Citus Blog

The Citus 13.0 release is out and includes PostgreSQL 17.2 support! We know you’ve been waiting, and we’ve been hard at work adding features we believe will take your experience to the next level, focusing on bringing the Postgres 17 exciting improvements to you at distributed scale.

The Citus database is an open-source extension of Postgres that brings the power of Postgres to any scale, from a single node to a distributed database cluster. Since Citus is an extension, using Citus means you're also using Postgres, giving you direct access to the Postgres features. And the latest of such features came with Postgres 17 release! In addition, Citus 13 will be made available on the elastic clusters (preview) feature on Azure Database for PostgreSQL - Flexible Server, along with PostgreSQL 17 support, in the near future.

PostgreSQL 17 highlights include performance improvements in query execution for indexes, a revamped memory management system for vacuum, new monitoring and analysis features, expanded functionality for managing data in partitions, optimizer improvements, and enhancements for high-concurrency workloads. PostgreSQL 17 also expands on SQL syntax that benefits both new workloads and mission-critical systems, such as the addition of the SQL/JSON JSON_TABLE() command for developers, and the expansion of the MERGE command. For those of you who are interested in upgrading to Postgres 17 and scaling these new features of Postgres: you can upgrade to Citus 13.0!

Along with Postgres 17 support, Citus 13.0 also fixes important bugs, and we are happy to say that we had many community contributions here as well. These bugfixes focus on data integrity and correctness, crash and fault tolerance, and cluster management, all of which are critical for ensuring reliable operations and user confidence in a distributed PostgreSQL environment.

Let's take a closer look at what's new in Citus 13.0:

The new PostgreSQL 16 release is out, packed with exciting improvements and features—and Citus 12.1 brings them to you at scale, within just one week of the PG16 release.

As many of you likely know, Citus is an open source PostgreSQL extension that turns Postgres into a distributed database. Our team started integrating Citus with the PG16 beta and release candidates early-on, so that you could have a new Citus 12.1 release that is compatible with Postgres 16 as quickly as possible after PG16 came out.

There are a lot of good reasons to upgrade to Postgres 16—huge thanks to everyone who contributed into this Postgres release! PG16 highlights include query performance boost with more parallelism; load balancing with multiple hosts in libpq (contributed by my Citus teammate, Jelte Fennema-Nio); I/O monitoring with pg_stat_io; developer experience enhancements; finer-grained options for access control; logical replication from standby servers and other replication improvements, like using btree indexes in the absence of a primary key (contributed by one of my teammates, Onder Kalaci.)

The good news for those of you who care about distributed Postgres: Citus 12.1 is now available and adds support for Postgres 16.

In addition to Postgres 16 support, Citus 12.1 includes enhancements to schema-based sharding, which was recently added to Citus 12.0—and is super useful for multi-tenant SaaS applications.

The topic of this month's PGSQL Phriday #011 community blogging event is partitioning vs. sharding in PostgreSQL. It seemed right to share a perspective on the question of "partitioning vs. sharding" from someone in the Citus open source team, since we eat, sleep, and breathe sharding for Postgres.

Postgres built-in "native" partitioning—and sharding via PG extensions like Citus—are both tools to grow your Postgres database, scale your application, and improve your application's performance.

What is partitioning and what is sharding? In Postgres, database partitioning and sharding are techniques for splitting collections of data into smaller sets, so the database only needs to process smaller chunks of data at a time. And as you might imagine, work gets done faster when you're processing less data.

In this post, you'll learn what partitioning and sharding are, why they matter, and when to use them. The table of contents:

Citus, a database scaling extension for PostgreSQL, is known for its ability to shard data tables and efficiently distribute workloads across multiple nodes. With Citus 12.0, Citus introduces a very exciting feature called schema-based sharding. The new schema-based sharding feature gives you a choice of how to distribute your data across a cluster, and for some data models (think: multi-tenant apps, microservices, etc.) this schema-based sharding approach may be significantly easier!

In this blog post, we will take a deep dive into the new schema-based sharding feature, and you will learn:

Postgres community released a new feature, in Postgres 15.0, that performs actions to modify rows in the target table, using the data from a source. MERGE provides a single SQL statement that can conditionally INSERT, UPDATE or DELETE rows, a task that would otherwise require multiple procedural language statements, using INSERT with ON CONFLICT clause etc.

In this blog post, you will learn a high-level overview of the functioning of Postgres MERGE. It will delve into some of the practical use-cases, and subsequently elaborate on the different strategies employed by Citus for handling MERGE in a distributed environment.

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.

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:

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 distributed database that is built entirely as an open source PostgreSQL extension. In fact, you can install it in your PostgreSQL server without changing any PostgreSQL functionality. Citus simply gives PostgreSQL additional superpowers.

Being an extension also means we can keep adding new Postgres superpowers at a high pace. In the last release (11.0), we focused on giving you the ability to query from any node, opening up Citus for many new use cases, and we also made Citus fully open source. That means you can see everything we do on the Citus GitHub page (and star the repo if you’re a fan 😊). It also means that everyone can take advantage of shard rebalancing without write-downtime.

In the latest release (11.1), our Citus database team at Microsoft improved the application’s experience and avoided blocking writes during important operations like distributing tables and tenant isolation. These new capabilities built on the experience gained from developing the shard rebalancer, which uses logical replication to avoid blocking writes. In addition, we made the shard rebalancer faster and more user-friendly; also, we prepared for the upcoming PostgreSQL 15 release. This post gives you a quick tour of the major changes in Citus 11.1, including:

From the beginning of the COVID-19 pandemic, the United Kingdom (UK) government has made it a top priority to track key health metrics and to share those metrics with the public.

And the citizens of the UK were hungry for information, as they tried to make sense of what was happening. Maps, graphs, and tables became the lingua franca of the pandemic. As a result, the GOV.UK Coronavirus dashboard became one of the most visited public service websites in the United Kingdom.

The list of people who rely on the UK Coronavirus dashboard is quite long: government personnel, public health officials, healthcare employees, journalists, and the public all use the same service.