From 01e2bceab436c8f1caa38673891f8841862dc3f7 Mon Sep 17 00:00:00 2001 From: Justin Joyce Date: Sun, 18 Oct 2026 15:25:47 +0100 Subject: [PATCH] Add Rust Avro benchmarks and blog post proving rugo outperforms arrow-avro in Python --- benchmarks/avro-rust/Cargo.lock | 1185 +++++++++++++++++ benchmarks/avro-rust/Cargo.toml | 25 + benchmarks/avro-rust/examples/inspect.rs | 18 + benchmarks/avro-rust/src/main.rs | 108 ++ benchmarks/avro_one.py | 6 +- benchmarks/measure_avro_rust.py | 128 ++ benchmarks/results/2026-10-18-avro-rust.json | 188 +++ docs/blog/index.html | 11 + ...ant-python-pipelines-without-the-rust.html | 445 +++++++ .../table-1.png | Bin 0 -> 43881 bytes .../table-2.png | Bin 0 -> 80176 bytes .../table-3.png | Bin 0 -> 64400 bytes tools/medium_table_images.py | 58 + 13 files changed, 2170 insertions(+), 2 deletions(-) create mode 100644 benchmarks/avro-rust/Cargo.lock create mode 100644 benchmarks/avro-rust/Cargo.toml create mode 100644 benchmarks/avro-rust/examples/inspect.rs create mode 100644 benchmarks/avro-rust/src/main.rs create mode 100644 benchmarks/measure_avro_rust.py create mode 100644 benchmarks/results/2026-10-18-avro-rust.json create mode 100644 docs/blog/performant-python-pipelines-without-the-rust.html create mode 100644 medium/performant-python-pipelines-without-the-rust/table-1.png create mode 100644 medium/performant-python-pipelines-without-the-rust/table-2.png create mode 100644 medium/performant-python-pipelines-without-the-rust/table-3.png create mode 100644 tools/medium_table_images.py diff --git a/benchmarks/avro-rust/Cargo.lock b/benchmarks/avro-rust/Cargo.lock new file mode 100644 index 0000000..a8800ae --- /dev/null +++ b/benchmarks/avro-rust/Cargo.lock @@ -0,0 +1,1185 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "adler2" +version = "2.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" 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measure_avro_rust.py: arrow-avro (Apache Arrow's columnar +# reader) and apache-avro (the Apache Avro project's Rust crate, one Value per +# record). Built twice by the measure script: default features (arrow-avro's own +# deflate backend, miniz_oxide) and `--features zlib-rs` (flate2 switched to +# zlib-rs, the fastest pure-Rust inflater) so arrow-avro gets its best deflate. + +[dependencies] +apache-avro = "0.22.0" +arrow-array = "60.0.0" +arrow-avro = "60.0.0" +flate2 = { version = "1.1", default-features = false, features = ["rust_backend"] } +serde_json = "1" + +[features] +zlib-rs = ["flate2/zlib-rs"] + +[profile.release] +lto = "fat" +codegen-units = 1 diff --git a/benchmarks/avro-rust/examples/inspect.rs b/benchmarks/avro-rust/examples/inspect.rs new file mode 100644 index 0000000..c40ffef --- /dev/null +++ b/benchmarks/avro-rust/examples/inspect.rs @@ -0,0 +1,18 @@ +//! Print arrow-avro's output schema and row 0 of the first batch, for checking +//! that it decodes gen_avro.py's file to the same values as rugo/fastavro. +//! +//! cargo run --release --example inspect -- + +use std::io::Cursor; + +fn main() { + let path = std::env::args().nth(1).expect("usage: inspect "); + let data = std::fs::read(path).expect("read file"); + let mut r = arrow_avro::reader::ReaderBuilder::new() + .build(Cursor::new(&data[..])) + .expect("arrow-avro reader"); + let batch = r.next().expect("a batch").expect("decode"); + for (field, col) in batch.schema().fields().iter().zip(batch.columns()) { + println!("{} {:?}\n {:?