Question: How do you implement multiple sharding in PostgreSQL?


Sharding in PostgreSQL is a strategy used to scale horizontally by distributing data across multiple databases or instances, which are known as shards. This approach can significantly improve performance for large-scale applications by parallelizing queries, spreading out the load, and reducing the amount of data stored on any single server.

Understanding Sharding in PostgreSQL

PostgreSQL itself does not have built-in automatic sharding capabilities; however, it supports partitioning and can be used in conjunction with external tools or additional modules to achieve sharding. One popular extension for this purpose is Citus, which transforms PostgreSQL into a distributed database system, enabling it to scale horizontally.

Implementing Multiple Sharding with Citus

Citus extends PostgreSQL, turning it into a distributed database system. Here's how you can set up multiple sharding with Citus:

  1. Install Citus: First, install Citus on your PostgreSQL server. You can find the installation instructions specific to your operating system on the Citus documentation website.

  2. Create Distributed Tables: Once Citus is installed, you can distribute your tables across multiple shards. For example:

    -- Connect to your Citus coordinator node \c my_database -- Enable Citus extension CREATE EXTENSION IF NOT EXISTS citus; -- Distribute your table SELECT create_distributed_table('my_table', 'distribution_column');

    In this code, 'my_table' is the name of your table, and 'distribution_column' is the column Citus will use to distribute the rows across different shards.

  3. Query Your Data: After distributing your tables, you can query them just like you would in a standard PostgreSQL database. Citus automatically manages the distribution of your queries across all the shards.

  4. Scaling Out: To scale out your database, you simply add more worker nodes to your Citus cluster. The command to add a new node looks like this:

    SELECT * FROM master_add_node('node_hostname_or_ip', node_port);


  • Choosing a Distribution Column: The choice of distribution column is critical. It should be a column that allows for even distribution of data across shards to prevent data skew.

  • Join Performance: When using multiple shards, joins between distributed tables are most efficient when they are based on the distribution column.

  • Data Consistency: Citus provides mechanisms to ensure data consistency across shards, but designing your application to work with distributed transactions can require some adjustments.

  • Maintenance and Monitoring: Operating a sharded PostgreSQL setup requires careful monitoring and maintenance. Tools like pgAdmin or Citus’s enterprise features can help manage and monitor the health and performance of your distributed database.

In conclusion, implementing multiple sharding in PostgreSQL, particularly with extensions like Citus, offers a powerful way to scale databases horizontally. Careful planning and consideration of your data distribution strategy are essential for success.

Was this content helpful?

White Paper

Free System Design on AWS E-Book

Download this early release of O'Reilly's latest cloud infrastructure e-book: System Design on AWS.

Free System Design on AWS E-Book
Start building today

Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.