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Python binding for omq.rs (Rust libzmq port). Drop-in pyzmq replacement on the common path.

Project description

pyomq

Python binding for omq.rs, a Rust libzmq port. Drop-in pyzmq replacement on the common path.

Install

uv pip install pyomq
uv pip install 'pyomq[test]'   # adds pytest, pyzmq for the interop suite

The published wheel includes all optional features (curve, blake3zmq, lz4, zstd). Use pyomq.has("curve") at runtime to check availability.

Usage

import pyomq as zmq  # drop-in for `import zmq` from pyzmq

ctx = zmq.Context()
push = ctx.socket(zmq.PUSH)
push.connect("tcp://127.0.0.1:5555")
push.send(b"hello")
push.close()
ctx.term()

For asynchronous code:

import pyomq.asyncio as zmq_async

ctx = zmq_async.Context()
sock = ctx.socket(pyomq.PUSH)
await sock.connect("tcp://127.0.0.1:5555")
await sock.send(b"hello")
await sock.close()

Status

Sync and asyncio APIs both ship in this release. All 19 ZMTP socket types are wired:

  • Standard (RFC 28 + 47): PAIR, PUB, SUB, REQ, REP, DEALER, ROUTER, PULL, PUSH, XPUB, XSUB.
  • Draft: SERVER, CLIENT (RFC 41), RADIO, DISH (RFC 48), GATHER, SCATTER (RFC 49), PEER, CHANNEL (RFC 51).

Transports: tcp://, ipc://, inproc://, and udp:// (RADIO/DISH only). Optional features built into the wheel: curve, blake3zmq, lz4, zstd.

DISH groups: use socket.join(b"group") / socket.leave(b"group") to manage subscriptions; messages are sent as multipart [group, body].

Backend

pyomq is built on omq-compio (single-threaded io_uring on Linux). The runtime runs on a dedicated background thread; every Python call releases the GIL across the runtime trip. This is the only backend pyomq supports — the omq-tokio backend exists in the upstream Rust workspace for callers that need a multi-thread tokio integration, but pyomq's per-call overhead is shaped around compio's single-thread invariant.

Performance

See BENCHMARKS.md for full tables.

PUSH/PULL throughput: Python bindings

Loopback PUSH/PULL throughput vs pyzmq, on a Linux 6.12 (Debian 13) VM on an Intel Mac Mini 2018 (i7-8700B, 3.2 GHz), Rust 1.95.0, default features:

Size inproc pyomq inproc pyzmq ratio tcp pyomq tcp pyzmq ratio
8 B 1.30 M/s 627 k/s 2.08× 1.36 M/s 565 k/s 2.41×
32 B 1.29 M/s 620 k/s 2.08× 1.36 M/s 576 k/s 2.37×
128 B 1.31 M/s 516 k/s 2.54× 1.29 M/s 496 k/s 2.61×
512 B 1.29 M/s 480 k/s 2.69× 1.21 M/s 461 k/s 2.62×
2 KiB 1.17 M/s 461 k/s 2.54× 908 k/s 342 k/s 2.65×
8 KiB 1.04 M/s 368 k/s 2.83× 349 k/s 102 k/s 3.41×
32 KiB 622 k/s 196 k/s 3.17× 116 k/s 46 k/s 2.50×
128 KiB 203 k/s 70 k/s 2.91× 32 k/s 24 k/s 1.32×

zmq.proxy() forwarding (128 B, TCP)

pyomq pyzmq ratio
PUSH/PULL msg/s 963 k/s 520 k/s 1.85×
REQ/REP rt/s 8,764/s 6,521/s 1.34×

pyomq's proxy() runs as a native Rust async loop on the compio thread — no Python per-message overhead. pyzmq's zmq.proxy() calls libzmq's C-level zmq_proxy. PUSH/PULL forwarding is throughput-bound and pyomq is ~1.9× faster. REQ/REP is latency-bound (4 TCP hops per round-trip) so both are similar.

Run scripts/update_perf.py (after maturin develop --release) to re-measure and update the tables above.

Compression transports

ØMQ adds two transparent compression transports on top of TCP: lz4+tcp:// (fast, low-latency) and zstd+tcp:// (higher ratio, better for large or structured payloads). Swap the scheme in your endpoint string and everything else stays the same:

push = ctx.socket(zmq.PUSH)
push.bind("lz4+tcp://127.0.0.1:5555")   # or zstd+tcp://

pull = ctx.socket(zmq.PULL)
pull.connect("lz4+tcp://127.0.0.1:5555")

Both peers must use a matching compression endpoint. Payloads below ~512 B are sent as-is (the codec detects that compression would expand them). For realistic JSON payloads at 2 KiB, lz4 yields ~3.8× and zstd ~4.5× on a bandwidth-limited link.

zstd+tcp:// also auto-trains a dictionary: it samples the first 1000 outbound messages (or 100 KiB of plaintext, whichever comes first), builds an 8 KiB dict, and ships it to the peer once. After that the compression threshold drops from 512 B to 64 B, so small structured messages start compressing too. lz4+tcp:// does not auto-train (LZ4 has no standard dict trainer).

Virtual throughput on bandwidth-limited links (JSON payloads, compio backend):

Compression throughput at 1 Gbps

Compression throughput at 100 Mbps

See BENCHMARKS_COMPRESSION.md for full tables including dict-trained ratios.

Develop

cd bindings/pyomq
uv venv && source .venv/bin/activate
uv pip install maturin pytest pyzmq
maturin develop --release
pytest -v

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