}", field.name(), field.data_type(), col.slice(0, 1)); + } +} diff --git a/benchmarks/avro-rust/src/main.rs b/benchmarks/avro-rust/src/main.rs new file mode 100644 index 0000000..e5fa139 --- /dev/null +++ b/benchmarks/avro-rust/src/main.rs @@ -0,0 +1,108 @@ +//! Run one (reader, shape) Avro measurement - the Rust counterpart of avro_one.py. +//! +//! avro_rust [batch_size] [utf8_view] +//! +//! Prints one JSON line: best/median wall time in ms and the row count. Called by +//! measure_avro_rust.py; not intended for direct use. +//! +//! Every reader decodes the same in-memory bytes; reading the file is outside the +//! timing. One thread throughout (neither crate parallelises a single file). +//! +//! arrow-avro arrow_avro::reader::Reader -> Arrow RecordBatches (columnar; the +//! counterpart of rugo's Morsels). batch_size defaults to the +//! crate's 1024; utf8_view=1 decodes strings to StringViewArray. +//! apache-avro apache_avro::Reader -> one Value per record (row-oriented) +//! +//! Shapes: `all` reads every field; `narrow` reads id, amount and country. +//! arrow-avro is given a projection (pruned reader schema); apache-avro is given a +//! reader schema of just those fields, which is how it skips the rest. + +use std::io::Cursor; +use std::time::Instant; + +const NARROW: [&str; 3] = ["id", "amount", "country"]; + +fn writer_schema_json(data: &[u8]) -> serde_json::Value { + let reader = apache_avro::Reader::new(Cursor::new(data)).expect("open avro"); + serde_json::from_str(&reader.writer_schema().canonical_form()).expect("schema json") +} + +fn main() { + let args: Vec = std::env::args().collect(); + if args.len() < 5 { + eprintln!("usage: avro_rust [batch_size] [utf8_view]"); + std::process::exit(2); + } + let (reader, shape, path) = (args[1].as_str(), args[2].as_str(), &args[3]); + let reps: usize = args[4].parse().expect("reps"); + let batch_size: usize = args.get(5).map(|s| s.parse().expect("batch_size")).unwrap_or(1024); + let utf8_view = args.get(6).map(|s| s == "1").unwrap_or(false); + let narrow = match shape { + "all" => false, + "narrow" => true, + _ => panic!("unknown shape {shape}"), + }; + let data = std::fs::read(path).expect("read file"); + + // Top-level field indexes / pruned schema for the narrow shape. + let schema = writer_schema_json(&data); + let fields = schema["fields"].as_array().expect("record fields"); + let projection: Vec = NARROW + .iter() + .map(|n| fields.iter().position(|f| f["name"] == *n).expect("narrow field")) + .collect(); + let mut pruned = schema.clone(); + pruned["fields"] = serde_json::Value::Array(projection.iter().map(|&i| fields[i].clone()).collect()); + let apache_reader_schema = apache_avro::Schema::parse_str(&pruned.to_string()).expect("reader schema"); + + let go = || -> usize { + match reader { + "arrow-avro" => { + let mut b = arrow_avro::reader::ReaderBuilder::new() + .with_batch_size(batch_size) + .with_utf8_view(utf8_view); + if narrow { + b = b.with_projection(projection.clone()); + } + let r = b.build(Cursor::new(&data[..])).expect("arrow-avro reader"); + r.map(|batch| batch.expect("arrow-avro batch").num_rows()).sum() + } + "apache-avro" => { + let r = if narrow { + apache_avro::Reader::builder(Cursor::new(&data[..])) + .reader_schema(&apache_reader_schema) + .build() + } else { + apache_avro::Reader::new(Cursor::new(&data[..])) + } + .expect("apache-avro reader"); + r.map(|v| { + v.expect("apache-avro record"); + 1usize + }) + .sum() + } + _ => panic!("unknown reader {reader}"), + } + }; + + let mut times = Vec::with_capacity(reps); + let mut rows = 0; + for _ in 0..reps { + let t = Instant::now(); + rows = std::hint::black_box(go()); + times.push(t.elapsed().as_secs_f64() * 1000.0); + } + let best = times.iter().cloned().fold(f64::INFINITY, f64::min); + let mut sorted = times.clone(); + sorted.sort_by(|a, b| a.partial_cmp(b).unwrap()); + let median = if reps % 2 == 1 { + sorted[reps / 2] + } else { + (sorted[reps / 2 - 1] + sorted[reps / 2]) / 2.0 + }; + println!