Citus Blog

When working on the internals of Citus, an open source extension to Postgres that transforms Postgres into a distributed database, we often get to talk with customers that have interesting challenges you won't find everywhere. Just a few months back, I encountered an analytics workload that was a really good fit for Citus.

But we had one problem: the percentile calculations on their data (over 300 TB of data) could not meet their SLA of 30 seconds.

To make things worse, the query performance was not even close to the target: the percentile calculations were taking about 6 minutes instead of the required 30 second SLA.

I recently gave a talk about the Citus extension to Postgres at the Warsaw PostgreSQL Users Group. Unfortunately, I did not get to go in person to beautiful Warsaw, but it was still a nice way to interact with the global Postgres community and talk about what Citus is, how it works, and what it can do for you.

Our latest release to the Citus extension to Postgres is Citus 9.4. If you’re not yet familiar, Citus transforms Postgres into a distributed database, distributing your data and your SQL queries across multiple nodes. This post is basically the Citus 9.4 release notes.

If you’re ready to get started with Citus, it’s easy to download Citus open source packages for 9.4.

I always recommend people check out docs.citusdata.com to learn more. The Citus documentation has rigorous tutorials, details on every Citus feature, explanations of key concepts—things like choosing the distribution column—tutorials on how you can set up Citus locally on a single server, how to install Citus on multiple servers, how to build a real-time analytics dashboard, how to build a multi-tenant database, and more...

Many of you rely on databases to return correct results for your SQL queries, however complex your queries might be. And you probably place your trust with no questions asked—since you know relational databases are built on top of proven mathematical foundations, and since there is no practical way to manually verify your SQL query output anyway.

Since it is possible that a database’s implementation of the SQL logic could have a few errors, database developers apply extensive testing methods to avoid such flaws. For instance, the Citus open source repo on GitHub has more than twice as many lines related to automated testing than lines of database code. However, checking correctness for all possible SQL queries is challenging because of the lack of a "ground truth" to compare their outputs against, and the infinite number of possible SQL queries.