TimescaleDB supports the ability to natively compress data stored in hypertables. Native compression does not require the use of any specific file system or external software. Compressing time-series data can significantly reduce the storage requirement of your data and, in many cases, speed up the responsiveness of queries on historical, compressed data.
Compression is powered by TimescaleDB’s built-in job scheduler framework. We leverage it to asynchronously convert individual chunks from an uncompressed row-based form to a compressed columnar form across a hypertable: Once a chunk is old enough, the chunk will be transactionally converted from the row to columnar form.
With native compression, even though a single hypertable in TimescaleDB will store data in both row and columnar forms, users don’t need to worry about this: they will continue to see a standard row-based schema when querying data. This is similar to building a view on the decompressed columnar data.
TimescaleDB enables this capability by both (1) transparently appending data stored in the standard row format with decompressed data from the columnar format, and (2) transparently decompressing individual columns from selected rows at query time.
During a query, uncompressed chunks will be processed normally, while data from compressed chunks will first be decompressed and converted to a standard row format at query time, before being appended or merged into other data. This approach is compatible with everything you expect from TimescaleDB, such as relational JOINs and analytical queries, as well as aggressive constraint exclusion to avoid processing chunks.
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