June 15, 2026
Compress to Impress?
How TimescaleDB compresses time-series data
This database says it can shrink old data by 98% — and commenters are side-eyeing the hype
TLDR: TimescaleDB says it can massively shrink old time-based data, which could cut storage costs and speed up some reads. Commenters were split between impressed, skeptical of the marketing, and eager to argue the real issue: whether this helps actual database performance or just makes a flashy headline.
A database story somehow turned into a comment-section cage match, and honestly, that’s where the real action is. The article says TimescaleDB can squeeze old sensor-style data down by as much as 98% by switching it into a more compact layout and using clever tricks tailored for repeating numbers and timestamps. In plain English: instead of storing every reading the same way, it packs older data much more tightly so companies can keep years of logs without their storage bill going feral.
But the community was not content to clap politely. One camp immediately went, “Hang on, this isn’t exactly new,” with a commenter pointing out that Facebook’s Gorilla already used similar ideas for storing values as changes from earlier values. Another group zoomed past the headline and into the eternal grown-up question: does compression actually make searches faster, or are we just saving disk space? That sparked the classic database drama: there are no magic tricks, only trade-offs.
Then came the niche-but-spicy side quest: could this make old-school, slightly lossy industrial data-compression methods for Internet-connected sensors basically unnecessary? And finally, the funniest swipe of the thread: a reader declaring that titles with “up to” are basically cursed, joking that “up to 100%” of them are nonsense. So yes, the tech is impressive — but the crowd’s verdict is a mix of “cool,” “seen it,” and “please stop with the marketing fluff.”
Key Points
- •The article says TimescaleDB can compress typical time-series data by up to 98% using algorithms tailored to cross-row patterns.
- •PostgreSQL TOAST and TimescaleDB compression address different problems: TOAST handles oversized individual values, while TimescaleDB targets patterns across rows in time-series workloads.
- •Hypercore stores new data in row-based PostgreSQL chunks and automatically converts older chunks into compressed columnar storage.
- •Compressed chunks are organized into batches of up to 1,000 rows, with each batch stored as a single row containing compressed column arrays.
- •The article reports much higher compression ratios for floats, timestamps, and repetitive text in TimescaleDB than with TOAST, especially for regular time-series structures.