCT<country_name STR, country_code STR>
-          `SELECT C.identifiers.country_name AS name FROM countries C` should match the
-          `identifier` -> `country_name` column in the table `countries` with the column namespace/
-          table alias `C`
         """
         if not self._columns:
             if ctx is None:
diff --git a/datajunction-server/tests/api/cubes_build_metrics_spec_test.py b/datajunction-server/tests/api/cubes_build_metrics_spec_test.py
index 64c14bb34..e196fa151 100644
--- a/datajunction-server/tests/api/cubes_build_metrics_spec_test.py
+++ b/datajunction-server/tests/api/cubes_build_metrics_spec_test.py
@@ -4,7 +4,19 @@
 This function builds Druid metricsSpec from measure columns and their components.
 """
 
+import pytest
+
 from datajunction_server.api.cubes import _build_metrics_spec
+from datajunction_server.models.cube_materialization import MetricMeasures
+from datajunction_server.models.materialization import (
+    DRUID_AGG_MAPPING,
+    DRUID_SKETCH_CONFIG,
+    DruidMeasuresCubeConfig,
+    Measure,
+    get_druid_aggregator_spec,
+    register_druid_aggregator,
+)
+from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.decompose import AggregationRule, MetricComponent
 from datajunction_server.models.query import ColumnMetadata
 
@@ -374,3 +386,293 @@ def test_preserves_column_order(self):
         result = _build_metrics_spec(columns, components, aliases)
 
         assert [r["name"] for r in result] == ["z_col", "a_col", "m_col"]
+
+
+class TestDruidAggregatorSpec:
+    """
+    Tests for the shared aggregator helper behind both metricsSpec builders.
+    """
+
+    def test_merge_is_preferred_over_accumulate(self):
+        """
+        Ingestion merges stored partials, so the merge function keys the lookup.
+        """
+        spec = get_druid_aggregator_spec(
+            column_name="orders_count",
+            column_type="bigint",
+            aggregation="COUNT",
+            merge="SUM",
+        )
+        assert spec == {
+            "fieldName": "orders_count",
+            "name": "orders_count",
+            "type": "longSum",
+        }
+
+    def test_falls_back_to_accumulate_without_merge(self):
+        """A component with no merge phase keys off its accumulate."""
+        spec = get_druid_aggregator_spec(
+            column_name="revenue_sum",
+            column_type="double",
+            aggregation="SUM",
+            merge=None,
+        )
+        assert spec is not None
+        assert spec["type"] == "doubleSum"
+
+    def test_unmappable_returns_none(self):
+        """
+        Return None for unmappable measures so callers determine fallback behavior.
+        """
+        assert (
+            get_druid_aggregator_spec(
+                column_name="x",
+                column_type="varchar",
+                aggregation="SUM",
+                merge="SUM",
+            )
+            is None
+        )
+        assert (
+            get_druid_aggregator_spec(
+                column_name="x",
+                column_type=None,
+                aggregation="SUM",
+                merge="SUM",
+            )
+            is None
+        )
+        assert (
+            get_druid_aggregator_spec(
+                column_name="x",
+                column_type="bigint",
+                aggregation=None,
+                merge=None,
+            )
+            is None
+        )
+
+    def test_sketch_gets_default_config(self):
+        """An HLL sketch carries its precision defaults into the spec."""
+        spec = get_druid_aggregator_spec(
+            column_name="accounts_hll",
+            column_type="binary",
+            aggregation="hll_sketch_agg",
+            merge="hll_union_agg",
+        )
+        assert spec == {
+            "fieldName": "accounts_hll",
+            "name": "accounts_hll",
+            "type": "HLLSketchMerge",
+            "lgK": 12,
+            "tgtHllType": "HLL_4",
+        }
+
+    def test_declared_params_override_defaults_partially(self):
+        """
+        Metric-level params override defaults for specific keys while retaining others.
+        """
+        spec = get_druid_aggregator_spec(
+            column_name="accounts_hll",
+            column_type="binary",
+            aggregation="hll_sketch_agg",
+            merge="hll_union_agg",
+            params={"lgK": 17},
+        )
+        assert spec is not None
+        assert spec["lgK"] == 17
+        assert spec["tgtHllType"] == "HLL_4"
+
+    def test_params_cannot_overwrite_structural_keys(self):
+        """
+        `params` must not overwrite structural keys like `type` or `fieldName`.
+        """
+        with pytest.raises(DJInvalidInputException) as excinfo:
+            get_druid_aggregator_spec(
+                column_name="accounts_hll",
+                column_type="binary",
+                aggregation="hll_sketch_agg",
+                merge="hll_union_agg",
+                params={"type": "doubleSum", "fieldName": "some_other_column"},
+            )
+        assert "does not accept" in str(excinfo.value)
+        assert "`fieldName`" in str(excinfo.value)
+        assert "`type`" in str(excinfo.value)
+
+    def test_unknown_param_is_rejected(self):
+        """Unknown parameters are rejected loudly."""
+        with pytest.raises(DJInvalidInputException) as excinfo:
+            get_druid_aggregator_spec(
+                column_name="accounts_hll",
+                column_type="binary",
+                aggregation="hll_sketch_agg",
+                merge="hll_union_agg",
+                params={"lgk": 17},  # lowercase k
+            )
+        assert "lgk" in str(excinfo.value)
+        assert "lgK" in str(excinfo.value)  # the supported spelling is named
+
+    def test_params_rejected_for_unparameterized_aggregator(self):
+        """Params passed to an unparameterized aggregator raise an error."""
+        with pytest.raises(DJInvalidInputException) as excinfo:
+            get_druid_aggregator_spec(
+                column_name="orders_count",
+                column_type="bigint",
+                aggregation="COUNT",
+                merge="SUM",
+                params={"compression": 200},
+            )
+        assert "takes no parameters" in str(excinfo.value)
+
+
+class TestRegisterDruidAggregator:
+    """
+    Tests for registering custom sketch families.
+    """
+
+    def test_register_and_use(self):
+        """A registered family is mappable and carries its default config."""
+        try:
+            register_druid_aggregator(
+                column_type="binary",
+                merge_func="test_tdigest_agg",
+                aggregator="testTDigestSketch",
+                default_config={"compression": 200},
+            )
+            spec = get_druid_aggregator_spec(
+                column_name="latency_tdigest",
+                column_type="binary",
+                aggregation="test_tdigest",
+                merge="test_tdigest_agg",
+            )
+            assert spec == {
+                "fieldName": "latency_tdigest",
+                "name": "latency_tdigest",
+                "type": "testTDigestSketch",
+                "compression": 200,
+            }
+            # A metric's own params win over the family default.
+            tuned = get_druid_aggregator_spec(
+                column_name="latency_tdigest",
+                column_type="binary",
+                aggregation="test_tdigest",
+                merge="test_tdigest_agg",
+                params={"compression": 1000},
+            )
+            assert tuned is not None
+            assert tuned["compression"] == 1000
+            assert "testTDigestSketch" in DRUID_SKETCH_CONFIG
+        finally:
+            DRUID_AGG_MAPPING.pop(("binary", "test_tdigest_agg"), None)
+            DRUID_SKETCH_CONFIG.pop("testTDigestSketch", None)
+
+    def test_register_without_config(self):
+        """A family with nothing to tune registers without extra config keys."""
+        try:
+            register_druid_aggregator(
+                column_type="bigint",
+                merge_func="test_plain_merge",
+                aggregator="testPlainAgg",
+            )
+            spec = get_druid_aggregator_spec(
+                column_name="c",
+                column_type="bigint",
+                aggregation="test_plain",
+                merge="test_plain_merge",
+            )
+            assert spec == {
+                "fieldName": "c",
+                "name": "c",
+                "type": "testPlainAgg",
+            }
+        finally:
+            DRUID_AGG_MAPPING.pop(("bigint", "test_plain_merge"), None)
+
+    def test_conflicting_defaults_are_rejected(self):
+        """
+        Re-registering an aggregator with different defaults raises an error.
+        """
+        try:
+            register_druid_aggregator(
+                column_type="binary",
+                merge_func="test_conflict_agg",
+                aggregator="testConflictSketch",
+                default_config={"compression": 100},
+            )
+            with pytest.raises(DJInvalidInputException) as excinfo:
+                register_druid_aggregator(
+                    column_type="binary",
+                    merge_func="test_conflict_agg",
+                    aggregator="testConflictSketch",
+                    default_config={"compression": 200},
+                )
+            assert "already registered with different defaults" in str(excinfo.value)
+            # Re-registering exactly what is there is a no-op.
+            register_druid_aggregator(
+                column_type="binary",
+                merge_func="test_conflict_agg",
+                aggregator="testConflictSketch",
+                default_config={"compression": 100},
+            )
+            assert DRUID_SKETCH_CONFIG["testConflictSketch"] == {"compression": 100}
+        finally:
+            DRUID_AGG_MAPPING.pop(("binary", "test_conflict_agg"), None)
+            DRUID_SKETCH_CONFIG.pop("testConflictSketch", None)
+
+    def test_conflicting_aggregator_is_rejected(self):
+        """Two families cannot claim the same (column type, merge func) pair."""
+        try:
+            register_druid_aggregator(
+                column_type="binary",
+                merge_func="test_claimed",
+                aggregator="testFirstSketch",
+            )
+            with pytest.raises(DJInvalidInputException) as excinfo:
+                register_druid_aggregator(
+                    column_type="binary",
+                    merge_func="test_claimed",
+                    aggregator="testSecondSketch",
+                )
+            assert "already registered as" in str(excinfo.value)
+        finally:
+            DRUID_AGG_MAPPING.pop(("binary", "test_claimed"), None)
+
+    def test_v2_measures_cube_config_gets_family_defaults(self):
+        """
+        The V2 path uses the aggregator fallback with `merge=None` to pull correct specs.
+        """
+        try:
+            register_druid_aggregator(
+                column_type="binary",
+                merge_func="test_v2_tdigest",
+                aggregator="testV2Sketch",
+                default_config={"compression": 200},
+            )
+            config = DruidMeasuresCubeConfig.model_construct(
+                dimensions=[],
+                measures={
+                    "some.metric": MetricMeasures.model_construct(
+                        metric="some.metric",
+                        combiner="x",
+                        measures=[
+                            Measure(
+                                name="latency_tdigest",
+                                field_name="latency_tdigest",
+                                agg="test_v2_tdigest",
+                                type="binary",
+                            ),
+                        ],
+                    ),
+                },
+            )
+            assert config.metrics_spec() == {
+                "latency_tdigest": {
+                    "fieldName": "latency_tdigest",
+                    "name": "latency_tdigest",
+                    "type": "testV2Sketch",
+                    "compression": 200,
+                },
+            }
+        finally:
+            DRUID_AGG_MAPPING.pop(("binary", "test_v2_tdigest"), None)
+            DRUID_SKETCH_CONFIG.pop("testV2Sketch", None)
diff --git a/datajunction-server/tests/api/cubes_test.py b/datajunction-server/tests/api/cubes_test.py
index aa00f9671..6d1b6245a 100644
--- a/datajunction-server/tests/api/cubes_test.py
+++ b/datajunction-server/tests/api/cubes_test.py
@@ -3584,10 +3584,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "COUNT",
                     "expression": "*",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "count_c8e42e74",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -3595,10 +3601,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "SUM",
                     "expression": "if(discount > 0.0, 1, 0)",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "discount_sum_30b84e6c",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -3606,10 +3618,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "COUNT",
                     "expression": "price",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "price_count_935e7117",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -3617,10 +3635,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "SUM",
                     "expression": "price * discount",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "price_discount_sum_e4ba5456",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -3628,10 +3652,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "SUM",
                     "expression": "price",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "price_sum_935e7117",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -3639,10 +3669,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "COUNT",
                     "expression": "repair_order_id",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "repair_order_id_count_bd241964",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -3650,10 +3686,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "SUM",
                     "expression": "total_repair_cost",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "total_repair_cost_sum_67874507",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -3769,10 +3811,16 @@ async def test_cube_materialization_metadata(
                     "aggregation": "SUM",
                     "expression": "price",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "merge": "SUM",
                     "name": "price_sum_252381cf",
                     "rule": {
                         "level": None,
+                        "reaggregate": None,
                         "type": "full",
                     },
                 },
@@ -4023,65 +4071,105 @@ async def test_cube_materialization_metadata(
                     "name": "count_c8e42e74",
                     "expression": "*",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "COUNT",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
                 {
                     "name": "discount_sum_30b84e6c",
                     "expression": "if(discount > 0.0, 1, 0)",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "SUM",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
                 {
                     "name": "price_count_935e7117",
                     "expression": "price",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "COUNT",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
                 {
                     "name": "price_discount_sum_e4ba5456",
                     "expression": "price * discount",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "SUM",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
                 {
                     "name": "price_sum_935e7117",
                     "expression": "price",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "SUM",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
                 {
                     "name": "repair_order_id_count_bd241964",
                     "expression": "repair_order_id",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "COUNT",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
                 {
                     "name": "total_repair_cost_sum_67874507",
                     "expression": "total_repair_cost",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "SUM",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
                 {
                     "name": "price_sum_252381cf",
                     "expression": "price",
                     "grain_alias": None,
+                    "params": None,
+                    "merge_args": [],
+                    "serialize": None,
+                    "serialize_targets": [],
+                    "serialize_type": None,
                     "aggregation": "SUM",
                     "merge": "SUM",
-                    "rule": {"type": "full", "level": None},
+                    "rule": {"type": "full", "level": None, "reaggregate": None},
                 },
             ],
             "timestamp_column": "hire_date",
@@ -4804,6 +4892,133 @@ class TestCubeMaterializeV2SuccessPaths:
     and mock the query service client to test the full endpoint flow.
     """
 
+    @pytest.fixture(autouse=True)
+    def _preserve_app_dependency_overrides(
+        self,
+        client_with_repairs_cube: AsyncClient,
+    ):
+        """Protect module-scoped client overrides from function-scoped clients."""
+        original_overrides = dict(client_with_repairs_cube.app.dependency_overrides)
+        yield
+        client_with_repairs_cube.app.dependency_overrides.clear()
+        client_with_repairs_cube.app.dependency_overrides.update(original_overrides)
+
+    @pytest.mark.asyncio
+    async def test_materialize_reaggregate_cube_without_protected_dimension_fails(
+        self,
+        client_with_build_v3: AsyncClient,
+        mocker,
+    ):
+        """Druid cube materialization refuses to bake in collapsed
+        semi-additive metrics without the protected dimension."""
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.daily_balance",
+                "description": "Semi-additive balance measured by order date",
+                "query": "SELECT SUM(line_total) FROM v3.order_details",
+                "mode": "published",
+                "reaggregate": {
+                    "rules": [
+                        {
+                            "dimension": "v3.date.date_id[order]",
+                            "fn": "last_value",
+                        },
+                    ],
+                },
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+        cube_name = "v3.test_daily_balance_materialization_guard"
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": cube_name,
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Unsafe semi-additive materialization",
+            },
+        )
+        assert response.status_code == 201, response.json()
+
+        combiner = mocker.patch(
+            "datajunction_server.api.cubes.build_combiner_sql_from_preaggs",
+        )
+
+        response = await client_with_build_v3.post(
+            f"/cubes/{cube_name}/materialize",
+            json={"strategy": "full", "schedule": "0 0 * * *"},
+        )
+
+        assert response.status_code == 400, response.json()
+        assert "protected dimension" in response.json()["message"]
+        combiner.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_materialize_derived_reaggregate_cube_without_protected_dimension_fails(
+        self,
+        client_with_build_v3: AsyncClient,
+        mocker,
+    ):
+        """Druid cube materialization also refuses derived metrics whose base
+        metric has an omitted semi-additive protected dimension."""
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.daily_balance",
+                "description": "Semi-additive balance measured by order date",
+                "query": "SELECT SUM(line_total) FROM v3.order_details",
+                "mode": "published",
+                "reaggregate": {
+                    "rules": [
+                        {
+                            "dimension": "v3.date.date_id[order]",
+                            "fn": "last_value",
+                        },
+                    ],
+                },
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.daily_balance_index",
+                "description": "Derived semi-additive balance index",
+                "query": "SELECT 10.0 / v3.daily_balance",
+                "mode": "published",
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+        cube_name = "v3.test_daily_balance_index_materialization_guard"
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": cube_name,
+                "metrics": ["v3.daily_balance_index"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Unsafe derived semi-additive materialization",
+            },
+        )
+        assert response.status_code == 201, response.json()
+
+        combiner = mocker.patch(
+            "datajunction_server.api.cubes.build_combiner_sql_from_preaggs",
+        )
+
+        response = await client_with_build_v3.post(
+            f"/cubes/{cube_name}/materialize",
+            json={"strategy": "full", "schedule": "0 0 * * *"},
+        )
+
+        assert response.status_code == 400, response.json()
+        assert "protected dimension" in response.json()["message"]
+        combiner.assert_not_called()
+
     @pytest.mark.asyncio
     async def test_materialize_cube_full_strategy_success(
         self,
@@ -5596,6 +5811,12 @@ async def test_materialize_cube_returns_metric_combiners(
         mock_qs_client.materialize_cube_v2.return_value = mocker.MagicMock(
             urls=["http://workflow/test-cube"],
         )
+        had_query_service_override = get_query_service_client in (
+            client.app.dependency_overrides
+        )
+        original_query_service_override = client.app.dependency_overrides.get(
+            get_query_service_client,
+        )
         client.app.dependency_overrides[get_query_service_client] = lambda: (
             mock_qs_client
         )
@@ -5783,8 +6004,12 @@ async def test_materialize_cube_returns_metric_combiners(
             ]
         finally:
             # Clean up dependency override
-            if get_query_service_client in client.app.dependency_overrides:
-                del client.app.dependency_overrides[get_query_service_client]
+            if had_query_service_override:
+                client.app.dependency_overrides[get_query_service_client] = (
+                    original_query_service_override
+                )
+            else:
+                client.app.dependency_overrides.pop(get_query_service_client, None)
 
 
 class TestCubeDeactivateSuccessPaths:
@@ -6020,6 +6245,12 @@ async def test_deactivate_cube_uses_stored_workflow_names(
             urls=["http://workflow/cube-workflow"],
             workflow_names=["cube_wf_name_1"],
         )
+        had_query_service_override = get_query_service_client in (
+            client.app.dependency_overrides
+        )
+        original_query_service_override = client.app.dependency_overrides.get(
+            get_query_service_client,
+        )
         client.app.dependency_overrides[get_query_service_client] = lambda: (
             mock_qs_client
         )
@@ -6047,8 +6278,12 @@ async def test_deactivate_cube_uses_stored_workflow_names(
             # The old path should NOT have been called
             mock_qs_client.deactivate_cube_workflow.assert_not_called()
         finally:
-            if get_query_service_client in client.app.dependency_overrides:
-                del client.app.dependency_overrides[get_query_service_client]
+            if had_query_service_override:
+                client.app.dependency_overrides[get_query_service_client] = (
+                    original_query_service_override
+                )
+            else:
+                client.app.dependency_overrides.pop(get_query_service_client, None)
 
 
 class TestCubeBackfillSuccessPaths:
diff --git a/datajunction-server/tests/api/graphql/resolvers/test_node_resolver.py b/datajunction-server/tests/api/graphql/resolvers/test_node_resolver.py
index 6a47982db..f38a5501a 100644
--- a/datajunction-server/tests/api/graphql/resolvers/test_node_resolver.py
+++ b/datajunction-server/tests/api/graphql/resolvers/test_node_resolver.py
@@ -70,6 +70,86 @@ def test_columns_resolver_filters_by_attribute():
     assert result[0].name == "user_id"
 
 
+def test_reaggregate_resolver_returns_none_for_non_metric():
+    """
+    Test that the reaggregate resolver only resolves for metric revisions.
+    """
+    from datajunction_server.api.graphql.scalars.node import NodeRevision
+    from datajunction_server.database import NodeRevision as DBNodeRevision
+    from datajunction_server.models.node_type import NodeType
+
+    db_node_revision = DBNodeRevision(
+        name="source_rev",
+        type=NodeType.SOURCE,
+        version="1",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+
+    result = NodeRevision.reaggregate(NodeRevision, root=db_node_revision)
+
+    assert result is None
+
+
+def test_reaggregate_resolver_returns_none_for_empty_metric_spec():
+    """
+    Test that the reaggregate resolver returns None for metrics without a spec.
+    """
+    from datajunction_server.api.graphql.scalars.node import NodeRevision
+    from datajunction_server.database import NodeRevision as DBNodeRevision
+    from datajunction_server.models.node_type import NodeType
+
+    db_node_revision = DBNodeRevision(
+        name="metric_rev",
+        type=NodeType.METRIC,
+        version="1",
+        reaggregate=None,
+    )
+
+    result = NodeRevision.reaggregate(NodeRevision, root=db_node_revision)
+
+    assert result is None
+
+
+def test_reaggregate_resolver_returns_metric_spec():
+    """
+    Test that the reaggregate resolver exposes metric reaggregation declarations.
+    """
+    from datajunction_server.api.graphql.scalars.node import NodeRevision
+    from datajunction_server.database import NodeRevision as DBNodeRevision
+    from datajunction_server.models.node_type import NodeType
+    from datajunction_server.models.reaggregate import ReaggregationFunction
+
+    db_node_revision = DBNodeRevision(
+        name="metric_rev",
+        type=NodeType.METRIC,
+        version="1",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+
+    result = NodeRevision.reaggregate(NodeRevision, root=db_node_revision)
+
+    assert result is not None
+    assert result.fn is None
+    assert result.weight is None
+    assert len(result.rules) == 1
+    assert result.rules[0].dimension == "default.date_dim.date"
+    assert result.rules[0].fn == ReaggregationFunction.LAST_VALUE
+
+
 @pytest.mark.asyncio
 @patch("datajunction_server.api.graphql.resolvers.nodes.DBNode.get_by_name")
 @patch("datajunction_server.api.graphql.resolvers.nodes.load_node_options")
diff --git a/datajunction-server/tests/api/measures_test.py b/datajunction-server/tests/api/measures_test.py
index 15cb18343..4d3c0d943 100644
--- a/datajunction-server/tests/api/measures_test.py
+++ b/datajunction-server/tests/api/measures_test.py
@@ -292,6 +292,7 @@ async def test_list_frozen_measures(
             "name": "repair_order_id_count_bd241964",
             "rule": {
                 "level": None,
+                "reaggregate": None,
                 "type": "full",
             },
             "upstream_revision": {
diff --git a/datajunction-server/tests/api/metrics_test.py b/datajunction-server/tests/api/metrics_test.py
index b6d939b76..49c39146a 100644
--- a/datajunction-server/tests/api/metrics_test.py
+++ b/datajunction-server/tests/api/metrics_test.py
@@ -3,6 +3,7 @@
 """
 
 from unittest.mock import patch
+from uuid import uuid4
 
 import pytest
 import pytest_asyncio
@@ -464,10 +465,16 @@ async def test_read_metrics(module__client_with_roads: AsyncClient) -> None:
             "aggregation": "SUM",
             "expression": "if(discount > 0.0, 1, 0)",
             "grain_alias": None,
+            "merge_args": [],
+            "serialize": None,
+            "serialize_targets": [],
+            "serialize_type": None,
+            "params": None,
             "name": "discount_sum_30b84e6c",
             "merge": "SUM",
             "rule": {
                 "level": None,
+                "reaggregate": None,
                 "type": "full",
             },
         },
@@ -475,10 +482,16 @@ async def test_read_metrics(module__client_with_roads: AsyncClient) -> None:
             "aggregation": "COUNT",
             "expression": "*",
             "grain_alias": None,
+            "merge_args": [],
+            "serialize": None,
+            "serialize_targets": [],
+            "serialize_type": None,
+            "params": None,
             "merge": "SUM",
             "name": "count_c8e42e74",
             "rule": {
                 "level": None,
+                "reaggregate": None,
                 "type": "full",
             },
         },
@@ -494,6 +507,111 @@ async def test_read_metrics(module__client_with_roads: AsyncClient) -> None:
     assert data["custom_metadata"] is None
 
