ringfire 🔥

ringfire is an ultra-low-latency, zero-copy, lock-free Inter-Process Communication (IPC) ring buffer and shared memory state bus for Linux, and since v0.5.0 it mirrors a ring to other hosts with the same sequence numbers.

It is engineered for high-frequency trading (HFT) engines, real-time market data ingestion, telemetry buses, and performance-critical distributed pipelines where microsecond socket latencies and kernel overhead are unacceptable.

⚡ Performance at a Glance

Corrected measurements, Linux ARM Neoverse-N1, 2026-09-27. Three runs per case on two physical cores, 64-byte messages, full reply validation. This was a shared machine; values below are medians of three Criterion run medians, with the full run range. All 28 cases, raw samples and machine settings.

Transport	Round trip, µs (range of runs)
ringfire, busy spin	0.29 (0.29–0.30)
ringfire, adaptive futex (32 spin attempts)	0.42 (0.36–0.44)
ringfire, zero-spin futex	4.46 (4.43–6.08)
Unix domain socket	6.55 (6.54–7.16)
Pipe	10.14 (9.61–10.22)
TCP loopback (`TCP_NODELAY`)	21.51 (21.48–21.67)

xychart-beta
    title "64-byte round trip on ARM, median of three runs"
    x-axis ["Spin", "Adaptive futex", "Zero-spin futex", "Unix socket", "Pipe", "TCP"]
    y-axis "microseconds (lower is better)" 0 --> 23
    bar [0.29, 0.42, 4.46, 6.55, 10.14, 21.51]

Futex's default policy spins briefly before sleeping. Zero-spin removes that budget; an already-ready reply can still bypass sleep. Neither mode forces a context switch for every message. Exact timing definitions.

Warm-cache operations on the same ARM host (ns per operation, range of runs). Receive loops include sequence validation; chunk refill happens outside the timer.

Operation	ns (range of runs)
`push` (no reader)	11.08 (10.26–11.32)
Successful `try_recv`	10.84 (10.83–10.91)
`recv_batch(32)`, per batch	363.10 (361.58–364.85)
`recv_batch(32)`, per message	11.35 (11.30–11.40)
`push` with a lossy reader	35.46 (35.01–36.49)
`push` with a lossless reader	36.38 (35.25–36.55)
Blackboard read	11.10 (11.08–11.13)
Blackboard write	7.06 (7.06–7.07)

The earlier Ryzen receive figures (6.4 ns and 2.3 ns/message) are withdrawn because the old harness mixed receives with empty polls and refills. The corrected ARM figures are a different-host measurement, not a before/after speed comparison. Historical Ryzen and LAN/WAN results remain below with their original context; per-stage network measurements used a timestamp-before-lock harness and await remeasurement.

Production test coverage: 96.67% of lines in the Linux ARM all-features run, including CLI, FFI and replication. CI enforces a 95% minimum and uploads its report. Coverage scope, per-file results and limitations.

Network mirrors — historical measurements (v0.5.0): the same ring, with the same sequence numbers, on other hosts. Readers there attach to it as if it were local.

Path, 64-byte records, 1,000 msg/s	push → read
consumer on the source host (same ring)	0.1 µs
mirror on the same host, multicast	3.8 µs
mirror on another host on a 1 GbE LAN, multicast, each of six	30–32 µs
mirror in Los Angeles from a source in Tokyo, UDP unicast	51.7 ms p50, 51.7 ms p99

Ordered, never duplicated, lost datagrams recovered from the ring itself; TCP, UDP multicast or UDP unicast (works from behind NAT). See Network Mirrors and docs/replication.md.

💡 The Problem: Why Traditional IPC Fails Under High Load

When communicating between processes on the same host, developers usually default to Unix Domain Sockets (UDS), TCP loopback, pipes, ZeroMQ, or broker-based message queues (NATS, Redis). Historical Ryzen RTT/2 estimates below assume symmetric paths; they are not measured one-way latencies. Kernel paths and backpressure differ:

IPC Mechanism	Kernel Overhead	Memory Copies	One-way Latency	Backpressure / Crash Behavior
Unix Domain Sockets (UDS)	2 syscalls (`send`/`recv`) + context switch	User $\to$ Kernel $\to$ User (2 copies)	~2,400 ns measured (RTT / 2)	Socket buffer fills up; blocks producer or drops packets
TCP Loopback (`127.0.0.1`)	Full TCP/IP stack + packetization	Multiple copies + TCP buffers	~5,100 ns measured (RTT / 2)	Heavy CPU jitter, flow control stalls
Pipes / FIFOs	Pipe inode lock + syscalls	Buffer copy through VFS	~2,500 ns measured (RTT / 2)	Blocking write when pipe buffer (64 KB) fills
Message Brokers (Redis / NATS)	Network stack + daemon context switch	Multi-hop serialization	not measured here	High GC/memory pressure, single point of failure
`ringfire` (Shared Memory)	0 syscalls on hot path	payload copied into slot and out to reader	~160 ns (historical RTT / 2 estimate)	Writer never blocks (lossy) or throttles on the slowest reader (lossless); crash-isolated