( + "{{\"best_ms\": {:.1}, \"median_ms\": {:.1}, \"rows\": {}}}", + best, median, rows + ); +} diff --git a/benchmarks/avro_one.py b/benchmarks/avro_one.py index e70bded..56ed425 100644 --- a/benchmarks/avro_one.py +++ b/benchmarks/avro_one.py @@ -9,6 +9,7 @@ timing. rugo rugo.avro.read_avro -> columnar Morsels (no Python object per value) + rugo-1t the same, max_threads=1 rugo+pylist the same, then every column .to_pylist() - the cost of ending in Python objects, which is what the other two return fastavro fastavro.reader -> one dict per record @@ -32,14 +33,15 @@ def narrow_schema(): return {**schema, "fields": [f for f in schema["fields"] if f["name"] in NARROW]} -if reader in ("rugo", "rugo+pylist"): +if reader in ("rugo", "rugo-1t", "rugo+pylist"): from rugo.avro import read_avro columns = NARROW if shape == "narrow" else None to_python = reader == "rugo+pylist" + threads = 1 if reader == "rugo-1t" else 0 def go(): n = 0 - with read_avro(data, columns=columns) as r: + with read_avro(data, columns=columns, max_threads=threads) as r: for morsel in r: n += morsel.num_rows if to_python: diff --git a/benchmarks/measure_avro_rust.py b/benchmarks/measure_avro_rust.py new file mode 100644 index 0000000..5a7418f --- /dev/null +++ b/benchmarks/measure_avro_rust.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +"""Race rugo's Avro reader against the Rust Avro readers on this machine. + + python3 benchmarks/measure_avro_rust.py [--rows 200000] [--rounds 5] [--reps 5] + [--python 3.14] [--rugo-src /path/to/opteryx-core] + +Needs `uv` and `cargo` on PATH. Same file and method as measure_avro.py: gen_avro.py +writes a deterministic 20-field file with fastavro (so no reader is timed on its own +writer's output), each reader decodes the same in-memory bytes in a fresh process, +reading the file is outside the timing. Both codecs, `deflate` and `null` (`null` +isolates decode from inflate). + +Arms - single thread unless the name says otherwise: + rugo-1t PyPI rugo, read_avro(max_threads=1) -> Morsels (avro_one.py) + rugo PyPI rugo, every core (rugo's default) - NOT part of the race + rugo-src-1t / -src the same from an opteryx-core source tree (--rugo-src) + arrow-avro Apache Arrow's arrow-avro -> RecordBatches, crate defaults + (batch 1024, StringArray, miniz_oxide inflate) + arrow-avro-tuned the same with batch 65,536 (rugo's morsel size) and flate2 on + zlib-rs, the fastest Rust inflater - arrow-avro at its best + arrow-avro-tuned-view tuned + StringViewArray strings + apache-avro the Apache Avro Rust crate -> one Value per record + +Rounds interleave the arms (order rotated each round) so thermal drift and order +bias land on every arm; each (round, arm) is a fresh process running `reps` reads. +Per arm: best = min over every read, median = median of the per-process medians. +Writes benchmarks/results/-avro-rust.json. Results are only comparable to +earlier runs from the same machine. +""" +import argparse, datetime, json, os, platform, statistics, subprocess, tempfile + +HERE = os.path.dirname(os.path.abspath(__file__)) +CRATE = f"{HERE}/avro-rust" +ap = argparse.ArgumentParser() +ap.add_argument("--rows", type=int, default=200_000) +ap.add_argument("--rounds", type=int, default=5) +ap.add_argument("--reps", type=int, default=5) +ap.add_argument("--python", default="3.14") +ap.add_argument("--rugo-src") +ap.add_argument("--workdir", default=tempfile.mkdtemp(prefix="rugo-avro-rust-")) +a = ap.parse_args() +W = a.workdir + + +def sh(*cmd, env=None): + return subprocess.run(cmd, check=True, capture_output=True, text=True, env=env).stdout + + +def venv(name, pkgs): + p = f"{W}/venv-{name}" + sh("uv", "venv", "--python", a.python, p) + sh("uv", "pip", "install", "--python", f"{p}/bin/python", *pkgs) + return f"{p}/bin/python" + + +# --- builds: one binary per flate2 backend +print("building Rust readers", flush=True) +BIN = {} +for variant, extra in (("default", []), ("zlib-rs", ["--features", "zlib-rs"])): + sh("cargo", "build", "--release", "--quiet", "--manifest-path", f"{CRATE}/Cargo.toml", + "--target-dir", f"{CRATE}/target/{variant}", *extra) + BIN[variant] = f"{CRATE}/target/{variant}/release/avro_rust" +lock = open(f"{CRATE}/Cargo.lock").read().split("[[package]]") +crate_versions = {} +for block in lock: + fields = dict(line.split(" = ", 1) for line in block.strip().splitlines() if " = " in line) + name = fields.get("name", "").strip('"') + if name in ("arrow-avro", "apache-avro", "flate2", "zlib-rs", "miniz_oxide"): + crate_versions[name] = fields["version"].strip('"') + +# --- Python side +pypi = venv("pypi", ["rugo", "fastavro"]) +ARMS = {"rugo-1t": [pypi, f"{HERE}/avro_one.py", "rugo-1t"], + "rugo": [pypi, f"{HERE}/avro_one.py", "rugo"]} +ENV = {} +versions = {"rugo": sh(pypi, "-c", "import rugo;print(rugo.__version__)").strip(), + "rustc": sh("rustc", "--version").strip(), **crate_versions} +if a.rugo_src: + srcpy = venv("src", ["fastavro"]) + ENV = {**os.environ, "PYTHONPATH": os.path.abspath(a.rugo_src)} + ARMS["rugo-src-1t"] = [srcpy, f"{HERE}/avro_one.py", "rugo-1t"] + ARMS["rugo-src"] = [srcpy, f"{HERE}/avro_one.py", "rugo"] + versions["rugo-src"] = sh(srcpy, "-c", "import rugo;print(rugo.__version__, rugo.__file__)", env=ENV).strip() +ARMS.update({ + "arrow-avro": [BIN["default"], "arrow-avro"], + "arrow-avro-tuned": [BIN["zlib-rs"], "arrow-avro"], + "arrow-avro-tuned-view": [BIN["zlib-rs"], "arrow-avro"], + "apache-avro": [BIN["default"], "apache-avro"], +}) +RUST_TAIL = {"arrow-avro": ["1024", "0"], "arrow-avro-tuned": ["65536", "0"], + "arrow-avro-tuned-view": ["65536", "1"], "apache-avro": []} + +res = {"date": datetime.datetime.now(datetime.UTC).isoformat(timespec="seconds"), + "machine": {"platform": platform.platform(), "cpu": platform.processor() or platform.machine(), + "cores": os.cpu_count(), "python": a.python}, + "rows": a.rows, "rounds": a.rounds, "reps": a.reps, "versions": versions, + "files_mb": {}, "reads": {}} + +for codec in ("deflate", "null"): + path = f"{W}/events-{codec}.avro" + print("generating", codec, flush=True) + sh(pypi, f"{HERE}/gen_avro.py", path, str(a.rows), codec) + res["files_mb"][codec] = round(os.path.getsize(path) / 1e6, 1) + for shape in ("all", "narrow"): + runs = {arm: [] for arm in ARMS} + names = list(ARMS) + for rnd in range(a.rounds): + order = names[rnd % len(names):] + names[:rnd % len(names)] + for arm in order: + print(f"read: {codec} {shape} round {rnd + 1}/{a.rounds} {arm}", flush=True) + cmd = ARMS[arm] + [shape, path, str(a.reps)] + RUST_TAIL.get(arm, []) + env = ENV if arm.startswith("rugo-src") else None + out = json.loads(sh(*cmd, env=env).strip().splitlines()[-1]) + if out["rows"] != a.rows: + raise SystemExit(f"{arm} read {out['rows']} rows, expected {a.rows}") + runs[arm].append(out) + for arm, outs in runs.items(): + res["reads"][f"{codec}/{shape}/{arm}"] = { + "best_ms": min(o["best_ms"] for o in outs), + "median_ms": round(statistics.median(o["median_ms"] for o in outs), 1), + "rows": a.rows} + +os.makedirs(f"{HERE}/results", exist_ok=True) +fn = f"{HERE}/results/{res['date'][:10]}-avro-rust.json" +json.dump(res, open(fn, "w"), indent=2) +print("wrote", fn) +for key, value in res["reads"].items(): + print(f" {key:40s} best {value['best_ms']:9.1f} median {value['median_ms']:9.1f} ms") diff --git a/benchmarks/results/2026-10-18-avro-rust.json b/benchmarks/results/2026-10-18-avro-rust.json new file mode 100644 index 0000000..ebccb87 --- /dev/null +++ b/benchmarks/results/2026-10-18-avro-rust.json @@ -0,0 +1,188 @@ +{ + "date": "2026-10-18T14:08:56+00:00", + "machine": { + "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O", + "cpu": "arm", + "cores": 18, + "python": "3.14" + }, + "rows": 200000, + "rounds": 5, + "reps": 5, + "versions": { + "rugo": "0.5.4", + "rustc": "rustc 1.96.1 (31fca3adb 2026-06-26)", + "apache-avro": "0.22.0", + "arrow-avro": "60.0.0", + "flate2": "1.1.10", + "miniz_oxide": "0.9.1", + "zlib-rs": "0.6.5", + "rugo-src": "0.5.4 /Users/justin/Nextcloud/opteryx-core/rugo/__init__.py" + }, + "files_mb": { + "deflate": 21.6, + "null": 38.2 + }, + "reads": { + "deflate/all/rugo-1t": { + "best_ms": 84.1, + "median_ms": 85.1, + "rows": 200000 + }, + "deflate/all/rugo": { + "best_ms": 30.5, + "median_ms": 30.9, + "rows": 200000 + }, + "deflate/all/rugo-src-1t": { + "best_ms": 82.8, + "median_ms": 83.9, + "rows": 200000 + }, + "deflate/all/rugo-src": { + "best_ms": 30.1, + "median_ms": 30.3, + "rows": 200000 + }, + "deflate/all/arrow-avro": { + "best_ms": 116.4, + "median_ms": 117.6, + "rows": 200000 + }, + "deflate/all/arrow-avro-tuned": { + "best_ms": 101.4, + "median_ms": 102.3, + "rows": 200000 + }, + "deflate/all/arrow-avro-tuned-view": { + "best_ms": 113.1, + "median_ms": 114.7, + "rows": 200000 + }, + "deflate/all/apache-avro": { + "best_ms": 267.4, + "median_ms": 272.0, + "rows": 200000 + }, + "deflate/narrow/rugo-1t": { + "best_ms": 52.3, + "median_ms": 52.9, + "rows": 200000 + }, + "deflate/narrow/rugo": { + "best_ms": 18.2, + "median_ms": 18.3, + "rows": 200000 + }, + "deflate/narrow/rugo-src-1t": { + "best_ms": 51.1, + "median_ms": 51.5, + "rows": 200000 + }, + "deflate/narrow/rugo-src": { + "best_ms": 17.8, + "median_ms": 18.0, + "rows": 200000 + }, + "deflate/narrow/arrow-avro": { + "best_ms": 97.8, + "median_ms": 98.3, + "rows": 200000 + }, + "deflate/narrow/arrow-avro-tuned": { + "best_ms": 82.8, + "median_ms": 83.2, + "rows": 200000 + }, + "deflate/narrow/arrow-avro-tuned-view": { + "best_ms": 82.8, + "median_ms": 83.2, + "rows": 200000 + }, + "deflate/narrow/apache-avro": { + "best_ms": 398.0, + "median_ms": 402.0, + "rows": 200000 + }, + "null/all/rugo-1t": { + "best_ms": 46.3, + "median_ms": 47.9, + "rows": 200000 + }, + "null/all/rugo": { + "best_ms": 17.3, + "median_ms": 17.7, + "rows": 200000 + }, + "null/all/rugo-src-1t": { + "best_ms": 45.5, + "median_ms": 46.3, + "rows": 200000 + }, + "null/all/rugo-src": { + "best_ms": 17.1, + "median_ms": 17.4, + "rows": 200000 + }, + "null/all/arrow-avro": { + "best_ms": 40.2, + "median_ms": 40.7, + "rows": 200000 + }, + "null/all/arrow-avro-tuned": { + "best_ms": 40.2, + "median_ms": 40.6, + "rows": 200000 + }, + "null/all/arrow-avro-tuned-view": { + "best_ms": 51.0, + "median_ms": 51.6, + "rows": 200000 + }, + "null/all/apache-avro": { + "best_ms": 200.4, + "median_ms": 204.3, + "rows": 200000 + }, + "null/narrow/rugo-1t": { + "best_ms": 14.2, + "median_ms": 14.5, + "rows": 200000 + }, + "null/narrow/rugo": { + "best_ms": 5.0, + "median_ms": 5.1, + "rows": 200000 + }, + "null/narrow/rugo-src-1t": { + "best_ms": 13.9, + "median_ms": 14.2, + "rows": 200000 + }, + "null/narrow/rugo-src": { + "best_ms": 4.8, + "median_ms": 4.9, + "rows": 200000 + }, + "null/narrow/arrow-avro": { + "best_ms": 20.6, + "median_ms": 21.1, + "rows": 200000 + }, + "null/narrow/arrow-avro-tuned": { + "best_ms": 20.7, + "median_ms": 21.2, + "rows": 200000 + }, + "null/narrow/arrow-avro-tuned-view": { + "best_ms": 20.8, + "median_ms": 21.0, + "rows": 200000 + }, + "null/narrow/apache-avro": { + "best_ms": 328.4, + "median_ms": 335.5, + "rows": 200000 + } + } +} \ No newline at end of file diff --git a/docs/blog/index.html b/docs/blog/index.html index bccbf3b..cca54a3 100644 --- a/docs/blog/index.html +++ b/docs/blog/index.html @@ -31,6 +31,17 @@

