Citus Blog

The latest episode of Path To Citus Con—the monthly podcast for developers who love Postgres—is now out. This episode featured guests Lukas Fittl (founder of pganalyze) and Rob Treat (an early Circonus developer) on the topic “My Journey into Postgres Monitoring” along with co-hosts Claire Giordano and Pino de Candia.

Have you ever asked yourself: “Why is my query so slow?” Or had to figure out which query is slowing things down? Or why your database server is at 90% CPU? According to Lukas, you might find these and many more answers by reviewing your error logs.

If you’re running Postgres on a managed service, what kinds of things do you need to monitor & optimize for? Versus what will your cloud service provider do? There is a discussion on this as well as a segue onto monitoring vs. observability: what’s the difference?

One of the top Citus features is the ability to run PostgreSQL at any scale, on a single node as well as a distributed database cluster.

As your application needs to scale, you can add more nodes to the Citus cluster, rebalance existing data to the new Postgres nodes, and seamlessly scale out. However, these operations require manual intervention: a) first you must create alerts on metrics, b) then, based on those alerts, you need to add more nodes, c) then you must kick off and monitor the shard rebalancer. Automating these steps will give you a complete auto scale experience—and make your life so much easier.

In this blog post, you will learn how to build a full-fledged auto scaling setup for the Citus database extension running as a managed service on Azure—called Azure Cosmos DB for PostgreSQL. You’ll also learn how you can easily add nodes to the Azure Cosmos DB for PostgreSQL cluster and use any metrics available to trigger actions in your cluster! Let’s dive into the following chapters:

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.

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.

We released Citus 11 in the previous weeks and it is packed. Citus went full open source, so now previously enterprise features like the non-blocking aspect of the shard rebalancer—and multi-user support—are all open source for everyone to enjoy. One other huge change in Citus 11 is now you can query your distributed Postgres tables from any Citus node, by default.

When using Citus to distribute Postgres before Citus 11, the coordinator node was your application’s only point of contact. Your application needed to connect to the coordinator to query your distributed Postgres tables. Coordinator node can handle high query throughput, about 100K per second but your application might need even more processing power. Thanks to our work in Citus 11 you can now query from any node in the Citus database cluster you want. In Citus 11 we sync the metadata to all nodes by default, so you can connect to any node and run queries on your tables.

Running queries from any node is awesome but you also need to be able to monitor and manage your queries from any node. Before, when you only connected the coordinator, using Postgres’ monitoring tools was enough but this is not the case anymore. So in Citus 11 we added some ways to observe your queries similar to you would do in a single Postgres instance.

Today we released Citus 11.0 beta, which is our first ever beta release of the Citus open source extension to Postgres. The reason we are releasing a beta version of 11.0 is that we are introducing a few fundamentally new capabilities, and we would like to get feedback from those of you who use Citus before we release Citus 11.0 to the world.

The biggest change in Citus 11.0 beta is that the schema and Citus metadata are now automatically synchronized throughout the database cluster. That means you can always query distributed tables from any node in a Citus cluster!

The easiest way to use Citus is to connect to the coordinator node and use it for both schema changes and distributed queries, but for very demanding applications, you now have the option to load balance distributed queries across the worker nodes in (parts of) your application by using a different connection string and factoring a few limitations.

I talk with a lot of folks that set their database up, start working with it, and then are surprised by issues that suddenly crop up out of nowhere. The reality is, so many don't want to have to be a DBA, instead you would rather build features and just have the database work. But your is that a database is a living breathing thing. As the data itself changes what is the right way to query and behave changes. Making sure your database is healthy and performing at it's maximum level doesn't require a giant overhaul constantly. In fact you can probably view it similar to how you approach personal health. Regular check-ups allow you to make small but important adjustments without having to make dramatic life altering changes to keep you on the right path.

After years of running and managing literally millions of Postgres databases, here's my breakdown of what your regular Postgres health check should look like. Consider running this on a monthly basis to be able to make small tweaks and adjustments and avoid the drastic changes.

Extensions are capable of extending, changing, and advancing the behavior of Postgres. How? By hooking into low level Postgres API hooks. The open source Citus database that scales out Postgres horizontally is itself implemented as a PostgreSQL extension, which allows Citus to stay current with Postgres releases without lagging behind like other Postgres forks. I've previously written about the various types of extensions, today though I want to take a deeper look at the most useful Postgres extension: pg_stat_statements.

Postgres keeps getting better and better. In recent years, the Postgres community has added JSONB support, improved performance, and added so many usability enhancements. The result: you can work even more powerfully with your database. Over the past 8 years, my favorite two enhancements have been JSONB and pg_stat_statements. Pg_stat_statements is a built-in extension that allows you to get high level insights into queries that are being run as well as their performance—without having to be an expert and without needing a PhD in databases.

Introducing the new landlord feature in Citus 7.5

With Citus 7.5, we've gone one step beyond the awesomeness of pg_stat_statements and Postgres, with the new landlord feature in Citus—to give you per-tenant stats.

At the heart of most applications is a database. Ensuring your database is performing well is key to ensuring your your customers receive a good experience when working with your app. It's likely you're already monitoring your systems today, and want to monitor your database using similar tooling. Today we're excited to release turnkey integration for one of the more popular tools out there to monitor Citus Cloud clusters: Datadog.