![]() ![]() ![]() Materialization in PostgreSQL allows you to pre-compute aggregations over your data and makes them available as cached values. Haven’t tried continuous aggregates yet? Start your free Timescale Cloud account now-we look forward to working with you! A Refresher on Continuous Aggregates This advice is based on our experience as Timescale Support engineers helping numerous customers get started with continuous aggregates. In this blog post, we’ll give you helpful tips on how to set up and work with continuous aggregates (we lovingly call them “caggs” at Timescale). It provides an efficient and low-impact mechanism to refresh materialized aggregates more frequently for up-to-date results. Querying a continuous aggregate: when you query a TimescaleDB continuous aggregate, it combines cached aggregated values from materialized aggregations with the newer data that has not been materialized. That leads us to the fourth data aggregation method:Ĥ. Continuous aggregates allow you to materialize aggregations well ahead of time so that your application can quickly retrieve the cached aggregates without waiting for them to be computed at query time. However, you may not get up-to-date results.Īs you can see, these options have shortcomings and may not get the job done-cue in TimescaleDB’s continuous aggregates. This will make your aggregate queries run faster since the materialized view will store previously computed results. Querying a PostgreSQL materialized view of cached aggregates, also known as materialized aggregations.Having your query saved as a view is handy, but it won’t actually improve your query latency since a view is a simple alias for your original query. Querying a view that in turn calls an aggregation function with the GROUP BY. ![]() You will probably find this option slow if you’re aggregating over large data volumes. Querying your data directly with an aggregation function and a GROUP BY.In PostgreSQL, you can retrieve data aggregations using various methods: One of the main challenges of working with time-series data is effectively running aggregations over high data volumes. ![]()
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