Getting Started with Citus

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 human 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. Watch our 15-min performance demo from SIGMOD to see an example of how Citus speeds up Postgres.

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.

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 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
• High-throughput transactional applications

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(
  ‘table_name’,
  ‘distribution_column’);

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.