How TimescaleDB compresses time-series data

Category: Engineering post
Status: discussion
Sources: roszigit.com, HN discussion
Summary: A write-up walks through TimescaleDB's Hypercore compression: row data in a hypertable chunk is reorganized into columnar batches of up to 1,000 rows, each column compressed with a type-appropriate scheme (delta and delta-of-delta for ordered integers and timestamps, dictionary encoding for low-cardinality text, Gorilla-style encoding for floats), trading per-row update cost for large storage and scan-throughput gains on append-mostly time-series workloads. It surfaced on Hacker News (133 points).
Why it matters: It explains the concrete tradeoffs (columnar batch layout, per-type codecs, slower in-place updates) that determine when Postgres-based time-series compression pays off for an observability or metrics store.