 
+@pytest.mark.asyncio
+async def test_metric_reaggregate_roundtrip_and_validation(
+    client_with_roads: AsyncClient,
+) -> None:
+    """
+    Semi-additive declarations round-trip and validate their protected dimension.
+    """
+    metric_name = f"default.reaggregate_repairs_{uuid4().hex}"
+    response = await client_with_roads.post(
+        "/nodes/metric/",
+        json={
+            "name": metric_name,
+            "description": "Repair orders with semi-additive declaration",
+            "query": "SELECT COUNT(repair_order_id) FROM default.repair_orders_fact",
+            "mode": "published",
+            "reaggregate": {
+                "rules": [
+                    {
+                        "dimension": "repair_order_id",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        },
+    )
+    assert response.status_code in (200, 201), response.json()
+    assert response.json()["reaggregate"] == {
+        "fn": None,
+        "weight": None,
+        "params": None,
+        "rules": [
+            {
+                "dimension": "repair_order_id",
+                "fn": "last_value",
+            },
+        ],
+    }
+
+    response = await client_with_roads.get(f"/nodes/{metric_name}/")
+    assert response.status_code == 200
+    assert response.json()["reaggregate"] == {
+        "fn": None,
+        "weight": None,
+        "params": None,
+        "rules": [
+            {
+                "dimension": "repair_order_id",
+                "fn": "last_value",
+            },
+        ],
+    }
+
+    response = await client_with_roads.get(f"/metrics/{metric_name}/")
+    assert response.status_code == 200
+    assert response.json()["reaggregate"] == {
+        "fn": None,
+        "weight": None,
+        "params": None,
+        "rules": [
+            {
+                "dimension": "repair_order_id",
+                "fn": "last_value",
+            },
+        ],
+    }
+
+    invalid_metric_name = f"default.invalid_reaggregate_{uuid4().hex}"
+    response = await client_with_roads.post(
+        "/nodes/metric/",
+        json={
+            "name": invalid_metric_name,
+            "description": "Invalid semi-additive declaration",
+            "query": "SELECT COUNT(repair_order_id) FROM default.repair_orders_fact",
+            "mode": "published",
+            "reaggregate": {
+                "rules": [
+                    {
+                        "dimension": "default.repair_orders_fact.nope",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        },
+    )
+    assert response.status_code == 422
+    assert response.json() == {
+        "message": "Node definition contains references to columns as "
+        "reaggregate dimensions that are not on parent nodes.",
+        "errors": [
+            {
+                "code": "INVALID_COLUMN",
+                "message": "Node definition contains references to columns as "
+                "reaggregate dimensions that are not on parent nodes.",
+                "debug": {
+                    "invalid_reaggregate_dimensions": [
+                        "default.repair_orders_fact.nope",
+                    ],
+                },
+                "context": "",
+            },
+        ],
+        "warnings": [],
+    }
+
+
 @pytest_asyncio.fixture(scope="module")
 async def module__current_user(module__session: AsyncSession) -> User:
     """
diff --git a/datajunction-server/tests/api/preaggregations_test.py b/datajunction-server/tests/api/preaggregations_test.py
index 72a8b9804..e87a1d523 100644
--- a/datajunction-server/tests/api/preaggregations_test.py
+++ b/datajunction-server/tests/api/preaggregations_test.py
@@ -736,11 +736,17 @@ async def test_get_preagg_by_id(self, client_with_preaggs):
                 "expr_hash": "83632b779d87",
                 "expression": "line_total",
                 "grain_alias": None,
+                "merge_args": [],
+                "serialize": None,
+                "serialize_targets": [],
+                "serialize_type": None,
+                "params": None,
                 "merge": "SUM",
                 "name": "line_total_sum_e1f61696",
                 "source_column": None,
                 "rule": {
                     "level": None,
+                    "reaggregate": None,
                     "type": "full",
                 },
                 "used_by_metrics": [
@@ -803,11 +809,17 @@ async def test_get_preagg_by_id(self, client_with_preaggs):
                 "expr_hash": "221d2a4bfdae",
                 "expression": "quantity",
                 "grain_alias": None,
+                "merge_args": [],
+                "serialize": None,
+                "serialize_targets": [],
+                "serialize_type": None,
+                "params": None,
                 "merge": "SUM",
                 "name": "quantity_sum_06b64d2e",
                 "source_column": None,
                 "rule": {
                     "level": None,
+                    "reaggregate": None,
                     "type": "full",
                 },
                 "used_by_metrics": [
diff --git a/datajunction-server/tests/api/sql_v2_test.py b/datajunction-server/tests/api/sql_v2_test.py
index 42c58eb52..fec7578f1 100644
--- a/datajunction-server/tests/api/sql_v2_test.py
+++ b/datajunction-server/tests/api/sql_v2_test.py
@@ -1201,9 +1201,18 @@ async def create_metric_distinct_single_column(client: AsyncClient):
             "aggregation": None,
             "expression": "hard_hat_id",
             "grain_alias": "hard_hat_id",
+            "merge_args": [],
+            "serialize": None,
+            "serialize_targets": [],
+            "serialize_type": None,
+            "params": None,
             "merge": None,
             "name": "hard_hat_id",
-            "rule": {"level": ["hard_hat_id"], "type": "limited"},
+            "rule": {
+                "level": ["hard_hat_id"],
+                "reaggregate": None,
+                "type": "limited",
+            },
         },
     ]
     assert metric_data["derived_expression"] == "COUNT( DISTINCT hard_hat_id)"
@@ -1228,9 +1237,18 @@ async def create_metric_distinct_expression(client: AsyncClient):
             "aggregation": None,
             "expression": "IF(hard_hat_id = 1, 1, 0)",
             "grain_alias": "hard_hat_id_distinct_0291ee39",
+            "merge_args": [],
+            "serialize": None,
+            "serialize_targets": [],
+            "serialize_type": None,
+            "params": None,
             "merge": None,
             "name": "hard_hat_id_distinct_0291ee39",
-            "rule": {"level": ["IF(hard_hat_id = 1, 1, 0)"], "type": "limited"},
+            "rule": {
+                "level": ["IF(hard_hat_id = 1, 1, 0)"],
+                "reaggregate": None,
+                "type": "limited",
+            },
         },
     ]
     assert (
@@ -1623,10 +1641,16 @@ async def test_metric_definitions_with_nonjoinable_dimensions(
                 "aggregation": "SUM",
                 "expression": "default.local_hard_hats_2.hard_hat_id",
                 "grain_alias": None,
+                "merge_args": [],
+                "serialize": None,
+                "serialize_targets": [],
+                "serialize_type": None,
+                "params": None,
                 "merge": "SUM",
                 "name": "default_DOT_local_hard_hats_2_DOT_hard_hat_id_sum_bf8a8419",
                 "rule": {
                     "level": None,
+                    "reaggregate": None,
                     "type": "full",
                 },
             },
@@ -1687,10 +1711,16 @@ async def test_metric_definitions_with_single_joinable_dimensions(
                 "aggregation": None,
                 "expression": "default.municipality_dim.contact_name",
                 "grain_alias": "contact_name",
+                "merge_args": [],
+                "serialize": None,
+                "serialize_targets": [],
+                "serialize_type": None,
+                "params": None,
                 "merge": None,
                 "name": "contact_name",
                 "rule": {
                     "level": ["default.municipality_dim.contact_name"],
+                    "reaggregate": None,
                     "type": "limited",
                 },
             },
@@ -1792,6 +1822,11 @@ async def test_metric_definition_with_multiple_joinable_dimensions(
                 "expression": "IF(default.hard_hat.state = 'NY', default.hard_hat.first_name, "
                 "NULL)",
                 "grain_alias": "default_DOT_hard_hat_DOT_state_default_DOT_hard_hat_DOT_first_name_distinct_1a99d6a7",
+                "merge_args": [],
+                "serialize": None,
+                "serialize_targets": [],
+                "serialize_type": None,
+                "params": None,
                 "merge": None,
                 "name": "default_DOT_hard_hat_DOT_state_default_DOT_hard_hat_DOT_first_name_distinct_1a99d6a7",
                 "rule": {
@@ -1799,6 +1834,7 @@ async def test_metric_definition_with_multiple_joinable_dimensions(
                         "IF(default.hard_hat.state = 'NY', "
                         "default.hard_hat.first_name, NULL)",
                     ],
+                    "reaggregate": None,
                     "type": "limited",
                 },
             },
diff --git a/datajunction-server/tests/construction/build_v3/accumulate_type_test.py b/datajunction-server/tests/construction/build_v3/accumulate_type_test.py
new file mode 100644
index 000000000..43c048ca4
--- /dev/null
+++ b/datajunction-server/tests/construction/build_v3/accumulate_type_test.py
@@ -0,0 +1,152 @@
+"""
+Tests for typing an accumulate whose outermost call takes several arguments.
+
+Type inference feeds a component exactly one input type. That is right for
+every aggregation DJ ships, including the templated ones -- ``SUM(POWER(x, 2))``
+is still a single-argument ``SUM`` on the outside. A sketch is the first shape
+that breaks it: ``nflx_tdigest(latency_ms, CAST(200.0 AS DOUBLE))`` needs two,
+so inference raised ``TypeError``, the caller swallowed it, and the column was
+recorded as the metric's own type.
+
+That mattered beyond cosmetics. The recorded type is persisted on the
+pre-aggregation row and forwarded to the query service, which uses it to
+create the materialized table -- so a struct-valued sketch column was being
+described as ``double``.
+
+``POWER`` stands in for a sketch accumulate here: OSS registers no sketch
+families, and it is the available two-argument function whose inferred result
+differs from the fallback.
+"""
+
+from types import SimpleNamespace
+
+import pytest
+
+from datajunction_server.construction.build_v3.measures import (
+    _multi_argument_accumulate_types,
+    infer_component_type,
+)
+from datajunction_server.models.decompose import AggregationRule, MetricComponent
+from datajunction_server.models.materialization import MaterializationTarget
+
+
+def _parent(column_type: str = "double"):
+    return SimpleNamespace(
+        current=SimpleNamespace(
+            columns=[SimpleNamespace(name="latency_ms", type=column_type)],
+        ),
+    )
+
+
+def _component(aggregation: str, **kwargs) -> MetricComponent:
+    return MetricComponent(
+        name="latency_acc_abc123",
+        expression="latency_ms",
+        aggregation=aggregation,
+        merge="SUM",
+        rule=AggregationRule(),
+        **kwargs,
+    )
+
+
+class TestMultiArgumentTypes:
+    """The helper resolves every argument, or declines entirely."""
+
+    def test_resolves_a_column_and_a_literal(self):
+        types = _multi_argument_accumulate_types("POWER(latency_ms, 2)", _parent())
+        assert [str(t) for t in types] == ["double", "int"]
+
+    def test_resolves_a_cast_without_binding_it_to_a_table(self):
+        # Literals and casts carry their own type; only columns need the parent.
+        types = _multi_argument_accumulate_types(
+            "POWER(latency_ms, CAST(200.0 AS DOUBLE))",
+            _parent(),
+        )
+        assert [str(t) for t in types] == ["double", "double"]
+
+    def test_declines_without_a_parent_to_resolve_columns_against(self):
+        assert _multi_argument_accumulate_types("POWER(latency_ms, 2)", None) is None
+
+    def test_declines_for_a_bare_function_name(self):
+        # "SUM" is a name, not a call -- the overwhelmingly common shape.
+        assert _multi_argument_accumulate_types("SUM", _parent()) is None
+
+    def test_declines_for_a_single_argument_call(self):
+        # Including templated ones, whose outermost call still takes one arg.
+        assert _multi_argument_accumulate_types("SUM(POWER(latency_ms, 2))", _parent()) is None
+
+    def test_declines_when_a_column_type_is_unrecognized(self):
+        # Better to fall back than to invent a type for the stored schema.
+        assert (
+            _multi_argument_accumulate_types(
+                "POWER(latency_ms, 2)",
+                _parent("some_unknown_type"),
+            )
+            is None
+        )
+
+
+class TestInferredColumnType:
+    """What the measures column ends up recorded as."""
+
+    def test_multi_argument_accumulate_is_typed_from_its_arguments(self):
+        # The regression: this used to return the metric type, because
+        # POWER.infer_type(double) raises and the error was swallowed.
+        assert (
+            infer_component_type(
+                _component("POWER(latency_ms, 2)"),
+                "bigint",
+                _parent(),
+            )
+            == "double"
+        )
+
+    def test_single_argument_accumulate_is_unchanged(self):
+        assert (
+            infer_component_type(_component("SUM"), "bigint", _parent()) == "double"
+        )
+
+    def test_unresolvable_multi_argument_accumulate_falls_back(self):
+        assert (
+            infer_component_type(
+                _component("POWER(latency_ms, 2)"),
+                "bigint",
+                _parent("some_unknown_type"),
+            )
+            == "bigint"
+        )
+
+    @pytest.mark.parametrize("target", [None, MaterializationTarget.ICEBERG])
+    def test_unconverted_targets_get_the_argument_derived_type(self, target):
+        assert (
+            infer_component_type(
+                _component("POWER(latency_ms, 2)"),
+                "bigint",
+                _parent(),
+                target,
+            )
+            == "double"
+        )
+
+    def test_declared_serialize_type_still_wins_for_its_target(self):
+        """
+        The conversion type takes precedence over the accumulated one.
+
+        Druid stores the converted representation, so the column must be
+        described as that -- the aggregator lookup keys on it.
+        """
+        component = _component(
+            "POWER(latency_ms, 2)",
+            serialize="to_druid_bytes({})",
+            serialize_targets=[MaterializationTarget.DRUID],
+            serialize_type="binary",
+        )
+        assert (
+            infer_component_type(
+                component,
+                "bigint",
+                _parent(),
+                MaterializationTarget.DRUID,
+            )
+            == "binary"
+        )
diff --git a/datajunction-server/tests/construction/build_v3/cube_matcher_test.py b/datajunction-server/tests/construction/build_v3/cube_matcher_test.py
index 89a3cf630..e8019e4b4 100644
--- a/datajunction-server/tests/construction/build_v3/cube_matcher_test.py
+++ b/datajunction-server/tests/construction/build_v3/cube_matcher_test.py
@@ -29,12 +29,49 @@
 from datajunction_server.construction.build_v3.utils import (
     extract_filter_dimension_refs,
 )
+from datajunction_server.database.node import Node
 from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.decompose import Aggregability
 from datajunction_server.models.dialect import Dialect
 from tests.construction.build_v3 import assert_sql_equal
 
 
+async def _create_daily_balance_metric(client_with_build_v3):
+    """Create a semi-additive metric protected by order date."""
+    response = await client_with_build_v3.post(
+        "/nodes/metric/",
+        json={
+            "name": "v3.daily_balance",
+            "description": "Semi-additive balance measured by order date",
+            "query": "SELECT SUM(line_total) FROM v3.order_details",
+            "mode": "published",
+            "reaggregate": {
+                "rules": [
+                    {
+                        "dimension": "v3.date.date_id[order]",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        },
+    )
+    assert response.status_code in (200, 201), response.json()
+
+
+async def _create_daily_balance_index_metric(client_with_build_v3):
+    """Create a derived metric that depends on the semi-additive balance."""
+    response = await client_with_build_v3.post(
+        "/nodes/metric/",
+        json={
+            "name": "v3.daily_balance_index",
+            "description": "Derived semi-additive balance index",
+            "query": "SELECT 10.0 / v3.daily_balance",
+            "mode": "published",
+        },
+    )
+    assert response.status_code in (200, 201), response.json()
+
+
 def test_materialized_dimension_lookup_preserves_roles():
     """Two roles with one short name resolve to distinct physical columns."""
     cube = SimpleNamespace(
@@ -1156,6 +1193,197 @@ def _boom(_filters):
             )
         assert "could not be parsed" in str(exc.value)
 
+    @pytest.mark.asyncio
+    async def test_reaggregate_cube_missing_protected_dimension_not_matched(
+        self,
+        client_with_build_v3,
+        session,
+    ):
+        """A materialized cube that dropped a semi-additive protected dimension
+        is not safe to auto-route to."""
+        await _create_daily_balance_metric(client_with_build_v3)
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": "v3.test_daily_balance_category_cube",
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Category cube missing semi-additive protected grain",
+            },
+        )
+        assert response.status_code == 201, response.json()
+
+        response = await client_with_build_v3.post(
+            "/data/v3.test_daily_balance_category_cube/availability/",
+            json={
+                "catalog": "default",
+                "schema_": "analytics",
+                "table": "daily_balance_category_cube",
+                "valid_through_ts": int(time.time() * 1000),
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        result = await find_matching_cube(
+            session,
+            metrics=["v3.daily_balance"],
+            dimensions=["v3.product.category"],
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_unsafe_cube_falls_back_to_live_sql(
+        self,
+        client_with_build_v3,
+        session,
+    ):
+        """An unsafe materialized cube is ignored, leaving the live path usable."""
+        await _create_daily_balance_metric(client_with_build_v3)
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": "v3.test_daily_balance_live_fallback_cube",
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Unsafe cube for live fallback",
+            },
+        )
+        assert response.status_code == 201, response.json()
+
+        response = await client_with_build_v3.post(
+            "/data/v3.test_daily_balance_live_fallback_cube/availability/",
+            json={
+                "catalog": "default",
+                "schema_": "analytics",
+                "table": "daily_balance_live_fallback_cube",
+                "valid_through_ts": int(time.time() * 1000),
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        result = await build_metrics_sql(
+            session=session,
+            metrics=["v3.daily_balance"],
+            dimensions=["v3.product.category"],
+        )
+
+        assert result.cube_name is None
+        assert "daily_balance_live_fallback_cube" not in result.sql
+        assert "MAX_BY(" in result.sql
+        assert "date_id_order" in result.sql
+
+    @pytest.mark.asyncio
+    async def test_derived_reaggregate_cube_missing_protected_dimension_not_matched(
+        self,
+        client_with_build_v3,
+        session,
+    ):
+        """Derived metric cubes inherit base semi-additive materialization needs."""
+        await _create_daily_balance_metric(client_with_build_v3)
+        await _create_daily_balance_index_metric(client_with_build_v3)
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": "v3.test_daily_balance_index_category_cube",
+                "metrics": ["v3.daily_balance_index"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Derived cube missing semi-additive protected grain",
+            },
+        )
+        assert response.status_code == 201, response.json()
+
+        response = await client_with_build_v3.post(
+            "/data/v3.test_daily_balance_index_category_cube/availability/",
+            json={
+                "catalog": "default",
+                "schema_": "analytics",
+                "table": "daily_balance_index_category_cube",
+                "valid_through_ts": int(time.time() * 1000),
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        result = await find_matching_cube(
+            session,
+            metrics=["v3.daily_balance_index"],
+            dimensions=["v3.product.category"],
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_pinned_reaggregate_cube_missing_protected_dimension_raises(
+        self,
+        client_with_build_v3,
+        session,
+    ):
+        """Pinned cube validation also blocks unsafe semi-additive materialization."""
+        await _create_daily_balance_metric(client_with_build_v3)
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": "v3.test_daily_balance_pinned_unsafe_cube",
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Pinned unsafe semi-additive cube",
+            },
+        )
+        assert response.status_code == 201, response.json()
+        cube_node = await Node.get_cube_by_name(
+            session,
+            "v3.test_daily_balance_pinned_unsafe_cube",
+        )
+        assert cube_node and cube_node.current
+
+        with pytest.raises(DJInvalidInputException) as exc:
+            await validate_pinned_cube_covers_filters(
+                session,
+                cube_node.current,
+                dimensions=["v3.product.category"],
+                filters=[],
+                metrics=["v3.daily_balance"],
+            )
+        assert "protected dimension" in str(exc.value)
+
+    @pytest.mark.asyncio
+    async def test_pinned_derived_reaggregate_cube_missing_protected_dimension_raises(
+        self,
+        client_with_build_v3,
+        session,
+    ):
+        """Pinned derived metric cubes also inherit base semi-additive needs."""
+        await _create_daily_balance_metric(client_with_build_v3)
+        await _create_daily_balance_index_metric(client_with_build_v3)
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": "v3.test_daily_balance_index_pinned_unsafe_cube",
+                "metrics": ["v3.daily_balance_index"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Pinned unsafe derived semi-additive cube",
+            },
+        )
+        assert response.status_code == 201, response.json()
+        cube_node = await Node.get_cube_by_name(
+            session,
+            "v3.test_daily_balance_index_pinned_unsafe_cube",
+        )
+        assert cube_node and cube_node.current
+
+        with pytest.raises(DJInvalidInputException) as exc:
+            await validate_pinned_cube_covers_filters(
+                session,
+                cube_node.current,
+                dimensions=["v3.product.category"],
+                filters=[],
+                metrics=["v3.daily_balance_index"],
+            )
+        assert "protected dimension" in str(exc.value)
+
     @pytest.mark.asyncio
     async def test_pinned_cube_non_druid_dialect_skips_validation(
         self,
diff --git a/datajunction-server/tests/construction/build_v3/decomposition_test.py b/datajunction-server/tests/construction/build_v3/decomposition_test.py
index 5a8a6af39..179a95372 100644
--- a/datajunction-server/tests/construction/build_v3/decomposition_test.py
+++ b/datajunction-server/tests/construction/build_v3/decomposition_test.py
@@ -5,9 +5,11 @@
 import pytest
 
 from datajunction_server.construction.build_v3.decomposition import (
+    _reaggregate_dimension_requested,
     get_base_metrics_for_derived,
     is_derived_metric,
 )
+from datajunction_server.construction.build_v3.metrics import _source_dimension_alias
 from datajunction_server.construction.build_v3.types import BuildContext
 from datajunction_server.models.node_type import NodeType
 
@@ -223,6 +225,42 @@ def test_metric_with_only_dimension_parent_not_derived(self):
         assert result is False
 
 
+@pytest.mark.parametrize(
+    ("protected_dimension", "requested_dimensions", "expected"),
+    [
+        ("v3.date.date_id[order]", ["v3.date.date_id[order]"], True),
+        ("v3.date.date_id[order]", ["v3.date.date_id[order]]"], True),
+        ("v3.date.date_id[order]", ["v3.date.date_id"], False),
+        ("v3.date.date_id[order]", ["v3.date.date_id[ship]"], False),
+        ("v3.date.date_id[order]", ["date_id"], False),
+        ("v3.date.date_id", ["v3.date.date_id"], True),
+        ("v3.date.date_id", ["date_id"], True),
+        ("v3.date.date_id", ["v3.date.date_id[order]"], False),
+    ],
+)
+def test_reaggregate_dimension_requested_is_role_sensitive(
+    protected_dimension,
+    requested_dimensions,
+    expected,
+):
+    """A role-less ref must not satisfy a role-qualified protected dimension."""
+    assert (
+        _reaggregate_dimension_requested(protected_dimension, requested_dimensions)
+        is expected
+    )
+
+
+def test_source_dimension_alias_does_not_fall_back_for_roled_refs():
+    """A roled protected dimension must collapse by that role's physical alias."""
+    ctx = BuildContext(session=MagicMock(), metrics=[], dimensions=[])
+    ctx.alias_registry.register("v3.date.date_id")
+    ctx.alias_registry.register("v3.date.date_id[ship]")
+
+    assert _source_dimension_alias(ctx, "v3.date.date_id[order]") is None
+    assert _source_dimension_alias(ctx, "v3.date.date_id[ship]") == "date_id_ship"
+    assert _source_dimension_alias(ctx, "v3.date.date_id") == "date_id"
+
+
 @pytest.mark.asyncio
 async def test_decomposition_with_dimension_parent_integration(
     module__client_with_build_v3,
diff --git a/datajunction-server/tests/construction/build_v3/merge_args_test.py b/datajunction-server/tests/construction/build_v3/merge_args_test.py
new file mode 100644
index 000000000..e044b4361
--- /dev/null
+++ b/datajunction-server/tests/construction/build_v3/merge_args_test.py
@@ -0,0 +1,129 @@
+"""
+Tests for fixed merge arguments on a component's Phase 2 call.
+
+Sketch families need tuning passed to the merge as well as the accumulate --
+`nflx_tdigest_agg(digest, compression)` -- but `merge` itself has to stay a bare
+function name, because the Druid aggregator mapping and the semi-additive
+rewrite both match on it. `merge_args` carries the rest.
+
+The omission this guards against is silent: a sketch merge called without its
+compression is accepted by the engine and returns a digest collapsed to a single
+centroid, so every quantile comes back equal to the mean.
+"""
+
+from datajunction_server.construction.build_v3.decomposition import build_merge_call
+from datajunction_server.models.decompose import (
+    AggregationRule,
+    Aggregability,
+    MetricComponent,
+)
+from datajunction_server.sql.decompose import ComponentDef
+from datajunction_server.sql.parsing import ast
+
+
+def _column(name: str = "latency_tdigest_ab12") -> ast.Column:
+    return ast.Column(name=ast.Name(name))
+
+
+class TestBuildMergeCall:
+    """The helper both emission sites use."""
+
+    def test_no_args_is_the_plain_single_argument_call(self):
+        # Every non-sketch aggregation takes this path and must be unchanged.
+        expr = build_merge_call("SUM", [], _column("revenue_sum_ab12"))
+        assert str(expr) == "SUM(revenue_sum_ab12)"
+
+    def test_one_fixed_argument_is_appended_after_the_column(self):
+        expr = build_merge_call(
+            "nflx_tdigest_agg",
+            ["CAST(200.0 AS DOUBLE)"],
+            _column(),
+        )
+        assert str(expr) == (
+            "nflx_tdigest_agg(latency_tdigest_ab12, CAST(200.0 AS DOUBLE))"
+        )
+
+    def test_several_fixed_arguments_keep_their_order(self):
+        expr = build_merge_call("sketch_merge", ["12", "'HLL_4'"], _column("s_ab12"))
+        assert str(expr) == "sketch_merge(s_ab12, 12, 'HLL_4')"
+
+    def test_the_column_is_always_the_first_argument(self):
+        expr = build_merge_call("f", ["1"], _column("c"))
+        assert isinstance(expr, ast.Function)
+        assert str(expr.args[0]) == "c"
+
+    def test_the_merge_name_is_preserved_verbatim(self):
+        # Matched on elsewhere, so casing must not be normalized.
+        expr = build_merge_call("nflx_tdigest_agg", ["1.0"], _column("c"))
+        assert expr.name.name == "nflx_tdigest_agg"
+
+    def test_arguments_are_parsed_not_pasted(self):
+        # A literal arrives as an expression node, not raw text, so it renders
+        # through the same dialect machinery as the rest of the tree.
+        expr = build_merge_call("f", ["1 + 2"], _column("c"))
+        assert isinstance(expr.args[1], ast.Expression)
+        assert str(expr) == "f(c, 1 + 2)"
+
+
+class TestComponentDefDefaults:
+    """Existing decompositions must be untouched by the new field."""
+
+    def test_merge_args_defaults_to_empty(self):
+        assert ComponentDef(suffix="_sum", accumulate="SUM", merge="SUM").merge_args == ()
+
+    def test_merge_args_is_a_tuple_so_the_default_cannot_be_mutated(self):
+        first = ComponentDef(suffix="_sum", accumulate="SUM", merge="SUM")
+        second = ComponentDef(suffix="_cnt", accumulate="COUNT", merge="SUM")
+        assert first.merge_args == second.merge_args == ()
+        assert isinstance(first.merge_args, tuple)
+
+    def test_merge_args_is_declared_on_the_component_def(self):
+        comp = ComponentDef(
+            suffix="_tdigest",
+            accumulate="nflx_tdigest({}, CAST(200.0 AS DOUBLE))",
+            merge="nflx_tdigest_agg",
+            merge_args=("CAST(200.0 AS DOUBLE)",),
+        )
+        assert comp.merge == "nflx_tdigest_agg"
+        assert comp.merge_args == ("CAST(200.0 AS DOUBLE)",)
+
+
+class TestMetricComponentField:
+    """`merge_args` survives the model, which is dumped to JSON in cube configs."""
+
+    def _component(self, merge_args):
+        return MetricComponent(
+            name="latency_tdigest_ab12",
+            expression="latency_ms",
+            aggregation="nflx_tdigest",
+            merge="nflx_tdigest_agg",
+            merge_args=merge_args,
+            rule=AggregationRule(type=Aggregability.FULL),
+        )
+
+    def test_defaults_to_empty_list(self):
+        component = MetricComponent(
+            name="revenue_sum_ab12",
+            expression="revenue",
+            aggregation="SUM",
+            merge="SUM",
+            rule=AggregationRule(type=Aggregability.FULL),
+        )
+        assert component.merge_args == []
+
+    def test_round_trips_through_json(self):
+        # A list rather than a tuple: these models are serialized straight into
+        # materialization configs.
+        component = self._component(["CAST(200.0 AS DOUBLE)"])
+        assert component.model_dump()["merge_args"] == ["CAST(200.0 AS DOUBLE)"]
+
+    def test_feeds_the_merge_call(self):
+        component = self._component(["CAST(200.0 AS DOUBLE)"])
+        expr = build_merge_call(
+            component.merge,
+            component.merge_args,
+            _column(component.name),
+        )
+        assert str(expr) == (
+            "nflx_tdigest_agg(latency_tdigest_ab12, CAST(200.0 AS DOUBLE))"
+        )
diff --git a/datajunction-server/tests/construction/build_v3/metrics_sql_test.py b/datajunction-server/tests/construction/build_v3/metrics_sql_test.py
index 697457e2d..847053723 100644
--- a/datajunction-server/tests/construction/build_v3/metrics_sql_test.py
+++ b/datajunction-server/tests/construction/build_v3/metrics_sql_test.py
@@ -1,3 +1,5 @@
+import time
+
 import pytest
 
 from . import assert_sql_equal
@@ -99,6 +101,651 @@ async def test_simple_single_metric(self, client_with_build_v3):
             },
         ]
 
