Citus 12.1 is out! Now with PG16 Support. Read all about it in Naisila’s 12.1 blog post. 💥
The Citus database distributes your Postgres tables or schemas across multiple nodes and parallelizes your queries and transactions. The combination of parallelism, keeping more data in memory, and higher I/O bandwidth often leads to dramatic speed ups. In this chart, we show a benchmark SQL query running ~40x faster with an 8-node Citus cluster vs. a single Postgres node.
Citus makes it possible to distribute your data, queries, and transactions across multiple nodes—by row or by schema. In addition, the architecture includes a distributed query planner and an adaptive query executor. You can shard on a single node, query from any node, and you can use the columnar feature to achieve compression ratios of 3x-10x or more. Citus is a 100% open source Postgres extension.
Citus is an open source extension to Postgres (not a fork.) So when you use Citus, you’re still using Postgres under the covers, along with the Citus extension on top. To your application, running on a Citus distributed database is like running on top of a single Postgres node. And because Citus is an extension, it’s easy for us to keep Citus current with the latest Postgres releases—plus you get the performance benefits of horizontal scale, while still being able to leverage your familiar SQL toolset and your Postgres expertise.
Because Citus distributes your data, parallelizes your queries, keeps more data in memory, and gives you higher I/O bandwidth—Citus can meet the demanding performance requirements of mixed OLTP and OLAP workloads. So you can simplify your architecture by using a single database for your app’s transactional and analytical workloads, even for data-intensive applications. Citus gives you more capabilities: you can now use both columnar and row-based tables in your Citus distributed database. And with Citus 12, you can now easily support microservices.
Find out more about the Citus concepts, architecture, cluster management, APIs, use cases, & performance tuning.
See how Citus scales out Postgres and parallelizes your workloads via these YouTube videos. Tip: turn on captions.
Using sharding and replication, the Citus extension distributes your data and queries across multiple nodes in a cluster, to give your app parallelism as well as more memory, compute, and disk. Citus is available as an open source download and in the cloud as a managed service. Azure Cosmos DB for PostgreSQL makes it easy to stand up a managed Citus cluster in minutes.
As of Citus 10, you can now shard Postgres on a single node, too. So you can adopt a distributed data model from the start to parallelize your queries—and be “scale-out ready.” Single-node Citus can also help to simplify your CI/CD pipelines.
Learn how Citus works in this talk about Citus table types, the PostgreSQL extension APIs, the Citus query planner, and performance benchmarks comparing multi-node Citus clusters to a single node.
Citus 12.1 blog post
Official release notes for Citus 12.1
Schema-based sharding comes to Postgres with Citus
Citus 12 blog post
Official release notes for Citus 12
How Citus supports the PostgreSQL MERGE command, as of Citus 12.0
Distributed PostgreSQL benchmarks using HammerDB, by GigaOM
Citus 11.3 blog post
Official release notes for Citus 11.3
Distributed Postgres goes full open source with Citus: why, what, & how
Open Source News
Open sourcing the Citus shard rebalancer
How to scale Postgres for time series data
|Citus Version||Compatible with PostgreSQL|
|9.5||11, 12, 13|
|10.0.x||11, 12, 13|
|10.2.x||12, 13, 14|
|11.1.x, 11.2.x, 11.3.x||13, 14, 15|
|12.1||14, 15, 16|
Citus achieves order-of-magnitude faster execution compared to vanilla PostgreSQL through a combination of parallelism, keeping more data in memory, and higher I/O bandwidth.
Citus enables real-time interaction with large datasets that span billions of records—and is a good fit for customer-facing workloads that often require low-latency response times. Performance increases as you add nodes to a Citus database cluster. This 15-min performance demo from SIGMOD shows how Citus speeds up Postgres, using the HammerDB benchmark. Recently GigaOm published a benchmark performance report for Citus. Find out why benchmarking databases is so hard in this blog post by the lead engineer for Citus. Columnar storage can speed up analytics workloads that benefit from compression, too.
The easiest way to start is by utilizing schema-based sharding, which assumes assigning each tenant to a separate schema. Citus then automatically distributes these among the nodes in your cluster and routes queries accordingly. The only change you will need to do in your application is to SET search_path when switching tenants. In some cases like with microservices, even that change may not be necessary if every microservice uses a separate user matching their schema name.
If you want the best performance, row-based sharding, using a distribution column is the best approach. The first step in migrating an application from Postgres to Citus is to choose your distribution column (sometimes called a distribution key, or a sharding key.) You’ll want to understand your workload in order to pinpoint a “good” distribution column, e.g., a column that enables you to get the maximum performance from Citus.
The second step is to prepare the Postgres tables and SQL queries for migration. The amount of effort involved depends (you’ve heard that before, right?) on whether your application is already centered around that distribution column in terms of queries and schema. If not, you may have to update some of your queries and/or add the distribution column to some of your tables.
Alternately, you can now shard your database on a single node. So you can build your application on single-node Citus from the very start and be “scale-out-ready”, able to easily add nodes and rebalance your Citus cluster as your application grows.
If you are ready to delve deeper, the Migrating to Citus guide in the Citus documentation should be useful.
The Citus extension to Postgres is commonly used with customer-facing applications that are growing fast, have demanding performance requirements, are starting to experience slow queries, need to plan for future scale—or all of the above. Common use cases for Citus—both self-hosted and in the cloud as the Azure Cosmos DB for PostgreSQL managed service—include:
As you’ll learn in the Citus concepts section of the documentation there are two ways of sharding Citus—row-based and schema-based. In both sharding methods Citus divides Postgres tables into multiple smaller tables, called shards. The shards are then spread across the nodes in the Citus database cluster.
In the case of row-based sharding you decide how tables are split using the
With schema-based sharding, the schema name acts as the grouping and you determine which schemas are distributed with
When new data is ingested or when queries come in, the Citus coordinator routes them to the correct shards based on the value of the distribution column (row-based) or the schema name (schema-based) depending on which sharding method you chose.