If you have ever used a database like Postgres, you know how important optimization is. Some minor changes in how the database is setup make all the difference between long waiting times and satisfied customers. And one crucial thing you need before doing the optimization is to monitor and understand how your database is being used.

Citus is an extension to Postgres that improves scalability and parallelization by distributing your Postgres database across nodes in a cluster. The Citus database extension is available as open source and as a managed service on the cloud, as Azure Cosmos DB for PostgreSQL. You can track your Citus nodes and the Postgres tables, but Citus 11.3 takes it one step further and introduces a new way to gather insight on your Citus database with tenant monitoring.

The new Citus 11.3 release, among many other features, introduces a new citus_stat_tenants view to track your most active tenants, for those with multi-tenant SaaS applications.

If you're building a software application that serves multiple tenants, you may have already encountered the challenges of managing and isolating tenant-specific data. That's where the django-multitenant library comes in. This library, actively used since 2017 and now downloaded more than 10K times per month, offers a simple and flexible solution for building multi-tenant Django applications.

In this blog post, we'll dive deeper into the concept of multi-tenancy and explore how Django-multitenant can help you build scalable, secure, and maintainable multi-tenant applications on top of PostgreSQL and the Citus database extension. We'll also provide a practical example of how to use Django-multitenant in a real-world scenario. So, if you're looking to simplify your multi-tenant development process, keep reading.

Citus enables several different PostgreSQL use cases, but one of the most popular ones is to build scalable multi-tenant software as a service (SaaS) applications. The most common way to build a multi-tenant application on Citus is to distribute all your Postgres tables by a “tenant ID” column. That way rows are (hash-)distributed across nodes, while rows with the same tenant ID value are co-located on the same node for fast local joins, transactions, and foreign keys.

For those of you who build SaaS apps, one question many of you have is how active your tenants are. More specifically: What are your busiest tenants? How many queries is your application doing on behalf of your tenants, and how much CPU do those queries use?

The new 11.3 release to the open source Citus database extension gives you tenant monitoring—with instant visibility into your top tenants using the new citus_stat_tenants feature, which shows query counts and CPU usage over a configurable time period.

Citus is a PostgreSQL extension that makes PostgreSQL scalable by transparently distributing and/or replicating tables across one or more PostgreSQL nodes. Citus could be used either on Azure cloud, or since the Citus database extension is fully open source, you can download and install Citus anywhere you like.

A typical Citus cluster consists of a special node called coordinator and a few worker nodes. Applications usually send their queries to the Citus coordinator node, which relays them to worker nodes and accumulates the results. (Unless of course you’re using the Citus query from any node feature, an optional feature introduced in Citus 11, in which case the queries can be routed to any of the nodes in the cluster.)

Anyway, one of the most frequently asked questions is: “How does Citus handle failures of the coordinator or worker nodes? What’s the HA story?”

And with the exception of when you’re running Citus in a managed service in the cloud, the answer so far was not great—just use PostgreSQL streaming to run coordinator and workers with HA and it is up to you how to handle a failover.

In this blog post, you’ll learn how Patroni 3.0+ can be used to deploy a highly available Citus database cluster—just by adding a few lines to the Patroni configuration file.

Our goal for the Citus extension is for you to be able to use all PostgreSQL features at any scale, with a seamless scaling experience. Distributed tables (or more generally “Citus tables”) are a powerful tool to get high performance at any scale. There are only a few remaining limitations when distributing a PostgreSQL table, but we are determined to solve them all. The Citus 11.2 release checks off another five SQL & DDL features that now work seamlessly on Citus tables. We also improved progress tracking for the shard rebalancer, so you know exactly what’s going on when rebalancing your cluster.

We also want PostgreSQL tools to work out-of-the-box even if you have a distributed PostgreSQL cluster. One of the most frequent questions we get on the Citus Slack from our open source users is how to set up high availability. Alexander Kukushkin, who is the primary maintainer of Patroni and recently joined the Citus database engine team, therefore developed a new version of Patroni which includes support for Citus!

Before we dive in, you can find detailed release notes for Citus 11.2 by the engineering team on our Updates page.

Postgres is one of the most widely used databases and supports a number of operating systems. When you are writing code for PostgreSQL, it's easy to test your changes locally, but it can be cumbersome to test it on all operating systems. A lot of times, you may encounter failures across platforms and it can get confusing to move forward while debugging. To make the dev/test process easier for you, you can use the Postgres CI.

When you test your changes on CI and see it fail, how do you proceed to debug from there? As a part of our work in the open source Postgres team at Microsoft, we often run into CI failures—and more often than not, the bug is not obvious, and requires further digging into.

In this blog post, you'll learn about techniques you can use to debug PostgreSQL CI failures faster. We'll be discussing these 4 tips in detail:

As you may have heard, we recently made PostgreSQL 15 generally available in Azure Cosmos DB for PostgreSQL within just 1 week of the PostgreSQL 15 release. The Postgres 15 version is available for you whether you need to create a new cluster in Azure Cosmos DB for PostgreSQL, or upgrade your existing cluster. (Note: you can do in-place major version upgrades in Azure Cosmos DB for PostgreSQL.) And the PostgreSQL 15 support is available in all Azure regions that support Azure Cosmos DB for PostgreSQL.

You may be surprised since it's usually not the norm for a managed database service to start supporting the new major PostgreSQL version that early... This post will walk you through what's going on behind the scenes that enables us to do such a feat. Some background before diving in:

Azure Cosmos DB for PostgreSQL is powered by native Postgres and Citus open source—and enables you to run PostgreSQL at any scale, from a single node to a large, distributed cluster. Customers can also scale out as much as they want depending on their needs with many additional features. The Hyperscale (Citus) managed service recently moved into Azure Cosmos DB family (more info on the launch of Azure Cosmos DB for PostgreSQL in this blog post) and with that introduced try Azure Cosmos DB for PostgreSQL for free where you can try out PostgreSQL 15 with Citus 11.1.

Reducing replication lag with IO concurrency in Postgres 15

PostgreSQL 15 improves crash recovery and physical replication performance of some large and very busy databases by trying to minimise I/O stalls. A standby server might now have an easier time keeping up with the primary.

How? The change in PostgreSQL15 is that recovery now uses the maintenance_io_concurrency setting (default is 10, but you can increase it) to decide how many concurrent I/Os to try to initiate, rather than doing random read I/Os one at a time. With big and busy databases, when I/O concurrency increases, replication lag can be reduced.

Big news from the Postgres and Citus team here at Microsoft! Just 1 week after PostgreSQL 15 was released, PostgreSQL 15 GA is generally available in the portal for the Azure Cosmos DB for PostgreSQL managed service—in all Azure regions. Whether you need to provision new clusters in Azure Cosmos DB for Postgres—or upgrade your existing database clusters—Postgres 15 is now a choice for you. Oh, and you can upgrade your existing cluster to Postgres 15 from any of the other supported major Postgres versions, using the in-place major version upgrade feature.

Have you ever played with Postgres source code and weren't sure if you broke anything? Postgres has a quite comprehensive regression test suite that helps to ensure that nothing is broken. You can, of course, run those tests on your machine and check if your version of Postgres works properly. But it always works on your machine, right? What about other environments?

In this blog post, you will learn about how to enable and use the Postgres CI (plus how to contribute to it!) based on my experience and learnings creating my first patch to Postgres. Specifically, you’ll learn: