SQL queries over raw FlatBuffer storage — A streaming query engine that keeps data in native FlatBuffer format while providing SQL access via SQLite virtual tables.
Try FlatSQL in your browser: https://digitalarsenal.github.io/flatsql/
npm install flatsqlflatsql 3.x ships the multithreaded partition store as one
wasm32-wasip1-threads artifact, flatsql/ps-threads.wasm: one writer per
(producer, SDS type) partition on a pinned pool of writer threads, durable
acks, and reader lanes that read committed snapshots and never wait on writers.
Hosts instantiate it over a shared memory and provide WASI preview1,
wasi.thread-spawn and the seven flatsql_io_* imports; its sha256 is in
flatsql/integrity.json.
import { loadFlatSQLPsThreads, getFlatSQLPsThreadsURL } from 'flatsql';
import { createPsNodeInstance } from 'flatsql/ps/node-host'; // Node; needs space-data-module-sdk >= 0.8.24
const bytes = await loadFlatSQLPsThreads(); // checked against integrity.json
const writer = await createPsNodeInstance(bytes, { root: './store-root' });See docs/PARTITION-STORE-WASM.md (artifact,
Node host, acceptance) and docs/PARTITION-STORE.md
(the engine). 3.0.0 removed the TypeScript engine (FlatSQLDatabase from
src/, DirectAccessor, FlatcAccessor), the node:sqlite artifact builder,
flatsql/standalone, flatsql/artifacts/standalone and the sql.js and
flatbuffers dependencies; the package root is the wasm bindings.
import { initFlatSQL } from 'flatsql/wasm';
// Initialize
const flatsql = await initFlatSQL();
// Create database with schema
const db = flatsql.createDatabase(`
table User {
id: int (id);
name: string;
email: string (key);
age: int;
}
`, 'myapp');
// Register file identifier for routing
db.registerFileId('USER', 'User');
// Ingest FlatBuffer data (streaming)
db.ingest(flatbufferStream);
// Query with SQL
const result = db.query('SELECT * FROM User WHERE age > 25');
console.log(result.columns, result.rows);FlatSQL bridges two technologies:
- FlatBuffers — Google's efficient cross-platform serialization library. Data is stored in binary format with zero-copy access (no parsing/unpacking needed).
- SQLite — The most widely deployed SQL database engine, used here only for SQL parsing and query execution.
The key insight: instead of converting FlatBuffers to SQLite rows (expensive), FlatSQL uses SQLite virtual tables to query FlatBuffer data directly. Your data stays in portable FlatBuffer format, readable by any FlatBuffer tooling, while you get SQL query capabilities.
Traditional approach:
FlatBuffer → Deserialize → SQLite rows → Query → Serialize → FlatBuffer
FlatSQL approach:
FlatBuffer → Query (via virtual table) → FlatBuffer
Benefits:
- Zero conversion overhead — Data stays in FlatBuffer format
- Streaming ingestion — Indexes built during data arrival, not after
- Portable output — Exported data is standard FlatBuffers, readable by any tooling
- Multi-source federation — Query across multiple FlatBuffer sources with automatic source tagging
flatsql_record_text(table, record, binary_schema) extracts search text using a
canonical FlatBuffers binary schema (.bfbs). This includes fields after vectors,
nested tables and unions, even when the SQL table exposes only an initial set of
columns. Records remain in their original binary format. The two-argument form
continues to extract the registered SQL columns.
Generate binary schemas from the same published IDL version used to produce the
records, using flatc -b --schema --bfbs-builtins. The schema and record must match
the registered table's file identifier. FlatSQL validates both inputs and indexes
public scalar values, strings and nested content. It excludes encrypted fields,
internal fields beginning with _, and opaque byte vectors.
Extraction rejects unsupported layouts and reports bounds failures instead of returning partial text. Limits are 1 MiB per binary schema, 16 MiB per record and output, 100,000 visited values/objects and 64 nesting levels. Schema metadata with 64-bit offsets or unknown advanced features is rejected. Consumers must include the binary schema and engine versions in their derived-index fingerprint so upgrades rebuild previously incomplete indexes. See the FlatBuffers reflection documentation.
Within an SDN module flow, FlatSQL always runs as a signed, pluggable WASM node. The exact same artifact bytes and hash run under the browser/JavaScript harness and WasmEdge. The pure TypeScript implementation is a library fallback and must not substitute for the FlatSQL node in an isomorphic flow.
FlatSQL remains independently instantiated and addressable by its signed manifest and artifact hash. A flow bundle may carry those bytes, but a compiler must not statically fold FlatSQL into the consuming application module.
The FlatSQL node owns SQL, tables, rows, indexes, compaction, snapshots, and
reload semantics. Hosts provide only generic opaque byte persistence,
shared-arena bounds checking, clocks/wakeups, network byte streams, and module
lifecycle. They must not expose a FlatSQL service, storage_engine_link, or
schema-aware row/query API.
Every FlatSQL module input and output port supports both canonical SDS
FlatBuffer and aligned-binary representations for the same schema identity.
PIV/TAB.WIRE_FORMAT selects per frame. Aligned frames are transient
shared-memory views; durable, network, and fallback records remain canonical
size-prefixed FlatBuffers.
- The SQLite-backed WASM path is
initFlatSQL→FlatSQL→createDatabase(the package root andflatsql/wasm). - Standalone deployments use the C++ WASI reactor at
flatsql/wasi.wasm; Node/WasmEdge hosts use a resident runner fromflatsql/standalone/wasmedge. The partition store isflatsql/ps-threads.wasm(Node host:flatsql/ps/node-host). - Artifact-builder cache behavior belongs to C++: query templates, result-cache keys, invalidation generation, hit/miss counters, SQL execution, and raw FlatBuffer lookup all live in the WASM database instance. JavaScript only marshals bytes and fills host capability gaps.
- Use
db.ingestBuffers([...])on the WASM path when your input is already a set of raw FlatBuffers. It feeds the native bulk-stream ingest path instead of looping throughingestOne(...). - Run
npm run bench(aliasnpm run bench:perf) for the WASM ingest benchmark (medians and records/s). Usenpm run bench:perf:profileto print the ingest phase breakdown (pack,decode,append,index,verify); profiling is diagnostic and slower by design. - Run
npm run test:clusterfor the native cluster validation workload, ornpm run test:cluster:smokefor the short preflight run. See docs/performance.md.
The Space Data Standards stress harness exercises every discoverable main.fbs under ../spacedatastandards.org/schema, records bandwidth and storage-fill metrics, and keeps canonical schemas database-neutral. Results are written under stress/results/<run-id>/.
# Local smoke path, no Docker required.
npm run stress:sds:smoke -- --run-id sds-smoke
# Existing single-controller Docker smoke path.
npm run stress:sds:docker-smoke -- --run-id sds-docker-smoke
# Container-per-node fleet path for small probes, e.g. 10 isolated nodes.
npm run stress:sds:docker-fleet -- --run-id sds-10-node --nodes 10 --storage-gb 1 --records-per-node 100000
# Generate and validate the 100-node / 1 GB-per-node production compose file without running it.
npm run stress:sds:full-config -- --run-id sds-full
docker compose -f stress/results/sds-full/docker-compose.full.yml config
# Run the full production gate. Requires Docker and enough local storage.
CONFIRM_FULL_STRESS=1 npm run stress:sds:full -- --run-id sds-fullThe production gate only passes when the aggregate report shows container isolation, Docker transport, at least 100 nodes, no metric errors, measured native/standalone/WasmEdge events, complete SDS schema-sweep coverage, at least 95% storage fill against the 1 GB/node target on every node, and bulk-ingest throughput at or above 75 fps on every node.
Full mode defaults to 20,000 records per node, or 2 million records across the 100-node fleet. In full mode the generated PublishEventRecord payload size scales from the configured storage budget so the run targets the requested per-node storage fill without requiring hundreds of millions of tiny records.