+    @staticmethod
+    async def _create_daily_balance_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.daily_balance",
+                "description": "Semi-additive balance measured by order date",
+                "query": "SELECT SUM(line_total) FROM v3.order_details",
+                "mode": "published",
+                "reaggregate": {
+                    "rules": [
+                        {
+                            "dimension": "v3.date.date_id[order]",
+                            "fn": "last_value",
+                        },
+                    ],
+                },
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @staticmethod
+    async def _create_scaled_daily_balance_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.scaled_daily_balance",
+                "description": "Scaled semi-additive balance measured by order date",
+                "query": "SELECT SUM(line_total) / 100 FROM v3.order_details",
+                "mode": "published",
+                "reaggregate": {
+                    "rules": [
+                        {
+                            "dimension": "v3.date.date_id[order]",
+                            "fn": "last_value",
+                        },
+                    ],
+                },
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @staticmethod
+    async def _create_first_daily_balance_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.first_daily_balance",
+                "description": "Semi-additive balance measured by first order date",
+                "query": "SELECT SUM(line_total) FROM v3.order_details",
+                "mode": "published",
+                "reaggregate": {
+                    "rules": [
+                        {
+                            "dimension": "v3.date.date_id[order]",
+                            "fn": "first_value",
+                        },
+                    ],
+                },
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @staticmethod
+    async def _create_daily_balance_index_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.daily_balance_index",
+                "description": "Derived semi-additive balance index",
+                "query": "SELECT 10.0 / v3.daily_balance",
+                "mode": "published",
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @staticmethod
+    async def _create_wow_daily_balance_index_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.wow_daily_balance_index",
+                "description": "Week-over-week daily balance index change",
+                "query": """
+                    SELECT
+                        (v3.daily_balance_index - LAG(v3.daily_balance_index, 1)
+                         OVER (ORDER BY v3.date.week[order]))
+                        / NULLIF(
+                            LAG(v3.daily_balance_index, 1)
+                            OVER (ORDER BY v3.date.week[order]),
+                            0
+                        ) * 100
+                """,
+                "mode": "published",
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @staticmethod
+    async def _create_product_balance_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.product_balance",
+                "description": "Semi-additive balance protected by product",
+                "query": "SELECT SUM(line_total) FROM v3.order_details",
+                "mode": "published",
+                "reaggregate": {
+                    "rules": [
+                        {
+                            "dimension": "v3.product.product_id",
+                            "fn": "last_value",
+                        },
+                    ],
+                },
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @staticmethod
+    async def _create_product_balance_index_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.product_balance_index",
+                "description": "Derived semi-additive product balance index",
+                "query": "SELECT 10.0 / v3.product_balance",
+                "mode": "published",
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @staticmethod
+    async def _create_wow_product_balance_index_metric(client_with_build_v3):
+        response = await client_with_build_v3.post(
+            "/nodes/metric/",
+            json={
+                "name": "v3.wow_product_balance_index",
+                "description": "Week-over-week product balance index change",
+                "query": """
+                    SELECT
+                        (v3.product_balance_index - LAG(v3.product_balance_index, 1)
+                         OVER (ORDER BY v3.date.week[order]))
+                        / NULLIF(
+                            LAG(v3.product_balance_index, 1)
+                            OVER (ORDER BY v3.date.week[order]),
+                            0
+                        ) * 100
+                """,
+                "mode": "published",
+            },
+        )
+        assert response.status_code in (200, 201), response.json()
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_collapses_when_protected_dimension_omitted(
+        self,
+        client_with_build_v3,
+    ):
+        """A semi-additive metric keeps its protected dimension as private grain."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        assert_sql_equal(
+            response.json()["sql"],
+            """
+            WITH
+            v3_order_details AS (
+                SELECT o.order_date, oi.product_id, oi.quantity * oi.unit_price AS line_total
+                FROM default.v3.orders o
+                JOIN default.v3.order_items oi ON o.order_id = oi.order_id
+            ),
+            v3_product AS (
+                SELECT product_id, category
+                FROM default.v3.products
+            ),
+            order_details_0 AS (
+                SELECT t2.category,
+                       t1.order_date AS date_id_order,
+                       SUM(t1.line_total) AS line_total_sum_e1f61696
+                FROM v3_order_details t1
+                LEFT OUTER JOIN v3_product t2 ON t1.product_id = t2.product_id
+                GROUP BY t2.category, t1.order_date
+            )
+            SELECT order_details_0.category AS category,
+                   MAX_BY(
+                       order_details_0.line_total_sum_e1f61696,
+                       order_details_0.date_id_order
+                   ) AS daily_balance
+            FROM order_details_0
+            GROUP BY order_details_0.category
+            """,
+        )
+        assert response.json()["columns"] == [
+            {
+                "name": "category",
+                "type": "string",
+                "semantic_entity": "v3.product.category",
+                "semantic_type": "dimension",
+            },
+            {
+                "name": "daily_balance",
+                "type": "double",
+                "semantic_entity": "v3.daily_balance",
+                "semantic_type": "metric",
+            },
+        ]
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_with_limited_metric_keeps_protected_grain(
+        self,
+        client_with_build_v3,
+    ):
+        """Merged metric queries must not add limited grain to the collapse CTE."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance", "v3.order_count"],
+                "dimensions": ["v3.product.category"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        normalized_sql = " ".join(sql.split())
+        assert "order_details_0 AS" in sql
+        assert "GROUP BY t2.category, t1.order_date" in normalized_sql
+        assert "GROUP BY t2.category, t1.order_date, t1.order_id" not in normalized_sql
+        assert "order_details_1_agg AS" in sql
+        assert "COUNT( DISTINCT order_id)" in normalized_sql
+        assert "MAX_BY(" in sql
+        assert "order_details_0.date_id_order" in sql
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_preserves_single_component_combiner_wrapper(
+        self,
+        client_with_build_v3,
+    ):
+        """Semi-additive collapse preserves arithmetic around the aggregate."""
+        await self._create_scaled_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.scaled_daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        normalized_sql = " ".join(sql.split())
+        assert "MAX_BY(" in sql
+        assert "order_details_0.date_id_order" in sql
+        assert "/ 100 AS scaled_daily_balance" in normalized_sql
+        assert (
+            "MAX_BY(order_details_0.line_total_sum_e1f61696, order_details_0.date_id_order) / 100"
+            in normalized_sql
+        )
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_druid_uses_latest_by_for_last_value(
+        self,
+        client_with_build_v3,
+    ):
+        """Druid live SQL uses Druid-native collapse functions."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "dialect": "druid",
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+        assert response.json()["dialect"] == "druid"
+
+        assert_sql_equal(
+            response.json()["sql"],
+            """
+            WITH
+            v3_order_details AS (
+                SELECT o.order_date, oi.product_id, oi.quantity * oi.unit_price AS line_total
+                FROM default.v3.orders o
+                JOIN default.v3.order_items oi ON o.order_id = oi.order_id
+            ),
+            v3_product AS (
+                SELECT product_id, category
+                FROM default.v3.products
+            ),
+            order_details_0 AS (
+                SELECT t2.category,
+                       t1.order_date AS date_id_order,
+                       SUM(t1.line_total) AS line_total_sum_e1f61696
+                FROM v3_order_details t1
+                LEFT OUTER JOIN v3_product t2 ON t1.product_id = t2.product_id
+                GROUP BY t2.category, t1.order_date
+            )
+            SELECT order_details_0.category AS category,
+                   LATEST_BY(
+                       order_details_0.line_total_sum_e1f61696,
+                       order_details_0.date_id_order
+                   ) AS daily_balance
+            FROM order_details_0
+            GROUP BY order_details_0.category
+            """,
+        )
+        assert "MAX_BY" not in response.json()["sql"]
+        assert "ARG_MAX" not in response.json()["sql"]
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_auto_routed_druid_uses_latest_by(
+        self,
+        client_with_build_v3,
+    ):
+        """Auto-routed Druid cube SQL uses Druid-native collapse functions."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": "v3.daily_balance_category_cube",
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "mode": "published",
+                "description": "Category-only decoy cube",
+            },
+        )
+        assert response.status_code == 201, response.json()
+
+        response = await client_with_build_v3.post(
+            "/data/v3.daily_balance_category_cube/availability/",
+            json={
+                "catalog": "default",
+                "schema_": "analytics",
+                "table": "daily_balance_category_cube",
+                "valid_through_ts": int(time.time() * 1000),
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        response = await client_with_build_v3.post(
+            "/nodes/cube/",
+            json={
+                "name": "v3.daily_balance_cube",
+                "metrics": ["v3.daily_balance"],
+                "dimensions": [
+                    "v3.product.category",
+                    "v3.date.date_id[order]",
+                ],
+                "mode": "published",
+                "description": "Daily balance cube at protected grain",
+            },
+        )
+        assert response.status_code == 201, response.json()
+
+        response = await client_with_build_v3.post(
+            "/data/v3.daily_balance_cube/availability/",
+            json={
+                "catalog": "default",
+                "schema_": "analytics",
+                "table": "daily_balance_cube",
+                "valid_through_ts": int(time.time() * 1000),
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+            },
+        )
+        assert response.status_code == 200, response.json()
+        assert response.json()["dialect"] == "druid"
+
+        sql = response.json()["sql"]
+        assert "FROM daily_balance_cube" in sql
+        assert "daily_balance_category_cube" not in sql
+        assert "LATEST_BY(" in sql
+        assert "date_id_order" in sql
+        assert "MAX_BY" not in sql
+        assert "ARG_MAX" not in sql
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_druid_uses_earliest_by_for_first_value(
+        self,
+        client_with_build_v3,
+    ):
+        """Druid first-value collapse renders as EARLIEST_BY."""
+        await self._create_first_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.first_daily_balance"],
+                "dimensions": ["v3.product.category"],
+                "dialect": "druid",
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        assert "EARLIEST_BY(" in sql
+        assert "MIN_BY" not in sql
+        assert "ARG_MIN" not in sql
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_derived_metric_uses_collapsed_base(
+        self,
+        client_with_build_v3,
+    ):
+        """Derived metrics inline semi-additive collapse in denominator position."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+        await self._create_daily_balance_index_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance_index"],
+                "dimensions": ["v3.product.category"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        assert "MAX_BY(" in sql
+        assert "order_details_0.date_id_order" in sql
+        assert "MAX_BY" in sql.split(" AS daily_balance_index")[0]
+        assert "10.0 / NULLIF(MAX_BY(" in sql
+        assert "10.0 / NULLIF(SUM(" not in sql
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_derived_metric_preserves_requested_protected_dimension(
+        self,
+        client_with_build_v3,
+    ):
+        """Derived metrics use normal aggregation when the protected dimension is requested."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+        await self._create_daily_balance_index_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance_index"],
+                "dimensions": ["v3.date.date_id[order]"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        assert "10.0 / NULLIF(SUM(" in sql
+        assert "date_id_order" in sql
+        assert "MAX_BY(" not in sql
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_nested_window_metric_reaggregates_with_collapse(
+        self,
+        client_with_build_v3,
+    ):
+        """Window reaggregation of derived metrics uses semi-additive parents."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+        await self._create_daily_balance_index_metric(client_with_build_v3)
+        await self._create_wow_daily_balance_index_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.wow_daily_balance_index"],
+                "dimensions": ["v3.product.category"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        assert "base_metrics AS" in sql
+        assert "MAX_BY(" in sql
+        assert "order_details_0.date_id_order" in sql
+        assert "10.0 / NULLIF(MAX_BY(" in sql
+        assert "LAG(base_metrics.daily_balance_index, 1)" in sql
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_window_grain_reaggregation_uses_collapse(
+        self,
+        client_with_build_v3,
+    ):
+        """Window aggregation CTEs collapse semi-additive derived parents."""
+        await self._create_product_balance_metric(client_with_build_v3)
+        await self._create_product_balance_index_metric(client_with_build_v3)
+        await self._create_wow_product_balance_index_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.wow_product_balance_index"],
+                "dimensions": ["v3.date.date_id[order]"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        assert "order_details_week_agg AS" in sql
+        assert "MAX_BY(" in sql
+        assert "product_id" in sql
+        assert "10.0 / NULLIF(MAX_BY(" in sql
+        assert "10.0 / NULLIF(SUM(" not in sql
+        assert "LAG(order_details_week_agg.product_balance_index, 1)" in sql
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_uses_normal_aggregation_when_protected_dimension_requested(
+        self,
+        client_with_build_v3,
+    ):
+        """Requesting the protected dimension means there is nothing to collapse."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.date.date_id[order]"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        assert_sql_equal(
+            response.json()["sql"],
+            """
+            WITH
+            v3_order_details AS (
+                SELECT o.order_date, oi.quantity * oi.unit_price AS line_total
+                FROM default.v3.orders o
+                JOIN default.v3.order_items oi ON o.order_id = oi.order_id
+            ),
+            order_details_0 AS (
+                SELECT t1.order_date AS date_id_order,
+                       SUM(t1.line_total) AS line_total_sum_e1f61696
+                FROM v3_order_details t1
+                GROUP BY t1.order_date
+            )
+            SELECT order_details_0.date_id_order AS date_id_order,
+                   SUM(order_details_0.line_total_sum_e1f61696) AS daily_balance
+            FROM order_details_0
+            GROUP BY order_details_0.date_id_order
+            """,
+        )
+        assert "MAX_BY" not in response.json()["sql"]
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_collapses_when_roleless_base_dimension_requested(
+        self,
+        client_with_build_v3,
+    ):
+        """A role-less dimension request does not satisfy a roled protected grain."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.date.date_id"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        assert "MAX_BY(" in sql
+        assert "order_details_0.date_id_order" in sql
+        assert (
+            "SUM(order_details_0.line_total_sum_e1f61696) AS daily_balance" not in sql
+        )
+
+    @pytest.mark.asyncio
+    async def test_reaggregate_collapses_with_coarser_time_dimension(
+        self,
+        client_with_build_v3,
+    ):
+        """Coarser time output still collapses across the protected date grain."""
+        await self._create_daily_balance_metric(client_with_build_v3)
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.date.month[order]"],
+                "use_materialized": "false",
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        assert_sql_equal(
+            response.json()["sql"],
+            """
+            WITH
+            v3_date AS (
+                SELECT date_id, month
+                FROM default.v3.dates
+            ),
+            v3_order_details AS (
+                SELECT o.order_date, oi.quantity * oi.unit_price AS line_total
+                FROM default.v3.orders o
+                JOIN default.v3.order_items oi ON o.order_id = oi.order_id
+            ),
+            order_details_0 AS (
+                SELECT t2.month AS month_order,
+                       COALESCE(t1.order_date, t2.date_id) AS date_id_order,
+                       SUM(t1.line_total) AS line_total_sum_e1f61696
+                FROM v3_order_details t1
+                LEFT OUTER JOIN v3_date t2 ON t1.order_date = t2.date_id
+                GROUP BY t2.month, COALESCE(t1.order_date, t2.date_id)
+            )
+            SELECT order_details_0.month_order AS month_order,
+                   MAX_BY(
+                       order_details_0.line_total_sum_e1f61696,
+                       order_details_0.date_id_order
+                   ) AS daily_balance
+            FROM order_details_0
+            GROUP BY order_details_0.month_order
+            """,
+        )
+
     @pytest.mark.asyncio
     async def test_multiple_metrics_same_grain(self, client_with_build_v3):
         """
@@ -3763,15 +4410,27 @@ async def test_cross_fact_window_metric_with_finer_grain(
                 CAST(COUNT( DISTINCT order_details_0.order_id) AS DOUBLE) / NULLIF(COUNT( DISTINCT page_views_enriched_0.customer_id), 0) AS conversion_rate
             FROM order_details_0 FULL OUTER JOIN page_views_enriched_0 ON order_details_0.date_id = page_views_enriched_0.date_id AND order_details_0.category = page_views_enriched_0.category AND order_details_0.week = page_views_enriched_0.week
             GROUP BY  1, 2, 3
+            ),
+            order_details_week_agg AS (
+            SELECT  base_metrics.category AS category,
+                base_metrics.week AS week,
+                CAST(SUM(base_metrics.order_count) AS DOUBLE) / NULLIF(SUM(base_metrics.visitor_count), 0) AS conversion_rate
+            FROM base_metrics
+            GROUP BY  base_metrics.category, base_metrics.week
+            ),
+            order_details_week AS (
+            SELECT  order_details_week_agg.category AS category,
+                order_details_week_agg.week AS week,
+                (order_details_week_agg.conversion_rate - LAG(order_details_week_agg.conversion_rate, 1) OVER ( PARTITION BY order_details_week_agg.category
+            ORDER BY order_details_week_agg.week) ) / NULLIF(LAG(order_details_week_agg.conversion_rate, 1) OVER ( PARTITION BY order_details_week_agg.category
+            ORDER BY order_details_week_agg.week) , 0) * 100 AS wow_conversion_rate_change
+            FROM order_details_week_agg
             )
-
             SELECT  base_metrics.date_id AS date_id,
                 base_metrics.category AS category,
                 base_metrics.week AS week,
-                (base_metrics.conversion_rate - LAG(base_metrics.conversion_rate, 1) OVER ( PARTITION BY base_metrics.category
-            ORDER BY base_metrics.week) ) / NULLIF(LAG(base_metrics.conversion_rate, 1) OVER ( PARTITION BY base_metrics.category
-            ORDER BY base_metrics.week) , 0) * 100 AS wow_conversion_rate_change
-            FROM base_metrics
+                order_details_week.wow_conversion_rate_change AS wow_conversion_rate_change
+            FROM base_metrics LEFT OUTER JOIN order_details_week ON base_metrics.category = order_details_week.category AND base_metrics.week = order_details_week.week
             """,
         )
 
@@ -3877,15 +4536,27 @@ async def test_cross_fact_window_on_derived_metric(self, client_with_build_v3):
                 SUM(page_views_enriched_0.view_id_count_f41e2db4) / NULLIF(COUNT( DISTINCT page_views_enriched_0.session_id), 0) AS pages_per_session
             FROM order_details_0 FULL OUTER JOIN page_views_enriched_0 ON order_details_0.date_id = page_views_enriched_0.date_id AND order_details_0.category = page_views_enriched_0.category AND order_details_0.week = page_views_enriched_0.week
             GROUP BY  1, 2, 3
+            ),
+            order_details_week_agg AS (
+            SELECT  base_metrics.category AS category,
+                base_metrics.week AS week,
+                SUM(base_metrics.total_revenue) / NULLIF(SUM(base_metrics.order_count), 0) / NULLIF(SUM(base_metrics.page_view_count) / NULLIF(SUM(base_metrics.session_count), 0), 0) AS efficiency_ratio
+            FROM base_metrics
+            GROUP BY  base_metrics.category, base_metrics.week
+            ),
+            order_details_week AS (
+            SELECT  order_details_week_agg.category AS category,
+                order_details_week_agg.week AS week,
+                (order_details_week_agg.efficiency_ratio - LAG(order_details_week_agg.efficiency_ratio, 1) OVER ( PARTITION BY order_details_week_agg.category
+            ORDER BY order_details_week_agg.week) ) / NULLIF(LAG(order_details_week_agg.efficiency_ratio, 1) OVER ( PARTITION BY order_details_week_agg.category
+            ORDER BY order_details_week_agg.week) , 0) * 100 AS wow_efficiency_ratio_change
+            FROM order_details_week_agg
             )
-
             SELECT  base_metrics.date_id AS date_id,
                 base_metrics.category AS category,
                 base_metrics.week AS week,
-                (base_metrics.efficiency_ratio - LAG(base_metrics.efficiency_ratio, 1) OVER ( PARTITION BY base_metrics.category
-            ORDER BY base_metrics.week) ) / NULLIF(LAG(base_metrics.efficiency_ratio, 1) OVER ( PARTITION BY base_metrics.category
-            ORDER BY base_metrics.week) , 0) * 100 AS wow_efficiency_ratio_change
-            FROM base_metrics""",
+                order_details_week.wow_efficiency_ratio_change AS wow_efficiency_ratio_change
+            FROM base_metrics LEFT OUTER JOIN order_details_week ON base_metrics.category = order_details_week.category AND base_metrics.week = order_details_week.week""",
         )
 
     @pytest.mark.asyncio
@@ -3989,8 +4660,8 @@ async def test_cross_fact_window_on_base_metrics(self, client_with_build_v3):
             order_details_week_agg AS (
             SELECT  base_metrics.category AS category,
                 base_metrics.week AS week,
-                COUNT( DISTINCT base_metrics.order_id) AS order_count,
-                COUNT( DISTINCT base_metrics.customer_id) AS visitor_count
+                SUM(base_metrics.order_count) AS order_count,
+                SUM(base_metrics.visitor_count) AS visitor_count
             FROM base_metrics
             GROUP BY  base_metrics.category, base_metrics.week
             ),
diff --git a/datajunction-server/tests/construction/build_v3/preagg_matcher_test.py b/datajunction-server/tests/construction/build_v3/preagg_matcher_test.py
index 294428b82..e88dfb1ec 100644
--- a/datajunction-server/tests/construction/build_v3/preagg_matcher_test.py
+++ b/datajunction-server/tests/construction/build_v3/preagg_matcher_test.py
@@ -32,6 +32,7 @@
 from datajunction_server.database.preaggregation import (
     PreAggregation,
     compute_expression_hash,
+    measure_identity_token,
 )
 from datajunction_server.database.user import User
 from datajunction_server.errors import DJInvalidInputException
@@ -220,7 +221,7 @@ async def test_returns_identities_for_all_components(
         parent_node: Node,
         metric_node: Node,
     ):
-        """Should return an (expression hash, aggregation) pair per component."""
+        """Should return one identity token per component."""
         components = [
             (metric_node, make_component("sum_revenue", "price * quantity")),
             (metric_node, make_component("sum_quantity", "quantity")),
@@ -230,8 +231,8 @@ async def test_returns_identities_for_all_components(
         identities = get_required_measure_identities(grain_group)
 
         assert identities == {
-            (compute_expression_hash("price * quantity"), "SUM"),
-            (compute_expression_hash("quantity"), "SUM"),
+            measure_identity_token(compute_expression_hash("price * quantity"), "SUM"),
+            measure_identity_token(compute_expression_hash("quantity"), "SUM"),
         }
 
     @pytest.mark.asyncio
@@ -264,7 +265,9 @@ async def test_deduplicates_same_expression_and_aggregation(
 
         identities = get_required_measure_identities(grain_group)
 
-        assert identities == {(compute_expression_hash("price * quantity"), "SUM")}
+        assert identities == {
+            measure_identity_token(compute_expression_hash("price * quantity"), "SUM"),
+        }
 
     @pytest.mark.asyncio
     async def test_same_expression_different_aggregation_are_distinct(
@@ -294,8 +297,8 @@ async def test_same_expression_different_aggregation_are_distinct(
         identities = get_required_measure_identities(grain_group)
 
         assert identities == {
-            (compute_expression_hash("unit_price"), "SUM"),
-            (compute_expression_hash("unit_price"), "MAX"),
+            measure_identity_token(compute_expression_hash("unit_price"), "SUM"),
+            measure_identity_token(compute_expression_hash("unit_price"), "MAX"),
         }
 
 
diff --git a/datajunction-server/tests/construction/build_v3/preagg_substitution_test.py b/datajunction-server/tests/construction/build_v3/preagg_substitution_test.py
index 0edd7753e..f8a045a70 100644
--- a/datajunction-server/tests/construction/build_v3/preagg_substitution_test.py
+++ b/datajunction-server/tests/construction/build_v3/preagg_substitution_test.py
@@ -86,6 +86,28 @@ async def _fake_columns(*args, **kwargs):
         del client.app.dependency_overrides[get_query_service_client]
 
 
+async def _create_daily_balance_metric(client_with_build_v3):
+    """Create a semi-additive metric protected by order date."""
+    response = await client_with_build_v3.post(
+        "/nodes/metric/",
+        json={
+            "name": "v3.daily_balance",
+            "description": "Semi-additive balance measured by order date",
+            "query": "SELECT SUM(line_total) FROM v3.order_details",
+            "mode": "published",
+            "reaggregate": {
+                "rules": [
+                    {
+                        "dimension": "v3.date.date_id[order]",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        },
+    )
+    assert response.status_code in (200, 201), response.json()
+
+
 class TestExternalPreAggRouting:
     """Queries route to externally-registered pre-agg tables via source_column."""
 
@@ -198,6 +220,98 @@ async def test_external_preagg_rolls_up_additive(self, client_with_build_v3):
             """,
         )
 
