Taking a Look at Compression Algorithms — Moncef Abboud

What happened

It has been reported that Moncef Abboud published a deep, 45-minute-read blog post dissecting compression algorithms after a self-described “delusional” hobby project: reimplementing a Kafka broker (MonKafka) and attempting to add compression for Kafka record batches. On his blog he walks readers from the basics — bytes and bits, lossless vs. lossy — into concrete techniques like run-length encoding, Lempel–Ziv back‑references, and Huffman coding. He also notes Kafka’s practical choices today: GZIP, Snappy, LZ4 and ZSTD.

The meat of the dive

Abboud’s piece is both a primer and an engineer’s notebook. He frames the tradeoffs—compression ratio, compression speed, decompression speed—and then drills into real-world schemes, including the gnarly details of DEFLATE (gzip). He even points readers to a helpful lecture by Professor Bill Bird; as Bird quips about DEFLATE’s bit-level hacks: “…maybe after months and months of working on this scheme… delirium was setting in.” The post reads like a developer learning by doing: importing libraries felt insufficient, so he rebuilt pieces to understand why they behave the way they do.

Why should you care? At scale, compression isn’t ivory‑tower theory — it’s millions of dollars and milliseconds. Efficient compression reduces storage and network costs and can change latency profiles for streaming systems like Kafka. Abboud also ties this curiosity to other deep dives he’s done (coding agents and LLM inference engines), suggesting that low-level tinkering still pays off for modern systems work. Who hasn’t fallen down a rabbit hole only to come out wiser? This one smells like the good kind.

Sources: cefboud.com, Hacker News