The Three Critical Pain Points:

The Syscall & Context Switch Tax: Every write() and read() triggers CPU privilege elevation from user-space to kernel-space and back, polluting CPU L1/L2 caches and branch predictors.
Buffer Bloat & Head-of-Line Blocking: If a consumer process stalls (e.g. garbage collection pause in Python, disk IO hiccup, or debug pause), standard socket buffers fill up immediately, stalling the critical producer or blowing up memory.
Serialization Overhead: Marshalling data to and from JSON, Protobuf, or even compact binary encoders consumes valuable CPU cycles and allocates memory on the hot path.

🎯 The Solution & Vision: What `ringfire` Solves

ringfire moves the communication fabric directly into physical RAM via POSIX shared memory (/dev/shm):

Pure Shared Memory (/dev/shm): Producer and consumers map the exact same physical memory region directly into their virtual address spaces.
Atomic Acquire/Release Synchronization: State is coordinated via 64-bit atomic sequence numbers using CPU-level memory barriers (core::sync::atomic), completely bypassing the operating system kernel.
Single-Writer Freedom (LatestWins Policy): The producer always writes to the ring. Slow, paused, or dead consumers can never block, stall, or crash the producer. If a consumer falls behind the ring buffer capacity, it detects that it was lapped and skips cleanly to the live stream.
Cache-Line Isolated Layout: Memory structures are aligned to 128-byte cache lines to eliminate false sharing between producer write heads and consumer read heads.
O(1) State Blackboard: Besides sequential stream events, ringfire provides a direct seqlock-synchronized slot table. Consumers inspect the latest state (e.g., current Best Bid & Offer for 500 coins) without replaying historical events.
Polyglot First-Class Support: Because the layout in /dev/shm is standard C-ABI memory, consumers can be written in Rust, C, C++, or Python (mmap + ctypes/numpy) with zero bridge penalty.

🤝 The Rapidfire Family

ringfire is designed as the cross-process counterpart to rapidfire:

┌─────────────────────────────────────────────────────────────────────────────┐
│                          IN-PROCESS (Single Process)                        │
│                                                                             │
│                                  rapidfire                                  │
│           • Intra-process MPMC / MPSC channels across threads & Tokio       │
│           • Ultra-low latency (< 15 ns), zero heap allocations              │
└──────────────────────────────────────┬──────────────────────────────────────┘
                                       │
                                       ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                         CROSS-PROCESS (Multi-Process IPC)                   │
│                                                                             │
│                                  ringfire                                   │
│           • Inter-process lock-free ring buffer via /dev/shm                │
│           • O(1) Shared State Blackboard (Seqlock)                          │
│           • ~160 ns cross-core latency, C11 header, Python bindings         │
└──────────────────────────────────────┬──────────────────────────────────────┘
                                       │
                                       ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                          CROSS-HOST (Network Mirrors)                       │
│                                                                             │
│                           ringfire serve / mirror                           │
│           • The same ring, same sequence numbers, on other hosts            │
│           • TCP, UDP multicast, UDP unicast (NAT, clouds, other sites)      │
│           • ~30 µs across a LAN, no reordering, loss repaired from the ring │
└─────────────────────────────────────────────────────────────────────────────┘

🚀 Detailed Benchmarks (AMD Ryzen 9 7950X on Linux `booster`)

Benchmarked using Criterion directly against POSIX shared memory (/dev/shm) on host booster (16 Cores / 32 Threads, Linux 6.8):

Measured with protocol v2 (v0.4.0), 64-byte messages:

Metric	Measured Value	Rate / Notes
SPMC Single-Message Push (no reader)	1.88 ns	533 Million msgs / sec
SPMC Non-Blocking `try_recv`	withdrawn	earlier 6.41 ns was invalid; corrected ARM results above
SPMC Batch Drain (`recv_batch(32)`)	withdrawn	earlier 74.2 ns was invalid; corrected ARM results above
Push with a reader draining on another core	41.2 ns lossy / 42.0 ns lossless	Cross-core cache-line transfer; the lossless gate adds < 1 ns
Roundtrip Latency (Ping-Pong RTT)	249.6 ns	~125 ns one-way cross-thread IPC
Blackboard Seqlock Read (O(1))	2.13 ns	Tear-free snapshot read
Blackboard Seqlock Write (O(1))	1.13 ns	Seqlock update
Multi-Process Saturation	1 writer + 8 readers	Zero gaps / zero corruption

Single-threaded figures measure the instruction path with a warm cache; real cross-process throughput is bounded by the cross-core transfer shown in the "with a reader" row. tests/regression_tests.rs checks that no torn record is ever returned under continuous lapping. What each bench and replication example measures, how to run them on an isolated host, and the status of every published figure: docs/benchmarking.md.