Use these entrypoints when wiring agents or deployments:
| Import | Runtime | Use for |
|---|---|---|
flatsql, flatsql/wasm |
Emscripten JS wrapper + C++ WASM | Browser/Node SQLite-backed engine |
flatsql/ps-threads.wasm, flatsql/ps |
The partition store artifact and its locator | Multithreaded partition store hosts |
flatsql/ps/node-host |
Node wasi-threads host (thread pool, WASI, flatsql_io, fault overlay) | Tests and Node processes |
flatsql/response |
Response artifact bytes | ETags, chunks, and raw byte artifacts keyed by native runtime cache keys |
flatsql/standalone/wasmedge |
Native WasmEdge runner builder/client | Persistent standalone runtime in Node |
For repeated FILE_ID-style traffic, register a cacheable query template once and call queryTemplate(...) with positional params. Identical query IDs and params hit the C++ result cache; ingest, load, template changes, and explicit clearQueryCache() invalidate stale entries by generation.
import { initFlatSQL } from 'flatsql';
const builder = (await initFlatSQL()).createDatabase(schema, 'users');
await builder.registerFileId('USER', 'User');
await builder.enableDemoExtractors();
await builder.ingestBuffers(buffers);
await builder.configureQueryCache({ maxEntries: 4096, maxRows: 5000 });
await builder.registerQueryTemplate('userByEmail', 'SELECT id FROM User WHERE email = ?', true);
await builder.queryTemplate('userByEmail', ['alice@example.com']); // miss
await builder.queryTemplate('userByEmail', ['alice@example.com']); // hit
console.log(await builder.getQueryCacheStats());The native cache holds up to 1024 results within a 64 MiB budget, evicting the least recently used result to stay under it; a result bigger than the whole budget is not cached. maxRows caps any single result (default 1,000,000 rows). Tune maxEntries and maxRows for production FILE_ID traffic instead of recompiling the C++ core.
For WasmEdge, build the small host runner and open a database through its process runtime. The runner keeps one WASI module instance resident so cache state survives across requests.
import { buildFlatSQLWasmEdgeRunner, createFlatSQLWasmEdgeProcessRuntime } from 'flatsql/standalone/wasmedge';
const runner = await buildFlatSQLWasmEdgeRunner({
outputPath: './build/flatsql-wasmedge-runner',
});
const runtime = createFlatSQLWasmEdgeProcessRuntime({ runnerPath: runner.outputPath });
const builder = await runtime.createDatabase(schema, 'users');WasmEdge must support WebAssembly exception handling for this artifact. The test runner uses wasmedge --enable-exception-handling --reactor wasm/flatsql-wasi.wasm _initialize for the smoke check and the native C API runner enables the same proposal programmatically. Some Node 24 builds require node --experimental-wasm-exnref when directly instantiating the standalone WASI artifact.
For high-volume repeated FILE_ID responses, build the cache key through the C++ runtime, wrap query results as response artifacts, and let edge/browser/WasmEdge host caches serve the immutable bytes by ETag or content hash. FlatSQL creates deterministic native cache keys and chunk metadata; deployment code owns CDN placement, routing, authorization, and global invalidation.
import { createQueryResponseArtifact } from 'flatsql/response';
const sql = 'SELECT _data FROM PublishEventRecord WHERE FILE_ID = ?';
const cacheKey = await builder.buildResponseArtifactCacheKey('PublishEventRecord', '1', sql, {
format: 'raw-flatbuffer-stream',
publishEventKey: 'publish-1',
projection: ['_data'],
params: ['publish-1'],
});
const result = await builder.query(sql, ['publish-1']);
const artifact = createQueryResponseArtifact(result, {
schemaName: 'PublishEventRecord',
schemaVersion: 1,
sql,
params: ['publish-1'],
format: 'raw-flatbuffer-stream',
publishEventKey: 'publish-1',
projection: ['_data'],
cacheKey,
});
console.log(artifact.metadata.cacheKey, artifact.metadata.etag);See docs/response-artifacts.md and docs/agents/response-artifacts.md for production and agent guidance.
