lzma vs bz2 Compress: Localhost Lab

Intro — what this post promises

Compress and decompress with lzma vs bz2, with zlib as a familiar reference (lab 102 covers zlib vs gzip). This lab reports MB/s and size ratios on Linux localhost.

Related links:

• reduce vs loop localhost lab
• selectors vs select localhost lab
• literal eval vs json localhost lab
• cmath vs math hypot localhost lab
• signal vs event wakeup localhost lab
• uuid4 vs uuid1 localhost lab
• platform vs uname localhost lab
• memoryview vs bytes localhost lab

Lab honesty (1 Oct 2026 IST): Python 3.13.5. Affiliates: 0. Payload: 1048536 bytes of a repeated log line (highly compressible — ratios look extreme on purpose).

Verdict up front: compress — lzma ~62.48 MB/s (out 356 B); bz2 ~4.53 MB/s (out 550 B); zlib ref ~320.27 MB/s. Decompress: lzma ~1708.96, bz2 ~144.08.

Arms

Seven rounds, p50. Round-trips OK (lzma=True, bz2=True).

Lab topology

raw=1048536 B · 7 rounds · p50
MB/s = (raw_MiB) / p50_s

Script: lab-evidence/143-lzma-vs-bz2/results/run_lab.py.

Lead table

Reading it for SRE work

• Archival / smallest artifacts on repetitive logs → lzma won both speed and size here.
• bz2 lagged badly on compress (~4.53 MB/s) — avoid for hot paths.
• Hot HTTP-ish payloads → stay on zlib/gzip (lab 102); this post is stdlib heavy compressors.
• Always measure on your corpus — random binary will not shrink like this repeated line.

Why ratios look tiny

The fixture repeats one log line to ~1 MiB. That is a realistic “agent dumps the same template” case, not a claim about JPEG. lzma’s 356-byte blob vs bz2’s 550 still shows lzma packing tighter on this pattern while compressing faster.

Decompress path

lzma decompress hit ~1708.96 MB/s vs bz2 ~144.08. Read-heavy pipelines that already store .xz stay fine; do not pick bz2 hoping decode will save you if encode was the bottleneck.

Put codec choice in the runbook next to retention: “xz for cold archive, gzip for hot ship.”

zlib reference context

zlib’s ~320.27 MB/s compress on this fixture is the speed ceiling among the three, with a larger 3656-byte blob. That matches the usual trade: gzip/zlib for hot ship, lzma for cold archive. Lab 102 remains the zlib-vs-gzip deep dive; this post only borrows zlib as a yardstick while crowning lzma over bz2 in the stdlib heavy tier.

On less repetitive data, absolute MB/s and ratios will move — rerun the script on a production sample before changing default codecs in an agent.

Pitfalls

• Comparing these MB/s to nginx gzip or zstd labs without stating codec.
• Using default presets without noting CPU vs size knobs.
• Compressing already-compressed blobs.
• Ignoring memory use of lzma presets on tiny hosts.

Reproduce

python3 lab-evidence/143-lzma-vs-bz2/results/run_lab.py

Evidence: summary.json, summary.txt.

Limits

One Linux box, default presets, one synthetic repetitive corpus. Not zstd (lab 33).

Takeaway

On this repetitive ~1 MiB log fixture, lzma compressed at ~62.48 MB/s to 356 bytes vs bz2 ~4.53 MB/s / 550 bytes. Prefer lzma over bz2 in stdlib; keep zlib/gzip for hotter paths.