📦 Quickstart (Rust)

All snippets below are compiled and run in CI as examples/quickstart.rs (cargo run --example quickstart).

1. Producer: Publish Fixed-Size Binary Records

use ringfire::RingProducer;

#[repr(C)]
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
struct MarketTicker {
    asset_id: u32,
    bid_px: u64,
    ask_px: u64,
    timestamp_ns: u64,
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Creates a 65,536 slot ring buffer in /dev/shm/hft_ticker_stream
    let mut producer = RingProducer::<MarketTicker>::create("/dev/shm/hft_ticker_stream", 65536)?;

    let ticker = MarketTicker {
        asset_id: 42,
        bid_px: 64_250_000_000,
        ask_px: 64_250_500_000,
        timestamp_ns: 1_726_870_000_000_000,
    };

    // Pushes ticker directly to shared memory (~2 ns)
    producer.push(&ticker);

    Ok(())
}

2. Tokio Async Consumer: Cooperative Non-Blocking Streaming

In async bots, busy loops starve the Tokio runtime. AsyncRingConsumer solves this with adaptive fast-path spinning, cooperative tokio::task::yield_now(), and 0% CPU idle sleep:

use ringfire::AsyncRingConsumer;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut consumer = AsyncRingConsumer::<MarketTicker>::attach("/dev/shm/hft_ticker_stream")?;

    println!("Attached async consumer. Streaming market data...");

    loop {
        // Yields cooperatively to other Tokio tasks when no traffic is present
        let ticker = consumer.recv().await;
        // Process ticker without stalling the Tokio runtime
    }
}

3. Synchronous Low-Latency Consumer (Dedicated Cores)

use ringfire::{RingConsumer, BusySpin};

let mut consumer = RingConsumer::<MarketTicker>::attach("/dev/shm/hft_ticker_stream")?;
let mut wait = BusySpin::new();

loop {
    // Spin polling for sub-20ns reaction time
    let ticker = consumer.recv_blocking(&mut wait);
    // Process ticker
}

4. Shared State Blackboard (O(1) Snapshot Table)

use ringfire::{BlackboardProducer, BlackboardConsumer};

// Producer updates snapshot table
let mut bb_prod = BlackboardProducer::<MarketTicker>::create("/dev/shm/hft_state_table", 1024)?;
bb_prod.write(42, &ticker)?; // ~1.1 ns write

// Consumer reads instantaneous current state by asset ID
let bb_cons = BlackboardConsumer::<MarketTicker>::attach("/dev/shm/hft_state_table")?;
if let Some(state) = bb_cons.read(42)? { // ~2.1 ns O(1) tear-free read
    println!("Current BBO for asset 42: bid={}, ask={}", state.bid_px, state.ask_px);
}

5. Raw Binary Payloads (`[u8; N]`) & Pointer Casting

You can also operate over raw byte buffers without declaring fixed structs:

use ringfire::{RingProducer, AsyncRingConsumer};

// Producer sends a raw 64-byte binary packet
let mut producer = RingProducer::<[u8; 64]>::create("/dev/shm/raw_stream", 65536)?;
let raw_bytes = [0xAAu8; 64];
producer.push(&raw_bytes);

// Consumer reads the 64 bytes and decodes the struct (byte arrays are not aligned for it)
let mut consumer = AsyncRingConsumer::<[u8; 64]>::attach("/dev/shm/raw_stream")?;
let bytes: [u8; 64] = consumer.recv().await;
let ticker: MarketTicker = unsafe { std::ptr::read_unaligned(bytes.as_ptr().cast()) };

6. Variable-Length Payloads (`BlobProducer` & `BlobConsumer`)

For variable-sized messages (e.g. L2/L3 order book snapshots, compressed frames, or variable trade batches), ringfire pairs fixed ring descriptors with a contiguous shared-memory PayloadArena:

use ringfire::{BlobProducerBuilder, BlobConsumer};

