July 29, 2026
Compressed data, inflated opinions
ALP: Adaptive lossless floating-point compression
This new number-squeezing trick won awards, but the comments instantly made it a meme
TLDR: ALP is an award-winning new way to shrink number-heavy data without losing accuracy, and it’s already being adopted by major data tools. The comments split between meme mode — “middle out?” — and practical skepticism about how often it really beats everyday compression options.
A very serious database paper showed up with a very un-serious side effect: the internet immediately tried to turn it into a punchline. ALP is a new way to shrink floating-point numbers — basically the messy decimal values used in tons of real-world data — without losing any information. The researchers say it’s faster to pack, faster to unpack, and better at saving space than rival methods, and the project has the academic trophies to flex it: SIGMOD 2024, full reproducibility badges, and even a Best Artifact Award. It’s also already landing in real systems like DuckDB, KuzuDB, FastLanes, liquid-cache, and, according to one commenter, even ClickHouse.
But the comment section? That’s where the real entertainment lives. One user drove straight into meme territory with the deadpan question, “is it middle out?” — a perfect Silicon Valley joke for a compression project, and honestly the kind of comment that can hijack an entire thread. Another commenter brought the buzz back down to earth with a classic engineer mood check: yes, ALP is impressive, but the situations where you’d actually choose it over more general compression tools “is not so large.” And there it is: the eternal tech-drama split between award-winning breakthrough and cool, but where do I really use it? So the vibe is half applause, half side-eye, with a dash of meme chaos — which, in internet terms, is basically a standing ovation.
Key Points
- •ALP is a lossless compression algorithm for IEEE 754 floating-point data and is presented with source code and benchmarks for an ACM SIGMOD 2024 paper.
- •The algorithm handles decimal floating-point values by mapping them to integers via powers of 10 and compressing them with a FastLanes variant of Frame-of-Reference encoding.
- •For high-precision floating-point values, ALP compresses the left part using FastLanes dictionary encoding while leaving the right part uncompressed to preserve precision.
- •The article states that ALP outperforms other schemes across decoding speed, compression ratio, and compression speed.
- •The project provides reproduction scripts, received all three SIGMOD artifact badges plus the SIGMOD Best Artifact Award, and is integrated into DuckDB, FastLanes, KuzuDB, and liquid-cache.