What is Citus?

Citus is basically worry-free Postgres that is built to scale out. It’s an extension to Postgres that distributes data and queries in a cluster of multiple machines. As an extension (rather than a fork), Citus supports new PostgreSQL releases, allowing users to benefit from new features while maintaining compatibility with existing PostgreSQL tools.

Citus horizontally scales PostgreSQL across multiple machines using sharding and replication. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.

Available in Three Ways:

  1. As open source to add to existing Postgres servers

  2. On-premise with additional enterprise grade security and cluster management tools

  3. In the Cloud, built into Azure Database for PostgreSQL — Hyperscale (Citus) a fully managed database as a service. (Citus Cloud on AWS is also available but is no longer onboarding new users.)

How Far Can Citus Scale?

Citus scales horizontally by adding worker nodes, vertically by upgrading workers/coordinator, and supports switching to Citus MX mode if needed. In practice our customers have achieved the following scale, with room to grow even more:

  • Algolia
    • 5-10B rows ingested per day

  • Heap
    • 700+ billion events

    • 1.4PB of data on a 70-node Citus database cluster

  • Chartbeat
    • >2.6B rows of data added per month

  • Pex
    • 80B rows updated/day

    • 20-node Citus database cluster on Google Cloud

    • 2.4TB memory, 1280 cores, and 80TB of data

    • …with plans to grow to 45 nodes

  • Mixrank
    • 1.6PB of time series data

For more customers and statistics, see our customer stories.

When to Use Citus

Multi-Tenant Database

Most B2B applications already have the notion of a tenant, customer, or account built into their data model. In this model, the database serves many tenants, each of whose data is separate from other tenants.

Citus provides full SQL coverage for this workload, and enables scaling out your relational database to 100K+ tenants. Citus also adds new features for multi-tenancy. For example, Citus supports tenant isolation to provide performance guarantees for large tenants, and has the concept of reference tables to reduce data duplication across tenants.

These capabilities allow you to scale out your tenants’ data across many machines, and easily add more CPU, memory, and disk resources. Further, sharing the same database schema across multiple tenants makes efficient use of hardware resources and simplifies database management.

Some advantages of Citus for multi-tenant applications:

  • Fast queries for all tenants

  • Sharding logic in the database, not the application

  • Hold more data than possible in single-node PostgreSQL

  • Scale out without giving up SQL

  • Maintain performance under high concurrency

  • Fast metrics analysis across customer base

  • Easily scale to handle new customer signups

  • Isolate resource usage of large and small customers

Real-Time Analytics

Citus supports real-time queries over large datasets. Commonly these queries occur in rapidly growing event systems or systems with time series data. Example use cases include:

  • Analytic dashboards with subsecond response times

  • Exploratory queries on unfolding events

  • Large dataset archival and reporting

  • Analyzing sessions with funnel, segmentation, and cohort queries

Citus’ benefits here are its ability to parallelize query execution and scale linearly with the number of worker databases in a cluster. Some advantages of Citus for real-time applications:

  • Maintain sub-second responses as the dataset grows

  • Analyze new events and new data as it happens, in real-time

  • Parallelize SQL queries

  • Scale out without giving up SQL

  • Maintain performance under high concurrency

  • Fast responses to dashboard queries

  • Use one database, not a patchwork

  • Rich PostgreSQL data types and extensions

Considerations for Use

Citus extends PostgreSQL with distributed functionality, but it is not a drop-in replacement that scales out all workloads. A performant Citus cluster involves thinking about the data model, tooling, and choice of SQL features used.

A good way to think about tools and SQL features is the following: if your workload aligns with use-cases described here and you happen to run into an unsupported tool or query, then there’s usually a good workaround.

When Citus is Inappropriate

Some workloads don’t need a powerful distributed database, while others require a large flow of information between worker nodes. In the first case Citus is unnecessary, and in the second not generally performant. Here are some examples:

  • When single-node Postgres can support your application and you do not expect to grow

  • Offline analytics, without the need for real-time ingest nor real-time queries

  • Analytics apps that do not need to support a large number of concurrent users

  • Queries that return data-heavy ETL results rather than summaries