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PostgreSQL: Creating and Using Multi-Column Indexes

Optimize queries with multi-column indexes in PostgreSQL. This guide explains the benefits and how to create composite indexes.

Creating and Using Multi-Column Indexes in PostgreSQL

In PostgreSQL, multi-column indexes (also known as composite indexes) significantly enhance query performance by indexing multiple columns within a table. This is especially beneficial when your queries frequently filter data based on combinations of columns.

Understanding Multi-Column Indexes

A multi-column index indexes multiple columns of a table together. This allows PostgreSQL to efficiently locate rows matching conditions on any subset of the indexed columns. The order of columns in a multi-column index is very important; it affects how the index is used for queries.

Creating Multi-Column Indexes

The syntax for creating a multi-column index is:


CREATE INDEX index_name ON table_name (column1, column2, ...);

You can include up to 32 columns (this limit can be adjusted during PostgreSQL compilation). PostgreSQL supports B-tree, GiST, GIN, and BRIN indexes for multi-column indexing. The order of columns is significant for query optimization.

Optimal Column Order

For best performance, list the columns in your index in order of frequency of use in your `WHERE` clauses. Columns frequently used in `WHERE` conditions should be placed earlier in the index definition. PostgreSQL will use the index for queries that match any prefix of the indexed columns.

Example:

  • Index: `(last_name, first_name)`
  • Index will be used for:
    • WHERE last_name = 'Smith' AND first_name = 'John'
    • WHERE last_name = 'Smith'
  • Index will *not* be used for:
    • WHERE first_name = 'John'

Example: Creating a Multi-Column Index

(Note: This example requires a pre-existing table named 'Person'. Screenshots in the original text cannot be displayed here; please refer to the original document for visual verification. The descriptions below aim to convey the information in those screenshots.)

  1. Create the Table: Create a `Person` table with `Person_id`, `first_name`, and `last_name` columns.
  2. Insert Data: Insert sample data into the table.
  3. Query Without Index: Execute a query to select people with a specific last name (`Smith`). Use `EXPLAIN` to see that PostgreSQL performs a sequential scan (no index used).
  4. Create the Index: Create a B-tree index on `last_name` and `first_name` (in that order).
  5. Query with Index: Execute the same query again using `EXPLAIN`. You'll see that PostgreSQL now uses the index (if there is sufficient data for the optimizer to choose index-based lookup).
  6. Query Using Both Indexed Columns: Query using both `last_name` and `first_name` in the `WHERE` clause. `EXPLAIN` shows the index is used.
  7. Query Using Only the Second Indexed Column: Query only using `first_name`. The index is not used because the index is based on `last_name` first.

Multi-Column Indexes with Different Index Types

The behavior of multi-column indexes differs slightly based on the index type (B-tree, GiST, GIN, BRIN):

  • B-tree: Uses the index efficiently for queries matching prefixes of the indexed columns.
  • GiST, GIN, BRIN: Can use the index even when only a subset of the indexed columns is specified in the query. However, the order of columns still matters for performance, as conditions on the first column typically determine how much of the index is scanned. GiST is less efficient with many distinct values in the first column.