Citus Blog

pg_cron is an open source PostgreSQL extension that provides a cron-based scheduler to periodically run SQL commands. Almost every managed PostgreSQL service supports pg_cron and it has become a standard tool for many PostgreSQL users. Since Citus has been my full-time job, pg_cron has always been a side project for me, and so I tried to architect it for simplicity, reliability, and low maintenance. Of course, with many users there is a long list of feature requests, and with the help of the Postgres community pg_cron keeps becoming more and more capable over time.

We recently added PostgreSQL 16 support (in version 1.6), but perhaps the most exciting feature added to pg_cron in the past year (in version 1.5) is the ability to schedule a job every few seconds. I shunned this feature idea for a while, because (a) it is not something regular cron can do; and (b) any issue in pg_cron would get much more severe if it were to happen every few seconds. However, by now pg_cron is reasonably battle-tested and second-granularity jobs had become the most popular pg_cron feature request by far.

On one of the Postgres community chat forums, a friend asked me: "Is there a blog post that outlines all the work that is being done on Postgres at Microsoft? It's hard to keep track these days."

And my friend is right: it is hard to keep track. Probably because there are multiple Postgres workstreams at Microsoft, spread across a few different teams.

In this post, you'll get a bird's eye view of all the Postgres work the Microsoft team has done over the last year. Our work includes some pretty significant improvements to the Postgres managed services on Azure, as well as contributions across the entire open source ecosystem—including commits to the Postgres core; new releases to Postgres open source extensions like Citus and pg_cron; plus ecosystem work on Patroni, PgBouncer, pgcopydb. And more.

Managing time series data at scale can be a challenge. PostgreSQL offers many powerful data processing features such as indexes, COPY and SQL—but the high data volumes and ever-growing nature of time series data can cause your database to slow down over time.

Fortunately, Postgres has a built-in solution to this problem: Partitioning tables by time range.

Partitioning with the Postgres declarative partitioning feature can help you speed up query and ingest times for your time series workloads. Range partitioning lets you create a table and break it up into smaller partitions, based on ranges (typically time ranges). Query performance improves since each query only has to deal with much smaller chunks. Though, you’ll still be limited by the memory, CPU, and storage resources of your Postgres server.

The good news is you can scale out your partitioned Postgres tables to handle enormous amounts of data by distributing the partitions across a cluster. How? By using the Citus extension to Postgres. In other words, with Citus you can create distributed time-partitioned tables. To save disk space on your nodes, you can also compress your partitions—without giving up indexes on them. Even better: the latest Citus 10.2 open-source release makes it a lot easier to manage your partitions in PostgreSQL.

Citus 10.2 is out! If you are not yet familiar with Citus, it is an open source extension to Postgres that transforms Postgres into a distributed database—so you can achieve high performance at any scale. The Citus open source packages are available for download. And Citus is also available in the cloud as a managed service, too.

You can see a bulleted list of all the changes in the CHANGELOG on GitHub. This post is your guide to what’s new in Citus 10.2, including some of these headline features.

One of the unique things about Postgres is that it is highly programmable via PL/pgSQL and extensions. Postgres is so programmable that I often think of Postgres as a computing platform rather than just a database (or a distributed computing platform—with Citus). As a computing platform, I always felt that Postgres should be able to take actions in an automated way. That is why I created the open source pg_cron extension back in 2016 to run periodic jobs in Postgres—and why I continue to maintain pg_cron now that I work on the Postgres team at Microsoft.

Using pg_cron, you can schedule Postgres queries to run periodically, according to the familiar cron syntax. Some typical examples:

Running periodic jobs such as vacuuming or removing old data is a common requirement in PostgreSQL. A simple way to achieve this is to configure cron or another external daemon to periodically connect to the database and run a command. However, with...