// Create a ring with 65,536 descriptor slots and a 16 MB byte arena
let mut producer = BlobProducerBuilder::new(65536, 16 * 1024 * 1024)
    .build::<u32, _>("/dev/shm/orderbook_stream")?;

// Push variable-length JSON, protobuf, or raw bytes directly into the arena
let raw_json = br#"{"event":"snapshot","bids":[[82000.5,1.2]],"asks":[[82001.0,0.8]]}"#;
producer.push(&42, raw_json)?;

// Consumer copies metadata + payload out of the arena, validated against overwrites
let mut consumer = BlobConsumer::<u32>::attach("/dev/shm/orderbook_stream")?;
let mut meta = 0u32;
let mut scratch_buffer = vec![0u8; 4096];
if let Some(len) = consumer.recv(&mut meta, &mut scratch_buffer)? {
    println!("Received {} byte snapshot for symbol {}", len, meta);
}

// Or inspect in place: the closure's result is discarded if the arena laps during it
let summary = consumer.view(|symbol, bytes| (*symbol, bytes.len()))?;

If the arena wraps around before a consumer gets to a message (payloads larger than arena_capacity / ring capacity on average), that message is skipped and counted in lapped_count(); size the arena for the backlog you need to retain.

7. Consumer Start Modes & Lock-Free SHM Checkpointing

Consumers can configure where in the stream to begin reading, and persist their read cursors lock-free into /dev/shm (a single atomic store plus a timestamp):

use ringfire::{RingConsumerBuilder, ConsumerStartMode};

let mut consumer = RingConsumerBuilder::<MarketTicker>::new()
    // Modes: Latest (instant jump), Head (wait for future), Oldest (replay backlog), Sequence(N)
    .start_mode(ConsumerStartMode::Latest)
    .attach("/dev/shm/hft_ticker_stream")?;

// Or attach with persistent offset checkpoint in /dev/shm:
let mut persistent_consumer = RingConsumerBuilder::<MarketTicker>::new()
    .offset_file("/dev/shm/hft_ticker_stream_worker1.offset")
    .attach("/dev/shm/hft_ticker_stream")?;

// In consumer loop, periodically or per-batch commit offset:
persistent_consumer.commit_offset()?;

8. Lossless Backpressure Flow Control

While default ringfire channels operate in LatestWins lossy mode (writer never blocks), streaming pipelines requiring zero message drops can enable LosslessBackpressure:

use ringfire::{RingProducerBuilder, FlowControl, RingfireError};

let mut producer = RingProducerBuilder::new(4096)
    .flow_control(FlowControl::LosslessBackpressure)
    .build::<MarketTicker, _>("/dev/shm/reliable_stream")?;

// Writer throttles (via spin/yield backoff) if slowest registered reader is about to be lapped:
producer.push(&ticker);

// Or use non-blocking try_push:
match producer.try_push(&ticker) {
    Ok(()) => println!("Pushed successfully"),
    Err(RingfireError::BackpressureBufferFull) => println!("Slow reader lag detected, backpressure applied"),
    Err(e) => return Err(e.into()),
}

What the guarantee covers:

Only Rust RingConsumers registered in the ring's reader registry hold the producer back (default 32 slots, RingProducerBuilder::max_readers). On a lossless ring, attaching when the registry is full fails with NoAvailableReaderSlots. Python and C readers do not register and can be lapped.
A reader is protected from the moment the producer observes its registration (the producer rescans at least every half ring); a reader starting from Oldest on a busy ring can still find its first messages gone and reports them via lapped_count().
Reader liveness is checked by PID. Readers in another PID namespace (a different container) look dead to the producer and are dropped from the registry: share the PID namespace when using lossless mode across containers.
The producer checks the registry only when it approaches the slowest cached cursor, so the lossless hot path has no syscalls and costs under 1 ns over lossy mode.

9. Multi-Channel Multiplexing (`RingMultiplexer` & `AsyncRingMultiplexer`)

Multiplex across multiple distinct ring buffer streams with fair Round-Robin or strict Priority scheduling:

use ringfire::{RingConsumer, RingMultiplexer};

let c1 = RingConsumer::<MarketTicker>::attach("/dev/shm/stream_btc")?;
let c2 = RingConsumer::<MarketTicker>::attach("/dev/shm/stream_eth")?;

let mut mux = RingMultiplexer::new();
mux.add(c1);
mux.add(c2);

// Fair Round-Robin across all channels
if let Some((channel_idx, ticker)) = mux.try_recv_any() {
    println!("Channel {} received ticker {}", channel_idx, ticker.asset_id);
}