+    @pytest.mark.asyncio
+    async def test_external_preagg_retaining_reaggregate_dimension_rolls_up(
+        self,
+        client_with_build_v3,
+    ):
+        """A semi-additive metric can read a fine pre-agg that still has the
+        protected dimension, then collapse in the final metrics query."""
+        await _create_daily_balance_metric(client_with_build_v3)
+        await _register_external_preagg(
+            client_with_build_v3,
+            metrics=["v3.daily_balance"],
+            dimensions=[
+                "v3.product.category",
+                "v3.date.date_id[order]",
+            ],
+            table_ref={
+                "catalog": "default",
+                "schema": "analytics",
+                "table": "daily_balance_by_category_day",
+                "valid_through_ts": 20250101,
+            },
+            measure_columns={"v3.daily_balance": "balance_sum"},
+            dimension_columns={"v3.date.date_id[order]": "order_day"},
+            table_columns={
+                "category": "string",
+                "order_day": "int",
+                "balance_sum": "double",
+            },
+        )
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+            },
+        )
+        assert response.status_code == 200, response.json()
+        assert_sql_equal(
+            response.json()["sql"],
+            """
+            WITH order_details_0 AS (
+                SELECT category,
+                       order_day date_id_order,
+                       SUM(balance_sum) balance_sum
+                FROM default.analytics.daily_balance_by_category_day
+                GROUP BY category, order_day
+            )
+            SELECT order_details_0.category AS category,
+                   MAX_BY(order_details_0.balance_sum, order_details_0.date_id_order)
+                       AS daily_balance
+            FROM order_details_0
+            GROUP BY order_details_0.category
+            """,
+        )
+
+    @pytest.mark.asyncio
+    async def test_external_preagg_missing_reaggregate_dimension_is_not_used(
+        self,
+        client_with_build_v3,
+    ):
+        """A semi-additive metric must not read a pre-agg that dropped the key
+        required by the final collapse."""
+        await _create_daily_balance_metric(client_with_build_v3)
+        await _register_external_preagg(
+            client_with_build_v3,
+            metrics=["v3.daily_balance"],
+            dimensions=["v3.product.category"],
+            table_ref={
+                "catalog": "default",
+                "schema": "analytics",
+                "table": "daily_balance_by_category",
+                "valid_through_ts": 20250101,
+            },
+            measure_columns={"v3.daily_balance": "balance_sum"},
+            table_columns={"category": "string", "balance_sum": "double"},
+        )
+
+        response = await client_with_build_v3.get(
+            "/sql/metrics/v3/",
+            params={
+                "metrics": ["v3.daily_balance"],
+                "dimensions": ["v3.product.category"],
+            },
+        )
+        assert response.status_code == 200, response.json()
+
+        sql = response.json()["sql"]
+        assert "default.analytics.daily_balance_by_category" not in sql
+        assert "MAX_BY(" in sql
+        assert "date_id_order" in sql
+
     @pytest.mark.asyncio
     async def test_external_non_additive_not_rolled_up(self, client_with_build_v3):
         """A non-additive measure (COUNT DISTINCT) does not roll up to a coarser
diff --git a/datajunction-server/tests/construction/build_v3/reaggregate_coverage_test.py b/datajunction-server/tests/construction/build_v3/reaggregate_coverage_test.py
new file mode 100644
index 000000000..358f14d11
--- /dev/null
+++ b/datajunction-server/tests/construction/build_v3/reaggregate_coverage_test.py
@@ -0,0 +1,764 @@
+"""Focused coverage tests for build-v3 reaggregation helpers."""
+
+from types import SimpleNamespace
+
+import pytest
+
+from datajunction_server.construction.build_v3.cube_matcher import (
+    _cube_dimension_covers_reaggregate_dimension,
+    _metric_graph_has_reaggregate,
+    _reaggregate_dimensions_for_cube_metrics,
+    _reaggregate_requirements_for_cube_metrics,
+    _reaggregate_requirements_for_metrics,
+    _reaggregate_requirements_for_decomposed_metrics,
+    _reaggregate_requirements_for_metrics_if_needed,
+    build_synthetic_grain_group,
+)
+from datajunction_server.construction.build_v3 import (
+    cube_matcher as cube_matcher_module,
+)
+from datajunction_server.construction.build_v3 import builder as builder_module
+from datajunction_server.construction.build_v3 import metrics as metrics_module
+from datajunction_server.construction.build_v3.builder import setup_build_context
+from datajunction_server.construction.build_v3.measures import (
+    build_grain_group_sql,
+    build_select_ast,
+    build_window_metric_grain_groups,
+)
+from datajunction_server.construction.build_v3.metrics import (
+    _build_reaggregate_collapse_expression,
+    _dimension_ref_base,
+    _dimension_ref_role,
+    _references_component,
+    _replace_reaggregate_merge_expression,
+    _metric_parent_refs,
+    _source_dimension_alias,
+    build_window_agg_cte_from_base_metrics,
+    generate_metrics_sql,
+)
+from datajunction_server.construction.build_v3.types import (
+    BuildContext,
+    ColumnMetadata,
+    DecomposedMetricInfo,
+    GeneratedMeasuresSQL,
+    GrainGroup,
+    GrainGroupSQL,
+    ResolvedDimension,
+)
+from datajunction_server.errors import DJInvalidInputException
+from datajunction_server.models.decompose import (
+    Aggregability,
+    AggregationRule,
+    MetricComponent,
+)
+from datajunction_server.models.dialect import Dialect
+from datajunction_server.models.node import NodeType
+from datajunction_server.models.reaggregate import (
+    DimensionReaggregateRule,
+    ReaggregationFunction,
+)
+from datajunction_server.sql.parsing import ast
+from datajunction_server.sql.parsing.backends.antlr4 import parse
+from datajunction_server.sql.parsing.types import BigIntType, DoubleType, StringType
+
+
+def _metric_node(name: str, query: str = "SELECT SUM(value) FROM test.parent"):
+    return SimpleNamespace(
+        name=name,
+        type=NodeType.METRIC,
+        current=SimpleNamespace(
+            query=query,
+            columns=[SimpleNamespace(type=DoubleType())],
+        ),
+    )
+
+
+def _source_node(name: str = "test.parent"):
+    return SimpleNamespace(
+        name=name,
+        type=NodeType.SOURCE,
+        current=SimpleNamespace(
+            catalog=SimpleNamespace(name="default"),
+            schema_="analytics",
+            table="parent",
+            columns=[
+                SimpleNamespace(name="id", type=BigIntType()),
+                SimpleNamespace(name="value", type=DoubleType()),
+                SimpleNamespace(name="region", type=StringType()),
+                SimpleNamespace(name="date_id", type=BigIntType()),
+            ],
+        ),
+    )
+
+
+def _semi_additive_component(
+    name: str = "balance_sum",
+    dimension: str = "v3.date.date_id",
+    fn: ReaggregationFunction = ReaggregationFunction.LAST_VALUE,
+) -> MetricComponent:
+    return MetricComponent(
+        name=name,
+        expression="value",
+        aggregation="SUM",
+        rule=AggregationRule(
+            type=Aggregability.FULL,
+            reaggregate=DimensionReaggregateRule(
+                dimension=dimension,
+                fn=fn,
+            ),
+        ),
+    )
+
+
+def _decomposed_metric(
+    metric_name: str,
+    component: MetricComponent | None = None,
+    query: str = "SELECT SUM(value) FROM test.parent",
+) -> DecomposedMetricInfo:
+    return DecomposedMetricInfo(
+        metric_node=_metric_node(metric_name, query),
+        components=[component or _semi_additive_component()],
+        aggregability=Aggregability.FULL,
+        combiner=query,
+        derived_ast=parse(query),
+    )
+
+
+def test_reaggregate_collapse_expression_handles_min_max_and_rejects_unsupported():
+    """MIN/MAX collapse ignore the protected dimension; unsupported functions fail."""
+    value_ref = ast.Column(name=ast.Name("balance"))
+    protected_ref = ast.Column(name=ast.Name("date_id"))
+
+    assert (
+        str(
+            _build_reaggregate_collapse_expression(
+                ReaggregationFunction.MIN,
+                Dialect.SPARK,
+                value_ref,
+                protected_ref,
+            ),
+        )
+        == "MIN(balance)"
+    )
+    assert (
+        str(
+            _build_reaggregate_collapse_expression(
+                ReaggregationFunction.MAX,
+                Dialect.SPARK,
+                value_ref,
+                protected_ref,
+            ),
+        )
+        == "MAX(balance)"
+    )
+
+    with pytest.raises(DJInvalidInputException, match="Unsupported semi-additive"):
+        _build_reaggregate_collapse_expression(
+            ReaggregationFunction.SUM,
+            Dialect.SPARK,
+            value_ref,
+            protected_ref,
+        )
+
+
+def test_references_component_checks_all_columns_and_handles_no_match():
+    """Component reference detection handles misses before matches."""
+    expression = parse(
+        "SELECT SUM(other_value) + SUM(balance_sum) FROM base",
+    ).select.projection[0]
+
+    assert _references_component(expression, "balance_sum")
+    assert not _references_component(expression, "missing_component")
+
+
+def test_replace_reaggregate_merge_expression_skips_nonmatching_functions():
+    """Combiner replacement skips unrelated functions before replacing the match."""
+    combiner = parse(
+        "SELECT SUM(other_value) + SUM(balance_sum) FROM base",
+    ).select.projection[0]
+    collapse_expr = ast.Function(
+        ast.Name("MAX"),
+        args=[ast.Column(name=ast.Name("balance_sum"))],
+    )
+
+    rendered = str(
+        _replace_reaggregate_merge_expression(
+            combiner,
+            "balance_sum",
+            "SUM",
+            collapse_expr,
+        ),
+    )
+
+    assert "SUM(other_value)" in rendered
+    assert "MAX(balance_sum)" in rendered
+
+
+def test_replace_reaggregate_merge_expression_rejects_missing_component():
+    """Combiner replacement fails if the expected merge function is absent."""
+    combiner = parse("SELECT AVG(balance_sum) FROM base").select.projection[0]
+
+    with pytest.raises(DJInvalidInputException, match="could not find"):
+        _replace_reaggregate_merge_expression(
+            combiner,
+            "balance_sum",
+            "SUM",
+            ast.Function(
+                ast.Name("MAX"),
+                args=[ast.Column(name=ast.Name("balance_sum"))],
+            ),
+        )
+
+
+def test_replace_reaggregate_merge_expression_skips_parentless_nested_match():
+    """A matching child without a parent is skipped before the final error."""
+
+    class ParentlessExpression(ast.Expression):
+        def __str__(self):
+            return "parentless"
+
+        @property
+        def children(self):
+            yield ast.Function(
+                ast.Name("SUM"),
+                args=[ast.Column(name=ast.Name("balance_sum"))],
+            )
+
+    with pytest.raises(DJInvalidInputException, match="could not find"):
+        _replace_reaggregate_merge_expression(
+            ParentlessExpression(),
+            "balance_sum",
+            "SUM",
+            ast.Function(
+                ast.Name("MAX"),
+                args=[ast.Column(name=ast.Name("balance_sum"))],
+            ),
+        )
+
+
+def test_reaggregate_dimension_ref_helpers_handle_roleless_refs_without_alias():
+    """Roleless refs return None for role and do not require an alias fallback."""
+    ctx = BuildContext(session=SimpleNamespace(), metrics=[], dimensions=[])
+
+    assert _dimension_ref_base("v3.date.date_id[order]") == "v3.date.date_id"
+    assert _dimension_ref_role("v3.date.date_id") is None
+    assert _source_dimension_alias(ctx, "v3.date.date_id") is None
+
+
+def test_metric_parent_refs_adds_metric_refs_discovered_from_query():
+    """Parsed-query metric references supplement the cached parent map."""
+    balance = _metric_node("balance")
+    extra = _metric_node("extra_metric")
+    derived = _metric_node(
+        "derived",
+        "SELECT balance + extra_metric + raw_col FROM test.parent",
+    )
+    ctx = BuildContext(session=SimpleNamespace(), metrics=[], dimensions=[])
+    ctx.nodes = {
+        "balance": balance,
+        "extra_metric": extra,
+        "derived": derived,
+    }
+    ctx.parent_map = {"derived": ["balance"]}
+
+    assert _metric_parent_refs(ctx, "derived") == ["balance", "extra_metric"]
+
+
+def test_window_agg_from_base_metrics_collapses_semi_additive_derived_parent(
+    monkeypatch,
+):
+    """Cross-fact window reaggregation replaces derived metric parents recursively."""
+    monkeypatch.setattr(metrics_module, "get_column_full_name", lambda _col: "")
+    balance = _metric_node("balance")
+    extra = _metric_node("extra_metric")
+    derived = _metric_node(
+        "derived",
+        "SELECT balance + extra_metric + raw_col FROM test.parent",
+    )
+    window = _metric_node("window_metric")
+    ctx = BuildContext(
+        session=SimpleNamespace(),
+        metrics=["window_metric"],
+        dimensions=["category"],
+    )
+    ctx.nodes = {
+        "balance": balance,
+        "extra_metric": extra,
+        "derived": derived,
+        "window_metric": window,
+    }
+    ctx.parent_map = {
+        "window_metric": ["derived"],
+        "derived": ["balance"],
+    }
+    ctx.alias_registry.register("v3.date.date_id")
+
+    query = build_window_agg_cte_from_base_metrics(
+        GrainGroupSQL(
+            query=parse("SELECT category FROM base_metrics"),
+            columns=[
+                ColumnMetadata(
+                    name="category",
+                    semantic_name="category",
+                    type="string",
+                    semantic_type="dimension",
+                ),
+            ],
+            grain=["category"],
+            aggregability=Aggregability.FULL,
+            metrics=[],
+            parent_name="cross_fact",
+            is_window_grain_group=True,
+            window_metrics_served=["window_metric"],
+        ),
+        "base_metrics",
+        ctx,
+        {
+            "balance": _decomposed_metric(
+                "balance",
+                _semi_additive_component(fn=ReaggregationFunction.MAX),
+            ),
+        },
+    )
+
+    rendered = str(query)
+    assert "MAX(base_metrics.balance)" in rendered
+    assert "extra_metric" in rendered
+    assert "raw_col" in rendered
+
+
+def test_generate_metrics_sql_rejects_multiple_base_grain_groups_with_reaggregate():
+    """Live SQL does not allow protected-dimension fanout across base groups."""
+    ctx = BuildContext(session=SimpleNamespace(), metrics=["balance"], dimensions=[])
+    grain_groups = [
+        GrainGroupSQL(
+            query=parse("SELECT category, balance_sum FROM fact_a"),
+            columns=[],
+            grain=["category"],
+            aggregability=Aggregability.FULL,
+            metrics=["balance"],
+            parent_name="fact_a",
+            reaggregate_dimension_aliases={"balance_sum": "date_id"},
+        ),
+        GrainGroupSQL(
+            query=parse("SELECT category, orders FROM fact_b"),
+            columns=[],
+            grain=["category"],
+            aggregability=Aggregability.FULL,
+            metrics=["orders"],
+            parent_name="fact_b",
+        ),
+    ]
+    measures_result = GeneratedMeasuresSQL(
+        grain_groups=grain_groups,
+        dialect=Dialect.SPARK,
+        requested_dimensions=[],
+        ctx=ctx,
+    )
+
+    with pytest.raises(DJInvalidInputException, match="multiple base grain groups"):
+        generate_metrics_sql(ctx, measures_result, {})
+
+
+def test_cube_reaggregate_requirements_skip_satisfied_dimensions_and_dedupe():
+    """Cube requirement helpers skip requested protected dims and de-duplicate."""
+    metric_revision = SimpleNamespace(
+        name="balance",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "v3.date.date_id[order]",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+    cube = SimpleNamespace(metric_node_revisions=lambda: [metric_revision])
+
+    assert (
+        _reaggregate_requirements_for_cube_metrics(
+            cube,
+            ["balance"],
+            ["v3.date.date_id[order]"],
+        )
+        == []
+    )
+
+    duplicate_cube = SimpleNamespace(
+        metric_node_revisions=lambda: [metric_revision, metric_revision],
+    )
+    assert _reaggregate_dimensions_for_cube_metrics(
+        duplicate_cube,
+        ["balance"],
+        ["v3.product.category"],
+    ) == ["v3.date.date_id[order]"]
+
+
+def test_cube_dimension_coverage_accepts_bare_protected_parent_column():
+    """Cube coverage accepts bare parent-column protected dimensions."""
+    assert _cube_dimension_covers_reaggregate_dimension(
+        "order_date",
+        "v3.order_details.order_date",
+    )
+    assert not _cube_dimension_covers_reaggregate_dimension(
+        "order_date",
+        "v3.order_details.ship_date",
+    )
+    assert not _cube_dimension_covers_reaggregate_dimension(
+        "order_date",
+        "v3.order_details.order_date[ship]",
+    )
+    assert not _cube_dimension_covers_reaggregate_dimension(
+        "v3.date.date_id",
+        "v3.order_details.order_date",
+    )
+
+
+@pytest.mark.asyncio
+async def test_metric_graph_has_reaggregate_skips_empty_metric_list():
+    """An empty metric list does not touch the database."""
+
+    class Session:
+        async def execute(self, _stmt):
+            raise AssertionError("empty metric graph should not query")
+
+    assert not await _metric_graph_has_reaggregate(Session(), [])
+
+
+@pytest.mark.asyncio
+async def test_cube_reaggregate_requirements_skip_full_extract_without_reaggregate(
+    monkeypatch,
+):
+    """No reaggregate in the metric graph skips full component extraction."""
+
+    async def fail_full_extract(*_args, **_kwargs):
+        raise AssertionError("full metric extraction should not run")
+
+    class Result:
+        def all(self):
+            return [("v3.total_revenue", None, None)]
+
+    class Session:
+        async def execute(self, _stmt):
+            return Result()
+
+    monkeypatch.setattr(
+        cube_matcher_module,
+        "_reaggregate_requirements_for_metrics",
+        fail_full_extract,
+    )
+
+    assert (
+        await _reaggregate_requirements_for_metrics_if_needed(
+            Session(),
+            ["v3.total_revenue"],
+            ["v3.product.category"],
+        )
+        == []
+    )
+
+
+@pytest.mark.asyncio
+async def test_cube_reaggregate_requirements_checks_derived_metric_ancestors(
+    monkeypatch,
+):
+    """Derived metrics still use full extraction when an ancestor has reaggregate."""
+    expected = [("v3.balance_index", "v3.date.date_id", ReaggregationFunction.MAX)]
+
+    async def full_extract(*_args, **_kwargs):
+        return expected
+
+    class Result:
+        def __init__(self, rows):
+            self.rows = rows
+
+        def all(self):
+            return self.rows
+
+    class Session:
+        def __init__(self):
+            self.calls = 0
+
+        async def execute(self, _stmt):
+            self.calls += 1
+            if self.calls == 1:
+                return Result([("v3.balance_index", None, "v3.daily_balance")])
+            return Result([("v3.daily_balance", {"rules": []}, None)])
+
+    monkeypatch.setattr(
+        cube_matcher_module,
+        "_reaggregate_requirements_for_metrics",
+        full_extract,
+    )
+
+    assert (
+        await _reaggregate_requirements_for_metrics_if_needed(
+            Session(),
+            ["v3.balance_index"],
+            ["v3.product.category"],
+        )
+        == expected
+    )
+
+
+@pytest.mark.asyncio
+async def test_reaggregate_requirements_skip_components_without_reaggregate(
+    monkeypatch,
+):
+    """Full extraction ignores ordinary components before collecting reaggregate."""
+
+    class Extractor:
+        async def extract(self, _session):
+            return [
+                MetricComponent(
+                    name="ordinary_sum",
+                    expression="value",
+                    aggregation="SUM",
+                    rule=AggregationRule(type=Aggregability.FULL),
+                ),
+                _semi_additive_component(
+                    "balance_sum",
+                    "v3.date.date_id",
+                    ReaggregationFunction.MAX,
+                ),
+            ], None
+
+    class FakeMetricComponentExtractor:
+        @classmethod
+        async def from_node_name(cls, _metric_name, _session):
+            return Extractor()
+
+    monkeypatch.setattr(
+        cube_matcher_module,
+        "MetricComponentExtractor",
+        FakeMetricComponentExtractor,
+    )
+
+    assert await _reaggregate_requirements_for_metrics(
+        SimpleNamespace(),
+        ["balance"],
+        ["v3.product.category"],
+    ) == [("balance", "v3.date.date_id", ReaggregationFunction.MAX)]
+
+
+def test_decomposed_reaggregate_requirements_dedupe_duplicate_components():
+    """Duplicate component rules produce one cube materialization requirement."""
+    rule = DimensionReaggregateRule(
+        dimension="v3.date.date_id",
+        fn=ReaggregationFunction.LAST_VALUE,
+    )
+    components = [
+        MetricComponent(
+            name=f"balance_sum_{idx}",
+            expression="value",
+            aggregation="SUM",
+            rule=AggregationRule(type=Aggregability.FULL, reaggregate=rule),
+        )
+        for idx in range(2)
+    ]
+    decomposed = _decomposed_metric("balance", components[0])
+    decomposed.components = components
+
+    assert _reaggregate_requirements_for_decomposed_metrics(
+        {"balance": decomposed},
+        ["balance"],
+        [],
+    ) == [("balance", "v3.date.date_id", ReaggregationFunction.LAST_VALUE)]
+
+
+def test_build_synthetic_grain_group_skips_duplicate_internal_dimension():
+    """Filter-only protected dims can already be present in ctx.dimensions."""
+    ctx = BuildContext(
+        session=SimpleNamespace(),
+        metrics=["balance"],
+        dimensions=["v3.date.date_id"],
+        dialect=Dialect.DRUID,
+    )
+    ctx.filter_dimensions = {"v3.date.date_id"}
+    ctx.nodes = {"balance": _metric_node("balance")}
+    ctx.parent_map = {"balance": ["test.parent"]}
+    cube = SimpleNamespace(
+        name="daily_balance_cube",
+        availability=SimpleNamespace(table="daily_balance_cube"),
+        materializations=[],
+    )
+
+    grain_group = build_synthetic_grain_group(
+        ctx,
+        {"balance": _decomposed_metric("balance")},
+        cube,
+    )
+
+    assert grain_group.grain == ["date_id"]
+    assert [
+        col.name for col in grain_group.columns if col.semantic_type == "dimension"
+    ] == [
+        "date_id",
+    ]
+
+
+def test_build_select_ast_skips_non_output_dimensions():
+    """Resolved dimensions outside output refs are join/group-only."""
+    ctx = BuildContext(session=SimpleNamespace(), metrics=[], dimensions=[])
+    parent = _source_node()
+    resolved_dimensions = [
+        ResolvedDimension(
+            original_ref="test.parent.region",
+            node_name="test.parent",
+            column_name="region",
+            role=None,
+            join_path=None,
+            is_local=True,
+        ),
+        ResolvedDimension(
+            original_ref="test.parent.date_id",
+            node_name="test.parent",
+            column_name="date_id",
+            role=None,
+            join_path=None,
+            is_local=True,
+        ),
+    ]
+
+    query, _ = build_select_ast(
+        ctx,
+        metric_expressions=[],
+        resolved_dimensions=resolved_dimensions,
+        parent_node=parent,
+        output_dimension_refs={"test.parent.region"},
+    )
+
+    rendered = str(query)
+    assert "t1.region" in rendered
+    assert "t1.date_id AS date_id" not in rendered
+
+
+def test_build_grain_group_sql_handles_internal_alias_already_in_grain():
+    """The internal reaggregate alias can match a requested dimension alias."""
+    ctx = BuildContext(
+        session=SimpleNamespace(),
+        metrics=["balance"],
+        dimensions=["test.parent.date_id"],
+        use_materialized=False,
+    )
+    parent = _source_node()
+    metric = _metric_node("balance")
+    component = _semi_additive_component(dimension="test.parent.date_id")
+    resolved_dimension = ResolvedDimension(
+        original_ref="test.parent.date_id",
+        node_name="test.parent",
+        column_name="date_id",
+        role=None,
+        join_path=None,
+        is_local=True,
+    )
+    grain_group = GrainGroup(
+        parent_node=parent,
+        aggregability=Aggregability.FULL,
+        grain_columns=[],
+        components=[(metric, component)],
+        reaggregate_component_dimensions={component.name: "test.parent.date_id"},
+    )
+
+    result = build_grain_group_sql(
+        ctx,
+        grain_group,
+        [resolved_dimension],
+        {"balance": 1},
+        output_dimension_refs={"test.parent.date_id"},
+    )
+
+    assert result.grain == ["date_id"]
+
+
+def test_build_window_metric_grain_groups_skips_missing_base_components():
+    """Window grain planning skips parent groups with no decomposed components."""
+    ctx = BuildContext(session=SimpleNamespace(), metrics=["window"], dimensions=[])
+    ctx.parent_map = {"window": ["missing_base"]}
+    existing_group = GrainGroupSQL(
+        query=parse("SELECT missing_base FROM test_parent_0"),
+        columns=[],
+        grain=[],
+        aggregability=Aggregability.FULL,
+        metrics=["missing_base"],
+        parent_name="test.parent",
+    )
+
+    assert (
+        build_window_metric_grain_groups(
+            ctx,
+            {"window": {"v3.date.week"}},
+            [existing_group],
+            {},
+        )
+        == []
+    )
+
+
+def test_build_window_metric_grain_groups_skips_when_parent_node_missing():
+    """Window grain planning skips groups whose source parent is not loaded."""
+    ctx = BuildContext(session=SimpleNamespace(), metrics=["window"], dimensions=[])
+    ctx.nodes = {"missing_base": _metric_node("missing_base")}
+    ctx.parent_map = {"window": ["missing_base"]}
+    existing_group = GrainGroupSQL(
+        query=parse("SELECT missing_base FROM missing_parent_0"),
+        columns=[],
+        grain=[],
+        aggregability=Aggregability.FULL,
+        metrics=["missing_base"],
+        parent_name="missing.parent",
+    )
+
+    assert (
+        build_window_metric_grain_groups(
+            ctx,
+            {"window": {"v3.date.week"}},
+            [existing_group],
+            {
+                "window": _decomposed_metric("window"),
+                "missing_base": _decomposed_metric("missing_base"),
+            },
+        )
+        == []
+    )
+
+
+@pytest.mark.asyncio
+async def test_setup_build_context_skips_duplicate_internal_reaggregate_dimension(
+    monkeypatch,
+):
+    """setup_build_context does not duplicate internal reaggregate dimensions."""
+    load_calls = []
+
+    async def fake_load_nodes(ctx):
+        load_calls.append(list(ctx.dimensions))
+        ctx.nodes = {"balance": _metric_node("balance")}
+
+    async def fake_decompose_and_group_metrics(ctx):
+        return [], {"balance": _decomposed_metric("balance")}
+
+    monkeypatch.setattr(builder_module, "load_nodes", fake_load_nodes)
+    monkeypatch.setattr(
+        builder_module,
+        "decompose_and_group_metrics",
+        fake_decompose_and_group_metrics,
+    )
+    monkeypatch.setattr(
+        builder_module,
+        "add_dimensions_from_metric_expressions",
+        lambda _ctx, _decomposed_metrics: None,
+    )
+    monkeypatch.setattr(
+        builder_module,
+        "missing_reaggregate_dimensions",
+        lambda _decomposed_metrics, _requested_dimensions: ["v3.date.date_id"],
+    )
+
+    ctx = await setup_build_context(
+        session=SimpleNamespace(),
+        metrics=["balance"],
+        dimensions=["v3.date.date_id"],
+    )
+
+    assert ctx.dimensions == ["v3.date.date_id"]
+    assert load_calls == [["v3.date.date_id"], ["v3.date.date_id"]]
diff --git a/datajunction-server/tests/construction/build_v3/serialize_target_test.py b/datajunction-server/tests/construction/build_v3/serialize_target_test.py
new file mode 100644
index 000000000..ef5bbcf76
--- /dev/null
+++ b/datajunction-server/tests/construction/build_v3/serialize_target_test.py
@@ -0,0 +1,244 @@
+"""
+Tests for the materialization-target serialize hook.
+
+Some components must be written to destinations in a representation different
+from their accumulated form. The fork keys off ``MaterializationTarget`` to
+determine if the expression needs to be serialized during measures table writing.
+"""
+
+import pytest
+
+from types import SimpleNamespace
+
+from datajunction_server.construction.build_v3.decomposition import (
+    build_component_expression,
+)
+from datajunction_server.construction.build_v3.measures import infer_component_type
+from datajunction_server.models.decompose import AggregationRule, MetricComponent
+from datajunction_server.models.materialization import MaterializationTarget
+
+
+def _component(
+    aggregation: str,
+    serialize: str | None = None,
+    serialize_targets: list[MaterializationTarget] | None = None,
+) -> MetricComponent:
+    return MetricComponent(
+        name="latency_td_abc123",
+        expression="latency_ms",
+        aggregation=aggregation,
+        merge="nflx_tdigest_agg",
+        rule=AggregationRule(),
+        serialize=serialize,
+        serialize_targets=serialize_targets or [],
+    )
+
+
+DRUID = [MaterializationTarget.DRUID]
+
+
+class TestSerializeIsApplied:
+    """When the component declares a conversion and the target asks for it."""
+
+    def test_simple_accumulate_is_wrapped(self):
+        """A bare function-name accumulate gets the conversion."""
+        component = _component(
+            "nflx_tdigest",
+            serialize="nflx_tdigest_sketch({})",
+            serialize_targets=DRUID,
+        )
+
+        expr = build_component_expression(component, MaterializationTarget.DRUID)
+
+        assert str(expr) == "nflx_tdigest_sketch(nflx_tdigest(latency_ms))"
+
+    def test_template_accumulate_is_wrapped(self):
+        """
+        A pre-expanded template accumulate gets the conversion too.
+        """
+        component = _component(
+            "nflx_tdigest(latency_ms, 200)",
+            serialize="nflx_tdigest_sketch({})",
+            serialize_targets=DRUID,
+        )
+
+        expr = build_component_expression(component, MaterializationTarget.DRUID)
+
+        assert str(expr) == "nflx_tdigest_sketch(nflx_tdigest(latency_ms, 200))"
+
+
+class TestSerializeIsSkipped:
+    """Every path that must leave the accumulated expression untouched."""
+
+    def test_no_target_means_no_conversion(self):
+        """
+        A query-time build passes no target and gets the unwrapped expression.
+        """
+        component = _component(
+            "nflx_tdigest(latency_ms, 200)",
+            serialize="nflx_tdigest_sketch({})",
+            serialize_targets=DRUID,
+        )
+
+        expr = build_component_expression(component)
+
+        assert str(expr) == "nflx_tdigest(latency_ms, 200)"
+
+    def test_different_target_means_no_conversion(self):
+        """
+        An Iceberg pre-agg keeps the in-engine representation.
+        """
+        component = _component(
+            "nflx_tdigest(latency_ms, 200)",
+            serialize="nflx_tdigest_sketch({})",
+            serialize_targets=DRUID,
+        )
+
+        expr = build_component_expression(component, MaterializationTarget.ICEBERG)
+
+        assert str(expr) == "nflx_tdigest(latency_ms, 200)"
+
+    def test_component_without_serialize_is_untouched(self):
+        """
+        A component declaring no conversion is unaffected even for Druid.
+        """
+        component = _component("SUM")
+
+        expr = build_component_expression(component, MaterializationTarget.DRUID)
+
+        assert str(expr) == "SUM(latency_ms)"
+
+    @pytest.mark.parametrize(
+        "target",
+        [None, MaterializationTarget.DRUID, MaterializationTarget.ICEBERG],
+    )
+    def test_plain_sum_is_stable_across_targets(self, target):
+        """
+        An ordinary measure renders identically whatever the target.
+        """
+        expr = build_component_expression(_component("SUM"), target)
+
+        assert str(expr) == "SUM(latency_ms)"
+
+
+class TestSerializeDeclaration:
+    """The declaration itself."""
+
+    def test_targets_default_to_empty(self):
+        """
+        A component declaring a conversion but no targets never applies it.
+
+        Defaulting to "no targets" rather than "all targets" prevents 
+        half-finished declarations from silently rewriting measures tables.
+        """
+        component = _component(
+            "nflx_tdigest(latency_ms, 200)",
+            serialize="nflx_tdigest_sketch({})",
+        )
+
+        expr = build_component_expression(component, MaterializationTarget.DRUID)
+
+        assert str(expr) == "nflx_tdigest(latency_ms, 200)"
+
+
+class TestSerializedColumnType:
+    """
+    The recorded column type must match the representation actually written.
+
+    These are set in different places (SQL by ``build_component_expression``,
+    type by ``infer_component_type``). A disagreement is silent and could cause
+    Druid ingestion to miss the correct aggregator mappings.
+    """
+
+    @staticmethod
+    def _parent():
+        return SimpleNamespace(
+            current=SimpleNamespace(
+                columns=[SimpleNamespace(name="latency_ms", type="double")],
+            ),
+        )
+
+    def _sketch_component(self, serialize_type: str | None = "binary"):
+        """
+        A component that converts on the way to Druid.
+        """
+        component = _component(
+            "SUM",
+            serialize="to_druid_bytes({})",
+            serialize_targets=DRUID,
+        )
+        component.serialize_type = serialize_type
+        return component
+
+    def test_druid_target_records_the_converted_type(self):
+        """The declared conversion type wins for the target that converts."""
+        assert (
+            infer_component_type(
+                self._sketch_component(),
+                "double",
+                self._parent(),
+                MaterializationTarget.DRUID,
+            )
+            == "binary"
+        )
+
+    @pytest.mark.parametrize(
+        "target",
+        [None, MaterializationTarget.ICEBERG],
+    )
+    def test_other_targets_keep_the_accumulated_type(self, target):
+        """Query-time and Iceberg store the unconverted value, so keep its type."""
+        assert (
+            infer_component_type(
+                self._sketch_component(),
+                "double",
+                self._parent(),
+                target,
+            )
+            == "double"
+        )
+
+    def test_conversion_without_a_declared_type_falls_through(self):
+        """
+        Declaring a conversion but no type leaves inference in charge.
+        """
+        assert (
+            infer_component_type(
+                self._sketch_component(serialize_type=None),
+                "double",
+                self._parent(),
+                MaterializationTarget.DRUID,
+            )
+            == "double"
+        )
+
+    def test_ordinary_component_is_unaffected(self):
+        """A component with no conversion types exactly as it did before."""
+        assert (
+            infer_component_type(
+                _component("SUM"),
+                "double",
+                self._parent(),
+                MaterializationTarget.DRUID,
+            )
+            == "double"
+        )
+
+
+class TestSerializesFor:
+    """The single predicate both the SQL and the type side consult."""
+
+    @pytest.mark.parametrize(
+        ("serialize", "targets", "target", "expected"),
+        [
+            ("f({})", DRUID, MaterializationTarget.DRUID, True),
+            ("f({})", DRUID, MaterializationTarget.ICEBERG, False),
+            ("f({})", DRUID, None, False),
+            ("f({})", [], MaterializationTarget.DRUID, False),
+            (None, DRUID, MaterializationTarget.DRUID, False),
+        ],
+    )
+    def test_predicate(self, serialize, targets, target, expected):
+        component = _component("SUM", serialize=serialize, serialize_targets=targets)
+
+        assert component.serializes_for(target) is expected
diff --git a/datajunction-server/tests/construction/build_v3/sketch_family_e2e_test.py b/datajunction-server/tests/construction/build_v3/sketch_family_e2e_test.py
new file mode 100644
index 000000000..95e7ca8f4
--- /dev/null
+++ b/datajunction-server/tests/construction/build_v3/sketch_family_e2e_test.py
@@ -0,0 +1,429 @@
+"""
+A sketch family, end to end, through the HTTP endpoints.
+
+The rest of the suite tests this machinery in pieces -- `merge_args_test` on
+the merge call, `accumulate_type_test` on inferred types, `serialize_target_test`
+on the write-time conversion, `reaggregate_test` on the spec. Each one holds a
+`ComponentDef` or a `MetricComponent` in its hand and asserts about it. None of
+them puts a family behind the server and asks what SQL comes out.
+
+This does. One metric, one grain, followed from raw rows to the number the
+engine returns:
+
+    accumulate   build_digest(line_total, 200.0)   from source, per partition
+    merge        merge_digest(digest, 200.0)       from the pre-agg table
+    combine      digest_quantiles(digest, ...)[0]  Spark / Trino
+                 DIGEST_QUANTILE(digest, 0.95)     Druid, merge fused in
+
+OSS registers no family of its own -- they live downstream with the
+engine-specific functions -- so the fixture below supplies one, the same way
+`decompose_test.registered_family` does for the unit tests. That makes this
+file the executable form of the extension contract: a downstream deployment
+has to register *both* a decomposition and the functions it names, and if
+either half is missing the failure is a 500 out of the measures endpoint, not
+something a type checker catches.
+
+`serialize` is the one hook not asserted here. It applies when writing a
+measures table for a target rather than when reading, so no query endpoint
+renders it; `serialize_target_test` and `cube_druid_sketch_spec_test` cover it.
+"""
+
+import re
+
+import pytest
+from httpx import AsyncClient
+
+from datajunction_server.models.dialect import Dialect
+from datajunction_server.models.materialization import MaterializationTarget
+from datajunction_server.models.reaggregate import ReaggregationFunction
+from datajunction_server.sql.decompose import (
+    FAMILY_DECOMPOSITION_REGISTRY,
+    AggDecomposition,
+    ComponentDef,
+    decomposes_family,
+    make_func,
+)
+from datajunction_server.sql.functions import Function, function_registry
+from datajunction_server.sql.parsing import ast
+from datajunction_server.sql.parsing import types as ct
+
+from . import assert_sql_equal
+
+METRIC = "v3.p95_line_total"
+DIMENSIONS = ["v3.product.category"]
+COMPRESSION = 200
+PREAGG_TABLE = "analytics.preaggs.v3_digest_by_cat"
+
+
+# ---------------------------------------------------------------------------
+# A stand-in sketch family: four functions and a decomposition that uses them.
+# Shaped after a real t-digest -- an accumulate that takes a tuning argument, a
+# merge that must repeat it, and a combiner whose form differs on Druid -- so
+# the parts of the machinery that only a sketch exercises are actually hit.
+# ---------------------------------------------------------------------------
+
+
+class _DigestFunction(Function):
+    """Base class, so the fixture can find these to register and unregister."""
+
+
+class BuildDigest(_DigestFunction):
+    """build_digest(col, compression) -> digest. Phase 1."""
+
+    is_aggregation = True
+    dialects = [Dialect.SPARK, Dialect.TRINO]
+
+
+class MergeDigest(_DigestFunction):
+    """merge_digest(digest, compression) -> digest. Phase 2."""
+
+    is_aggregation = True
+    dialects = [Dialect.SPARK, Dialect.TRINO]
+
+
+class DigestQuantiles(_DigestFunction):
+    """digest_quantiles(digest, quantiles) -> array<double>. Phase 3, non-Druid."""
+
+    is_aggregation = False
+    dialects = [Dialect.SPARK, Dialect.TRINO]
+
+
+class DigestQuantile(_DigestFunction):
+    """DIGEST_QUANTILE(digest, q) -> double. Phase 2+3 fused, Druid only."""
+
+    is_aggregation = True
+    dialects = [Dialect.DRUID]
+
+
+@BuildDigest.register
+def infer_type(col: ct.ColumnType, compression: ct.ColumnType) -> ct.BinaryType:
+    return ct.BinaryType()
+
+
+@MergeDigest.register
+def infer_type(digest: ct.ColumnType, compression: ct.ColumnType) -> ct.BinaryType:
+    return ct.BinaryType()
+
+
+@DigestQuantiles.register
+def infer_type(digest: ct.ColumnType, quantiles: ct.ColumnType) -> ct.ListType:
+    return ct.ListType(element_type=ct.DoubleType())
+
+
+@DigestQuantile.register
+def infer_type(digest: ct.ColumnType, quantile: ct.ColumnType) -> ct.DoubleType:
+    return ct.DoubleType()
+
+
+@pytest.fixture
+def digest_family():
+    """
+    Register the family and its functions, then put the registries back.
+
+    Both registries are process-global, so leaving either populated would leak
+    into every later test in the session.
+    """
+    saved: dict[str, type | None] = {}
+    for cls in _DigestFunction.__subclasses__():
+        snake_cased = re.sub(r"(?<!^)(?=[A-Z])", "_", cls.__name__).upper()
+        for key in (cls.__name__.upper(), snake_cased):
+            saved[key] = function_registry.get(key)
+            function_registry[key] = cls
+
+    @decomposes_family(ReaggregationFunction.TDIGEST)
+    class _DigestDecomposition(AggDecomposition):
+        @property
+        def compression(self) -> int:
+            return int(self.params.get("compression", 100))
+
+        @property
+        def compression_literal(self) -> str:
+            return f"CAST({self.compression}.0 AS DOUBLE)"
+
+        @property
+        def components(self) -> list[ComponentDef]:
+            return [
+                ComponentDef(
+                    # Compression rides in the suffix so two differently tuned
+                    # sketches over the same column get distinct columns.
+                    suffix=f"_digest_c{self.compression}",
+                    accumulate=f"build_digest({{}}, {self.compression_literal})",
+                    merge="merge_digest",
+                    merge_args=(self.compression_literal,),
+                    serialize="digest_to_bytes({})",
+                    serialize_targets=(MaterializationTarget.DRUID,),
+                    serialize_type="binary",
+                ),
+            ]
+
+        def combine(self, components, func, dialect=Dialect.SPARK):
+            column, fraction = components[0].name, func.args[1]
+            if dialect == Dialect.DRUID:
+                return make_func("DIGEST_QUANTILE", column, fraction)
+            return ast.Subscript(
+                expr=make_func(
+                    "digest_quantiles",
+                    column,
+                    make_func("array", fraction),
+                ),
+                index=ast.Number(0),
+            )
+
+    try:
+        yield _DigestDecomposition
+    finally:
+        FAMILY_DECOMPOSITION_REGISTRY.pop(ReaggregationFunction.TDIGEST, None)
+        for key, previous in saved.items():
+            if previous is None:
+                function_registry.pop(key, None)
+            else:
+                function_registry[key] = previous
+
+
+@pytest.fixture
+async def percentile_metric(client_with_build_v3: AsyncClient, digest_family):
+    """
+    A percentile metric that opts into the family.
+
+    The aggregation is a plain APPROX_PERCENTILE -- nothing about the query
+    mentions a sketch. Only `reaggregate.fn` selects the family, which is the
+    point of the family registry: the metric keeps its readable definition.
+    """
+    response = await client_with_build_v3.post(
+        "/nodes/metric/",
+        json={
+            "name": METRIC,
+            "description": "95th percentile line total",
+            "query": (
+                "SELECT APPROX_PERCENTILE(line_total, 0.95) FROM v3.order_details"
+            ),
+            "mode": "published",
+            "reaggregate": {
+                "fn": "tdigest",
+                "params": {"compression": COMPRESSION},
+            },
+        },
+    )
+    assert response.status_code in (200, 201), response.json()
+    return client_with_build_v3
+
+
+async def _measures(client: AsyncClient, dialect: str = "spark") -> dict:
+    response = await client.get(
+        "/sql/measures/v3/",
+        params={"metrics": [METRIC], "dimensions": DIMENSIONS, "dialect": dialect},
+    )
+    assert response.status_code == 200, response.json()
+    return response.json()
+
+
+async def _publish_preagg(client: AsyncClient) -> None:
+    response = await client.post(
+        "/preaggs/plan",
+        json={
+            "metrics": [METRIC],
+            "dimensions": DIMENSIONS,
+            "strategy": "full",
+            "schedule": "0 0 * * *",
+        },
+    )
+    assert response.status_code == 201, response.text
+
+    catalog, schema, table = PREAGG_TABLE.split(".")
+    response = await client.post(
+        f"/preaggs/{response.json()['preaggs'][0]['id']}/availability/",
+        json={
+            "catalog": catalog,
+            "schema": schema,
+            "table": table,
+            "valid_through_ts": 1704067200,
+        },
+    )
+    assert response.status_code == 200, response.text
+
+
+@pytest.mark.asyncio
+async def test_accumulate_renders_the_tuning_param(percentile_metric):
+    """
+    Phase 1: `params` reaches the generated SQL as a real argument.
+
+    `compression` is declared once on the metric's reaggregate spec and has to
+    survive into the accumulate call. If it were dropped the query would still
+    run -- `build_digest` has a one-argument form -- and quietly build sketches
+    at the wrong accuracy, so this is worth pinning on the SQL rather than on
+    the spec.
+    """
+    (group,) = (await _measures(percentile_metric))["grain_groups"]
+
+    assert_sql_equal(
+        group["sql"],
+        f"""
+        WITH v3_order_details AS (
+          SELECT oi.product_id,
+                 oi.quantity * oi.unit_price AS line_total
+          FROM default.v3.orders o
+          JOIN default.v3.order_items oi ON o.order_id = oi.order_id
+        ),
+        v3_product AS (
+          SELECT product_id, category FROM default.v3.products
+        )
+        SELECT t2.category,
+               build_digest(t1.line_total, CAST({COMPRESSION}.0 AS DOUBLE))
+                 line_total_digest_c{COMPRESSION}_e1f61696
+        FROM v3_order_details t1
+        LEFT OUTER JOIN v3_product t2 ON t1.product_id = t2.product_id
+        GROUP BY t2.category
+        """,
+    )
+
+
+@pytest.mark.asyncio
+async def test_component_identity_carries_the_compression(percentile_metric):
+    """
+    The column name encodes the tuning, and the recorded type is the sketch's.
+
+    Both matter downstream: the name keeps a sketch built at one accuracy from
+    satisfying a query that asked for another, and the type is what the
+    materialized table is created from.
+    """
+    (group,) = (await _measures(percentile_metric))["grain_groups"]
+    (component,) = group["components"]
+    (column,) = [c for c in group["columns"] if c["semantic_type"] != "dimension"]
+
+    assert component["name"] == f"line_total_digest_c{COMPRESSION}_e1f61696"
+    assert component["merge"] == "merge_digest"
+    assert component["aggregability"] == "full"
+    assert column["type"] == "binary"
+
+
+@pytest.mark.asyncio
+async def test_merge_from_preagg_repeats_the_tuning_param(percentile_metric):
+    """
+    Phase 2: reading stored sketches back, with `merge_args` applied.
+
+    The accumulate is gone and the source CTEs with it -- a single scan of the
+    pre-agg table. The compression has to appear a second time here: `merge` is
+    a bare function name (the Druid aggregator lookup and the semi-additive
+    rewrite both match on it), so the argument cannot be folded into it and
+    travels as `merge_args` instead.
+    """
+    await _publish_preagg(percentile_metric)
+    (group,) = (await _measures(percentile_metric))["grain_groups"]
+
+    assert_sql_equal(
+        group["sql"],
+        f"""
+        SELECT category,
+               merge_digest(line_total_digest_c{COMPRESSION}_e1f61696,
+                            CAST({COMPRESSION}.0 AS DOUBLE))
+                 line_total_digest_c{COMPRESSION}_e1f61696
+        FROM {PREAGG_TABLE}
+        GROUP BY category
+        """,
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "dialect, expected",
+    [
+        (
+            "spark",
+            f"digest_quantiles(line_total_digest_c{COMPRESSION}_e1f61696, "
+            "array(0.95))[0]",
+        ),
+        (
+            "druid",
+            f"DIGEST_QUANTILE(line_total_digest_c{COMPRESSION}_e1f61696, 0.95)",
+        ),
+    ],
+)
+async def test_combiner_takes_the_shape_the_engine_needs(
+    percentile_metric,
+    dialect,
+    expected,
+):
+    """
+    Phase 3: the combiner, and the one place the dialect legitimately matters.
+
+    Druid has no scalar reader for the sketch, so it fuses merge and combine
+    into one aggregating call; Spark and Trino read the merged column with a
+    separate extractor. That is a difference in shape, which no transpiler can
+    derive -- unlike function spelling or array index base, which the
+    transpilation layer handles and a decomposition must therefore leave alone.
+
+    The requested quantile is authored in the metric query, not the reaggregate
+    spec, so it has to survive decomposition and reappear here. That is what
+    lets p50/p95/p99 share one sketch column.
+    """
+    payload = await _measures(percentile_metric, dialect=dialect)
+    (formula,) = payload["metric_formulas"]
+
+    assert formula["combiner"] == expected
+
+
+@pytest.mark.asyncio
+async def test_metrics_layer_recomputes_from_source(percentile_metric):
+    """
+    Final metrics layer: recomputes from source, pre-agg or not.
+
+    Pinning current behaviour, not endorsing it. The pre-agg published here is
+    the same one `test_merge_from_preagg_repeats_the_tuning_param` reads, and
+    the measures layer picks it up. The metrics layer never consults the
+    matcher: `build_metrics_sql` takes the cube branch only when
+    `use_materialized and dialect == Dialect.DRUID`, and otherwise builds grain
+    groups from source.
+
+    So no sketch appears below at all -- a bare APPROX_PERCENTILE off the fact
+    -- and the combiner asserted above never reaches rendered SQL on this path.
+    Defensible if scanning source on Trino is cheap enough, but it does mean
+    the sketch columns are written for Druid's benefit alone.
+    """
+    await _publish_preagg(percentile_metric)
+
+    response = await percentile_metric.get(
+        "/sql/",
+        params={"metrics": [METRIC], "dimensions": DIMENSIONS},
+    )
+    assert response.status_code == 200, response.json()
+
+    assert_sql_equal(
+        response.json()["sql"],
+        """
+        WITH v3_DOT_order_details AS (
+          SELECT o.order_id,
+                 oi.line_number,
+                 o.customer_id,
+                 o.order_date,
+                 o.from_location_id,
+                 o.to_location_id,
+                 o.status,
+                 oi.product_id,
+                 oi.quantity,
+                 oi.unit_price,
+                 oi.quantity * oi.unit_price AS line_total
+          FROM v3.orders AS o
+          JOIN v3.order_items AS oi ON o.order_id = oi.order_id
+        ),
+        v3_DOT_product AS (
+          SELECT v3_DOT_src_products.product_id,
+                 v3_DOT_src_products.name,
+                 v3_DOT_src_products.category,
+                 v3_DOT_src_products.subcategory,
+                 v3_DOT_src_products.price
+          FROM v3.products AS v3_DOT_src_products
+        ),
+        v3_DOT_order_details_metrics AS (
+          SELECT v3_DOT_product.category v3_DOT_product_DOT_category,
+                 APPROX_PERCENTILE(v3_DOT_order_details.line_total, 0.95)
+                   v3_DOT_p95_line_total
+          FROM v3_DOT_order_details
+          LEFT JOIN v3_DOT_product
+            ON v3_DOT_order_details.product_id = v3_DOT_product.product_id
+          GROUP BY v3_DOT_product.category
+        )
+        SELECT v3_DOT_order_details_metrics.v3_DOT_product_DOT_category,
+               v3_DOT_order_details_metrics.v3_DOT_p95_line_total
+        FROM v3_DOT_order_details_metrics
+        """,
+    )
diff --git a/datajunction-server/tests/database/preaggregation_test.py b/datajunction-server/tests/database/preaggregation_test.py
index 0364ab65f..a88bac361 100644
--- a/datajunction-server/tests/database/preaggregation_test.py
+++ b/datajunction-server/tests/database/preaggregation_test.py
@@ -716,3 +716,75 @@ async def test_find_matching_no_candidates(
             },
         )
         assert result is None
+
+
+class TestMeasureIdentityParams:
+    """Tuning parameters participate in measure identity."""
+
+    def test_existing_tokens_are_unchanged(self):
+        """A component without params produces the identical token."""
+        assert measure_identity_token("abc123", "SUM") == "abc123:SUM"
+        assert measure_identity_token("abc123", "SUM", None) == "abc123:SUM"
+        assert measure_identity_token("abc123", "SUM", {}) == "abc123:SUM"
+        assert measure_identity_token("abc123", None) == "abc123:"
+        assert measure_identity_token("abc123", " count ") == "abc123:COUNT"
+
+    def test_differing_params_differ(self):
+        """Two sketches over one column at different accuracies are distinct."""
+        low = measure_identity_token("abc123", "TDIGEST_AGG", {"compression": 100})
+        high = measure_identity_token("abc123", "TDIGEST_AGG", {"compression": 1000})
+        assert low != high
+        # ...and neither collides with the un-parameterized token.
+        assert low != measure_identity_token("abc123", "TDIGEST_AGG")
+
+    def test_params_are_order_and_spelling_insensitive(self):
+        """Order and type variations (e.g. 200 vs 200.0) do not change the identity token."""
+        assert measure_identity_token(
+            "abc123",
+            "TDIGEST_AGG",
+            {"compression": 200, "k": 12},
+        ) == measure_identity_token(
+            "abc123",
+            "TDIGEST_AGG",
+            {"k": 12, "compression": 200},
+        )
+        assert measure_identity_token(
+            "abc123",
+            "TDIGEST_AGG",
+            {"compression": 200.0},
+        ) == measure_identity_token("abc123", "TDIGEST_AGG", {"compression": 200})
+        # Fractional values are preserved.
+        assert measure_identity_token(
+            "abc123",
+            "TDIGEST_AGG",
+            {"compression": 200.5},
+        ) != measure_identity_token("abc123", "TDIGEST_AGG", {"compression": 200})
+
+    def test_get_measure_identities_carries_params(self):
+        """Stored measures round-trip their params into the identity set."""
+        plain = make_measure("latency_sum", "latency_ms")
+        sketched = make_measure(
+            "latency_tdigest",
+            "latency_ms",
+            aggregation="TDIGEST_AGG",
+            merge="TDIGEST_AGG",
+        )
+        sketched.params = {"compression": 200}
+        identities = get_measure_identities([plain, sketched])
+        assert identities == {
+            measure_identity_token(compute_expression_hash("latency_ms"), "SUM"),
+            measure_identity_token(
+                compute_expression_hash("latency_ms"),
+                "TDIGEST_AGG",
+                {"compression": 200},
+            ),
+        }
+        # Ensure the sketch identity distinguishes different compressions.
+        assert (
+            measure_identity_token(
+                compute_expression_hash("latency_ms"),
+                "TDIGEST_AGG",
+                {"compression": 1000},
+            )
+            not in identities
+        )
diff --git a/datajunction-server/tests/internal/deployment/orchestration_test.py b/datajunction-server/tests/internal/deployment/orchestration_test.py
index a163ba5ab..974895046 100644
--- a/datajunction-server/tests/internal/deployment/orchestration_test.py
+++ b/datajunction-server/tests/internal/deployment/orchestration_test.py
@@ -27,6 +27,7 @@
 from datajunction_server.internal.deployment.orchestrator import (
     DeploymentOrchestrator,
     DeploymentPlan,
+    DeploymentTimer,
     ResourceRegistry,
     column_changed,
     tag_needs_update,
@@ -82,6 +83,26 @@ def mock_deployment_context(current_user: User):
     return context
 
