The Numbers
Logtide, an open-source log management platform, deployed TimescaleDB compression in December 2025. Storage dropped from 220GB to 25GB over 60 days. That's 88.6% reduction on real production data: 500,000 logs daily, 300 bytes average.
The breakdown: 135GB raw data plus 85GB of indexes became 18GB compressed columnstore plus 7GB sparse indexes. Query performance held steady, full-text searches still completing in 145ms.
Why This Works
TimescaleDB's columnar compression groups similar values together. Sequential timestamps compress well. Log levels (info, error, warn) repeat constantly. Metadata fields duplicate across entries. The math is simple: similar data stored together compresses better than row-by-row storage.
The implementation uses hypertables with automatic chunk partitioning. Data older than seven days moves to compressed columnstore. Recent data stays in rowstore for fast writes. A background job handles the transition automatically.
The Trade-offs
Storage multiplication from indexes is the real problem here. Primary keys double storage. GIN indexes triple it. B-tree adds another copy. That 6x multiplier turns 4.5GB monthly into 27GB. Compression attacks the index overhead directly.
TimescaleDB 2.22+ dynamically adjusts compression settings as data patterns change. The segmentby grouping by organization_id and level matches query patterns. The orderby on timestamp DESC aligns with how logs get accessed: newest first.
What This Means
Time-series databases handle this problem differently. InfluxDB and ClickHouse claim higher compression ratios in benchmarks, sometimes hitting 95%. The PostgreSQL foundation means TimescaleDB inherits Postgres overhead but keeps Postgres compatibility. That matters if your stack is already Postgres-based.
The technique applies beyond logs. Metrics, sensor data, financial ticks. Anything with timestamps and repeating values. The 7-day compression threshold is configurable. Heavy write workloads might push to 14-30 days. Read-heavy systems can compress after one day.
Worth Noting
Compression isn't free. Schema changes require pausing policies and recompressing chunks. Complex analytics on compressed data adds query overhead. Full-text search efficiency drops compared to raw rowstore. These are engineering trade-offs, not marketing claims.
The storage math is compelling: 324GB yearly projection without compression versus 36GB with it. For self-hosted deployments, that's the difference between expensive NVMe and commodity storage. For managed databases, it's IOPS charges and backup costs.
TimescaleDB remains open-source. No recent funding disclosed, but the time-series database market is growing at 25% CAGR. Production deployments like this validate the compression claims we've seen since the feature launched in 2019.