| Repository | Description |
|---|---|
| digitalarsenal/flatsql | This project — FlatSQL query engine |
| digitalarsenal/flatbuffers | Fork of Google FlatBuffers with WASM support |
| flatc-wasm | FlatBuffer compiler running in WebAssembly |
The C++ engine compiles to WebAssembly for cross-platform deployment:
import { initFlatSQL } from 'flatsql/wasm';
const flatsql = await initFlatSQL();
// Create database with schema
const db = flatsql.createDatabase(`
table User {
id: int (id);
name: string;
email: string (key);
age: int;
}
`, 'myapp');
// Register file identifier routing
db.registerFileId('USER', 'User');
// Enable demo field extractors (for testing)
db.enableDemoExtractors();
// Ingest FlatBuffer stream
// Format: [4-byte size LE][FlatBuffer][4-byte size LE][FlatBuffer]...
db.ingest(streamData);
// Or ingest pre-built FlatBuffers directly through the bulk path.
db.ingestBuffers([
flatsql.createTestUser(1, 'Alice', 'alice@example.com', 30),
flatsql.createTestUser(2, 'Bob', 'bob@example.com', 25),
]);
// Query with SQL
const result = db.query('SELECT id, name, email FROM User WHERE age > 25');
console.log(result.columns); // ['id', 'name', 'email']
console.log(result.rows); // [[1, 'Alice', 'alice@example.com'], ...]
// Export database
const exported = db.exportData();
// Cleanup
db.destroy();The pure TypeScript engine (FlatSQLDatabase with DirectAccessor /
FlatcAccessor) was removed in 3.0.0; flatsql 2.x still carries it. The
BTree, stacked store and schema parser remain as flatsql/btree,
flatsql/storage and flatsql/schema.
Use the runtime guard helpers before attempting browser cluster mode:
import { detectClusterEnvironment, isClusterModeSupported } from 'flatsql/cluster';
const env = detectClusterEnvironment();
if (!isClusterModeSupported(env)) {
throw new Error('Cluster mode unsupported on this runtime');
}For performance-critical applications, link the C++ library directly:
#include <flatsql/database.h>
auto db = flatsql::FlatSQLDatabase::fromSchema(schema, "mydb");
// Register file ID routing
db.registerFileId("USER", "User");
// Set field extractor
db.setFieldExtractor("User", extractUserField);
// Ingest streaming data
size_t recordsIngested = 0;
db.ingest(data, length, &recordsIngested);
// Query
auto result = db.query("SELECT * FROM User WHERE id = 5");
for (size_t i = 0; i < result.rowCount(); i++) {
std::cout << result.getString(i, "name") << std::endl;
}┌─────────────────────────────────────────────────────────────┐
│ FlatSQLDatabase │
├─────────────────────────────────────────────────────────────┤
│ SchemaParser │ SQLiteEngine │
│ (FlatBuffers IDL) │ (Virtual Tables) │
├─────────────────────────────────────────────────────────────┤
│ TableStore (per table) │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ SQLite Indexes Field Extractors │ │
│ │ (id, email, timestamp) (getField callbacks) │ │
│ └────────────────────────────────────────────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ StackedFlatBufferStore (append-only) │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ [Header][FB₁][FB₂][FB₃]... │ │
│ └──────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
- JSON schemas that reference other definitions now produce explicit
From_Target_jointables with{FromRowId, TargetRowId}columns. - This keeps referenced schemas in the same normalized storage layer so queries can
JOINthe virtual tables instead of embedding opaque JSON blobs.
FlatSQL ingests size-prefixed FlatBuffer streams:
[4-byte size LE][FlatBuffer with file_id][4-byte size LE][FlatBuffer]...
The 4-byte file identifier in each FlatBuffer determines which table receives the record.