 
+def test_deployment_timer_logs_unaccounted_overhead(monkeypatch, caplog):
+    """
+    Deployment timing summary includes unaccounted overhead when it is meaningful.
+    """
+    timer = DeploymentTimer()
+    timer.record("validate", 10, "1 node")
+    monkeypatch.setattr(
+        "datajunction_server.internal.deployment.orchestrator.time.perf_counter",
+        lambda: timer._start + 0.2,
+    )
+
+    with caplog.at_level(
+        "INFO",
+        logger="datajunction_server.internal.deployment.orchestrator",
+    ):
+        timer.log_summary("default", "deployment-1")
+
+    assert "(unaccounted overhead)" in caplog.text
+
+
 @pytest.fixture
 def sample_deployment_spec():
     """Sample deployment specification for testing"""
diff --git a/datajunction-server/tests/internal/deployment/validation_test.py b/datajunction-server/tests/internal/deployment/validation_test.py
index c252035a5..6a270b7c7 100644
--- a/datajunction-server/tests/internal/deployment/validation_test.py
+++ b/datajunction-server/tests/internal/deployment/validation_test.py
@@ -793,6 +793,97 @@ async def test_invalid_short_name_not_in_parent_columns(
         assert err.debug is not None
         assert "no_such_col" in err.debug["invalid_required_dimensions"]
 
+    @pytest.mark.asyncio
+    async def test_valid_reaggregate_dimension_full_path(
+        self,
+        session: AsyncSession,
+        parent_node: Node,
+    ):
+        """reaggregate.rules[].dimension full-path column found in dim nodes is valid."""
+        dim_node = self._make_dim_node("test.dim", ["dateint"])
+        context = self._make_context(session, parent_node)
+        spec = MetricSpec(
+            name="test.metric",
+            query="SELECT SUM(value) FROM test.parent",
+            reaggregate={
+                "rules": [
+                    {
+                        "dimension": "test.dim.dateint",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        )
+        validator = NodeSpecBulkValidator(context)
+        validator._all_dim_nodes = {**context.dependency_nodes, "test.dim": dim_node}
+        result = validator.validate_query_node(spec)
+
+        assert result.status == NodeStatus.VALID
+        error_codes = [e.code for e in result.errors]
+        assert ErrorCode.INVALID_COLUMN not in error_codes
+
+    @pytest.mark.asyncio
+    async def test_invalid_reaggregate_dimension_column(
+        self,
+        session: AsyncSession,
+        parent_node: Node,
+    ):
+        """reaggregate.rules[].dimension full-path column absent from dim node is invalid."""
+        dim_node = self._make_dim_node("test.dim", ["dateint"])
+        context = self._make_context(session, parent_node)
+        spec = MetricSpec(
+            name="test.metric",
+            query="SELECT SUM(value) FROM test.parent",
+            reaggregate={
+                "rules": [
+                    {
+                        "dimension": "test.dim.nonexistent_col",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        )
+        validator = NodeSpecBulkValidator(context)
+        validator._all_dim_nodes = {**context.dependency_nodes, "test.dim": dim_node}
+        result = validator.validate_query_node(spec)
+
+        assert result.status == NodeStatus.INVALID
+        err = next(e for e in result.errors if e.code == ErrorCode.INVALID_COLUMN)
+        assert err.debug is not None
+        assert "test.dim.nonexistent_col" in err.debug["invalid_reaggregate_dimensions"]
+
+    @pytest.mark.asyncio
+    async def test_invalid_reaggregate_function(
+        self,
+        session: AsyncSession,
+        parent_node: Node,
+    ):
+        """unsupported dimension-specific reaggregate functions are invalid."""
+        context = self._make_context(session, parent_node)
+        spec = MetricSpec(
+            name="test.metric",
+            query="SELECT SUM(value) FROM test.parent",
+            reaggregate={
+                "rules": [
+                    {
+                        "dimension": "id",
+                        "fn": "sum",
+                    },
+                ],
+            },
+        )
+        validator = NodeSpecBulkValidator(context)
+        validator._all_dim_nodes = dict(context.dependency_nodes)
+        result = validator.validate_query_node(spec)
+
+        assert result.status == NodeStatus.INVALID
+        err = next(
+            e
+            for e in result.errors
+            if e.code == ErrorCode.INVALID_ARGUMENTS_TO_FUNCTION
+        )
+        assert err.debug == {"invalid_reaggregate_functions": ["sum"]}
+
     @pytest.mark.asyncio
     async def test_no_required_dimensions_is_noop(
         self,
@@ -866,6 +957,64 @@ async def test_prefetch_fetches_dim_node_from_db(
         col_names = {c.name for c in fetched.current.columns}
         assert "region" in col_names
 
+    @pytest.mark.asyncio
+    async def test_prefetch_fetches_reaggregate_dim_node_from_db(
+        self,
+        session: AsyncSession,
+        parent_node: Node,
+        dim_node_in_db: Node,
+    ):
+        """_prefetch_required_dimension_nodes includes reaggregate dimensions."""
+        context = self._make_context(session, parent_node)
+        spec = MetricSpec(
+            name="test.metric",
+            query="SELECT SUM(value) FROM test.parent",
+            reaggregate={
+                "rules": [
+                    {
+                        "dimension": "test.external_dim.region",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        )
+        validator = NodeSpecBulkValidator(context)
+
+        await validator._prefetch_required_dimension_nodes([spec])
+
+        assert "test.external_dim" in validator._all_dim_nodes
+        fetched = validator._all_dim_nodes["test.external_dim"]
+        assert fetched.current is not None
+        assert {c.name for c in fetched.current.columns} == {"region"}
+
+    @pytest.mark.asyncio
+    async def test_prefetch_skips_short_reaggregate_dimension(
+        self,
+        session: AsyncSession,
+        parent_node: Node,
+    ):
+        """Short-form reaggregate dimensions do not trigger dimension-node prefetch."""
+        context = self._make_context(session, parent_node)
+        spec = MetricSpec(
+            name="test.metric",
+            query="SELECT SUM(value) FROM test.parent",
+            reaggregate={
+                "rules": [
+                    {
+                        "dimension": "value",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        )
+        validator = NodeSpecBulkValidator(context)
+
+        await validator._prefetch_required_dimension_nodes([spec])
+
+        assert set(validator._all_dim_nodes.keys()) == set(
+            context.dependency_nodes.keys(),
+        )
+
     @pytest.mark.asyncio
     async def test_prefetch_with_no_required_dimensions(
         self,
diff --git a/datajunction-server/tests/internal/namespaces_test.py b/datajunction-server/tests/internal/namespaces_test.py
index 1c563211d..839f99086 100644
--- a/datajunction-server/tests/internal/namespaces_test.py
+++ b/datajunction-server/tests/internal/namespaces_test.py
@@ -3,6 +3,7 @@
 """
 
 from datetime import UTC, datetime
+from types import SimpleNamespace
 
 import pytest
 from ruamel.yaml import YAML
@@ -22,6 +23,7 @@
     _merge_list_with_key,
     _merge_yaml_preserving_comments,
     create_or_reactivate_namespace,
+    get_node_specs_for_export,
     node_spec_to_yaml,
     provision_namespace_boundary,
 )
@@ -1108,6 +1110,81 @@ def test_no_yaml_document_start_marker(self):
             "query: SELECT SUM(rev) FROM ns.transforms.t",
         ]
 
+    def test_metric_reaggregate_exports_dimension_rules(self):
+        """metric reaggregate exports the rule list without null future fields"""
+        spec = MetricSpec(
+            name="ns.metrics.daily_balance",
+            node_type=NodeType.METRIC,
+            query="SELECT SUM(balance) FROM ns.transforms.accounts",
+            reaggregate={
+                "rules": [
+                    {
+                        "dimension": "${prefix}v3.date.date_id[order]",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        )
+        assert node_spec_to_yaml(spec).splitlines() == [
+            "name: ns.metrics.daily_balance",
+            "node_type: metric",
+            "mode: published",
+            "query: SELECT SUM(balance) FROM ns.transforms.accounts",
+            "reaggregate:",
+            "  rules:",
+            "    - dimension: ${prefix}v3.date.date_id[order]",
+            "      fn: last_value",
+        ]
+
+    @pytest.mark.asyncio
+    async def test_metric_reaggregate_export_injects_dimension_prefix(
+        self,
+        monkeypatch,
+    ):
+        """namespace export parameterizes reaggregate rule dimensions."""
+        spec = MetricSpec(
+            name="demo.branch.metrics.daily_balance",
+            node_type=NodeType.METRIC,
+            query="SELECT SUM(balance) FROM demo.branch.transforms.accounts",
+            reaggregate={
+                "rules": [
+                    {
+                        "dimension": "demo.branch.dims.date.date_id[order]",
+                        "fn": "last_value",
+                    },
+                ],
+            },
+        )
+
+        async def to_spec(_session):
+            return spec
+
+        fake_node = SimpleNamespace(
+            name="demo.branch.metrics.daily_balance",
+            current=SimpleNamespace(parents=[], status="valid"),
+            to_spec=to_spec,
+        )
+
+        async def namespace_get(_session, _namespace, raise_if_not_exists=False):
+            return SimpleNamespace(parent_namespace="demo.main")
+
+        async def list_all_nodes(_session, _namespace, options=None):
+            return [fake_node]
+
+        monkeypatch.setattr(NodeNamespace, "get", namespace_get)
+        monkeypatch.setattr(NodeNamespace, "list_all_nodes", list_all_nodes)
+
+        exported = await get_node_specs_for_export(SimpleNamespace(), "demo.branch")
+
+        assert exported[0].name == "${prefix}metrics.daily_balance"
+        assert exported[0].query == (
+            "SELECT SUM(balance) FROM ${prefix}transforms.accounts"
+        )
+        assert (
+            exported[0].reaggregate.rules[0].dimension
+            == "${prefix}dims.date.date_id[order]"
+        )
+
     def test_multiline_query_uses_literal_block_style(self):
         """multiline queries are serialized with |- literal block style"""
         spec = MetricSpec(
diff --git a/datajunction-server/tests/internal/node_validation_test.py b/datajunction-server/tests/internal/node_validation_test.py
index 5711a6ea7..7f765c7fe 100644
--- a/datajunction-server/tests/internal/node_validation_test.py
+++ b/datajunction-server/tests/internal/node_validation_test.py
@@ -1019,6 +1019,133 @@ async def test_validate_node_data_v2_flags_invalid_required_dimensions(
     ), [(e.code, e.message) for e in validator.errors]
 
