Citus BlogCitus Blog

What if you could automatically shard your PostgreSQL database across any number of servers and get industry-leading performance at scale without any special data modelling steps?

Our latest Citus open source release, Citus 12, adds a new and easy way to transparently scale your Postgres database: Schema-based sharding, where the database is transparently sharded by schema name.

Schema-based sharding gives an easy path for scaling out several important classes of applications that can divide their data across schemas:

• Multi-tenant SaaS applications
• Microservices that use the same database
• Vertical partitioning by groups of tables

Each of these scenarios can now be enabled on Citus using regular CREATE SCHEMA commands. That way, many existing applications and libraries (e.g. django-tenants) can scale out without any changes, and developing new applications can be much easier. Moreover, you keep all the other benefits of Citus, including distributed transactions, reference tables, rebalancing, and more.

Introducing Path To Citus Con, a podcast for developers who love Postgres. Why? Because sometimes, something you build gets bigger than you thought it would. The monthly podcast Path To Citus Con (renamed to Talking Postgres in July 2024) was originally meant to be a “pre-event” to build excitement and give a hands-on experience for people who would be attending Citus Con: An Event for Postgres. The audience would get a chance to talk to speakers for the conference and hear a deep dive conversation.

It’s now its own monthly podcast with guests from around the world. Guests have been deep in the world of databases and the Citus database extension to Postgres, and also people in the Postgres community and technology more generally. It’s the human side of open source, PostgreSQL, and the many PG extensions (including Citus.)

In this blog post, you’ll learn about what Path To Citus Con is, how you can participate, listen, and read each episode, and about episodes like “Working in public on open source,” “Why giving talks at Postgres conferences matters,” and more (details below.)

Distributed PostgreSQL has become a hot topic. Several distributed database vendors have added support for the PostgreSQL protocol as a convenient way to gain access to the PostgreSQL ecosystem. Others (like us) have built a distributed database on top of PostgreSQL itself.

For the Citus database team, distributed PostgreSQL is primarily about achieving high performance at scale. The unique thing about Citus, the technology powering Azure Cosmos DB for PostgreSQL, is that it is fully implemented as an open-source extension to PostgreSQL. It also leans entirely on PostgreSQL for storage, indexing, low-level query planning and execution, and various performance features. As such, Citus inherits the performance characteristics of a single PostgreSQL server but applies them at scale.

That all sounds good in theory, but to see whether this holds up in practice, you need benchmark numbers. We therefore asked GigaOM to run performance benchmarks comparing Azure Cosmos DB for PostgreSQL to other distributed implementations. GigaOM compared the transaction performance and price-performance of these popular managed services of distributed PostgreSQL, using the HammerDB benchmark software:

One of the most important improvements in Citus 11.3 is that Citus offers more reliable metadata sync. Before 11.3, when a Citus cluster had thousands of distributed objects (such as distributed tables), Citus occasionally experienced memory problems while running metadata sync. Due to these memory errors, some users with very large numbers of tables were sometimes unable to add new nodes or upgrade beyond Citus 11.0.

To address the memory issues, we added an alternative "non-transactional" mode to the current metadata sync in Citus 11.3.

The default mode for metadata sync is still the original single transaction mode that we introduced in Citus 11.0. But now in 11.3 or later, if you have a very large number of tables and you run into the memory error, you can choose to optionally switch to the non-transactional mode, which syncs the metadata via many transactions. While most of you who use Citus will not need to enable this alternative metadata sync mode, this is how to do it:

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.

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.

A developer friend of mine prefers to read about what to expect at upcoming events in the narrative form of a blog, rather than having to click in and out of different abstracts on a schedule page.

So this ultimate guide post is my gift to those of you who want to know more about the 37 talks that will be presented at this year’s 2nd annual Citus Con: An Event for Postgres 2023—and who want to read about it in blog post form.

And yes, Citus Con is virtual again this year! This means you can watch all the livestream & on-demand talks from the comfort of your very own desk—and chit-chat in the virtual hallway track on the #cituscon channel on Discord.

[Update in May 2023]: It's a wrap! The categories in this ultimate guide will help you find the talks which are most useful to you and your work/interests. Or you can jump straight to the playlist of all 37 Citus Con 2023 talks on YouTube.

So what’s on the schedule at Citus Con: An Event for Postgres 2023, exactly? Be sure to check out both tabs on the Schedule page, both the Live Sessions & the On-Demand Sessions tabs, to learn about the:

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.