Columnar batches

TimescaleDB uses columnar collocation and columnar compression within row-based storage to optimize analytical query performance while maintaining full Postgres compatibility. This approach ensures efficient storage, high compression ratios, and rapid query execution.

A rowstore chunk is converted to a columnstore chunk by successfully grouping together sets of rows (typically up to 1000) into a single batch, then converting the batch into columnar form.

Each compressed batch does the following:

• Encapsulates columnar data in compressed arrays of up to 1,000 values per column, stored as a single entry in the underlying compressed table
• Uses a column-major format within the batch, enabling efficient scans by co-locating values of the same column and allowing the selection of individual columns without reading the entire batch
• Applies advanced compression techniques at the column level, including run-length encoding, delta encoding, and Gorilla compression, to significantly reduce storage footprint (by up to 95%) and improve I/O performance.

While the chunk interval of rowstore and columnstore batches usually remains the same, TimescaleDB can also combine columnstore batches so they use a different chunk interval.

This architecture provides the benefits of columnar storage—optimized scans, reduced disk I/O, and improved analytical performance—while seamlessly integrating with Postgres’s row-based execution model.