 
+@pytest.mark.asyncio
+async def test_validate_node_data_v2_flags_invalid_reaggregate_dimensions(
+    session: AsyncSession,
+    user: User,
+):
+    """reaggregate dimensions must resolve to columns on parent dimension nodes."""
+    from datajunction_server.errors import ErrorCode
+    from datajunction_server.internal.validation import validate_node_data_v2
+
+    source = Node(
+        name="test.v2_reagg_dim_parent",
+        type=NodeType.SOURCE,
+        created_by_id=user.id,
+        current_version="v1.0",
+    )
+    source_rev = NodeRevision(
+        name="test.v2_reagg_dim_parent",
+        display_name="reaggregate dim parent",
+        type=NodeType.SOURCE,
+        query=None,
+        status=NodeStatus.VALID,
+        version="v1.0",
+        node=source,
+        columns=[Column(name="id", type=ct.BigIntType(), order=0)],
+        created_by_id=user.id,
+    )
+    dim = Node(
+        name="test.v2_reagg_dim",
+        type=NodeType.DIMENSION,
+        created_by_id=user.id,
+        current_version="v1.0",
+    )
+    dim_rev = NodeRevision(
+        name="test.v2_reagg_dim",
+        display_name="tiny reaggregate dim",
+        type=NodeType.DIMENSION,
+        query="SELECT 1 AS id",
+        status=NodeStatus.VALID,
+        version="v1.0",
+        node=dim,
+        columns=[Column(name="id", type=ct.BigIntType(), order=0)],
+        created_by_id=user.id,
+    )
+    session.add_all([source, source_rev, dim, dim_rev])
+    await session.commit()
+
+    child = NodeRevision(
+        name="test.v2_reagg_dim_child",
+        display_name="reaggregate dim child",
+        type=NodeType.METRIC,
+        query="SELECT SUM(id) FROM test.v2_reagg_dim_parent",
+        status=NodeStatus.VALID,
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "test.v2_reagg_dim.ghost_col",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+    validator = await validate_node_data_v2(child, session)
+
+    assert validator.status == NodeStatus.INVALID
+    assert any(
+        err.code == ErrorCode.INVALID_COLUMN and "reaggregate dimensions" in err.message
+        for err in validator.errors
+    ), [(e.code, e.message) for e in validator.errors]
+
+
+@pytest.mark.asyncio
+async def test_validate_node_data_v2_flags_unsupported_reaggregate_function(
+    session: AsyncSession,
+    user: User,
+):
+    """dimension-specific reaggregate only accepts supported collapse functions."""
+    from datajunction_server.errors import ErrorCode
+    from datajunction_server.internal.validation import validate_node_data_v2
+
+    source = Node(
+        name="test.v2_reagg_fn_parent",
+        type=NodeType.SOURCE,
+        created_by_id=user.id,
+        current_version="v1.0",
+    )
+    source_rev = NodeRevision(
+        name="test.v2_reagg_fn_parent",
+        display_name="reaggregate function parent",
+        type=NodeType.SOURCE,
+        query=None,
+        status=NodeStatus.VALID,
+        version="v1.0",
+        node=source,
+        columns=[
+            Column(name="id", type=ct.BigIntType(), order=0),
+            Column(name="order_date", type=ct.BigIntType(), order=1),
+        ],
+        created_by_id=user.id,
+    )
+    session.add_all([source, source_rev])
+    await session.commit()
+
+    child = NodeRevision(
+        name="test.v2_reagg_fn_child",
+        display_name="reaggregate function child",
+        type=NodeType.METRIC,
+        query="SELECT SUM(id) FROM test.v2_reagg_fn_parent",
+        status=NodeStatus.VALID,
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "order_date",
+                    "fn": "sum",
+                },
+            ],
+        },
+    )
+    validator = await validate_node_data_v2(child, session)
+
+    assert validator.status == NodeStatus.INVALID
+    assert any(
+        err.code == ErrorCode.INVALID_ARGUMENTS_TO_FUNCTION
+        and err.debug == {"invalid_reaggregate_functions": ["sum"]}
+        for err in validator.errors
+    ), [(e.code, e.message, e.debug) for e in validator.errors]
+
+
 @pytest.mark.asyncio
 async def test_validate_node_data_v2_cross_fact_metrics_no_shared_dims(
     session: AsyncSession,
diff --git a/datajunction-server/tests/internal/nodes/derive_frozen_measures_test.py b/datajunction-server/tests/internal/nodes/derive_frozen_measures_test.py
index 292be4521..c0000b83e 100644
--- a/datajunction-server/tests/internal/nodes/derive_frozen_measures_test.py
+++ b/datajunction-server/tests/internal/nodes/derive_frozen_measures_test.py
@@ -16,7 +16,16 @@
 from datajunction_server.database.measure import FrozenMeasure
 from datajunction_server.database.node import Node, NodeRevision
 from datajunction_server.database.user import OAuthProvider, User
-from datajunction_server.internal.nodes import derive_frozen_measures_bulk
+from datajunction_server.errors import DJInvalidInputException
+from datajunction_server.internal.nodes import (
+    _raise_if_frozen_measure_conflicts,
+    derive_frozen_measures_bulk,
+)
+from datajunction_server.models.decompose import (
+    Aggregability,
+    AggregationRule,
+    MetricComponent,
+)
 from datajunction_server.models.node import NodeStatus
 from datajunction_server.models.node_type import NodeType
 
@@ -62,6 +71,7 @@ async def _make_metric(
     name: str,
     query: str,
     parents: list[Node],
+    reaggregate: dict[str, object] | None = None,
 ) -> Node:
     node = Node(
         name=name,
@@ -75,6 +85,7 @@ async def _make_metric(
         type=NodeType.METRIC,
         version="v1.0",
         query=query,
+        reaggregate=reaggregate,
         status=NodeStatus.VALID,
         parents=parents,
         created_by_id=user.id,
@@ -123,6 +134,180 @@ async def test_base_metric_populates_derived_expression_and_measure(
     assert any(fm.aggregation == "SUM" for fm in metric.current.frozen_measures)
 
 
+@pytest.mark.asyncio
+async def test_frozen_measure_name_collision_allows_reaggregate_only_difference(
+    session: AsyncSession,
+    user: User,
+):
+    """Metric-level reaggregate metadata does not change measure identity."""
+    src = await _make_source(
+        session,
+        user,
+        "src_reaggregate_collision",
+        [Column(name="semi_amount", type=ct.DoubleType(), order=0)],
+    )
+    reaggregate = await _make_metric(
+        session,
+        user,
+        "m.semi_amount_eod",
+        "SELECT SUM(semi_amount) FROM src_reaggregate_collision",
+        [src],
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+    additive = await _make_metric(
+        session,
+        user,
+        "m.semi_amount_total",
+        "SELECT SUM(semi_amount) FROM src_reaggregate_collision",
+        [src],
+    )
+    await session.refresh(reaggregate, ["current"])
+    await session.refresh(additive, ["current"])
+
+    await derive_frozen_measures_bulk(session, [additive.current.id])
+    await session.commit()
+
+    await session.refresh(additive.current, ["frozen_measures"])
+    assert all(fm.rule.reaggregate is None for fm in additive.current.frozen_measures)
+
+    await derive_frozen_measures_bulk(session, [reaggregate.current.id])
+    await session.commit()
+    await session.refresh(reaggregate.current, ["frozen_measures"])
+
+    assert {fm.name for fm in additive.current.frozen_measures} == {
+        fm.name for fm in reaggregate.current.frozen_measures
+    }
+
+
+@pytest.mark.asyncio
+async def test_reaggregate_rule_is_not_persisted_on_shared_frozen_measure(
+    session: AsyncSession,
+    user: User,
+):
+    """FrozenMeasure.rule stays metric-independent even for semi-additive metrics."""
+    src = await _make_source(
+        session,
+        user,
+        "src_reaggregate_storage",
+        [Column(name="semi_amount", type=ct.DoubleType(), order=0)],
+    )
+    reaggregate = await _make_metric(
+        session,
+        user,
+        "m.semi_amount_snapshot",
+        "SELECT SUM(semi_amount) FROM src_reaggregate_storage",
+        [src],
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+    await session.refresh(reaggregate, ["current"])
+
+    await derive_frozen_measures_bulk(session, [reaggregate.current.id])
+    await session.commit()
+
+    await session.refresh(reaggregate.current, ["frozen_measures"])
+    assert all(
+        fm.rule.reaggregate is None for fm in reaggregate.current.frozen_measures
+    )
+
+
+def test_frozen_measure_conflict_rejects_different_measure_identity():
+    """FrozenMeasure name collisions fail when the metric-independent rule differs."""
+    frozen_measure = FrozenMeasure(
+        name="amount_sum",
+        upstream_revision_id=1,
+        expression="amount",
+        aggregation="SUM",
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+    measure = MetricComponent(
+        name="amount_sum",
+        expression="discounted_amount",
+        aggregation="SUM",
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+
+    with pytest.raises(DJInvalidInputException, match="already exists"):
+        _raise_if_frozen_measure_conflicts(frozen_measure, measure)
+
+
+def test_frozen_measure_conflict_rejects_different_tuning_params():
+    """
+    A name collision fails when only tuning parameters differ, preventing
+    a metric from silently reusing a sketch built with a different accuracy.
+    """
+    frozen_measure = FrozenMeasure(
+        name="latency_tdigest",
+        upstream_revision_id=1,
+        expression="latency_ms",
+        aggregation="TDIGEST_AGG",
+        params={"compression": 200},
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+    measure = MetricComponent(
+        name="latency_tdigest",
+        expression="latency_ms",
+        aggregation="TDIGEST_AGG",
+        params={"compression": 1000},
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+
+    with pytest.raises(DJInvalidInputException, match="tuning parameters"):
+        _raise_if_frozen_measure_conflicts(frozen_measure, measure)
+
+
+def test_frozen_measure_reuse_allows_matching_params():
+    """
+    Identical params are reusable; absent-vs-empty is not a conflict.
+    """
+    frozen_measure = FrozenMeasure(
+        name="latency_tdigest",
+        upstream_revision_id=1,
+        expression="latency_ms",
+        aggregation="TDIGEST_AGG",
+        params={"compression": 200},
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+    measure = MetricComponent(
+        name="latency_tdigest",
+        expression="latency_ms",
+        aggregation="TDIGEST_AGG",
+        params={"compression": 200},
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+    _raise_if_frozen_measure_conflicts(frozen_measure, measure)
+
+    unparameterized = FrozenMeasure(
+        name="amount_sum",
+        upstream_revision_id=1,
+        expression="amount",
+        aggregation="SUM",
+        params=None,
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+    empty_params = MetricComponent(
+        name="amount_sum",
+        expression="amount",
+        aggregation="SUM",
+        params={},
+        rule=AggregationRule(type=Aggregability.FULL),
+    )
+    _raise_if_frozen_measure_conflicts(unparameterized, empty_params)
+
+
 @pytest.mark.asyncio
 async def test_derived_metric_expands_parent_cache(
     session: AsyncSession,
diff --git a/datajunction-server/tests/models/cube_druid_sketch_spec_test.py b/datajunction-server/tests/models/cube_druid_sketch_spec_test.py
new file mode 100644
index 000000000..09a3bb067
--- /dev/null
+++ b/datajunction-server/tests/models/cube_druid_sketch_spec_test.py
@@ -0,0 +1,196 @@
+"""
+Regression tests for sketch measures reaching Druid ingestion.
+"""
+
+from datajunction_server.api.cubes import _build_metrics_spec
+from datajunction_server.models.cube_materialization import CombineMaterialization
+from datajunction_server.models.decompose import AggregationRule, MetricComponent
+from datajunction_server.models.node_type import NodeNameVersion
+from datajunction_server.models.partition import Granularity
+from datajunction_server.models.query import ColumnMetadata
+
+TIMESTAMP_COLUMN = ColumnMetadata(name="order_date", type="int")
+
+
+def _measure(
+    name: str,
+    aggregation: str | None,
+    merge: str | None = None,
+) -> MetricComponent:
+    """A measure component as the decomposer would emit it."""
+    return MetricComponent(
+        name=name,
+        expression=name,
+        aggregation=aggregation,
+        merge=merge,
+        rule=AggregationRule(),
+    )
+
+
+def _combiner(
+    measures: list[MetricComponent],
+    measure_columns: list[ColumnMetadata],
+) -> CombineMaterialization:
+    """A combiner stage carrying real measures, ready to build a Druid spec."""
+    return CombineMaterialization(
+        node=NodeNameVersion(name="default.repairs_cube", version="v1.0"),
+        columns=[TIMESTAMP_COLUMN, *measure_columns],
+        grain=["order_date"],
+        dimensions=["order_date"],
+        measures=measures,
+        timestamp_column="order_date",
+        timestamp_format="yyyyMMdd",
+        granularity=Granularity.DAY,
+    )
+
+
+class TestCombinerSketchMetricsSpec:
+    """``CombineMaterialization.metrics_spec`` with measures that carry sketches."""
+
+    def test_hll_measure_maps_to_hll_sketch_merge(self):
+        """
+        An HLL sketch measure reaches Druid as an HLLSketchMerge aggregator.
+        """
+        combiner = _combiner(
+            measures=[
+                _measure("customer_id_hll_23002251", "hll_sketch_agg", "hll_union_agg"),
+            ],
+            measure_columns=[
+                ColumnMetadata(name="customer_id_hll_23002251", type="binary"),
+            ],
+        )
+
+        assert combiner.metrics_spec() == [
+            {
+                "fieldName": "customer_id_hll_23002251",
+                "name": "customer_id_hll_23002251",
+                "type": "HLLSketchMerge",
+                "lgK": 12,
+                "tgtHllType": "HLL_4",
+            },
+        ]
+
+    def test_hll_measure_is_neither_dropped_nor_downgraded(self):
+        """
+        The sketch measure survives as a sketch aggregator.
+        """
+        combiner = _combiner(
+            measures=[
+                _measure("customer_id_hll_23002251", "hll_sketch_agg", "hll_union_agg"),
+            ],
+            measure_columns=[
+                ColumnMetadata(name="customer_id_hll_23002251", type="binary"),
+            ],
+        )
+
+        spec = combiner.metrics_spec()
+
+        assert len(spec) == 1, "sketch measure was dropped from the metricsSpec"
+        assert spec[0]["type"] != "longSum"
+
+    def test_plain_sum_measure_maps_to_long_sum(self):
+        """A non-sketch measure still maps as before."""
+        combiner = _combiner(
+            measures=[_measure("total_repair_cost", "SUM", "SUM")],
+            measure_columns=[
+                ColumnMetadata(name="total_repair_cost", type="bigint"),
+            ],
+        )
+
+        assert combiner.metrics_spec() == [
+            {
+                "fieldName": "total_repair_cost",
+                "name": "total_repair_cost",
+                "type": "longSum",
+            },
+        ]
+
+    def test_merge_phase_takes_precedence_over_accumulate(self):
+        """
+        Pins which phase the aggregator lookup uses: ``merge``, not ``aggregation``.
+        Druid ingests already-accumulated partials, so the merge function is the
+        correct phase to key off.
+        """
+        combiner = _combiner(
+            measures=[_measure("order_count", "COUNT", "SUM")],
+            measure_columns=[ColumnMetadata(name="order_count", type="double")],
+        )
+
+        assert combiner.metrics_spec() == [
+            {
+                "fieldName": "order_count",
+                "name": "order_count",
+                "type": "doubleSum",
+            },
+        ]
+
+    def test_percentile_measure_is_dropped_silently(self):
+        """
+        A percentile measure disappears from the ingestion spec without error.
+        """
+        combiner = _combiner(
+            measures=[_measure("p95_latency", "approx_percentile")],
+            measure_columns=[ColumnMetadata(name="p95_latency", type="double")],
+        )
+
+        assert combiner.metrics_spec() == []
+
+
+class TestMetricsSpecBuildersAgree:
+    """
+    The two Druid metricsSpec builders, compared directly.
+
+    ``CombineMaterialization.metrics_spec`` and ``api.cubes._build_metrics_spec``
+    map measures for different entry points. They share ``get_druid_aggregator_spec`` 
+    so the aggregator type and family config agree.
+    """
+
+    def test_builders_agree_on_hll_aggregator_type(self):
+        """Both resolve an HLL sketch measure to the same aggregator type."""
+        measure = _measure(
+            "customer_id_hll_23002251",
+            "hll_sketch_agg",
+            "hll_union_agg",
+        )
+        measure_column = ColumnMetadata(
+            name="customer_id_hll_23002251",
+            type="binary",
+        )
+
+        v3_spec = _combiner([measure], [measure_column]).metrics_spec()
+        cubes_api_spec = _build_metrics_spec([measure_column], [measure], {})
+
+        assert v3_spec[0]["type"] == cubes_api_spec[0]["type"] == "HLLSketchMerge"
+
+    def test_builders_agree_on_hll_tuning_config(self):
+        """
+        Both builders attach the same HLL tuning config.
+        """
+        measure = _measure(
+            "customer_id_hll_23002251",
+            "hll_sketch_agg",
+            "hll_union_agg",
+        )
+        measure_column = ColumnMetadata(
+            name="customer_id_hll_23002251",
+            type="binary",
+        )
+
+        v3_entry = _combiner([measure], [measure_column]).metrics_spec()[0]
+        cubes_api_entry = _build_metrics_spec([measure_column], [measure], {})[0]
+
+        assert v3_entry["lgK"] == cubes_api_entry["lgK"] == 12
+        assert v3_entry["tgtHllType"] == cubes_api_entry["tgtHllType"] == "HLL_4"
+
+    def test_builders_disagree_on_unmappable_measures(self):
+        """
+        An unmappable measure is dropped by one builder and defaulted by the other.
+        """
+        measure = _measure("p95_latency", "approx_percentile")
+        measure_column = ColumnMetadata(name="p95_latency", type="double")
+
+        v3_spec = _combiner([measure], [measure_column]).metrics_spec()
+        cubes_api_spec = _build_metrics_spec([measure_column], [measure], {})
+
+        assert v3_spec == []
+        assert cubes_api_spec[0]["type"] == "longSum"
diff --git a/datajunction-server/tests/models/deployment_test.py b/datajunction-server/tests/models/deployment_test.py
index 91507b62d..a371e2f61 100644
--- a/datajunction-server/tests/models/deployment_test.py
+++ b/datajunction-server/tests/models/deployment_test.py
@@ -1706,7 +1706,7 @@ def fingerprint(spec: NodeSpec) -> SemanticFingerprint:
     "source": "71dcbc388988c2bdd850670427710384687b58565ee38ca392dc220adfed868d",
     "transform": "978e692880c7bcfb1bd78ece85895a1ec1558e85377b064a8dac3f3719cff2a5",
     "dimension": "f7b3c87a61fdadf9997432fd9334befdf43f2488874ef555e3f7d4c4ba86e3e1",
-    "metric": "a3fde7af5dbd00d194805af33fc213bca52244c7cdedf1f7363ec52d2f6d4116",
+    "metric": "f0b5356d75b1f6a39a2809581a9167b9e1298a41980ee8e83040cf6bd0996851",
     "cube": "9b0a56d974d1e3769bc2db94e2cfbae7a6a4839f664eebd2a4387ef112ceea81",
 }
 
@@ -1853,6 +1853,38 @@ def test_semantic_fingerprint_renders_prefixes_and_normalizes_sql():
     assert fingerprint(explicit) != fingerprint(implicit)
 
 
+def test_metric_spec_equality_compares_rendered_reaggregate():
+    """Parameterized reaggregate dimensions should not cause perpetual deploys."""
+    parameterized = MetricSpec(
+        namespace="analytics",
+        name="daily_balance",
+        query="SELECT SUM(balance) FROM analytics.daily_balances",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "${prefix}date.date_id[order]",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+    rendered = MetricSpec(
+        namespace="analytics",
+        name="daily_balance",
+        query="SELECT SUM(balance) FROM analytics.daily_balances",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "analytics.date.date_id[order]",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+
+    assert parameterized == rendered
+
+
 def test_semantic_diff_and_fingerprint_share_change_rules():
     original = TransformSpec(name="node", query="SELECT id AS value FROM source")
     formatted = TransformSpec(
@@ -1958,6 +1990,7 @@ def test_metric_presentation_fields_preserve_semantic_fingerprint():
             "query",
             "columns",
             "required_dimensions",
+            "reaggregate",
         }
     )
     assert all(
diff --git a/datajunction-server/tests/models/node_test.py b/datajunction-server/tests/models/node_test.py
index 1489d3928..3a630811c 100644
--- a/datajunction-server/tests/models/node_test.py
+++ b/datajunction-server/tests/models/node_test.py
@@ -227,6 +227,30 @@ def test_extra_validation() -> None:
         "bound dimensions which are only for metrics."
     )
 
+    node = Node(name="A", type=NodeType.TRANSFORM, current_version="1")
+    node_revision = NodeRevision(
+        name=node.name,
+        type=node.type,
+        node=node,
+        version="1",
+        query="SELECT * FROM B",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "B.date_id",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+    with pytest.raises(Exception) as excinfo:
+        node_revision.extra_validation()
+
+    assert str(excinfo.value) == (
+        "Node A of type transform cannot have "
+        "reaggregate settings which are only for metrics."
+    )
+
 
 def test_merging_availability_simple_no_partitions() -> None:
     """
diff --git a/datajunction-server/tests/models/reaggregate_test.py b/datajunction-server/tests/models/reaggregate_test.py
new file mode 100644
index 000000000..3cf21b60d
--- /dev/null
+++ b/datajunction-server/tests/models/reaggregate_test.py
@@ -0,0 +1,188 @@
+"""Tests for reaggregation models."""
+
+import pytest
+from pydantic import ValidationError
+
+from datajunction_server.models import reaggregate as reaggregate_module
+from datajunction_server.models.reaggregate import (
+    DimensionReaggregateRule,
+    ReaggregateSpec,
+    ReaggregationFunction,
+    dimension_reaggregate_rules,
+    dump_reaggregate_spec,
+    is_parameterized_reaggregate_function,
+    parse_reaggregate_spec,
+    unsupported_dimension_reaggregate_functions,
+)
+
+
+def test_dump_reaggregate_spec_from_dict():
+    """
+    Reaggregate specs passed as dictionaries are validated and serialized.
+    """
+    assert dump_reaggregate_spec(
+        {
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    ) == {
+        "fn": None,
+        "weight": None,
+        "params": None,
+        "rules": [
+            {
+                "dimension": "default.date_dim.date",
+                "fn": "last_value",
+            },
+        ],
+    }
+
+
+def test_dump_reaggregate_spec_from_model():
+    """
+    Reaggregate spec models are serialized to JSON-compatible dictionaries.
+    """
+    assert dump_reaggregate_spec(
+        ReaggregateSpec(
+            rules=[
+                DimensionReaggregateRule(
+                    dimension="default.date_dim.date",
+                    fn=ReaggregationFunction.LAST_VALUE,
+                ),
+            ],
+        ),
+    ) == {
+        "fn": None,
+        "weight": None,
+        "params": None,
+        "rules": [
+            {
+                "dimension": "default.date_dim.date",
+                "fn": "last_value",
+            },
+        ],
+    }
+
+
+def test_dump_reaggregate_spec_none():
+    """
+    Missing reaggregate specs pass through unchanged.
+    """
+    assert dump_reaggregate_spec(None) is None
+
+
+def test_parse_reaggregate_spec_from_model():
+    """
+    Parsed model inputs are returned unchanged.
+    """
+    spec = ReaggregateSpec(
+        rules=[
+            DimensionReaggregateRule(
+                dimension="default.date_dim.date",
+                fn=ReaggregationFunction.LAST_VALUE,
+            ),
+        ],
+    )
+
+    assert parse_reaggregate_spec(spec) is spec
+
+
+def test_dimension_reaggregate_rules_empty_for_none():
+    """
+    Empty reaggregate specs have no dimension-specific rules.
+    """
+    assert dimension_reaggregate_rules(None) == []
+
+
+def test_unsupported_dimension_reaggregate_functions_handles_empty_and_invalid():
+    """
+    Unsupported dimension collapse functions are reported from parsed specs.
+    """
+    assert unsupported_dimension_reaggregate_functions(None) == []
+    assert unsupported_dimension_reaggregate_functions(
+        {
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "sum",
+                },
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    ) == ["sum"]
+
+
+def test_params_rejected_for_unparameterized_function():
+    """
+    Functions without tuneable parameters reject `params`.
+    """
+    with pytest.raises(ValidationError) as excinfo:
+        ReaggregateSpec(fn=ReaggregationFunction.SUM, params={"compression": 200})
+    assert "`sum` does not accept parameters" in str(excinfo.value)
+    assert "compression" in str(excinfo.value)
+
+
+def test_params_rejected_when_no_function_given():
+    """
+    `params` without an `fn` is rejected.
+    """
+    with pytest.raises(ValidationError) as excinfo:
+        ReaggregateSpec(params={"k": 256})
+    assert "no reaggregation function does not accept parameters" in str(excinfo.value)
+
+
+def test_empty_params_allowed():
+    """
+    An absent or empty `params` is fine on any function.
+    """
+    assert ReaggregateSpec(fn=ReaggregationFunction.SUM).params is None
+    assert ReaggregateSpec(fn=ReaggregationFunction.SUM, params={}).params == {}
+
+
+def test_params_accepted_for_parameterized_function(monkeypatch):
+    """
+    Registering a function as parameterized enables `params` assignment.
+    """
+    monkeypatch.setattr(
+        reaggregate_module,
+        "PARAMETERIZED_REAGGREGATE_FUNCTIONS",
+        frozenset({ReaggregationFunction.AVG}),
+    )
+    spec = ReaggregateSpec(fn=ReaggregationFunction.AVG, params={"compression": 200})
+    assert spec.params == {"compression": 200}
+    assert dump_reaggregate_spec(spec)["params"] == {"compression": 200}
+
+
+def test_only_sketch_families_are_parameterized():
+    """
+    Only a sketch family takes tuning parameters.
+    """
+    parameterized = {
+        fn for fn in ReaggregationFunction if is_parameterized_reaggregate_function(fn)
+    }
+
+    assert parameterized == {ReaggregationFunction.TDIGEST}
+
+
+def test_params_round_trip_through_parse():
+    """
+    `params` survives dict -> model -> dict without loss.
+    """
+    monkeypatched = frozenset({ReaggregationFunction.AVG})
+    original = reaggregate_module.PARAMETERIZED_REAGGREGATE_FUNCTIONS
+    reaggregate_module.PARAMETERIZED_REAGGREGATE_FUNCTIONS = monkeypatched
+    try:
+        spec = parse_reaggregate_spec({"fn": "avg", "params": {"compression": 200}})
+        assert spec.params == {"compression": 200}
+        assert parse_reaggregate_spec(dump_reaggregate_spec(spec)).params == {
+            "compression": 200,
+        }
+    finally:
+        reaggregate_module.PARAMETERIZED_REAGGREGATE_FUNCTIONS = original
diff --git a/datajunction-server/tests/sql/decompose_test.py b/datajunction-server/tests/sql/decompose_test.py
index b2c93644b..af8607eb0 100644
--- a/datajunction-server/tests/sql/decompose_test.py
+++ b/datajunction-server/tests/sql/decompose_test.py
@@ -2,23 +2,38 @@
 Tests for ``datajunction_server.sql.decompose``.
 """
 
+from types import SimpleNamespace
+
 import pytest
 import pytest_asyncio
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from datajunction_server.database.node import Node, NodeRelationship, NodeRevision
+from datajunction_server.errors import DJInvalidInputException
 from datajunction_server.models.cube_materialization import (
     Aggregability,
     AggregationRule,
     MetricComponent,
 )
 from datajunction_server.models.engine import Dialect
+from datajunction_server.models.materialization import MaterializationTarget
 from datajunction_server.models.node_type import NodeType
+from datajunction_server.models.reaggregate import (
+    DimensionReaggregateRule,
+    ReaggregateSpec,
+    ReaggregationFunction,
+)
 from datajunction_server.sql import functions as dj_functions
 from datajunction_server.sql.decompose import (
+    DECOMPOSITION_REGISTRY,
+    FAMILY_DECOMPOSITION_REGISTRY,
     DUPLICATION_INVARIANT_AGGREGATIONS,
+    AggDecomposition,
+    ComponentDef,
     MetricComponentExtractor,
+    decomposes_family,
     get_decomposition,
+    make_func,
     is_duplication_invariant,
     is_metric_duplication_sensitive,
     safe_denominator,
@@ -62,7 +77,12 @@ async def create_metric(session: AsyncSession, current_user, parent_node):
     """Fixture to create a metric node with a query."""
     created_metrics: list[NodeRevision] = []
 