// Or Tokio async multiplexing:
// let (idx, ticker) = async_mux.recv_any().await;

10. CLI Diagnostic & Monitoring Tool (`ringfire`)

The bundled ringfire binary provides real-time terminal monitoring and inspection:

# View buffer configuration, sequence counters, and registered consumer lag
cargo run --bin ringfire -- stat /dev/shm/hft_ticker_stream

# Machine-readable JSON output for automated telemetry:
cargo run --bin ringfire -- stat /dev/shm/hft_ticker_stream --json

# Real-time interactive terminal dashboard with msg/s and MB/s throughput:
cargo run --bin ringfire -- top /dev/shm/hft_ticker_stream --interval-ms 500

# Dump recent slots and payloads in hex or ASCII:
cargo run --bin ringfire -- dump /dev/shm/hft_ticker_stream --tail 10 --hex

# Clean up dead reader slots from crashed processes:
cargo run --bin ringfire -- prune /dev/shm/hft_ticker_stream

11. Network Mirrors: One Source Ring, Identical Copies on Other Hosts

One process writes a ring. Other hosts get a mirror: a ring with the same geometry and the same sequence numbers, kept up to date over the network. Readers on a mirror host attach to it with RingConsumer or BlobConsumer exactly as they would on the source host and never touch the network. Records travel as raw slot bytes, so one ringfire serve / ringfire mirror pair works for any element type, fixed-size or variable-length (BlobProducer rings included). The full design, the story of what the stress tests found and every measurement are in docs/replication.md.

Choosing how records travel

Route	Flag	Why
Processes on one host	none needed	they share the ring: 0.1 µs, no network
Your own LAN with your own switch	`--multicast GROUP:PORT`	one datagram whatever the number of mirrors; ~30 µs to every mirror, flat
Between sites, into a cloud, from behind NAT	`--udp PORT` on the source, `--unicast` on the mirror, `--dup 2` on long links	clouds and hosters do not route multicast; TCP pays a full round trip per lost segment, this path pays nothing
One mirror over a link that only passes TCP	default	simplest; a thread and a `write` per mirror on the source

Whatever the transport: records are written to a mirror strictly in sequence order, never reordered, never duplicated. A lost datagram is asked back with NAK over the TCP control connection and answered from the source ring; the ring is the retransmission buffer, so a mirror can be behind by up to capacity records (262,144 slots is 262 ms at 1 M msg/s, 26 s at 10 k msg/s). Only beyond that a mirror gets a GAP and its readers see the same lapped count a slow reader on the source host would.

A site hub: cross the network once per site

A mirror is an ordinary ring, so a site runs one mirror over the WAN and serves it again locally. One copy crosses the ocean however many readers the site has, and every hop keeps the source's sequence numbers.

Measured through such a hub on the LAN: 52.9 µs p50 end to end against 10.3 µs for a direct mirror, This is an end-to-end observation, not an isolated measurement of hub processing cost. (VPCs have no native multicast, only Transit Gateway multicast domains, so inside a cloud the hub sends unicast to each instance: one sendto per instance per frame.)

CLI

# Source host, LAN with multicast
ringfire serve /dev/shm/ticks --bind 0.0.0.0:7400 --multicast 239.255.0.1:7401 --iface 10.0.0.5 --spin
# Mirror hosts on that LAN (join on the NIC facing the source)
ringfire mirror 10.0.0.5:7400 /dev/shm/ticks --iface 10.0.0.7 --spin

# Source host, mirrors anywhere (other sites, clouds, behind NAT)
ringfire serve /dev/shm/ticks --bind 0.0.0.0:7400 --udp 7403 --dup 2 --spin
ringfire mirror source.example:7400 /dev/shm/ticks --unicast --spin

# Site hub: mirror the source, then serve the mirror ring to local readers
ringfire mirror source.example:7400 /dev/shm/ticks --unicast --spin &
ringfire serve  /dev/shm/ticks --bind 0.0.0.0:7400 --udp 7403 --spin

# Resume after a restart from the last record in the local ring
ringfire mirror source.example:7400 /dev/shm/ticks --from resume --unicast

serve options: --batch N records per frame, --linger-us N (frame pacing, adaptive by default), --mtu N datagram budget (8972 with jumbo frames on a LAN, ~1400 through a tunnel), --ttl, --iface. mirror options: --from latest|oldest|resume|N, --iface, --once, --reconnect-ms.

