Why Citus?

Learn Your Way: Read, Watch, or Do

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Read the docs

Find out more about the Citus concepts, architecture, cluster management, APIs, use cases, & performance tuning.

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Watch the videos

See how Citus scales out Postgres and parallelizes your workloads via these YouTube videos. Tip: turn on captions.

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Try the tutorials

Learn how to use Citus by using sample data in these short tutorials. For time series data, check out the use case guide.

Try Citus Right Now

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Citus Open Source

You can download and install Citus open source packages for Docker, Ubuntu, Debian, Fedora, CentOS, and Red Hat via these simple steps.

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Citus on Azure

You can stand up a Citus cluster in minutes with the Azure Cosmos DB for PostgreSQL managed service.

Sharding on a Single Node or Multiple Nodes

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.

Citus single node to distributed Citus cluster diagram
A Citus database cluster contains a Citus coordinator node and multiple worker nodes. Each node contains small Postgres tables called shards. Learn more in the animated Citus architecture graphic—or in the Citus GitHub repo.

How Citus Works

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.

Video thumbnail: screen with Citus performance benchmarks

Frequently Asked Questions

  1. Citus Version Compatible with PostgreSQL
    5.2 9.5 only
    6.x 9.5, 9.6
    7.x 9.6, 10
    8.x 10, 11
    9.0-9.4 11, 12
    9.5 11, 12, 13
    10.0.x 11, 12, 13
    10.1.x 12, 13
    10.2.x 12, 13, 14
    11.0.x 13, 14
    11.1.x, 11.2.x, 11.3.x 13, 14, 15
  2. 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. And new to Citus 10, columnar storage can speed up analytics workloads that benefit from compression, too.

  3. 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, as of Citus 10, 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.

  4. 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 on-prem and in the cloud where Hyperscale (Citus) is an option in the Azure Database for PostgreSQL managed service—include:

    • Customer-facing analytics dashboards
    • SaaS applications—usually multi-tenant
    • Time series workloads
    • IOT workloads—that need UPDATEs & JOINs
    • Mixed transactional & analytical workloads
  5. As you’ll learn in the Citus concepts section of the documentation, Citus divides Postgres tables into multiple smaller tables, called shards. The shards are then spread across the nodes in the Citus database cluster when you configure Citus with the create_distributed_table() function. 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.

    SELECT create_distributed_table(

    Another way of thinking about shards: Each shard contains a portion of the larger Postgres table that you have distributed. Imagine you previously had a 1 TB Postgres table. Now imagine you have distributed that 1 TB table across 100 shards in a Citus cluster. Each shard—which is just a smaller Postgres table—would be a 10 GB Postgres table.

    Citus does more than simply shard and distribute your data, however. Citus also parallelizes your SQL queries across different nodes in the Citus cluster, giving you an order-of-magnitude increase in query response times for many use cases. New as of Citus 10, you can be “scale-out-ready” by sharding your Postgres database on a single Citus node, using a distributed data model from the very start—so you can easily add nodes later as your application grows.