Async streaming Python bindings for lol_html, a low-output-latency HTML rewriter

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Project description

lol-html-py

Async streaming Python bindings for lol_html, the low-output-latency HTML rewriter from Cloudflare. Built on PyO3 and Tokio, integrates with asyncio via pyo3-async-runtimes.

Why

lol_html is designed for streaming: it can begin emitting output before the full input has been received. Exposing that property to Python requires more than a thin FFI wrapper: per-chunk round trips across the PyO3 boundary will dominate runtime for small chunks. This package solves that by running the rewriter on a long-lived Tokio task, coalescing output in Rust, and crossing the FFI boundary only at channel endpoints with symmetric backpressure on both input and output. The chunk sizes that control the properties are all configurable.

Install

pip install lol-html-py

Usage

import asyncio
from lol_html import AsyncRewriter

async def main():
    rw = AsyncRewriter()

    async def produce():
        await rw.feed(b"<html><script>bad()</script><p>ok</p></html>")
        rw.close()

    async def consume():
        out = bytearray()
        async for chunk in rw:
            out += chunk
        return bytes(out)

    _, result = await asyncio.gather(produce(), consume())
    print(result)

asyncio.run(main())

Configuration

To control the latency/throughput trade-off we can vary the I/O and flushing parameters. These have sensible defaults and can be overridden per instance and via environment variables.

Constructor arg	Env var	Default	Meaning
`input_capacity`	`LOL_HTML_INPUT_CAPACITY`	`8`	Bounded input channel depth (producer backpressure)
`output_capacity`	`LOL_HTML_OUTPUT_CAPACITY`	`8`	Bounded output channel depth (consumer backpressure)
`flush_threshold`	`LOL_HTML_FLUSH_THRESHOLD`	`16384`	Bytes accumulated before flushing to the output channel
`flush_every_chunk`	`LOL_HTML_FLUSH_EVERY_CHUNK`	`false`	Force a flush after every input chunk, regardless of buffer size

Note that environment variables are read at library load time via a Rust constructor. Per-instance constructor arguments always take priority over env vars.

Operating points

Goal	`flush_threshold`	`flush_every_chunk`
SSE / per-token streaming	`1`	`True`
General low-latency streaming	`4096`–`16384`	`False`
High-throughput batch	`65536`+	`False`
Single output blob at end	`0`	`False`

flush_threshold=0 disables size-based flushing entirely; output emits only at end-of-stream or per-chunk forced flush.

Architecture

Python producer ──feed()──▶ [input channel, bounded] ──▶ parser task
                                                           │
                                                           ├─ HtmlRewriter.write()
                                                           ├─ sink coalesces into Vec
                                                           └─ flush ──▶ [output channel, bounded]
                                                                            │
                                                       Python consumer ◀────┘ (async for)

The parser task runs on a shared multi-threaded Tokio runtime.
FFI crossings happen only at channel enqueue/dequeue points.
Backpressure is symmetric: slow consumer → full output channel → parser blocks → full input channel → feed() awaits.
Cancellation via cancel() or Drop triggers a CancellationToken that unblocks every select arm, ensuring clean shutdown.

Performance

Benchmarks are split across two harnesses to isolate parse cost from PyO3 FFI + asyncio cost:

just bench          # Python async pipeline via pytest-benchmark
just bench-native   # Pure Rust baseline — no Python, no FFI

On a 110 KB HTML payload (medium in the bench suite) we get:

Scenario	Throughput	vs. native
`null_copy` — memcpy ceiling	~58,000 MB/s	—
`lol_html_native` — Rust only	~164 MB/s	1.00×
`lol_html_py` — async Python	~113 MB/s	0.69×

The 1.45× gap between native and Python comes from crossing the FFI boundary, sending data over Tokio channels, asyncio scheduling, and allocating a PyBytes object for each output chunk. On this payload that adds up to roughly 300 μs per run, most of which is fixed per invocation (not per byte), so will matter less on larger documents and more on smaller ones.

How input chunk size affects throughput

In the native Rust bench, feeding the payload in 256-byte writes or 64 KB writes both measure ~164 MB/s — chunk size doesn't affect native performance.

In the Python bench, 256-byte chunks run at ~5.7 MB/s; 16 KB chunks run at ~113 MB/s. That's a 20× difference, and it's entirely FFI overhead: each await rw.feed(chunk) crosses into Rust, sends on a channel, and returns an awaitable. At 256 bytes per call you're doing ~430 of those round-trips per document; at 16 KB you're doing ~7.

If you're feeding the rewriter from an HTTP response or a file read, chunks are already ≥ 4 KB by default and this isn't something you need to think about. If you're feeding it from a source that produces tiny writes (a tokenizer, an SSE stream with small events), buffer them in Python before calling feed().

How flush policy affects throughput

With flush_every_chunk=True and flush_threshold=1, the bench runs at ~52 MB/s instead of ~113 MB/s — roughly 2× slower. Every input chunk causes one output channel send, one PyBytes allocation, and one GIL reacquisition on the Python side.

With the defaults (threshold 16 KB, no per-chunk flush) the rewriter batches output until it has ~16 KB to send, which is usually one or two sends for a document this size.

Pick eager flushing when you specifically need to forward each input chunk's output before the next one arrives — server-sent events, incremental rendering, anything the downstream consumer treats as a real-time stream. Otherwise the default will be faster.

Is 113 MB/s good?

Compared to raw memcpy (~58,000 MB/s), native lol_html is 355× slower. That reflects the work of tokenizing HTML and matching element handlers — lol_html isn't memory-bound, it's CPU-bound on parse logic. The Python bindings achieve ~70% of native throughput, so the async layer is taking a meaningful but not dominant share of total time.

Development

Build from source, with uv:

uv sync

or with pip:

git clone https://github.com/lmmx/lol-html
cd lol-html
pip install maturin
maturin develop --release

License

MIT © Louis Maddox

Project details

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lmmx

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Release history Release notifications | RSS feed

0.1.2

Apr 14, 2026

This version

0.1.1

Apr 14, 2026

0.1.0

Apr 14, 2026

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Download URL: lol_html_py-0.1.1.tar.gz
Upload date: Apr 14, 2026
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lol-html-py 0.1.1

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