-    async def _create(query: str, name: str | None = None, parent=None):
+    async def _create(
+        query: str,
+        name: str | None = None,
+        parent=None,
+        reaggregate: dict[str, object] | None = None,
+    ):
         parent_to_use = parent if parent else parent_node
         metric_name = name or f"test_metric_{len(created_metrics)}"
 
@@ -81,6 +101,7 @@ async def _create(query: str, name: str | None = None, parent=None):
             name=metric_name,
             type=NodeType.METRIC,
             query=query,
+            reaggregate=reaggregate,
             created_by_id=current_user.id,
         )
         session.add(metric_rev)
@@ -120,6 +141,105 @@ async def test_simple_sum(session: AsyncSession, create_metric):
     )
 
 
+@pytest.mark.asyncio
+async def test_reaggregate_sum_attaches_spec(session: AsyncSession, create_metric):
+    """
+    Semi-additive declarations attach to the single extracted base measure.
+    """
+    metric_rev = await create_metric(
+        "SELECT SUM(account_balance) FROM parent_node",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+
+    extractor = MetricComponentExtractor(metric_rev.id)
+    measures, derived_sql = await extractor.extract(session)
+
+    expected_spec = DimensionReaggregateRule(
+        dimension="default.date_dim.date",
+        fn=ReaggregationFunction.LAST_VALUE,
+    )
+    assert measures == [
+        MetricComponent(
+            name="account_balance_sum_8e611a76",
+            expression="account_balance",
+            aggregation="SUM",
+            merge="SUM",
+            rule=AggregationRule(
+                type=Aggregability.FULL,
+                reaggregate=expected_spec,
+            ),
+        ),
+    ]
+    assert_sql_equal(
+        str(derived_sql),
+        "SELECT SUM(account_balance_sum_8e611a76) FROM parent_node",
+    )
+
+
+@pytest.mark.asyncio
+async def test_reaggregate_multiple_rules_not_supported(
+    session: AsyncSession,
+    create_metric,
+):
+    """
+    V1 semi-additive metrics support one dimension-specific rule.
+    """
+    metric_rev = await create_metric(
+        "SELECT SUM(account_balance) FROM parent_node",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+                {
+                    "dimension": "default.region_dim.region",
+                    "fn": "first_value",
+                },
+            ],
+        },
+    )
+
+    extractor = MetricComponentExtractor(metric_rev.id)
+    with pytest.raises(DJInvalidInputException, match="exactly one rule"):
+        await extractor.extract(session)
+
+
+@pytest.mark.asyncio
+async def test_reaggregate_unsupported_dimension_function_not_supported(
+    session: AsyncSession,
+    create_metric,
+):
+    """
+    Dimension-specific reaggregate accepts only collapse-safe functions.
+    """
+    metric_rev = await create_metric(
+        "SELECT SUM(account_balance) FROM parent_node",
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "sum",
+                },
+            ],
+        },
+    )
+
+    extractor = MetricComponentExtractor(metric_rev.id)
+    with pytest.raises(
+        DJInvalidInputException,
+        match="unsupported dimension reaggregation function",
+    ):
+        await extractor.extract(session)
+
+
 @pytest.mark.asyncio
 async def test_sum_with_cast(session: AsyncSession, create_metric):
     """
@@ -854,6 +974,50 @@ async def test_unsupported_aggregation_function(session: AsyncSession, create_me
     )
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    ("query", "match"),
+    [
+        (
+            "SELECT AVG(sales_amount) FROM parent_node",
+            "exactly one component",
+        ),
+        (
+            "SELECT COUNT(DISTINCT user_id) FROM parent_node",
+            "one non-distinct fully-aggregatable component",
+        ),
+        (
+            "SELECT MEDIAN(sales_amount) FROM parent_node",
+            "decomposable aggregation",
+        ),
+    ],
+)
+async def test_unsupported_reaggregate_shapes(
+    session: AsyncSession,
+    create_metric,
+    query: str,
+    match: str,
+):
+    """
+    V1 semi-additive metrics only support one ordinary decomposable component.
+    """
+    metric_rev = await create_metric(
+        query,
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+
+    extractor = MetricComponentExtractor(metric_rev.id)
+    with pytest.raises(DJInvalidInputException, match=match):
+        await extractor.extract(session)
+
+
 @pytest.mark.asyncio
 async def test_count_if(session: AsyncSession, create_metric):
     """
@@ -1539,7 +1703,12 @@ async def create_derived_metric(clean_session: AsyncSession, clean_current_user)
     session = clean_session
     current_user = clean_current_user
 
-    async def _create(name: str, query: str, base_metric_nodes: list[Node]):
+    async def _create(
+        name: str,
+        query: str,
+        base_metric_nodes: list[Node],
+        reaggregate: dict[str, object] | None = None,
+    ):
         metric_node = Node(
             name=name,
             type=NodeType.METRIC,
@@ -1555,6 +1724,7 @@ async def _create(name: str, query: str, base_metric_nodes: list[Node]):
             name=name,
             type=NodeType.METRIC,
             query=query,
+            reaggregate=reaggregate,
             created_by_id=current_user.id,
         )
         session.add(metric_rev)
@@ -1571,6 +1741,116 @@ async def _create(name: str, query: str, base_metric_nodes: list[Node]):
     return _create
 
 
+@pytest.mark.asyncio
+async def test_derived_metric_reaggregate_not_supported_db_load(
+    clean_session: AsyncSession,
+    create_base_metric,
+    create_derived_metric,
+):
+    """
+    Semi-additive declarations are currently limited to base metrics.
+    """
+    session = clean_session
+    balance_node, _ = await create_base_metric(
+        "default.balance",
+        "SELECT SUM(account_balance) FROM parent_node",
+    )
+    _, derived_rev = await create_derived_metric(
+        "default.double_balance",
+        "SELECT default.balance * 2",
+        [balance_node],
+        reaggregate={
+            "rules": [
+                {
+                    "dimension": "default.date_dim.date",
+                    "fn": "last_value",
+                },
+            ],
+        },
+    )
+
+    extractor = MetricComponentExtractor(derived_rev.id)
+    with pytest.raises(
+        DJInvalidInputException,
+        match="Derived metric `default.double_balance` declares reaggregate",
+    ):
+        await extractor.extract(session)
+
+
+def test_derived_metric_reaggregate_not_supported_cache_load():
+    """
+    Cache-based extraction enforces the same base-metric-only constraint.
+    """
+    reaggregate = {
+        "rules": [
+            {
+                "dimension": "default.date_dim.date",
+                "fn": "last_value",
+            },
+        ],
+    }
+    base_node = SimpleNamespace(
+        name="default.balance",
+        type=NodeType.METRIC,
+        current=SimpleNamespace(
+            query="SELECT SUM(account_balance) FROM parent_node",
+            reaggregate=None,
+        ),
+    )
+    derived_node = SimpleNamespace(
+        name="default.double_balance",
+        type=NodeType.METRIC,
+        current=SimpleNamespace(
+            query="SELECT default.balance * 2",
+            reaggregate=reaggregate,
+        ),
+    )
+
+    extractor = MetricComponentExtractor(0)
+    with pytest.raises(
+        DJInvalidInputException,
+        match="Derived metric `default.double_balance` declares reaggregate",
+    ):
+        extractor._build_metric_data_from_cache(
+            derived_node,
+            {
+                "default.balance": base_node,
+                "default.double_balance": derived_node,
+            },
+            {"default.double_balance": ["default.balance"]},
+        )
+
+
+def test_normalize_aliases_leaves_other_namespaces_qualified():
+    """
+    Alias normalization only strips the primary parent alias from column refs.
+    """
+    extractor = MetricComponentExtractor(0)
+    query = parse("SELECT p.value, other.value FROM parent_node p")
+
+    normalized = extractor._normalize_aliases(query)
+
+    assert_sql_equal(str(normalized), "SELECT value, other.value FROM parent_node")
+
+
+def test_substitute_metric_references_ignores_non_metric_columns():
+    """
+    Derived metric substitution leaves ordinary columns in place.
+    """
+    extractor = MetricComponentExtractor(0)
+    query = parse("SELECT default.balance + raw_value FROM parent_node")
+
+    substituted = extractor._substitute_metric_references(
+        query,
+        {"default.balance": ([], "SUM(balance)")},
+    )
+
+    assert_sql_equal(
+        str(substituted),
+        "SELECT SUM(balance) + raw_value FROM parent_node",
+    )
+
+
 @pytest.mark.asyncio
 async def test_extract_derived_metric_revenue_per_order(
     clean_session: AsyncSession,
@@ -2404,3 +2684,437 @@ async def test_sum_abs_decomposes(session: AsyncSession, create_metric):
     assert comp.rule.type == Aggregability.FULL
     assert "ABS" in comp.expression
     assert_sql_equal(str(derived_sql), f"SELECT SUM({comp.name}) FROM parent_node")
+
+
+class TestReaggregateParams:
+    """
+    Tests that sketch tuning parameters flow through to components.
+    """
+
+    @staticmethod
+    def _spec(params):
+        """A spec carrying params."""
+        return ReaggregateSpec.model_construct(
+            fn=ReaggregationFunction.SUM,
+            weight=None,
+            rules=[],
+            params=params,
+        )
+
+    def test_params_reach_components(self):
+        extractor = MetricComponentExtractor(1)
+        components, _ = extractor._extract_base(
+            parse("SELECT SUM(latency_ms) FROM t"),
+            self._spec({"compression": 200}),
+        )
+        assert [component.params for component in components] == [
+            {"compression": 200},
+        ]
+
+    def test_params_reach_every_component_of_a_multi_component_metric(self):
+        """AVG decomposes to a sum and a count; both are the same sketch family."""
+        extractor = MetricComponentExtractor(1)
+        components, _ = extractor._extract_base(
+            parse("SELECT AVG(latency_ms) FROM t"),
+            self._spec({"compression": 200}),
+        )
+        assert len(components) == 2
+        assert all(c.params == {"compression": 200} for c in components)
+
+    def test_params_are_copied_not_shared(self):
+        """
+        Each component receives an independent copy of its params dict.
+        """
+        extractor = MetricComponentExtractor(1)
+        params = {"compression": 200}
+        components, _ = extractor._extract_base(
+            parse("SELECT AVG(latency_ms) FROM t"),
+            self._spec(params),
+        )
+        params["compression"] = 999
+        assert all(c.params == {"compression": 200} for c in components)
+        assert components[0].params is not components[1].params
+
+    def test_no_params_leaves_components_untouched(self):
+        """Omitting params applies no configuration to components."""
+        extractor = MetricComponentExtractor(1)
+        components, _ = extractor._extract_base(
+            parse("SELECT SUM(latency_ms) FROM t"),
+            self._spec(None),
+        )
+        assert [component.params for component in components] == [None]
+
+    def test_params_without_an_aggregating_component_is_rejected(self):
+        """
+        Declaring params on a non-aggregating metric is rejected.
+        """
+        extractor = MetricComponentExtractor(1)
+        with pytest.raises(DJInvalidInputException) as excinfo:
+            extractor._extract_base(
+                parse("SELECT COUNT(DISTINCT order_id) FROM t"),
+                self._spec({"compression": 200}),
+            )
+        assert "requires an aggregating component" in str(excinfo.value)
+
+
+# =============================================================================
+# Dialect-aware combiners
+# =============================================================================
+
+
+class _DialectAwareSum(AggDecomposition):
+    """
+    A SUM whose combiner renders differently per dialect, recording the call it
+    was handed. Tests the pipeline for dialect-specific extraction logic.
+    """
+
+    seen: list[tuple["ast.Function", Dialect]] = []
+
+    @property
+    def components(self) -> list[ComponentDef]:
+        return [ComponentDef(suffix="_sum", accumulate="SUM", merge="SUM")]
+
+    def combine(self, components, func, dialect=Dialect.SPARK):
+        type(self).seen.append((func, dialect))
+        
+        combiners = {
+            Dialect.DRUID: self._combine_druid,
+            Dialect.SPARK: self._combine_spark,
+        }
+        return combiners.get(dialect, self._combine_spark)(components)
+        
+    def _combine_druid(self, components):
+        return make_func("druid_combiner", components[0].name)
+        
+    def _combine_spark(self, components):
+        return make_func("spark_combiner", components[0].name)
+
+
+@pytest.fixture
+def dialect_aware_sum():
+    """Swap SUM's decomposition for one that branches on dialect, then restore."""
+    original = DECOMPOSITION_REGISTRY.get(dj_functions.Sum)
+    _DialectAwareSum.seen = []
+    DECOMPOSITION_REGISTRY[dj_functions.Sum] = _DialectAwareSum
+    try:
+        yield _DialectAwareSum
+    finally:
+        DECOMPOSITION_REGISTRY[dj_functions.Sum] = original
+
+
+def test_combine_renders_per_dialect():
+    """A decomposition can key its combiner off the target dialect."""
+    components = [
+        MetricComponent(
+            name="price_sum_abc",
+            expression="price",
+            aggregation="SUM",
+            merge="SUM",
+            rule=AggregationRule(),
+        ),
+    ]
+    func = parse("SELECT SUM(price) FROM parent_node").select.projection[0]
+    decomposition = _DialectAwareSum()
+
+    spark = decomposition.combine(components, func, Dialect.SPARK)
+    druid = decomposition.combine(components, func, Dialect.DRUID)
+
+    assert "spark_combiner" in str(spark)
+    assert "druid_combiner" in str(druid)
+    assert str(spark) != str(druid)
+
+
+def test_combine_defaults_to_spark():
+    """
+    Omitting the dialect yields the Spark rendering.
+    """
+    components = [
+        MetricComponent(
+            name="price_sum_abc",
+            expression="price",
+            aggregation="SUM",
+            merge="SUM",
+            rule=AggregationRule(),
+        ),
+    ]
+    func = parse("SELECT SUM(price) FROM parent_node").select.projection[0]
+
+    assert "spark_combiner" in str(_DialectAwareSum().combine(components, func))
+
+
+def test_combine_receives_the_originating_call():
+    """
+    The combiner is handed the authored ``ast.Function``, arguments included.
+    """
+    components = [
+        MetricComponent(
+            name="latency_p50",
+            expression="latency_ms",
+            aggregation="SUM",
+            merge="SUM",
+            rule=AggregationRule(),
+        ),
+    ]
+    func = parse(
+        "SELECT APPROX_PERCENTILE(latency_ms, 0.5) FROM parent_node",
+    ).select.projection[0]
+
+    decomposition = _DialectAwareSum()
+    decomposition.seen = []
+    decomposition.combine(components, func, Dialect.DRUID)
+
+    seen_func, seen_dialect = _DialectAwareSum.seen[-1]
+    assert seen_dialect == Dialect.DRUID
+    assert str(seen_func.args[1]) == "0.5"
+
+
+def test_extractor_defaults_to_spark_dialect():
+    """An extractor built without a dialect renders for Spark."""
+    assert MetricComponentExtractor(1)._dialect == Dialect.SPARK
+
+
+@pytest.mark.asyncio
+async def test_extractor_threads_dialect_into_combiner(
+    session: AsyncSession,
+    create_metric,
+    dialect_aware_sum,
+):
+    """
+    The dialect handed to the extractor reaches the combiner.
+    """
+    metric_rev = await create_metric("SELECT SUM(price) FROM parent_node")
+
+    _, spark_sql = await MetricComponentExtractor(metric_rev.id).extract(session)
+    _, druid_sql = await MetricComponentExtractor(
+        metric_rev.id,
+        dialect=Dialect.DRUID,
+    ).extract(session)
+
+    assert "spark_combiner" in str(spark_sql)
+    assert "druid_combiner" in str(druid_sql)
+
+
+@pytest.mark.asyncio
+async def test_extractor_dialect_does_not_leak_between_instances(
+    session: AsyncSession,
+    create_metric,
+    dialect_aware_sum,
+):
+    """
+    Dialect is per-extractor configuration, not shared state.
+    """
+    metric_rev = await create_metric("SELECT SUM(price) FROM parent_node")
+
+    await MetricComponentExtractor(metric_rev.id, dialect=Dialect.DRUID).extract(
+        session,
+    )
+    _, after = await MetricComponentExtractor(metric_rev.id).extract(session)
+
+    assert "spark_combiner" in str(after)
+    assert "druid_combiner" not in str(after)
+
+
+class _SerializingSum(AggDecomposition):
+    """A SUM that declares a Druid-only serialize conversion."""
+
+    @property
+    def components(self) -> list[ComponentDef]:
+        return [
+            ComponentDef(
+                suffix="_sum",
+                accumulate="SUM",
+                merge="SUM",
+                serialize="to_druid_bytes({})",
+                serialize_targets=(MaterializationTarget.DRUID,),
+            ),
+        ]
+
+    def combine(self, components, func, dialect=Dialect.SPARK):
+        return make_func("SUM", components[0].name)
+
+
+@pytest.fixture
+def serializing_sum():
+    """Swap SUM's decomposition for one declaring a serialize, then restore."""
+    original = DECOMPOSITION_REGISTRY.get(dj_functions.Sum)
+    DECOMPOSITION_REGISTRY[dj_functions.Sum] = _SerializingSum
+    try:
+        yield
+    finally:
+        DECOMPOSITION_REGISTRY[dj_functions.Sum] = original
+
+
+@pytest.mark.asyncio
+async def test_serialize_propagates_from_component_def(
+    session: AsyncSession,
+    create_metric,
+    serializing_sum,
+):
+    """
+    A ``serialize`` declared on ``ComponentDef`` reaches the extracted component.
+    """
+    metric_rev = await create_metric("SELECT SUM(price) FROM parent_node")
+
+    components, _ = await MetricComponentExtractor(metric_rev.id).extract(session)
+
+    assert len(components) == 1
+    assert components[0].serialize == "to_druid_bytes({})"
+    assert components[0].serialize_targets == [MaterializationTarget.DRUID]
+
+
+@pytest.mark.asyncio
+async def test_components_without_serialize_declare_none(
+    session: AsyncSession,
+    create_metric,
+):
+    """An ordinary decomposition leaves the conversion unset."""
+    metric_rev = await create_metric("SELECT SUM(price) FROM parent_node")
+
+    components, _ = await MetricComponentExtractor(metric_rev.id).extract(session)
+
+    assert components[0].serialize is None
+    assert components[0].serialize_targets == []
+
+
+# =============================================================================
+# Family-gated decompositions
+# =============================================================================
+
+
+@pytest.fixture
+def registered_family():
+    """
+    Register a decomposition for a reaggregation family, then remove it.
+
+    OSS ships no family implementations -- they live downstream with the
+    engine-specific functions -- so a test has to supply one to exercise the
+    selection path at all.
+    """
+
+    @decomposes_family(ReaggregationFunction.TDIGEST)
+    class _FamilyDecomposition(AggDecomposition):
+        @property
+        def components(self) -> list[ComponentDef]:
+            return [
+                ComponentDef(
+                    suffix="_sketch",
+                    accumulate=f"build_sketch({{}}, {self.params.get('compression', 1)})",
+                    merge="merge_sketch",
+                ),
+            ]
+
+        def combine(self, components, func, dialect=Dialect.SPARK):
+            return make_func("read_sketch", components[0].name)
+
+    try:
+        yield _FamilyDecomposition
+    finally:
+        FAMILY_DECOMPOSITION_REGISTRY.pop(ReaggregationFunction.TDIGEST, None)
+
+
+def test_declared_family_overrides_the_function_registry(registered_family):
+    """
+    A metric declaring a family gets that family's decomposition.
+
+    This is what lets `APPROX_PERCENTILE(x, 0.95)` keep its spelling and still
+    decompose: the aggregation function is unchanged, the family does the work.
+    """
+    spec = ReaggregateSpec(fn=ReaggregationFunction.TDIGEST)
+
+    decomposition = get_decomposition(dj_functions.ApproxPercentile, spec)
+
+    assert isinstance(decomposition, registered_family)
+
+
+def test_family_receives_its_tuning_params(registered_family):
+    """
+    `reaggregate.params` reaches the decomposition, and thus the accumulate.
+    """
+    spec = ReaggregateSpec(
+        fn=ReaggregationFunction.TDIGEST,
+        params={"compression": 500},
+    )
+
+    decomposition = get_decomposition(dj_functions.ApproxPercentile, spec)
+
+    assert decomposition.params == {"compression": 500}
+    assert decomposition.components[0].accumulate == "build_sketch({}, 500)"
+
+
+def test_undeclared_metric_is_untouched_by_a_registered_family(registered_family):
+    """
+    A metric without `reaggregate` keeps the by-function decomposition.
+    """
+    assert get_decomposition(dj_functions.ApproxPercentile) is None
+    assert isinstance(
+        get_decomposition(dj_functions.Sum),
+        type(get_decomposition(dj_functions.Sum)),
+    )
+    assert get_decomposition(dj_functions.Sum) is not None
+
+
+def test_unregistered_family_falls_through():
+    """
+    Declaring a family nothing implements degrades rather than raising.
+    """
+    spec = ReaggregateSpec(fn=ReaggregationFunction.TDIGEST)
+
+    assert get_decomposition(dj_functions.ApproxPercentile, spec) is None
+    assert get_decomposition(dj_functions.Sum, spec) is not None
+
+
+@pytest.mark.asyncio
+async def test_family_gated_percentile_decomposes_through_the_extractor(
+    session: AsyncSession,
+    create_metric,
+    registered_family,
+):
+    """
+    A family-gated metric survives the decomposability gate.
+
+    The regression this guards: `_extract_base` aborts early if any aggregation
+    is non-decomposable, and that check used to ask `get_decomposition(fn)`
+    without the declared family. A family-gated metric is precisely one whose
+    aggregation function has no entry of its own, so every one of them aborted
+    there and fell through to native grain -- the family registry was correct
+    and simply never reached.
+
+    Testing `get_decomposition` directly cannot catch this, and neither can a
+    family test over `SUM`, which passes the gate on its own merits. It needs a
+    non-decomposable function driven through the extractor.
+    """
+    metric_rev = await create_metric(
+        "SELECT APPROX_PERCENTILE(price, 0.95) FROM parent_node",
+        reaggregate={"fn": "tdigest", "params": {"compression": 200}},
+    )
+
+    components, combiner = await MetricComponentExtractor(metric_rev.id).extract(
+        session,
+    )
+
+    assert components, "family-gated percentile produced no components"
+    assert components[0].merge == "merge_sketch"
+    assert "build_sketch" in components[0].aggregation
+    assert "read_sketch" in str(combiner)
+
+
+@pytest.mark.asyncio
+async def test_percentile_without_a_family_still_does_not_decompose(
+    session: AsyncSession,
+    create_metric,
+    registered_family,
+):
+    """
+    The gate still stops metrics that did not opt in.
+
+    The fix must not make every percentile decomposable -- only the ones that
+    declared the family. A registered family that nobody references changes
+    nothing.
+    """
+    metric_rev = await create_metric(
+        "SELECT APPROX_PERCENTILE(price, 0.95) FROM parent_node",
+    )
+
+    components, _ = await MetricComponentExtractor(metric_rev.id).extract(session)
+
+    assert components == []
diff --git a/datajunction-server/tests/sql/functions_test.py b/datajunction-server/tests/sql/functions_test.py
index 10a1290df..964d2815b 100644
--- a/datajunction-server/tests/sql/functions_test.py
+++ b/datajunction-server/tests/sql/functions_test.py
@@ -288,6 +288,33 @@ async def test_approx_percentile(session: AsyncSession):
     assert not exc.errors
     assert query_with_list.select.projection[0].type == ct.FloatType()  # type: ignore
 
+    # The two-argument form -- `accuracy` is optional in Spark, and this is the
+    # spelling metric authors actually use.
+    query = parse("SELECT approx_percentile(10.0, 0.5)")
+    exc = DJException()
+    ctx = ast.CompileContext(session=session, exception=exc)
+    await query.compile(ctx)
+    assert not exc.errors
+    assert query.select.projection[0].type == ct.FloatType()  # type: ignore
+
+    query = parse("SELECT approx_percentile(10.0, array(0.5, 0.9))")
+    exc = DJException()
+    ctx = ast.CompileContext(session=session, exception=exc)
+    await query.compile(ctx)
+    assert not exc.errors
+    assert query.select.projection[0].type == ct.ListType(  # type: ignore
+        element_type=ct.FloatType(),
+    )
+
+    # A double-typed percentage dispatches too: DoubleType and FloatType are
+    # siblings under FloatingBase, so a FloatType-only registration missed it.
+    query = parse("SELECT approx_percentile(10.0, CAST(0.5 AS DOUBLE))")
+    exc = DJException()
+    ctx = ast.CompileContext(session=session, exception=exc)
+    await query.compile(ctx)
+    assert not exc.errors
+    assert query.select.projection[0].type == ct.FloatType()  # type: ignore
+
 
 @pytest.mark.asyncio
 async def test_array(session: AsyncSession):
diff --git a/datajunction-ui/src/app/pages/AddEditNodePage/ReaggregateFields.jsx b/datajunction-ui/src/app/pages/AddEditNodePage/ReaggregateFields.jsx
new file mode 100644
index 000000000..d163361b9
--- /dev/null
+++ b/datajunction-ui/src/app/pages/AddEditNodePage/ReaggregateFields.jsx
@@ -0,0 +1,79 @@
+/**
+ * Semi-additive metric controls.
+ */
+import { ErrorMessage, Field, useField, useFormikContext } from 'formik';
+import { useContext, useEffect, useMemo, useState } from 'react';
+import DJClientContext from '../../providers/djclient';
+import { labelize } from '../../../utils/form';
+import { FormikSelect } from './FormikSelect';
+
+const SEMI_ADDITIVE_FUNCTIONS = ['last_value', 'first_value', 'min', 'max'];
+
+export const ReaggregateFields = () => {
+  const djClient = useContext(DJClientContext).DataJunctionAPI;
+  const { values } = useFormikContext();
+  const [dimensionField] = useField('reaggregate_dimension');
+  const [dimensionOptions, setDimensionOptions] = useState([]);
+
+  useEffect(() => {
+    const fetchData = async () => {
+      if (values.upstream_node) {
+        const data = await djClient.node(values.upstream_node);
+        setDimensionOptions(
+          data.columns.map(col => ({
+            value: col.name,
+            label: col.name,
+          })),
+        );
+      } else {
+        setDimensionOptions([]);
+      }
+    };
+    fetchData().catch(console.error);
+  }, [djClient, values.upstream_node]);
+
+  const selectOptions = useMemo(() => {
+    if (
+      !dimensionField.value ||
+      dimensionOptions.some(option => option.value === dimensionField.value)
+    ) {
+      return dimensionOptions;
+    }
+    return [
+      { value: dimensionField.value, label: dimensionField.value },
+      ...dimensionOptions,
+    ];
+  }, [dimensionField.value, dimensionOptions]);
+
+  return (
+    <div className="ReaggregateFields node-row">
+      <div className="NodeCreationInput">
+        <ErrorMessage name="reaggregate_dimension" component="span" />
+        <label htmlFor="reaggregate_dimension">Semi-Additive Dimension</label>
+        <span data-testid="select-semi-additive-dimension">
+          <FormikSelect
+            className=""
+            classNamePrefix="ReaggregateDimension"
+            selectOptions={selectOptions}
+            formikFieldName="reaggregate_dimension"
+            placeholder="Choose Semi-Additive Dimension"
+            isMulti={false}
+            isClearable={true}
+          />
+        </span>
+      </div>
+      <div className="NodeCreationInput NodeModeInput">
+        <ErrorMessage name="reaggregate_function" component="span" />
+        <label htmlFor="ReaggregateFunction">Semi-Additive Type</label>
+        <Field as="select" name="reaggregate_function" id="ReaggregateFunction">
+          <option value=""></option>
+          {SEMI_ADDITIVE_FUNCTIONS.map(func => (
+            <option value={func} key={func}>
+              {labelize(func)}
+            </option>
+          ))}
+        </Field>
+      </div>
+    </div>
+  );
+};
diff --git a/datajunction-ui/src/app/pages/AddEditNodePage/__tests__/AddEditNodePageFormSuccess.test.jsx b/datajunction-ui/src/app/pages/AddEditNodePage/__tests__/AddEditNodePageFormSuccess.test.jsx
index f399c4b8d..a7bbdd578 100644
--- a/datajunction-ui/src/app/pages/AddEditNodePage/__tests__/AddEditNodePageFormSuccess.test.jsx
+++ b/datajunction-ui/src/app/pages/AddEditNodePage/__tests__/AddEditNodePageFormSuccess.test.jsx
@@ -156,6 +156,99 @@ describe('AddEditNodePage submission succeeded', () => {
     );
   }, 60000);
 