FlatSQL includes a full spatial computation engine with SpatiaLite-compatible functions, polygon boolean operations, Voronoi/Delaunay tessellation, and coordinate transforms.
| Category | Functions |
|---|---|
| Point ops | geo_distance, geo_bearing, geo_destination, geo_midpoint, geo_area_bbox, geo_within_radius, geo_bbox_contains |
| Geohash | geo_geohash_encode, geo_geohash_decode_lat, geo_geohash_decode_lon |
| Geometry I/O | geo_from_text (WKT→blob), geo_as_text (blob→WKT), geo_from_geojson, geo_as_geojson |
| Predicates | geo_contains (point-in-polygon, ray casting with hole support) |
| Boolean ops | geo_intersection, geo_union, geo_difference, geo_sym_difference, geo_buffer |
| Analysis | geo_area_geom, geo_centroid, geo_length_geom, geo_envelope, geo_convex_hull |
| Voronoi/Delaunay | geo_voronoi, geo_delaunay |
| Coord transforms | geo_to_ecef (WGS84→ECEF), geo_from_ecef (ECEF→WGS84) |
-- Distance between NYC and DC (~328 km)
SELECT geo_distance(40.7128, -74.0060, 38.9072, -77.0369);
-- Bearing from NYC to LA
SELECT geo_bearing(40.7128, -74.0060, 34.0522, -118.2437);
-- Geohash encode/decode
SELECT geo_geohash_encode(42.6, -5.6, 5); -- 'ezs42'
-- Point-in-polygon
SELECT geo_contains('POLYGON((0 0, 10 0, 10 10, 0 10, 0 0))', 5, 5); -- 1
-- Polygon intersection (accepts WKT or blob)
SELECT geo_as_text(geo_intersection(
'POLYGON((0 0, 10 0, 10 10, 0 10, 0 0))',
'POLYGON((5 5, 15 5, 15 15, 5 15, 5 5))'
));
-- WGS84 to ECEF coordinate transform
SELECT geo_to_ecef(48.8566, 2.3522, 35); -- '4200952.xxx,172458.xxx,4780111.xxx'
-- Voronoi diagram
SELECT geo_as_text(geo_voronoi(
'MULTIPOINT((0 0), (10 0), (10 10), (0 10))',
'POLYGON((-5 -5, 15 -5, 15 15, -5 15, -5 -5))'
));FlatSQL automatically creates R-Tree shadow tables for spatial columns during ingest. Spatial columns are detected by:
- Convention: columns named
latitude/lat+longitude/lon/lng - Schema attribute: fields marked with
(spatial)in FlatBuffers IDL
-- R-Tree pre-filter + precise radius check (72x faster than full scan)
SELECT p.* FROM points p
INNER JOIN _rtree_points r ON p.rowid = r.id
WHERE r.minLat >= 36.2 AND r.maxLat <= 45.2
AND r.minLon >= -80 AND r.maxLon <= -68
AND geo_within_radius(40.7, -74.0, p.lat, p.lon, 500) = 1;| Operation | Throughput | Latency |
|---|---|---|
| Point operations (distance, bearing) | 733K–1.07M ops/sec | ~1 µs |
| Geohash encode/decode | 631K–746K ops/sec | ~1.4 µs |
| Point-in-polygon | 127K–255K ops/sec | ~4 µs |
| Polygon intersection | 81K ops/sec | ~12 µs |
| Voronoi (4 sites) | 34K ops/sec | ~29 µs |
| Coordinate transforms | 556K–609K ops/sec | ~1.7 µs |
| R-Tree bbox query (10K records) | 77K ops/sec | ~13 µs |
| R-Tree vs full scan speedup | 58–72x |
FlatSQL Spatial is also available as a standalone WASM module compliant with the Space Data Module SDK:
import { initSpatialSDM } from 'flatsql/sdm';
const sdm = await initSpatialSDM();
// Read the embedded manifest
console.log(sdm.getManifest().pluginId);
// → 'com.digitalarsenal.flatsql.spatial'
// Compute distance
const km = sdm.computeDistance(40.7128, -74.006, 38.9072, -77.0369);
// Point-in-polygon
const inside = sdm.pointInPolygon('POLYGON((0 0, 10 0, 10 10, 0 10, 0 0))', 5, 5);
// Coordinate transform
const ecef = sdm.toECEF(48.8566, 2.3522, 35);
const back = sdm.fromECEF(ecef.x, ecef.y, ecef.z);The WASM engines (initFlatSQL(), the partition store's reader lanes) embed
SQLite. (The pure-JS engine and its SQL subset were removed in 3.0.0.)
