Citus Blog

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:

In my work as an engineer on the Postgres team at Microsoft, I get to meet all sorts of customers going through many challenging projects. One recent database migration project I worked on is a story that just needs to be told. The customer—in the retail space—was using Redshift as the data warehouse and Databricks as their ETL engine. Their setup was deployed on AWS and GCP, across different data centers in different regions. And they'd been running into performance bottlenecks and also was incurring unnecessary egress cost.

Specifically, the amount of data in our customer's analytic store was growing faster than the compute required to process that data. AWS Redshift was not able to offer independent scaling of storage and compute—hence our customer was paying extra cost by being forced to scale up the Redshift nodes to account for growing data volumes. To address these issues, they decided to migrate their analytics landscape to Azure.

I recently analyzed the limits of connection scalability, to understand the most effective way to improve Postgres' handling of large numbers of connections, and why that is important. I concluded that the most pressing issue is snapshot scalability.

This post details the improvements I recently contributed to Postgres 14 (to be released Q3 of 2021), significantly reducing the identified snapshot scalability bottleneck.

As the explanation of the implementation details is fairly long, I thought it'd be more fun for of you if I start with the results of the work, instead of the technical details (I'm cheating, I know ;)).

Postgres is an amazing RDBMS implementation. Postgres is open source and it's one of the most standard-compliant SQL implementations that you will find (if not the most compliant.) Postgres is packed with extensions to the standard, and it makes writing and deploying your applications simple and easy. After all, Postgres has your back and manages all the complexities of concurrent transactions for you.

In this post I am excited to announce that a new version of pg_auto_failover has been released, pg_auto_failover 1.4.

pg_auto_failover is an extension to Postgres built for high availability (HA), that monitors and manages failover for Postgres clusters. Our guiding principles from day one have been simplicity and correctness. Since pg_auto_failover is open source, you can find it on GitHub and it's easy to try out. Let's walk through what's new in pg_auto_failover, and let's explore the new capabilities you can take advantage of.

One common challenge with Postgres for those of you who manage busy Postgres databases, and those of you who foresee being in that situation, is that Postgres does not handle large numbers of connections particularly well.

While it is possible to have a few thousand established connections without running into problems, there are some real and hard-to-avoid problems.

Since joining Microsoft last year in the Azure Database for PostgreSQL team—where I work on open source Postgres—I have spent a lot of time analyzing and addressing some of the issues with connection scalability in Postgres.