+  it('for creating a semi-additive metric node', async () => {
+    const mockDjClient = initializeMockDJClient();
+    mockDjClient.DataJunctionAPI.createNode.mockReturnValue({
+      status: 200,
+      json: { name: 'default.some_test_metric' },
+    });
+
+    mockDjClient.DataJunctionAPI.tagsNode.mockReturnValue({
+      status: 200,
+      json: { message: 'Success' },
+    });
+
+    mockDjClient.DataJunctionAPI.node.mockResolvedValue({
+      columns: [{ name: 'v3.date.date_id[order]' }],
+    });
+
+    mockDjClient.DataJunctionAPI.listTags.mockReturnValue([
+      { name: 'purpose', display_name: 'Purpose' },
+      { name: 'intent', display_name: 'Intent' },
+    ]);
+
+    mockDjClient.DataJunctionAPI.listMetricMetadata.mockReturnValue(
+      mocks.metricMetadata,
+    );
+
+    mockDjClient.DataJunctionAPI.whoami.mockReturnValue({
+      id: 123,
+      username: 'test_user',
+    });
+
+    const element = testElement(mockDjClient);
+    const { getByTestId } = renderCreateMetric(element);
+
+    await userEvent.type(
+      screen.getByLabelText('Display Name *'),
+      'Some Test Metric',
+    );
+    const selectUpstream = screen.getByTestId('select-upstream-node');
+    fireEvent.keyDown(selectUpstream.firstChild, { key: 'ArrowDown' });
+    fireEvent.click(screen.getByText('default.repair_orders'));
+
+    await waitFor(() => {
+      expect(mockDjClient.DataJunctionAPI.node).toHaveBeenCalledWith(
+        'default.repair_orders',
+      );
+    });
+
+    const selectReaggregateDimension = getByTestId(
+      'select-semi-additive-dimension',
+    );
+    fireEvent.keyDown(selectReaggregateDimension.firstChild, {
+      key: 'ArrowDown',
+    });
+    fireEvent.click(await screen.findByText('v3.date.date_id[order]'));
+    fireEvent.change(screen.getByLabelText('Semi-Additive Type'), {
+      target: { value: 'last_value' },
+    });
+
+    await userEvent.type(
+      screen.getByLabelText('Aggregate Expression *'),
+      'SUM(balance)',
+    );
+    await userEvent.click(screen.getByText('Create metric'));
+
+    await waitFor(
+      () => {
+        expect(mockDjClient.DataJunctionAPI.createNode).toBeCalledWith(
+          'metric',
+          'default.some_test_metric',
+          'Some Test Metric',
+          '',
+          'SELECT SUM(balance) \n FROM default.repair_orders',
+          'published',
+          'default',
+          null,
+          undefined,
+          undefined,
+          undefined,
+          null,
+          {
+            rules: [
+              {
+                dimension: 'v3.date.date_id[order]',
+                fn: 'last_value',
+              },
+            ],
+          },
+        );
+      },
+      { timeout: 10000 },
+    );
+  }, 60000);
+
   it('for editing a transform or dimension node', async () => {
     const mockDjClient = initializeMockDJClient();
 
@@ -288,4 +381,159 @@ describe('AddEditNodePage submission succeeded', () => {
       ).toBeInTheDocument();
     });
   }, 1000000);
+
+  it('for editing a semi-additive metric node', async () => {
+    const mockDjClient = initializeMockDJClient();
+
+    mockDjClient.DataJunctionAPI.getNodeForEditing.mockReturnValue({
+      ...mocks.mockGetMetricNode,
+      current: {
+        ...mocks.mockGetMetricNode.current,
+        reaggregate: {
+          rules: [
+            {
+              dimension: 'v3.date.date_id[order]',
+              fn: 'LAST_VALUE',
+            },
+          ],
+        },
+      },
+    });
+    mockDjClient.DataJunctionAPI.patchNode = vi.fn();
+    mockDjClient.DataJunctionAPI.patchNode.mockReturnValue({
+      status: 201,
+      json: { name: 'default.num_repair_orders', type: 'metric' },
+    });
+
+    mockDjClient.DataJunctionAPI.tagsNode.mockReturnValue({
+      status: 200,
+      json: { message: 'Success' },
+    });
+
+    mockDjClient.DataJunctionAPI.listTags.mockReturnValue([
+      { name: 'purpose', display_name: 'Purpose' },
+      { name: 'intent', display_name: 'Intent' },
+    ]);
+
+    mockDjClient.DataJunctionAPI.whoami.mockReturnValue({
+      id: 123,
+      username: 'test_user',
+    });
+
+    const element = testElement(mockDjClient);
+    renderEditNode(element);
+
+    await waitFor(() => {
+      expect(screen.getByLabelText('Semi-Additive Type')).toHaveValue(
+        'last_value',
+      );
+      expect(screen.getByText('v3.date.date_id[order]')).toBeInTheDocument();
+    });
+
+    await userEvent.type(screen.getByLabelText('Description'), '!!!');
+    await userEvent.click(screen.getByText('Save'));
+
+    await waitFor(() => {
+      expect(mockDjClient.DataJunctionAPI.patchNode).toBeCalledWith(
+        'default.num_repair_orders',
+        'Default: Num Repair Orders',
+        'Number of repair orders!!!',
+        'SELECT count(repair_order_id) \n FROM default.repair_orders',
+        'published',
+        ['repair_order_id', 'country'],
+        'neutral',
+        'unitless',
+        5,
+        [],
+        ['dj'],
+        { key1: 'value1', key2: 'value2' },
+        {
+          rules: [
+            {
+              dimension: 'v3.date.date_id[order]',
+              fn: 'last_value',
+            },
+          ],
+        },
+      );
+    });
+  }, 1000000);
+
+  it('for clearing semi-additive fields from a metric node', async () => {
+    const mockDjClient = initializeMockDJClient();
+
+    mockDjClient.DataJunctionAPI.getNodeForEditing.mockReturnValue({
+      ...mocks.mockGetMetricNode,
+      current: {
+        ...mocks.mockGetMetricNode.current,
+        reaggregate: {
+          rules: [
+            {
+              dimension: 'v3.date.date_id[order]',
+              fn: 'LAST_VALUE',
+            },
+          ],
+        },
+      },
+    });
+    mockDjClient.DataJunctionAPI.patchNode = vi.fn();
+    mockDjClient.DataJunctionAPI.patchNode.mockReturnValue({
+      status: 201,
+      json: { name: 'default.num_repair_orders', type: 'metric' },
+    });
+
+    mockDjClient.DataJunctionAPI.tagsNode.mockReturnValue({
+      status: 200,
+      json: { message: 'Success' },
+    });
+
+    mockDjClient.DataJunctionAPI.listTags.mockReturnValue([
+      { name: 'purpose', display_name: 'Purpose' },
+      { name: 'intent', display_name: 'Intent' },
+    ]);
+
+    mockDjClient.DataJunctionAPI.whoami.mockReturnValue({
+      id: 123,
+      username: 'test_user',
+    });
+
+    const element = testElement(mockDjClient);
+    renderEditNode(element);
+
+    await waitFor(() => {
+      expect(screen.getByLabelText('Semi-Additive Type')).toHaveValue(
+        'last_value',
+      );
+      expect(screen.getByText('v3.date.date_id[order]')).toBeInTheDocument();
+    });
+
+    const clearReaggregateDimension = screen
+      .getByTestId('select-semi-additive-dimension')
+      .querySelector('.ReaggregateDimension__clear-indicator');
+    expect(clearReaggregateDimension).toBeInTheDocument();
+    fireEvent.mouseDown(clearReaggregateDimension, { button: 0 });
+    fireEvent.change(screen.getByLabelText('Semi-Additive Type'), {
+      target: { value: '' },
+    });
+
+    await userEvent.click(screen.getByText('Save'));
+
+    await waitFor(() => {
+      expect(mockDjClient.DataJunctionAPI.patchNode).toBeCalledWith(
+        'default.num_repair_orders',
+        'Default: Num Repair Orders',
+        'Number of repair orders',
+        'SELECT count(repair_order_id) \n FROM default.repair_orders',
+        'published',
+        ['repair_order_id', 'country'],
+        'neutral',
+        'unitless',
+        5,
+        [],
+        ['dj'],
+        { key1: 'value1', key2: 'value2' },
+        null,
+      );
+    });
+  }, 1000000);
 });
diff --git a/datajunction-ui/src/app/pages/AddEditNodePage/index.jsx b/datajunction-ui/src/app/pages/AddEditNodePage/index.jsx
index 2dc0acb22..fcb0f3088 100644
--- a/datajunction-ui/src/app/pages/AddEditNodePage/index.jsx
+++ b/datajunction-ui/src/app/pages/AddEditNodePage/index.jsx
@@ -27,6 +27,7 @@ import { RequiredDimensionsSelect } from './RequiredDimensionsSelect';
 import LoadingIcon from '../../icons/LoadingIcon';
 import { ColumnsSelect } from './ColumnsSelect';
 import { CustomMetadataField } from './CustomMetadataField';
+import { ReaggregateFields } from './ReaggregateFields';
 
 class Action {
   static Add = new Action('add');
@@ -71,6 +72,9 @@ export function AddEditNodePage({ extensions = {} }) {
     mode: 'published',
     owners: [],
     custom_metadata: '',
+    reaggregate_dimension: '',
+    reaggregate_function: '',
+    had_reaggregate: false,
   };
 
   const validator = values => {
@@ -92,6 +96,14 @@ export function AddEditNodePage({ extensions = {} }) {
     if (values.type !== 'metric' && !values.query) {
       errors.query = 'Required';
     }
+    if (values.type === 'metric') {
+      if (values.reaggregate_dimension && !values.reaggregate_function) {
+        errors.reaggregate_function = 'Required';
+      }
+      if (values.reaggregate_function && !values.reaggregate_dimension) {
+        errors.reaggregate_dimension = 'Required';
+      }
+    }
     return errors;
   };
 
@@ -161,8 +173,28 @@ export function AddEditNodePage({ extensions = {} }) {
     return `SELECT ${aggregateExpression}`;
   };
 
+  const buildReaggregateSpec = values => {
+    if (values.reaggregate_dimension && values.reaggregate_function) {
+      return {
+        rules: [
+          {
+            dimension: values.reaggregate_dimension,
+            fn: values.reaggregate_function,
+          },
+        ],
+      };
+    }
+    return values.had_reaggregate ? null : undefined;
+  };
+
+  const firstReaggregateRule = reaggregate => reaggregate?.rules?.[0];
+
+  const normalizeReaggregateFunction = reaggregateFunction =>
+    reaggregateFunction ? reaggregateFunction.toLowerCase() : '';
+
   const createNode = async (values, setStatus) => {
-    const { status, json } = await djClient.createNode(
+    const reaggregate = buildReaggregateSpec(values);
+    const createNodeArgs = [
       nodeType,
       values.name,
       values.display_name,
@@ -180,7 +212,11 @@ export function AddEditNodePage({ extensions = {} }) {
         ? values.required_dimensions
         : undefined,
       parseCustomMetadata(values.custom_metadata),
-    );
+    ];
+    if (reaggregate !== undefined) {
+      createNodeArgs.push(reaggregate);
+    }
+    const { status, json } = await djClient.createNode(...createNodeArgs);
     if (status === 200 || status === 201) {
       if (values.tags) {
         await djClient.tagsNode(values.name, values.tags);
@@ -201,7 +237,8 @@ export function AddEditNodePage({ extensions = {} }) {
   };
 
   const patchNode = async (values, setStatus) => {
-    const { status, json } = await djClient.patchNode(
+    const reaggregate = buildReaggregateSpec(values);
+    const patchNodeArgs = [
       values.name,
       values.display_name,
       values.description,
@@ -219,7 +256,11 @@ export function AddEditNodePage({ extensions = {} }) {
         : undefined,
       values.owners,
       parseCustomMetadata(values.custom_metadata),
-    );
+    ];
+    if (reaggregate !== undefined) {
+      patchNodeArgs.push(reaggregate);
+    }
+    const { status, json } = await djClient.patchNode(...patchNodeArgs);
     const tagsResponse = await djClient.tagsNode(
       values.name,
       values.tags.map(tag => tag),
@@ -296,6 +337,12 @@ export function AddEditNodePage({ extensions = {} }) {
           required_dimensions: node.current.requiredDimensions.map(
             dim => dim.name,
           ),
+          reaggregate_dimension:
+            firstReaggregateRule(node.current.reaggregate)?.dimension || '',
+          reaggregate_function: normalizeReaggregateFunction(
+            firstReaggregateRule(node.current.reaggregate)?.fn,
+          ),
+          had_reaggregate: Boolean(node.current.reaggregate),
           upstream_node: '', // Derived metrics have no upstream node
           aggregate_expression: derivedExpression,
         };
@@ -310,6 +357,12 @@ export function AddEditNodePage({ extensions = {} }) {
           required_dimensions: node.current.requiredDimensions.map(
             dim => dim.name,
           ),
+          reaggregate_dimension:
+            firstReaggregateRule(node.current.reaggregate)?.dimension || '',
+          reaggregate_function: normalizeReaggregateFunction(
+            firstReaggregateRule(node.current.reaggregate)?.fn,
+          ),
+          had_reaggregate: Boolean(node.current.reaggregate),
           upstream_node: nonMetricParent?.name || '',
           aggregate_expression: node.current.metricMetadata?.expression,
         };
@@ -360,6 +413,9 @@ export function AddEditNodePage({ extensions = {} }) {
       'metric_direction',
       'significant_digits',
       'required_dimensions',
+      'reaggregate_dimension',
+      'reaggregate_function',
+      'had_reaggregate',
       'owners',
       'custom_metadata',
     ];
@@ -495,16 +551,21 @@ export function AddEditNodePage({ extensions = {} }) {
                   if (action === Action.Edit) {
                     const data = await getExistingNodeData(name);
                     runValidityChecks(data, setNode, setMessage);
-                    updateFieldsWithNodeData(
-                      data,
-                      setFieldValue,
-                      setNode,
-                      setSelectTags,
-                      setSelectPrimaryKey,
-                      setSelectUpstreamNode,
-                      setSelectRequiredDims,
-                      setSelectOwners,
-                    );
+                    if (
+                      data.message === undefined &&
+                      nodeCanBeEdited(data.type)
+                    ) {
+                      updateFieldsWithNodeData(
+                        data,
+                        setFieldValue,
+                        setNode,
+                        setSelectTags,
+                        setSelectPrimaryKey,
+                        setSelectUpstreamNode,
+                        setSelectRequiredDims,
+                        setSelectOwners,
+                      );
+                    }
                   }
                 };
                 fetchData().catch(console.error);
@@ -560,7 +621,10 @@ export function AddEditNodePage({ extensions = {} }) {
                           />
                         )}
                         {(nodeType === 'metric' || node.type === 'metric') && (
-                          <MetricMetadataFields />
+                          <>
+                            <MetricMetadataFields />
+                            <ReaggregateFields />
+                          </>
                         )}
                       </div>
 
diff --git a/datajunction-ui/src/app/pages/AddEditNodePage/styles.css b/datajunction-ui/src/app/pages/AddEditNodePage/styles.css
index f18307426..1eb0b846c 100644
--- a/datajunction-ui/src/app/pages/AddEditNodePage/styles.css
+++ b/datajunction-ui/src/app/pages/AddEditNodePage/styles.css
@@ -139,6 +139,16 @@
   margin: 0 !important;
 }
 
+.node-builder .ReaggregateFields {
+  gap: 12px;
+}
+
+.node-builder .ReaggregateFields > * {
+  flex: 1;
+  min-width: 0;
+  margin: 0 !important;
+}
+
 /* Override legacy form-scoped element styling for inputs/selects/buttons.
  * Scope to the specific wrapper classes so we DON'T accidentally restyle
  * the hidden <input> that lives inside react-select's Control — that input
diff --git a/datajunction-ui/src/app/pages/NodePage/NodeInfoTab.jsx b/datajunction-ui/src/app/pages/NodePage/NodeInfoTab.jsx
index cd18f4fb7..bf5667944 100644
--- a/datajunction-ui/src/app/pages/NodePage/NodeInfoTab.jsx
+++ b/datajunction-ui/src/app/pages/NodePage/NodeInfoTab.jsx
@@ -10,6 +10,23 @@ import { labelize } from '../../../utils/form';
 SyntaxHighlighter.registerLanguage('sql', sql);
 foundation.hljs['padding'] = '2rem';
 
+const dimensionNodePath = dimension => {
+  if (!dimension) {
+    return null;
+  }
+  const dimensionWithoutRole = dimension.replace(/\[[^\]]+\]$/, '');
+  const parts = dimensionWithoutRole.split('.');
+  if (parts.length <= 1) {
+    return null;
+  }
+  return parts.slice(0, -1).join('.');
+};
+
+const reaggregateFunctionLabel = func =>
+  func ? labelize(func.toLowerCase()) : null;
+
+const firstReaggregateRule = reaggregate => reaggregate?.rules?.[0];
+
 // interface MetricInfo {
 //   name: string;
 //   current: MetricRevision;
@@ -59,6 +76,7 @@ export default function NodeInfoTab({ node }) {
         expression: metric.current.metricMetadata?.expression,
         incompatible_druid_functions:
           metric.current.metricMetadata?.incompatibleDruidFunctions || [],
+        reaggregate: metric.current.reaggregate,
       });
     };
     if (node.type === 'metric') {
@@ -267,6 +285,39 @@ export default function NodeInfoTab({ node }) {
               {metricInfo?.metric_metadata?.significantDigits || 'None'}
             </p>
           </div>
+          <div style={{ marginRight: '2rem' }}>
+            <h6 className="mb-0 w-100">Semi-Additive</h6>
+            <p
+              className="mb-0 opacity-75"
+              role="dialog"
+              aria-hidden="false"
+              aria-label="Reaggregate"
+            >
+              {firstReaggregateRule(metricInfo?.reaggregate) ? (
+                <>
+                  {reaggregateFunctionLabel(
+                    firstReaggregateRule(metricInfo.reaggregate).fn,
+                  )}
+                  {' on '}
+                  {dimensionNodePath(
+                    firstReaggregateRule(metricInfo.reaggregate).dimension,
+                  ) ? (
+                    <a
+                      href={`/nodes/${dimensionNodePath(
+                        firstReaggregateRule(metricInfo.reaggregate).dimension,
+                      )}`}
+                    >
+                      {firstReaggregateRule(metricInfo.reaggregate).dimension}
+                    </a>
+                  ) : (
+                    firstReaggregateRule(metricInfo.reaggregate).dimension
+                  )}
+                </>
+              ) : (
+                'None'
+              )}
+            </p>
+          </div>
         </div>
       </div>
     ) : (
diff --git a/datajunction-ui/src/app/pages/NodePage/__tests__/NodePage.test.jsx b/datajunction-ui/src/app/pages/NodePage/__tests__/NodePage.test.jsx
index 7fe47c5ff..66e676b95 100644
--- a/datajunction-ui/src/app/pages/NodePage/__tests__/NodePage.test.jsx
+++ b/datajunction-ui/src/app/pages/NodePage/__tests__/NodePage.test.jsx
@@ -388,6 +388,10 @@ describe('<NodePage />', () => {
           screen.getByRole('dialog', { name: 'RequiredDimensions' }),
         ).toHaveTextContent('');
 
+        expect(
+          screen.getByRole('dialog', { name: 'Reaggregate' }),
+        ).toHaveTextContent('None');
+
         expect(
           screen.getByRole('dialog', { name: 'DisplayName' }),
         ).toHaveTextContent('Default: Num Repair Orders');
@@ -414,6 +418,50 @@ describe('<NodePage />', () => {
     expect(container.getElementsByClassName('language-sql')).toMatchSnapshot();
   }, 60000);
 
+  it('renders semi-additive metric information with a dimension link', async () => {
+    const djClient = mockDJClient();
+    djClient.DataJunctionAPI.node.mockReturnValue(mocks.mockMetricNode);
+    djClient.DataJunctionAPI.getMetric.mockResolvedValue({
+      ...mocks.mockMetricNodeJson,
+      current: {
+        ...mocks.mockMetricNodeJson.current,
+        reaggregate: {
+          rules: [
+            {
+              dimension: 'v3.date.date_id[order]',
+              fn: 'LAST_VALUE',
+            },
+          ],
+        },
+      },
+    });
+    const element = (
+      <DJClientContext.Provider value={djClient}>
+        <NodePage {...defaultProps} />
+      </DJClientContext.Provider>
+    );
+    render(
+      <MemoryRouter initialEntries={['/nodes/default.num_repair_orders/info']}>
+        <Routes>
+          <Route path="nodes/:name/:tab" element={element} />
+        </Routes>
+      </MemoryRouter>,
+    );
+
+    const reaggregate = await screen.findByRole('dialog', {
+      name: 'Reaggregate',
+    });
+    await waitFor(() => {
+      expect(reaggregate).toHaveTextContent(
+        'Last Value on v3.date.date_id[order]',
+      );
+    });
+
+    expect(
+      screen.getByRole('link', { name: 'v3.date.date_id[order]' }),
+    ).toHaveAttribute('href', '/nodes/v3.date');
+  }, 60000);
+
   it('hides Edit and shows the read-only badge for a node in a read-only (flat git) namespace', async () => {
     const djClient = mockDJClient();
     djClient.DataJunctionAPI.node.mockReturnValue(mocks.mockMetricNode);
diff --git a/datajunction-ui/src/app/services/DJService.js b/datajunction-ui/src/app/services/DJService.js
index 86e0cc1d0..ed3ac4399 100644
--- a/datajunction-ui/src/app/services/DJService.js
+++ b/datajunction-ui/src/app/services/DJService.js
@@ -520,6 +520,14 @@ export const DataJunctionAPI = {
             requiredDimensions {
               name
             }
+            reaggregate {
+              fn
+              weight
+              rules {
+                dimension
+                fn
+              }
+            }
             mode
             customMetadata
           }
@@ -608,6 +616,14 @@ export const DataJunctionAPI = {
             requiredDimensions {
               name
             }
+            reaggregate {
+              fn
+              weight
+              rules {
+                dimension
+                fn
+              }
+            }
           }
         }
       }
@@ -747,6 +763,7 @@ export const DataJunctionAPI = {
     metric_unit,
     required_dimensions,
     custom_metadata,
+    reaggregate,
   ) {
     const metricMetadata =
       metric_direction || metric_unit
@@ -767,6 +784,7 @@ export const DataJunctionAPI = {
       metric_metadata: metricMetadata,
       required_dimensions: required_dimensions,
       custom_metadata: custom_metadata,
+      reaggregate: reaggregate,
     };
     // Remove undefined fields to avoid sending them to the API
     Object.keys(requestBody).forEach(
@@ -797,6 +815,7 @@ export const DataJunctionAPI = {
     required_dimensions,
     owners,
     custom_metadata,
+    reaggregate,
   ) {
     try {
       const metricMetadata =
@@ -818,6 +837,7 @@ export const DataJunctionAPI = {
         required_dimensions: required_dimensions,
         owners: owners,
         custom_metadata: custom_metadata,
+        reaggregate: reaggregate,
       };
       // Remove undefined fields to avoid sending them to the API
       Object.keys(requestBody).forEach(
diff --git a/datajunction-ui/src/app/services/__tests__/DJService.test.jsx b/datajunction-ui/src/app/services/__tests__/DJService.test.jsx
index 33bf11af1..5d022bbf5 100644
--- a/datajunction-ui/src/app/services/__tests__/DJService.test.jsx
+++ b/datajunction-ui/src/app/services/__tests__/DJService.test.jsx
@@ -182,6 +182,59 @@ describe('DataJunctionAPI', () => {
     });
   });
 
+  it('calls createNode with semi-additive metadata correctly', async () => {
+    fetch.mockResponseOnce(JSON.stringify({}));
+    await DataJunctionAPI.createNode(
+      'metric',
+      'default.daily_balance',
+      'Daily Balance',
+      'Daily balance',
+      'SELECT sum(balance) FROM default.accounts',
+      'published',
+      'default',
+      null,
+      undefined,
+      undefined,
+      undefined,
+      null,
+      {
+        rules: [
+          {
+            dimension: 'v3.date.date_id[order]',
+            fn: 'last_value',
+          },
+        ],
+      },
+    );
+    expect(fetch).toHaveBeenCalledWith(`${DJ_URL}/nodes/metric`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        name: 'default.daily_balance',
+        display_name: 'Daily Balance',
+        description: 'Daily balance',
+        query: 'SELECT sum(balance) FROM default.accounts',
+        mode: 'published',
+        namespace: 'default',
+        primary_key: null,
+        metric_metadata: null,
+        required_dimensions: undefined,
+        custom_metadata: null,
+        reaggregate: {
+          rules: [
+            {
+              dimension: 'v3.date.date_id[order]',
+              fn: 'last_value',
+            },
+          ],
+        },
+      }),
+      credentials: 'include',
+    });
+  });
+
   it('calls patchNode correctly', async () => {
     const sampleArgs = [
       'name',
@@ -233,6 +286,65 @@ describe('DataJunctionAPI', () => {
     });
   });
 
+  it('calls patchNode with semi-additive metadata correctly', async () => {
+    fetch.mockResponseOnce(JSON.stringify({}));
+    await DataJunctionAPI.patchNode(
+      'default.daily_balance',
+      'Daily Balance',
+      'Daily balance',
+      'SELECT sum(balance) FROM default.accounts',
+      'published',
+      null,
+      'neutral',
+      'unitless',
+      5,
+      [],
+      ['dj'],
+      null,
+      {
+        rules: [
+          {
+            dimension: 'v3.date.date_id[order]',
+            fn: 'last_value',
+          },
+        ],
+      },
+    );
+    expect(fetch).toHaveBeenCalledWith(
+      `${DJ_URL}/nodes/default.daily_balance`,
+      {
+        method: 'PATCH',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({
+          display_name: 'Daily Balance',
+          description: 'Daily balance',
+          query: 'SELECT sum(balance) FROM default.accounts',
+          mode: 'published',
+          primary_key: null,
+          metric_metadata: {
+            direction: 'neutral',
+            unit: 'unitless',
+            significant_digits: 5,
+          },
+          required_dimensions: [],
+          owners: ['dj'],
+          custom_metadata: null,
+          reaggregate: {
+            rules: [
+              {
+                dimension: 'v3.date.date_id[order]',
+                fn: 'last_value',
+              },
+            ],
+          },
+        }),
+        credentials: 'include',
+      },
+    );
+  });
+
   it('calls createCube correctly', async () => {
     const sampleArgs = [
       'default.node_name',
@@ -1265,6 +1377,7 @@ describe('DataJunctionAPI', () => {
       }),
     );
     await DataJunctionAPI.getMetric('default.num_repair_orders');
+    const requestBody = JSON.parse(fetch.mock.calls[0][1].body);
     expect(fetch).toHaveBeenCalledWith(
       `${DJ_URL}/graphql`,
       expect.objectContaining({
@@ -1275,6 +1388,13 @@ describe('DataJunctionAPI', () => {
         },
       }),
     );
+    expect(requestBody.variables).toEqual({
+      name: 'default.num_repair_orders',
+    });
+    expect(requestBody.query).toContain('reaggregate');
+    expect(requestBody.query).toContain('dimension');
+    expect(requestBody.query).toContain('rules');
+    expect(requestBody.query).toContain('fn');
   });
 
   it('calls notebookExportCube correctly', async () => {
@@ -1892,7 +2012,13 @@ describe('DataJunctionAPI', () => {
     );
 
     const result = await DataJunctionAPI.getNodeForEditing('default.node1');
+    const requestBody = JSON.parse(fetch.mock.calls[0][1].body);
     expect(result).toHaveProperty('name', 'default.node1');
+    expect(requestBody.variables).toEqual({ name: 'default.node1' });
+    expect(requestBody.query).toContain('reaggregate');
+    expect(requestBody.query).toContain('dimension');
+    expect(requestBody.query).toContain('rules');
+    expect(requestBody.query).toContain('fn');
   });
 
   it('returns null when getNodeForEditing finds no nodes', async () => {
diff --git a/docker-compose.yml b/docker-compose.yml
index 82020adac..845bf7a2f 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -106,7 +106,13 @@ services:
     stdin_open: true
     volumes:
       - ./datajunction-ui:/usr/src/app/
-      - ./datajunction-ui/node_modules:/usr/src/app/node_modules
+      # Keep platform-specific packages inside Docker. `nocopy` prevents the
+      # host's macOS node_modules from seeding this Linux volume.
+      - type: volume
+        source: djui_node_modules
+        target: /usr/src/app/node_modules
+        volume:
+          nocopy: true
     environment:
       - NODE_OPTIONS=--max-old-space-size=4096
       - WATCHPACK_POLLING=true
@@ -327,3 +333,4 @@ services:
 
 volumes:
   postgres_metadata:
+  djui_node_modules: