timescaledb

Hypertables are TimescaleDB's core data structure for time-series data, automatically partitioning time-stamped rows across chunks for fast ingestion and queries. Create a hypertable by converting a standard PostgreSQL table, then insert time-series data normally—TimescaleDB handles chunking transparently to optimize storage and query performance.

Continuous aggregates pre-compute and materialize time-series rollups—like hourly or daily summaries—automatically as new data arrives. Use them to avoid recalculating aggregations on massive datasets; they dramatically speed up dashboards and reporting queries on historical time-series data.

Optimize with compression to reduce storage, continuous aggregates to avoid expensive recalculations, appropriate chunk intervals to balance query speed and memory use, and hyperfunctions for vectorized analytics. Installation and configuration tuning are covered in depth to match your workload scale.

Hyperfunctions are specialized SQL functions for time-series analysis—like time-weighted averages, percentile approximations, and gap-filling. They execute efficiently within the database, eliminating expensive data export and application-layer computation for common time-series patterns.

Yes. Compression reduces raw hypertable storage by encoding time-series chunks, while continuous aggregates pre-compute rollups; both techniques work together. Compress older chunks to free space while materialized aggregates serve fast queries on summarized data.