The engines reject what they cannot execute: a caller that asks for a subset gets that subset or an error, never the whole table with no indication that the filter was dropped.
SELECTwith column selectionWHEREwith=,!=/<>,<,>,<=,>=,BETWEEN,IN,LIKE,IS NULL, andAND/OR/NOT/ parenthesesLIKEwith%and_wildcards, ASCII case-insensitive (the SQLite default)ORDER BY(ASC/DESC, multiple terms)LIMITandOFFSETCOUNT(*)aggregate (WASM engine)- Index-accelerated queries on
(id)and(key)columns, including as one conjunct of a largerANDpredicate
JOIN(query one table at a time)GROUP BY,HAVING, most aggregatesINSERT,UPDATE,DELETE(use API methods instead)- Subqueries, CTEs, window functions
FlatSQL outperforms traditional SQLite on query operations:
| Operation | FlatSQL | SQLite | Speedup |
|---|---|---|---|
| Point query (by id) | 3.50 ms | 3.93 ms | 1.1x |
| Point query (by key) | 5.23 ms | 6.94 ms | 1.3x |
| Direct index lookup | 1.56 ms | 3.93 ms | 2.5x |
| Full scan | 0.84 ms | 1.25 ms | 1.5x |
| Direct iteration | 0.05 ms | 1.25 ms | 25x |
Benchmarks: 10,000 records, 10,000 query iterations, Apple M3 Ultra
FlatSQL uses SQLite's virtual table (VTable) API to expose FlatBuffer data as queryable tables. This architecture enables SQL queries over raw binary data, but comes with fundamental trade-offs that affect performance characteristics.
FlatSQL provides three access paths with different performance profiles:
| Access Path | Latency | Throughput | Use Case |
|---|---|---|---|
| Zero-Copy API | 1.7 µs | 580K ops/sec | Direct index lookup, returns raw FlatBuffer pointer |
| VTable SQL | 12.9 µs | 78K ops/sec | Full SQL queries via SQLite |
| Pure SQLite | 2.5 µs | 400K ops/sec | Baseline comparison |
SQLite's VTable API has fundamental limitations that prevent FlatSQL from matching pure SQLite query performance:
-
Per-Column Extraction — The
xColumn()callback is invoked once per column per row. There is no batch API to extract multiple fields at once, meaning each field access has function call overhead. -
Mandatory Value Conversion — All values must be converted to SQLite's internal format via
sqlite3_result_*()functions. Even though FlatBuffers already store data in an efficient binary format, we must convert strings to SQLite strings, integers to SQLite integers, etc. -
Row-by-Row Processing — The
xNext()callback advances one row at a time with no vectorized or batch iteration. This prevents SIMD optimizations or processing multiple records per call. -
No Direct Memory Access — SQLite cannot read FlatBuffer memory directly; all data must flow through the VTable callback interface, adding overhead for every field access.
Use the Zero-Copy API when:
- You need maximum throughput for point lookups
- You're building hot paths that query by indexed keys
- You can work directly with FlatBuffer data structures
// Zero-copy: returns pointer to raw FlatBuffer (1.7 µs)
const uint8_t* data = db.findRawByIndex("User", "email", email, &len);
auto user = GetUser(data); // Direct FlatBuffer accessUse VTable SQL when:
- You need complex queries (filtering, sorting, aggregation)
- Query flexibility is more important than raw speed
- You're doing ad-hoc exploration of the data
// VTable SQL: full query capability (12.9 µs)
auto result = db.query("SELECT * FROM User WHERE age > 25 ORDER BY name");FlatSQL uses SQLite's highly optimized B-tree for indexing (not a custom implementation). This provides:
- Battle-tested performance — SQLite's B-tree is used by billions of devices
- Consistent behavior — Same indexing code path as pure SQLite
- Fast path optimization — Type-specific lookups bypass
std::variantoverhead
The index stores (key, sequence) → (offset, length) mappings, allowing O(log n) lookups that return pointers directly into the FlatBuffer storage.