Rust

use ringfire::{Mirror, MirrorStart, MulticastConfig, ReplicaServer, RingConsumer};
use std::net::Ipv4Addr;

// Source host, LAN: one datagram for all mirrors.
let server = ReplicaServer::bind("/dev/shm/ticks", "0.0.0.0:7400")?
    .multicast(MulticastConfig::new(Ipv4Addr::new(239, 255, 0, 1), 7401).interface(source_nic))
    .spin(true);
server.spawn()?;

// Source host, mirrors anywhere: UDP unicast from port 7403, every datagram twice.
let server = ReplicaServer::bind("/dev/shm/ticks", "0.0.0.0:7400")?
    .unicast(7403, 1400)
    .duplicate(2)
    .spin(true);
server.spawn()?;

// Mirror host.
let mut mirror = Mirror::builder()
    .start(MirrorStart::Oldest)   // everything the source still retains, then live
    .unicast(true)                // ask for UDP unicast (ignored if the source has none)
    .spin(true)
    .connect("source.example:7400", "/dev/shm/ticks")?;
std::thread::spawn(move || mirror.run());

// Readers on the mirror host: the same code as on the source host.
let mut reader = RingConsumer::<Tick>::attach("/dev/shm/ticks")?;
while let Some(tick) = reader.try_recv() { /* ... */ }

BlobProducer rings need nothing extra: readers use BlobConsumer on the mirror, blobs keep their bytes, length, flags and sequence, and only sit at a different offset in the mirror's arena.

What it costs

64-byte records, one message every 100 µs unless noted, everything busy-polling, Linux 6.8, kernel network stack, two Ryzen 9 7950X hosts on a 1 GbE LAN.

Stage	p50	p99
push → read by a consumer on the source (same ring)	0.1 µs	0.1 µs
push → read on a mirror on the same host, multicast	3.8 µs	4.9 µs
push → read on a mirror on the other host, multicast, each of six	30–32 µs	33–36 µs
push → read on a mirror on the other host, UDP unicast, each of six	38–48 µs	50–63 µs
push → read on a mirror on the other host, TCP	31–34 µs	35–38 µs
push → read on a leaf behind a hub, unicast both hops	52.9 µs	57.3 µs

Round trip, 20,000 samples	Transport	p50	p99	max
two hosts, one mirror each way	TCP	54.0 µs	59.1 µs	1.3 ms
two hosts, one mirror each way	multicast	53.1 µs	59.9 µs	86.9 µs
the same with 16 more mirrors on the second host	TCP	53.5 µs	235 µs	11.0 ms
the same with 16 more mirrors on the second host	multicast	63.7 µs	72.1 µs	84 µs

Sustained, open loop, 5 s per point	Transport, frame linger	Delivered	RTT p50	RTT p99
1,000/s	multicast, adaptive	100 %	55 µs	62 µs
20,000/s	multicast, none	100 %	51 µs	65 µs
100,000/s	multicast, 100 µs	100 %	221 µs	272 µs
1,000,000/s	multicast, 300 µs	100 %	0.93 ms	1.8 ms

Tokyo → Los Angeles, 100 ms ping, 1,000/s	one-way p50	p99	max
TCP	50.4 ms	99.6 ms	132 ms
UDP unicast	51.7 ms	51.7 ms	61 ms
UDP unicast, every datagram twice	51.5 ms	51.6 ms	58 ms

No record was lost or reordered at any point of any of these runs. Above ~20,000 msg/s every unbatched record costs a datagram and a system call, and the kernel path here sustains about 40,000 datagrams/s; frame pacing (--linger-us, adaptive by default: frames leave at most once per 50 µs unless full) is what keeps 100,000/s at 221 µs instead of over a millisecond. Near 1 M/s the 1500-byte MTU is the limit; jumbo frames raise it six-fold. Going below the kernel stack means bypassing it (AF_XDP, DPDK, Onload), which is the planned next transport.

Tuning checklist

Give every mirror process two cores when it busy-polls (--spin): the mirror thread and the reader are both spinning; one core for both turns microseconds into scheduler slices of milliseconds.
--iface on multi-homed hosts (docker bridges, several NICs); on Linux a socket bound to a port receives every multicast group joined on that port, so give each stream its own port.
--mtu 8972 once the NICs and the switch pass jumbo frames; --mtu 1400 through WireGuard or other tunnels, never above the path MTU on a WAN.
Size the source ring for the burst you want mirrors to survive: retention is capacity records.
--linger-us 0 for a steady stream near 20,000 msg/s where every microsecond counts; a fixed --linger-us 100 for a steady 100,000 msg/s.

