About data retention with continuous aggregates

You can downsample your data by combining a data retention policy with continuous aggregates. If you set your refresh policies correctly, you can delete old data from a hypertable without deleting it from any continuous aggregates. This lets you save on raw data storage while keeping summarized data for historical analysis.

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To keep your aggregates while dropping raw data, you must be careful about refreshing your aggregates. You can delete raw data from the underlying table without deleting data from continuous aggregates, so long as you don't refresh the aggregate over the deleted data. When you refresh a continuous aggregate, TimescaleDB updates the aggregate based on changes in the raw data for the refresh window. If it sees that the raw data was deleted, it also deletes the aggregate data. To prevent this, make sure that the aggregate's refresh window doesn't overlap with any deleted data. For more information, see the following example.

As an example, say that you add a continuous aggregate to a conditions hypertable that stores device temperatures:

CREATE MATERIALIZED VIEW conditions_summary_daily (day, device, temp)
WITH (timescaledb.continuous) AS
  SELECT time_bucket('1 day', time), device, avg(temperature)
  FROM conditions
  GROUP BY (1, 2);

SELECT add_continuous_aggregate_policy('conditions_summary_daily', '7 days', '1 day', '1 day');

This creates a conditions_summary_daily aggregate which stores the daily temperature per device. The aggregate refreshes every day. Every time it refreshes, it updates with any data changes from 7 days ago to 1 day ago.

You should not set a 24-hour retention policy on the conditions hypertable. If you do, chunks older than 1 day are dropped. Then the aggregate refreshes based on data changes. Since the data change was to delete data older than 1 day, the aggregate also deletes the data. You end up with no data in the conditions_summary_daily table.

To fix this, set a longer retention policy, for example 30 days:

SELECT add_retention_policy('conditions', INTERVAL '30 days');

Now, chunks older than 30 days are dropped. But when the aggregate refreshes, it doesn't look for changes older than 30 days. It only looks for changes between 7 days and 1 day ago. The raw hypertable still contains data for that time period. So your aggregate retains the data.

Data retention on a continuous aggregate itself

You can also apply data retention on a continuous aggregate itself. For example, you can keep raw data for 30 days, as above. Meanwhile, you can keep daily data for 600 days, and no data beyond that.

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