PostgreSQL: Identifying Missing or Unused Indexes

Indexes are crucial for database performance, acting like a book's index to quickly locate data. However, both missing and unused indexes can negatively impact your PostgreSQL database. Missing indexes can lead to slow queries, while unused indexes consume disk space and slow down write operations (INSERT, UPDATE, DELETE) without providing any benefit.

Why Identify Missing Indexes?

When queries are slow, it's often because PostgreSQL has to perform a full table scan instead of using an index. This happens when there's no suitable index for the

code

WHERE

clause,

code

JOIN

conditions, or

code

ORDER BY

clauses in your SQL statements. Identifying these missing indexes allows you to create them, significantly speeding up data retrieval.

Why Identify Unused Indexes?

Indexes aren't free. They require disk space and add overhead to data modification operations. If an index is never used by any query, it's a prime candidate for removal. Dropping unused indexes can free up disk space and improve the performance of your INSERT, UPDATE, and DELETE statements.

Tools and Techniques for Identification

PostgreSQL provides several ways to help you identify index usage and potential candidates for creation or removal. These include built-in views, extensions, and analyzing query execution plans.

Using `pg_stat_user_indexes` and `pg_statio_user_indexes`

The

code

pg_stat_user_indexes

view provides statistics on index usage, specifically the number of times an index has been scanned (

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idx_scan

). A low or zero

code

idx_scan

count for an index might indicate it's unused. The

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pg_statio_user_indexes

view provides I/O statistics, showing how often an index has been read from or written to disk.

Which PostgreSQL system view shows the number of times an index has been scanned?

pg_stat_user_indexes

Leveraging `pg_stat_statements`

The

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pg_stat_statements

extension tracks execution statistics for all SQL statements executed by the server. By joining this with information about your indexes, you can identify queries that are performing full table scans or are otherwise inefficient, suggesting the need for new indexes. It also helps in identifying which indexes are frequently used by which queries.

Analyzing `EXPLAIN` and `EXPLAIN ANALYZE`

The

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EXPLAIN

command shows the execution plan for a query without actually running it, while

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EXPLAIN ANALYZE

runs the query and shows the actual execution plan and timing. Look for 'Seq Scan' (Sequential Scan) on large tables, which indicates a full table scan and a potential missing index. Conversely, if an index is used but the scan is very slow or inefficient, it might be a poorly chosen index.

Understanding query plans is key. A 'Seq Scan' means the database reads every row in the table. An 'Index Scan' or 'Bitmap Heap Scan' means it's using an index to find the relevant rows more efficiently. The cost estimates and actual times provided by EXPLAIN ANALYZE help pinpoint performance bottlenecks.

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Using Index Advisor Extensions

Extensions like

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pg_qualstats

can help identify queries with missing predicates that could benefit from indexes. Other tools and scripts exist that analyze

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pg_stat_statements

and

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pg_stat_user_indexes

to suggest potentially unused or missing indexes.

Remember to monitor index usage over time. An index that is unused today might become critical tomorrow as your application evolves.

Best Practices for Index Management

Regularly review your index usage. Create indexes based on query patterns, especially for columns used in WHERE, JOIN, ORDER BY, and GROUP BY clauses. Periodically identify and drop indexes that are not being used to maintain database health and performance.

What is the primary indicator of a missing index in an EXPLAIN plan?

Sequential Scan (Seq Scan) on a large table.

Learning Resources

PostgreSQL Documentation: System Statistics Views(documentation)

Official PostgreSQL documentation detailing system statistics views like pg_stat_user_indexes and pg_stat_statements, essential for monitoring index usage.

PostgreSQL: Understanding EXPLAIN(documentation)

Comprehensive guide from PostgreSQL on how to interpret the output of EXPLAIN and EXPLAIN ANALYZE to understand query execution plans.

PostgreSQL Indexing: The Definitive Guide(blog)

An in-depth blog post covering various aspects of PostgreSQL indexing, including identifying unused indexes and performance tuning strategies.

Finding Unused Indexes in PostgreSQL(wikipedia)

A Stack Exchange discussion providing practical SQL queries and approaches for identifying unused indexes in a PostgreSQL database.

PostgreSQL Performance Tuning: Indexes(blog)

A blog post from Percona focusing on performance tuning in PostgreSQL, with a significant section dedicated to effective index usage and management.

pg_stat_statements: The Missing Link in PostgreSQL Performance Monitoring(documentation)

While not a direct link to the extension's page, this section of the PostgreSQL manual explains the pg_stat_statements view and its utility for query analysis.

PostgreSQL: How to Find Missing Indexes(blog)

A blog post from Citus Data offering practical advice and SQL queries for identifying queries that would benefit from new indexes.

SQL Indexing Strategies for PostgreSQL(blog)

This article discusses various indexing strategies in PostgreSQL, including how to analyze query performance and identify index needs.

PostgreSQL Indexing Best Practices(blog)

Crunchy Data provides a guide to PostgreSQL indexing best practices, covering creation, maintenance, and performance considerations.

PostgreSQL Performance Tuning with pg_stat_statements(blog)

A tutorial on how to leverage the pg_stat_statements extension to identify slow queries and optimize database performance through index analysis.