🛡️ Memory Safety: Why Raw Pointers into Shared Memory Are Dangerous

In cross-process shared memory with a non-blocking writer (LatestWins), returning a raw pointer (*const T) directly into the mapped /dev/shm buffer is fundamentally unsafe:

If a consumer holds a raw pointer to slot $K$, and the writer laps the buffer and begins overwriting slot $K$ on another CPU core, the consumer will observe a torn read (half old data, half new data).
In high-frequency trading and order book streaming, a torn read corrupts prices and sizes, leading to disastrous trading errors.

The `ringfire` Solution: Slot Seqlock (protocol v2)

ringfire enforces tear-free reads without locks. The writer:

Stores SLOT_WRITING (u64::MAX) into the slot sequence, then a release fence.
Copies the payload.
Stores the message sequence with Ordering::Release.

The reader:

Loads the slot sequence $s_1$ with Ordering::Acquire; proceeds only if $s_1$ is the sequence it wants.
Copies the payload into its own stack/registers.
Issues an acquire fence and reloads the sequence $s_2$.
If $s_1 = s_2$, the copy is consistent. Otherwise the writer lapped the reader mid-copy: the copy is discarded and the reader jumps to the oldest retained message, counting the gap in lapped_count().

Before v0.4.0 the writer skipped step 1, so a reader exactly one slot short of being lapped could accept a half-overwritten payload; the regression suite now hammers this case.

Warning

Anti-Pattern: Returning Raw Pointers into Shared Memory (*const T) Some naive IPC designs attempt to return a direct pointer or slice &[u8] into /dev/shm to claim "zero-memcpy". In a multi-process architecture with a non-blocking writer (LatestWins), this is a dangerous anti-pattern: the writer can overwrite that memory slot at any microsecond while the reader is parsing it, causing undefined behavior, silent data races, and torn reads.

ringfire deliberately copies the slot payload into the reader's stack/register space inside a seqlock validation boundary (s1 == s2). Copy costs depend on payload size, compiler and cache state; the benchmarks measure the complete validated receive loop.

⚡ Wait Strategies

ringfire supports selectable wait strategies depending on CPU budget:

BusySpin: Polls continuously with core::hint::spin_loop(), consuming a core while idle.
YieldBackoff: Spins for $K$ iterations then calls std::thread::yield_now(). Wake-up latency depends on scheduling and load.
FutexWait: Sleeps on Linux futex when the queue is idle (timed sleep elsewhere). The consumer uses an asymmetric memory barrier before sleeping on supported Linux kernels. Sleeps remain bounded (10 ms when no timeout is set); latency depends on scheduling and the configured spin limit.

🐍 Polyglot Access (C / C++ / Python)

C11 Header: Include include/ringfire.h in any C/C++ project without linking overhead (header-only consumer), or link the cdylib/staticlib for the FFI producer, consumer and blackboard.
Python (python/ringfire): RingConsumer, RingProducer, BlobConsumer (zero-copy try_recv or validated try_recv_copy) and the blackboard, via mmap + ctypes.Structure.

All bindings speak protocol v2 and locate slots through the header's slots_offset; v1 and v2 peers refuse each other (VersionMismatch) instead of misreading memory. Python producers rely on x86-64 store ordering (Python has no fences); use a Rust or C producer on AArch64.

📐 Delivery Semantics at a Glance

Channel	Producer blocks?	Slow consumer	Delivery
`RingProducer` (default `LossyLatestWins`)	Never	Lapped: skips to the oldest retained message, `lapped_count()`	At-most-once per reader, in order
`RingProducer` + `LosslessBackpressure`	When the slowest registered reader is a full ring behind	Holds the producer	Exactly-once, in order, for registered Rust readers
`BlobProducer` / `BlobConsumer`	Never	Skips messages whose ring slot or arena bytes were overwritten	At-most-once per reader, in order
`MpmcProducer` / `MpmcQueueConsumer`	Never	Overrun items are dropped, `dropped_count()`	At-most-once, each item to one consumer
`BlackboardProducer` / `BlackboardConsumer`	Never	Always reads the latest value	Latest-value snapshot per key

📚 Citation

If you use ringfire in academic research or technical publications, please cite this verified source archive snapshot:

@software{panasenko_ringfire_2026,
  author    = {Panasenko, Alexander},
  title     = {ringfire: Shared-memory ring buffers for Rust},
  month     = sep,
  year      = 2026,
  publisher = {Zenodo},
  version   = {0.5.3+licenses.462b66d},
  doi       = {10.5281/zenodo.23028631},
  url       = {https://doi.org/10.5281/zenodo.23028631}
}

Note

This archival snapshot (0.5.3+licenses.462b66d, commit 462b66d5cb6e400e7a406e872e7f2409ac861d7b) differs from original tag v0.5.3 only by adding standard LICENSE-MIT and LICENSE-APACHE license texts; it is not a separate tagged release. For complete metadata, see CITATION.cff or the Zenodo record (Concept DOI: 10.5281/zenodo.23028630, Version DOI: 10.5281/zenodo.23028631).

