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Claire Giordano: Beginner's Guide to Partitioning vs. Sharding in Postgres (PGConf.EU 2023)
Discover the ins and outs of partitioning and sharding in Postgres, two techniques for optimizing database performance and scalability. Learn how to leverage native partitioning and Citus's row-based sharding to handle large datasets effectively.
- Partitioning and sharding are two techniques used to improve the performance and scalability of large databases.
- Partitioning divides a large table into smaller, more manageable chunks, while sharding distributes data across multiple servers or nodes.
- Postgres native partitioning is a built-in feature that allows you to create partitions based on a specified key.
- Citus is an open-source extension to Postgres that provides row-based sharding, as well as other features such as distributed queries and automatic failover.
- Partitioning can help improve query performance by reducing the amount of data that needs to be scanned, while sharding can help improve scalability by distributing the load across multiple servers.
- Both partitioning and sharding have their own advantages and disadvantages, and the best approach for a particular application will depend on the specific requirements.
- Partitioning and sharding can be used together to achieve even greater performance and scalability benefits.
- There are a number of tools and techniques available to help manage partitioned and sharded databases, such as pgPartMan and Citus’s built-in management tools.
- Partitioning and sharding are powerful techniques that can be used to improve the performance and scalability of large databases, but they also require careful planning and implementation.