Blog

PyArrow, pandas or NumPy. Measurements, not estimates.

+ +

Performant Python pipelines without the Rust

+

18 October 2026

+

+ rugo's Avro reader, called from Python, against Apache + Arrow's Rust reader. On one thread Python won three of + four races, by 1.2–1.6×. On its defaults rugo was + 2.3–4.5× faster than any Rust result. +

+ +

When rugo makes your Lambda cheaper, and when it diff --git a/docs/blog/performant-python-pipelines-without-the-rust.html b/docs/blog/performant-python-pipelines-without-the-rust.html new file mode 100644 index 0000000..b13643a --- /dev/null +++ b/docs/blog/performant-python-pipelines-without-the-rust.html @@ -0,0 +1,445 @@ + + + + + + Performant Python pipelines without the Rust + + + + + + + +
+
+
+

Performant Python pipelines without the Rust

+

+ Justin Joyce · 18 October 2026 + · 6 min read +

+
+ avro + rust + performance + benchmarks +
+

+ Correct at time of publishing. For up-to-date + performance and feature information, see + rugo.dev. +

+
+ +

+ Called from Python, rugo reads Avro faster than + arrow-avro, Apache Arrow's own Rust reader. + On one thread it won three of four races, + by 1.2× to 1.6×. Left on its defaults, it uses every core + and was 2.3× to 4.5× faster than any Rust + result. +

+ +

+ There's a familiar arc in data engineering. The Python + pipeline gets slow, someone benchmarks it, and the + conclusion is always the same: rewrite the hot path in Rust. + Weeks later you have a second language, a build toolchain, + FFI glue, and a team that can't all read the code. +

+ +

+ We think that trade is usually unnecessary. To test it, we + raced rugo's Avro reader, called from Python, against the + best Avro readers Rust has, called from Rust. +

+ +

The contenders

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
ReaderCalled fromReturns
rugo 0.5.4Pythoncolumnar Morsels
+ arrow-avro + 60.0.0 + RustArrow RecordBatches
apache-avro 0.22.0Rustone Value per record
+
+ +

+ arrow-avro is the one to beat. It decodes straight into + columns, as rugo does, and in our runs it was 2.7× to 16× + faster than the older apache-avro crate. We ran it twice: + once on its defaults, and once tuned as far as we could push + it, with 65,536-row batches and its deflate switched to + zlib-rs, a faster Rust inflater than its default. +

+ +

The test

+ +

+ One file: 200,000 rows of a 20-field event record, the mix + you'd see in a Kafka topic or a log stream. Longs, doubles, + strings, enums, nullable unions, timestamps, dates, a + decimal, a fixed(16), a nested record, an array + and a map. fastavro wrote it, so no reader is timed on its + own output. +

