Aggregate queries which touch large swathes of time-series data can take a long time to compute because the system needs to scan large amounts of data on every query execution. To make such queries faster, a continuous aggregate allows materializing the computed aggregates, while also providing means to continuously, and with low overhead, keep them up-to-date as the underlying source data changes.
Continuous aggregates are somewhat similar to PostgreSQL's materialized views, but, unlike a materialized view, a continuous aggregate can be continuously and incrementally refreshed. The refreshing can be done either manually or via a policy that runs in the background, and can cover the entire continuous aggregate or just a specific time range. In either case, the refresh only recomputes the aggregate buckets that have changed since the last refresh.
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