How We Made Data Aggregation Better and Faster on PostgreSQL With TimescaleDB 2.7

It’s time for another #AlwaysBeLaunching week! 🥳🚀✨ In our #AlwaysBeLaunching initiatives, we challenge ourselves to bring you an array of new features and content. Today, we are introducing TimescaleDB 2.7 and the performance boost it brings for aggregate queries. 🔥 Expect more news this week about further performance improvements, developer productivity, SQL, and more. Make sure you follow us on Twitter (@TimescaleDB), so you don’t miss any of it!

Time-series data is the lifeblood of the analytics revolution in nearly every industry today. One of the most difficult challenges for application developers and data scientists is aggregating data efficiently without always having to query billions (or trillions) of raw data rows. Over the years, developers and databases have created numerous ways to solve this problem, usually similar to one of the following options:

Most developers head down one of these paths because we learn, often the hard way, that running reports and analytic queries over the same raw data, request after request, doesn't perform well under heavy load. In truth, most raw time-series data doesn't change after it's been saved, so these complex aggregate calculations return the same results each time.

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