How to use Partial Indexes in PostgreSQL?

Partial indexes in PostgreSQL allow you to create an index on a subset of a table’s rows that meet a certain condition. This can be useful for improving query performance on a subset of data without incurring the overhead of an index on the entire table.

Here’s an example of how you can use partial indexes in PostgreSQL:

Suppose you have a table of orders with a status column that indicates whether the order has been shipped or not. You want to create an index on the shipped orders to improve query performance. In PostgreSQL, you can create a partial index using the following SQL statement:

CREATE INDEX idx_shipped_orders ON orders (order_date)
WHERE status = 'shipped';

This creates an index on the order_date column, but only for rows where the status column is set to ‘shipped’. This allows you to query the shipped orders more efficiently without incurring the overhead of an index on the entire table.

Note that partial indexes can only be used when the WHERE clause of the query matches the condition specified in the index definition. If the WHERE clause does not match the condition, PostgreSQL will not use the partial index and will instead scan the entire table.

Partial indexes can also be useful for reducing the storage requirements of indexes. By creating an index on a subset of rows, you can reduce the size of the index and potentially save disk space.

In summary, partial indexes in PostgreSQL can be a useful tool for improving query performance on a subset of data and reducing the storage requirements of indexes. However, it’s important to ensure that the condition specified in the index definition matches the WHERE clause of the queries you want to optimize.

About Shiv Iyer 452 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.