An intrinsic part of time-series data is that new data is accumulated and old data is rarely, if ever, updated and the relevance of the data diminishes over time. It is therefore often desirable to delete old data to save disk space.
As an example, if you have a hypertable definition of
where you collect raw data into chunks of one day:
CREATE TABLE conditions( time TIMESTAMPTZ NOT NULL, device INTEGER, temperature FLOAT ); SELECT * FROM create_hypertable('conditions', 'time', chunk_time_interval => INTERVAL '1 day');
If you collect a lot of data and realize that you never actually use
raw data older than 30 days, you might want to delete data older than
30 days from
However, deleting large swaths of data from tables can be costly and
slow if done row-by-row using the standard
DELETE command. Instead,
TimescaleDB provides a function
drop_chunks that quickly drop data
at the granularity of chunks without incurring the same overhead.
SELECT drop_chunks('conditions', INTERVAL '24 hours');
This drops all chunks from the hypertable
conditions that only
include data older than this duration, and does not delete any
individual rows of data in chunks.
TimescaleDB also includes a background job scheduling framework for automating data management tasks, such as enabling easy data retention policies. With policies, you can set data retention standards on each hypertable and allow TimescaleDB to drop data as necessary.
It's worth noting that continuous aggregates are also valid targets retention policies.
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