👥 Author

Alexander Panasenko

Email: alex@prod.codes
GitHub: @alex09x

📜 License

Licensed under either of:

Apache License, Version 2.0 (LICENSE-APACHE)
MIT License (LICENSE-MIT)

at your option.

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
benches		benches
docs		docs
examples		examples
include		include
python/ringfire		python/ringfire
scripts		scripts
src		src
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE-APACHE		LICENSE-APACHE
LICENSE-MIT		LICENSE-MIT
README.md		README.md
ROADMAP.md		ROADMAP.md

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Repository files navigation

ringfire 🔥

⚡ Performance at a Glance

💡 The Problem: Why Traditional IPC Fails Under High Load

The Three Critical Pain Points:

🎯 The Solution & Vision: What `ringfire` Solves

🤝 The Rapidfire Family

🚀 Detailed Benchmarks (AMD Ryzen 9 7950X on Linux `booster`)

📦 Quickstart (Rust)

1. Producer: Publish Fixed-Size Binary Records

2. Tokio Async Consumer: Cooperative Non-Blocking Streaming

3. Synchronous Low-Latency Consumer (Dedicated Cores)

4. Shared State Blackboard (O(1) Snapshot Table)

5. Raw Binary Payloads (`[u8; N]`) & Pointer Casting

6. Variable-Length Payloads (`BlobProducer` & `BlobConsumer`)

7. Consumer Start Modes & Lock-Free SHM Checkpointing

8. Lossless Backpressure Flow Control

9. Multi-Channel Multiplexing (`RingMultiplexer` & `AsyncRingMultiplexer`)

10. CLI Diagnostic & Monitoring Tool (`ringfire`)

11. Network Mirrors: One Source Ring, Identical Copies on Other Hosts

Choosing how records travel

A site hub: cross the network once per site

CLI

Rust

What it costs

Tuning checklist

🛡️ Memory Safety: Why Raw Pointers into Shared Memory Are Dangerous

The `ringfire` Solution: Slot Seqlock (protocol v2)

⚡ Wait Strategies

🐍 Polyglot Access (C / C++ / Python)

📐 Delivery Semantics at a Glance

📚 Citation

👥 Author

📜 License

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ringfire 🔥

⚡ Performance at a Glance

💡 The Problem: Why Traditional IPC Fails Under High Load

The Three Critical Pain Points:

🎯 The Solution & Vision: What ringfire Solves

🤝 The Rapidfire Family

🚀 Detailed Benchmarks (AMD Ryzen 9 7950X on Linux booster)

📦 Quickstart (Rust)

1. Producer: Publish Fixed-Size Binary Records

2. Tokio Async Consumer: Cooperative Non-Blocking Streaming

3. Synchronous Low-Latency Consumer (Dedicated Cores)

4. Shared State Blackboard (O(1) Snapshot Table)

5. Raw Binary Payloads ([u8; N]) & Pointer Casting

6. Variable-Length Payloads (BlobProducer & BlobConsumer)

7. Consumer Start Modes & Lock-Free SHM Checkpointing

8. Lossless Backpressure Flow Control

9. Multi-Channel Multiplexing (RingMultiplexer & AsyncRingMultiplexer)

10. CLI Diagnostic & Monitoring Tool (ringfire)

11. Network Mirrors: One Source Ring, Identical Copies on Other Hosts

Choosing how records travel

A site hub: cross the network once per site

CLI

Rust

What it costs

Tuning checklist

🛡️ Memory Safety: Why Raw Pointers into Shared Memory Are Dangerous

The ringfire Solution: Slot Seqlock (protocol v2)

⚡ Wait Strategies

🐍 Polyglot Access (C / C++ / Python)

📐 Delivery Semantics at a Glance

📚 Citation

👥 Author

📜 License

About

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🎯 The Solution & Vision: What `ringfire` Solves

🚀 Detailed Benchmarks (AMD Ryzen 9 7950X on Linux `booster`)

5. Raw Binary Payloads (`[u8; N]`) & Pointer Casting

6. Variable-Length Payloads (`BlobProducer` & `BlobConsumer`)

9. Multi-Channel Multiplexing (`RingMultiplexer` & `AsyncRingMultiplexer`)

10. CLI Diagnostic & Monitoring Tool (`ringfire`)

The `ringfire` Solution: Slot Seqlock (protocol v2)