+ +

+ We wrote it twice, once with deflate (21.6 MB) and once + uncompressed (38.2 MB), and read it two ways: +

+ +
    +
  • all fields: every one of the 20.
    • +
  • + 3 fields: id, + amount and country. rugo is + given columns; arrow-avro a projection; + apache-avro a reader schema of just those fields. +
    • +
+ +

+ Every reader decoded the same in-memory bytes, so disk wasn't + part of the timing. Each run was a fresh process on one + thread. The readers took turns across five rounds, and the + figures are medians. The machine is an 18-core Apple Silicon + Mac. +

+ +

One thread each

+ +

Median read time, shorter is faster:

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Racerugo (Python)arrow-avroapache-avrorugo vs arrow-avro
deflate, all fields85.1 ms102.3 ms272.0 ms1.20× faster
deflate, 3 fields52.9 ms83.2 ms402.0 ms1.57× faster
uncompressed, all fields47.9 ms40.6 ms204.3 ms1.18× slower
uncompressed, 3 fields14.5 ms21.0 ms335.5 ms1.45× faster
+
+ +

+ Python won three of the four. The arrow-avro figures are its + tuned build; on its defaults it was 15 ms slower on the + deflate races and level on the uncompressed ones. + apache-avro was never in it: it came last in every race, up + to 23× slower than rugo. +

+ +

Every core

+ +

+ One thread each keeps the race fair, but it isn't how anyone + runs rugo. On its defaults, rugo spreads a file's blocks + across every core. Neither Rust reader can do that for a + single file, so this compares rugo, untouched, with + arrow-avro at its best: +

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Racerugo, defaultsarrow-avro, tunedrugo vs arrow-avro
deflate, all fields30.9 ms102.3 ms3.3× faster
deflate, 3 fields18.3 ms83.2 ms4.5× faster
uncompressed, all fields17.7 ms40.6 ms2.3× faster
uncompressed, 3 fields5.1 ms21.0 ms4.1× faster
+
+ +

That's what you get without changing a setting.

+ +

Where we lost

+ +

+ One race went to Rust. Reading every field of the + uncompressed file on one thread, arrow-avro took 40.6 ms and + rugo took 47.9 ms, so arrow-avro was 18% faster. +

+ +

+ We're showing it because it's the purest test of the decoder + itself: no inflate, no skipping, just bytes into columns. + It's also the one we most want to win. +

+ +

+ Our best guess, not yet tested: rugo turns the nested record + and the map into JSON text, which is extra work. arrow-avro + builds native struct and map arrays instead. Reading the file + without those two fields would tell us whether the decoder + itself is behind. +

+ +

+ It doesn't change the headline. Kafka Connect, Spark and + Iceberg usually write Avro compressed, and on compressed + files Python wins. +

+ +

Why Python can be this fast

+ +

+ Python isn't doing the work. rugo is a native C++ engine with + a Python front door: you call it from Python, and the bytes + never pass through the interpreter. +

+ +
    +
  • + Compiled schema. rugo turns the Avro + schema into a flat program of decode steps once, then + runs that program over every block. Nothing is looked up + per row. +
    • +
  • + Straight into columns. Values land in + typed columnar vectors, with no Python object per value. + That's what you'd write in Rust; you just don't have to. +
    • +
  • + Fast inflate. Deflate blocks go through + libdeflate, a dedicated, heavily tuned inflate library. + On compressed files that's most of the time, and most of + the lead. +
    • +
  • + Cheap skipping. Fields you don't ask for + are stepped over, not decoded. On the uncompressed file, + reading 3 of 20 fields takes about a third of the time + of reading them all. +
    • +
  • + Every core. A file's blocks are + independent, so rugo spreads them across threads and + still hands back batches in file order. +
    • +
+ +

+ The point isn't that Rust is slow. arrow-avro is excellent + work. The point is that "fast" is a property of the engine, + not of the language you call it from. +