| Aspect | FlatSQL | Pure SQLite |
|---|---|---|
| Point lookup (indexed) | 1.4x faster (zero-copy API) | Baseline |
| SQL queries | 5-7x slower (VTable overhead) | Baseline |
| Storage format | FlatBuffers (portable, zero-copy) | SQLite pages |
| Data conversion | None (zero-copy) or on-demand | Always required |
| Streaming ingest | Append-only, real-time indexing | Row-by-row inserts |
Bottom line: FlatSQL excels when you need streaming ingestion of FlatBuffer data with SQL query capability. Use the zero-copy API for performance-critical lookups; use SQL for complex queries where flexibility matters more than speed.
- Node.js 24+
- CMake 3.20+ (for WASM builds)
- Emscripten (for WASM builds)
npm install
npm run build
npm test# Clone DA-FlatBuffers (required dependency)
git clone https://github.com/DigitalArsenal/flatbuffers.git ../flatbuffers
# Build browser, standalone WASI, and SDM artifacts
npm run build:wasm
# Verify standalone and package surfaces
npm run test:wasi
npm run test:wasmedge
npm test -- --runInBand --runTestsByPath test/package-exports.test.tsOutput: wasm/flatsql.js, wasm/flatsql.wasm, wasm/flatsql-wasi.wasm, sdm/flatsql-spatial.wasm, and docs copies for GitHub Pages.
The partition store artifact wasm/flatsql-ps-threads.wasm needs wasi-sdk 30 (WASI_SDK_PATH) and builds with npm run build:wasm:ps (npm run build:wasm:ps-tests adds the wasm test commands); see docs/PARTITION-STORE-WASM.md.
npm run serve
# Open http://localhost:8081import { initFlatSQL } from 'flatsql/wasm';
const flatsql = await initFlatSQL();const db = flatsql.createDatabase(schemaString, 'dbname');db.registerFileId('USER', 'User'); // Route "USER" FlatBuffers to User tableconst bytesConsumed = db.ingest(uint8ArrayStream);
// Or with source tagging (requires registerSource first):
const bytesConsumed = db.ingest(uint8ArrayStream, 'satellite-1');const result = db.query('SELECT * FROM User WHERE age > 25');
// result.columns: string[]
// result.rows: any[][]const data = db.exportData(); // Returns Uint8Arraydb.registerSource('satellite-1'); // Creates User@satellite-1, Post@satellite-1, etc.db.createUnifiedViews(); // Creates unified views with _source columnconst sources = db.listSources(); // ['satellite-1', 'satellite-2', ...]FlatSQL supports federating multiple data sources with the same schema. Each source gets its own set of tables, and you can query them individually or across all sources.
Imagine you have multiple satellites streaming telemetry data with the same schema:
// Register sources
db.registerSource('satellite-1');
db.registerSource('satellite-2');
db.registerSource('ground-station');
// Register file IDs and extractors (must be done after registerSource)
db.registerFileId('TELE', 'Telemetry');
db.enableDemoExtractors();
// Create unified views (call once after all sources registered)
db.createUnifiedViews();
// Ingest from different sources
db.ingest(satellite1Stream, 'satellite-1');
db.ingest(satellite2Stream, 'satellite-2');
db.ingest(groundStream, 'ground-station');
// Query a specific source
db.query('SELECT * FROM "Telemetry@satellite-1" WHERE signal > 50');
// Query across all sources (unified view)
db.query('SELECT * FROM Telemetry WHERE timestamp > 1000');- Source-specific tables:
TableName@sourceName(e.g.,User@siteA,Telemetry@satellite-1) - Unified views:
TableName(e.g.,User,Telemetry) - combines all source tables with a_sourcecolumn
Unified views include a _source column that identifies which source each row came from:
-- See source for each record
SELECT _source, id, name FROM User LIMIT 10;
-- Count records by source
SELECT _source, COUNT(*) as count FROM User GROUP BY _source;
-- Filter by source in unified view
SELECT * FROM User WHERE _source = 'satellite-1';Fields marked (encrypted) in the schema are stored encrypted under the database key and decrypted when SQL reads them. The format is FlatBuffers field-encryption format 3: each record gets its own key, derived from the database key and the record's index, and each encrypted value gets its own IV, its position in the record. No two records and no two values share a key stream.
The record index is the record's sequence, its rowid. Records are numbered 1, 2, 3, ... in stream order, so the stream carries the index: an exported stream loaded into an empty database (loadAndRebuild) gives every record the index it was encrypted with. Load an encrypted stream into an empty database only.
db.registerFileId('SECR', 'Secret');
db.setEncryptionKey(key); // 32 bytes
const seq = db.ingestOneEncrypted(record, bfbs); // encrypted as record `seq`
db.query('SELECT code FROM Secret'); // plaintextbfbsis the table's binary schema with builtin attributes (flatc -b --schema --bfbs-builtins). It must mark the same fields(encrypted)as the database schema, or the call is refused.ingestOneEncrypted(record, bfbs, source?)assigns the sequence and encrypts under it in one step.encryptBuffer(record, bfbs, recordIndex)anddecryptBuffer(record, bfbs, recordIndex)take the index explicitly.(encrypted)columns are never indexed. A predicate on one scans and decrypts.- A database that already holds records in tables with
(encrypted)columns needs their format declared:setEncryptionKey(key, { format: 3 }). Nothing in the stream marks the format.
Crypto backend. Format 3 derives keys with HKDF-SHA256 and encrypts with AES-256-CTR, and computeHMAC/verifyHMAC are HMAC-SHA256 under the database key. All three come from the FlatBuffers crypto backend. Native builds use OpenSSL when CMake finds it. The wasm builds (flatsql.wasm, flatsql-wasi.wasm, flatsql-wasi-noeh.wasm) and native builds without OpenSSL compile the FlatBuffers fallback backend, which implements them from FlatBuffers 8af3053e on and derives the same keys as OpenSSL: the same key and record give the same stored bytes in every build. Built against an older FlatBuffers, the fallback's key derivation is not HKDF-SHA256 (it had 256 outputs, so records would share key streams); such a build refuses a key for a database with (encrypted) columns, with an error that names HKDF-SHA256. FlatSQLDatabase::recordEncryptionAvailable() checks the backend against an HKDF-SHA256 known answer.
Format 2 (flatsql 3.2.0 and earlier). Those versions encrypted every record under the same field keys, those of record 0, derived by the fallback backend. This version refuses format 2: setEncryptionKey(key, { format: 2 }) throws, and a database that holds records must declare format 3. To migrate, use flatsql 3.2.0 and the old key to read each record of exportData() back as plaintext with decryptBuffer(record, bfbs). Then ingest the plaintext records into a new database with ingestOneEncrypted.
PolyForm Noncommercial License 1.0.0 — source-available, free for any noncommercial purpose. Commercial use requires a separate license from Edgesource Corporation See LICENSE.
Contributions welcome. Please open an issue first to discuss significant changes.
For questions, licensing inquiries, or commercial support: tj@edgesource.com
Built on DA-FlatBuffers and SQLite.
The bundled SQLite engine includes FTS5 on native, browser and WASI targets.
SELECT flatsql_record_text('CAT', ?) extracts searchable scalar/text fields
from a bound FlatBuffer blob using the registered table schema. Both raw and
size-prefixed records are accepted; the file identifier must match the table.
Encrypted columns, opaque byte vectors and internal columns are excluded.
Scalar defaults retain their declared type. Enums use their declared integer
width when their definition is present; unresolved named types and non-byte
vectors stay opaque instead of being treated as strings.
Unknown schemas and malformed supported fields return SQL errors.
A host can persist that text in an FTS5 table keyed by its durable record ID, including records outside the engine's active record window. Index lifecycle, source permissions, coverage and cursor identity remain the host's responsibility. The function covers the fields declared in the registered schema; it does not recursively decode nested tables or invent a text representation for binary data.