TimescaleDB achieves up to 98% compression on typical time-series datasets through its specialized hypercore engine. Unlike general-purpose database compression, hypercore exploits the inherent patterns in temporal data using algorithms like delta encoding, delta-of-delta, and run-length encoding. New data arrives in row-based chunks for fast writes, then automatically converts to columnar format for analytical queries.

PostgreSQL's TOAST mechanism handles individual large values like JSON or text fields, but focuses on single-value compression rather than cross-row patterns. Hypercore takes a fundamentally different approach by grouping rows into batches of roughly 1000 and compressing entire columns together. This column-major organization allows queries to fetch only needed fields, dramatically reducing I/O for time-series workloads.

The engine selects compression algorithms per column type automatically. Timestamps at regular intervals compress 50-100× using delta-of-delta techniques, while repetitive strings achieve 5-10× with dictionary compression and RLE. Float sensor readings see 10-20× improvement through XOR-based methods that exploit similar neighboring values.

This matters because IoT and monitoring applications generate millions of repetitive readings daily. Traditional databases waste storage on unchanged values and timestamps, while hypercore's targeted approach can reduce multi-terabyte retention costs to gigabytes while accelerating analytical queries by orders of magnitude.