+ +

What this leaves out

+ +
    +
  • + One schema. A wide, flat record, or one + that's mostly strings, could move the margins either way. +
    • +
  • + One machine. Every figure here is from + Apple Silicon. We haven't run x86 yet. +
    • +
  • + Different outputs. rugo returns nested + records and maps as JSON text; arrow-avro returns native + struct and map arrays. Both end in columns, but they + aren't the same columns. +
    • +
  • + Other languages. We didn't test Go, + Java or C++ readers. The ones we know of decode a row at + a time, as apache-avro does. +
    • +
  • + Reading the file. Every reader got the + bytes already in memory. Reading from disk or object + storage adds the same time to each. +
    • +
+ +

Try it

+ +

+ pip install rugo is the whole install. There's + no toolchain to set up, and no PyArrow or NumPy: +

+ + 
from rugo.avro import read_avro
+
+with read_avro(
+    "events.avro", columns=["id", "amount", "country"]
+) as reader:
+    for morsel in reader:
+        print(morsel.num_rows, morsel.column("amount").to_pylist()[:3])
+ +

Reproducing this

+ +

+ Measured 18 October 2026 on an 18-core Apple Silicon Mac, + against rugo 0.5.4 from PyPI on CPython 3.14, and arrow-avro + 60.0.0 and apache-avro 0.22.0 built with Rust 1.96.1 + (release profile, fat LTO). The tuned arrow-avro build + switches flate2 to zlib-rs + 0.6.5; the default build uses miniz_oxide. + rugo runs with max_threads=1 for the + one-thread races. +

+ +

+ Each of the five rounds runs every reader in a fresh + process, five reads each, with the order rotated each round + so no reader always goes first. The figures are the median + of the per-process medians. +

+ +

+ The harness, the data generator and the Rust readers are in + benchmarks: measure_avro_rust.py builds both Rust + readers, writes the file and runs the race on your machine. + We'd like to hear what you get, especially on x86. +

+
+
+ +
+
+ rugo is part of the + Opteryx project — Apache + 2.0 + + GitHub + · + PyPI + +
+
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pillow python tools/medium_table_images.py docs/blog/.html + +Medium has no tables: a story imported from rugo.dev keeps its headings, text and +code, but each table has to go back in as an image. This renders each +`
` in the post on its own page with the blog's own +stylesheet (headless Microsoft Edge, 2x scale), trims the empty background, and +writes /table-1.png, table-2.png, ... in page order, printing which +section each one belongs under. +""" +import os, re, subprocess, sys, tempfile + +from PIL import Image + +EDGE = "/Applications/Microsoft Edge.app/Contents/MacOS/Microsoft Edge" +DOCS = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "docs")) +PAD = 32 # px of background kept round the table, at 2x scale + +post, out = sys.argv[1], sys.argv[2] +html = open(post, encoding="utf-8").read() +os.makedirs(out, exist_ok=True) + +# Each table with the last

above it, so the caller knows where it goes. +tables = [] +for m in re.finditer(r'
.*?\s*
', html, re.S): + headings = re.findall(r"

(.*?)

", html[: m.start()], re.S) + tables.append((headings[-1].strip() if headings else "(top)", m.group(0))) +if not tables: + sys.exit(f"no tables in {post}") + +with tempfile.TemporaryDirectory() as tmp: + for n, (section, table) in enumerate(tables, 1): + page = f"{tmp}/table-{n}.html" + with open(page, "w", encoding="utf-8") as f: + f.write(f""" + + +{table}""") + raw = f"{tmp}/raw-{n}.png" + subprocess.run([EDGE, "--headless=new", "--disable-gpu", "--hide-scrollbars", + "--force-device-scale-factor=2", "--window-size=760,1600", + f"--screenshot={raw}", f"file://{page}"], + check=True, capture_output=True) + img = Image.open(raw).convert("RGB") + bg = img.getpixel((0, 0)) + diff = [(x, y) for y in range(0, img.height, 2) for x in range(0, img.width, 4) + if img.getpixel((x, y)) != bg] + if not diff: + sys.exit(f"table {n} rendered empty") + x0 = max(min(x for x, _ in diff) - PAD, 0) + y0 = max(min(y for _, y in diff) - PAD, 0) + x1 = min(max(x for x, _ in diff) + PAD, img.width) + y1 = min(max(y for _, y in diff) + PAD, img.height) + dest = f"{out}/table-{n}.png" + img.crop((x0, y0, x1, y1)).save(dest) + print(f"{dest} -> under '{section}'")