Understanding and Using Indexes in PostgreSQL

Indexes are one of the most powerful features in PostgreSQL for improving query performance. Without proper indexing, even a well-designed database can become slow and inefficient as data grows. However, indexes are not magic — they must be understood and used carefully to avoid unnecessary overhead.

In this article, you will learn what indexes are, how they work in PostgreSQL, the different types of indexes available, and best practices for using them effectively in real-world scenarios.

What Is an Index in PostgreSQL?

An index is a special data structure that PostgreSQL uses to speed up data retrieval. Instead of scanning an entire table (sequential scan), PostgreSQL can use an index to quickly locate rows that match a query condition.

You can think of an index like a book’s table of contents:

• Without an index → PostgreSQL reads every row
• With an index → PostgreSQL jumps directly to the relevant rows

Indexes are most commonly used with:

• WHERE clauses
• JOIN conditions
• ORDER BY
• GROUP BY

How Indexes Work Internally

PostgreSQL stores index data separately from table data. When a query is executed, the query planner decides whether using an index is faster than scanning the entire table.

Key points:

• Indexes store column values and pointers to table rows
• Indexes increase read performance
• Indexes add overhead to write operations (INSERT, UPDATE, DELETE)

That’s why indexing is always a trade-off between read speed and write cost.

Creating a Basic Index

The most common way to create an index is using the CREATE INDEX command.

Example:

CREATE INDEX idx_users_email ON users(email);

This index helps PostgreSQL quickly find rows when filtering by the email column.

To see existing indexes:

\d users

Common Index Types in PostgreSQL

PostgreSQL provides several index types, each optimized for different use cases.

B-Tree Index (Default)

B-Tree is the default and most widely used index type.

Best for:

• Equality (=)
• Range queries (<, >, BETWEEN)
• Sorting (ORDER BY)

Example:

CREATE INDEX idx_orders_created_at 
ON orders(created_at);

Use B-Tree when you are unsure — it works for most scenarios.

Hash Index

Hash indexes are optimized for equality comparisons only.

Best for:

• = operator

Limitations:

• Not useful for range queries
• Rarely used compared to B-Tree

Example:

CREATE INDEX idx_users_id_hash 
ON users USING HASH (id);

GIN Index (Generalized Inverted Index)

GIN indexes are designed for complex data types.

Best for:

• JSONB
• ARRAY
• Full-text search (tsvector)

Example for JSONB:

CREATE INDEX idx_products_tags 
ON products USING GIN (tags);

GIN indexes are powerful but can be expensive in terms of storage and write performance.

GiST Index (Generalized Search Tree)

GiST indexes are flexible and support advanced data types.

Best for:

• Geospatial data (PostGIS)
• Range types
• Similarity searches

Example:

CREATE INDEX idx_locations_geom 
ON locations USING GiST (geom);

BRIN Index (Block Range Index)

BRIN indexes are lightweight and work well for very large tables.

Best for:

• Columns with natural ordering (timestamps, IDs)
• Huge tables with sequential data

Example:

CREATE INDEX idx_logs_created_at 
ON logs USING BRIN (created_at);

BRIN indexes consume minimal space but are less precise than B-Tree.

Multi-Column Indexes

PostgreSQL supports indexes on multiple columns.

Example:

CREATE INDEX idx_orders_customer_date 
ON orders(customer_id, created_at);

Important rule:

• Index works best when query conditions match the column order

Good:

WHERE customer_id = 10 AND created_at > '2025-01-01'

Less effective:

WHERE created_at > '2025-01-01'

Partial Indexes

Partial indexes index only a subset of rows.

Best for:

• Filtering frequently queried data
• Reducing index size

Example:

CREATE INDEX idx_active_users 
ON users(email)
WHERE status = 'active';

This index is smaller and faster than indexing the entire table.

Expression Indexes

Expression indexes index computed values instead of raw columns.

Example:

CREATE INDEX idx_lower_email 
ON users (LOWER(email));

Useful when queries use functions in WHERE clauses.

When PostgreSQL Uses an Index

PostgreSQL does not always use an index, even if it exists.

Reasons:

• Table is very small
• Query returns most rows
• Planner estimates sequential scan is faster

To analyze query plans:

EXPLAIN ANALYZE
SELECT * FROM users WHERE email = 'test@example.com';

This command shows whether PostgreSQL uses an index and how long the query takes.

Index Maintenance and Performance Impact

Indexes must be maintained during data changes.

Impact:

• Slower INSERT, UPDATE, DELETE
• Increased disk usage

Best practices:

• Avoid indexing columns with low selectivity (e.g., boolean)
• Remove unused indexes
• Monitor index usage

Check index usage:

SELECT relname, idx_scan
FROM pg_stat_user_indexes;

Best Practices for Using Indexes

1. Index columns used frequently in WHERE and JOIN
2. Avoid over-indexing
3. Use partial indexes for filtered data
4. Prefer B-Tree unless a specific use case exists
5. Use EXPLAIN ANALYZE before and after adding indexes
6. Periodically review and clean unused indexes

Common Indexing Mistakes

• Indexing every column blindly
• Ignoring write performance impact
• Using wrong index type
• Not analyzing query patterns
• Forgetting to maintain indexes

Good indexing starts with understanding how your application queries data.

Conclusion

Indexes are essential for building high-performance PostgreSQL databases. By understanding how indexes work and choosing the right type for each use case, you can dramatically improve query speed without sacrificing system stability.

The key is balance — use indexes strategically, monitor their impact, and continuously optimize based on real query behavior.

If you master PostgreSQL indexing, you unlock one of the most powerful performance tools available in modern relational databases.