---
title: "How to Monitor and Optimize PostgreSQL Index Performance"
description: "Learn how to monitor and optimize PostgreSQL index performance using pg_stat views, planner settings, and Timescale best practices."
section: "Postgres guides"
---

> **TimescaleDB is now Tiger Data.**

PostgreSQL indexes can be the difference between a blazing-fast query and a frustrating performance bottleneck. But how can you tell if your indexes are helping—or hurting—your database?

In this post, we’ll explore practical strategies to evaluate and optimize your PostgreSQL indexes using tools like `pg_stat_user_indexes` and `pg_stat_statements`. We’ll also share best practices and how Timescale simplifies index management for time-series and partitioned workloads.

> Looking to improve your PostgreSQL performance? Check out our performance tuning series, [including our index-focused article](https://www.timescale.com/learn/postgresql-performance-tuning-optimizing-database-indexes).



## 
Why PostgreSQL Index Monitoring Matters

Indexes in PostgreSQL aren’t "set and forget." As your data and query patterns evolve, your indexing strategy must evolve too. Outdated, underused, or excessive indexes can bring all sorts of issues:

- Slowing down write operations
- Wasting disk space
- Misleading the query planner
- Leading to suboptimal query plans

Let’s walk through how to spot problematic indexes and fix them before they impact your app.



## Find Unused Indexes With pg_stat_user_indexes

To identify indexes that aren’t being used effectively, the `pg_stat_user_indexes` view is your best friend. The `idx_scan` column shows how often each index has been scanned.

Here’s a query to find low-usage indexes:

`SELECT
  relname AS table_name,
  indexrelname AS index_name,
  pg_size_pretty(pg_relation_size(indexrelid)) AS index_size,
  idx_scan AS index_scan_count
FROM
  pg_stat_user_indexes
WHERE
  idx_scan < 50  -- Adjust based on your workload and retention window
ORDER BY
  index_scan_count ASC,
  pg_relation_size(indexrelid) DESC;
`

In the example above, we're looking for indexes with fewer than 50 scans (which is an arbitrary number for this example—you should adjust it based on your application's usage patterns and the duration since the last statistics reset.


> **Tip:** Don't immediately drop indexes based on this query alone. Some may be used infrequently (e.g., by weekly reports) but are still critical.



## Spot Queries That Need Indexes Using pg_stat_statements

If a query is reading a lot of data from disk, it might benefit from better indexing. That’s where `pg_stat_statements` comes in. Two key metrics to watch:

- `shared_blks_read`: how many blocks PostgreSQL had to read from disk
- `blk_read_time`: time (in ms) spent reading those blocks

A high value in either can signal that the planner isn't using indexes efficiently.

> Read how you can optimize [array queries with PostgreSQL GIN indexes](https://www.timescale.com/learn/optimizing-array-queries-with-gin-indexes-in-postgresql).



## 

🔍 Real-World Example: Slow Query With No Index

Suppose you run a daily report:

`SELECT * FROM purchases WHERE purchase_date > NOW() - INTERVAL '1 day';

`

From `pg_stat_statements`, you see:

| Query | shared_blks_read | blk_read_time |
| --- | --- | --- |
| `SELECT * FROM purchases...` | 5,000 | 300 ms |

This suggests PostgreSQL is reading a lot of disk blocks—slowly. Upon inspection, you realize there’s **no index on **`purchase_date`. Creating one might help:

`CREATE INDEX idx_purchase_date ON purchases(purchase_date);
`

After creation and some usage, re-check `pg_stat_statements`—you should see those values drop, confirming improved performance.



## Tuning PostgreSQL Index Performance With Server Parameters

PostgreSQL’s planner relies on server parameters to decide whether (and how) to use indexes. Here’s a quick rundown of the most important ones:



### work_mem and shared_buffers

- `work_mem`: It controls memory per operation (e.g., sort, hash). Too low, and operations spill to disk.
- `shared_buffers`: It caches table and index pages in RAM. A higher value reduces disk I/O. 

Check out [<u>Part II</u>](https://www.timescale.com/blog/postgresql-performance-tuning-part-ii-adjusting-key-parameters/) of our series on PostgreSQL performance tuning for more detailed configuration recommendations for both of these parameters.



### effective_cache_size

This doesn’t allocate memory—it estimates how much is available for caching. A higher value makes the planner more likely to use indexes.

> 🧠 Best Practice: Set it to 50–70 % of your system's total memory.



### random_page_cost and seq_page_cost

These parameters influence the planner’s cost estimates:

- `random_page_cost`: Default is 4 (ideal for spinning disks). On SSDs, set to ~1.1.
- `seq_page_cost`: Usually left at 1.0.

Reducing `random_page_cost` makes index scans more attractive.



### cpu_index_tuple_cost

Determines the CPU cost of processing an index tuple (default: 0.005). Generally best left unchanged unless you're doing advanced tuning with benchmarking.



## Best Practices for PostgreSQL Index Optimization

✅ **Index only what you need**
 Avoid over-indexing—it slows inserts/updates and increases storage.

✅ **Avoid indexes on low-cardinality columns**
 Columns like `gender` or booleans often don’t benefit from indexes—use partial or multicolumn indexes instead.

✅ **Update statistics after changing indexes**
 Run `ANALYZE` after adding or dropping indexes so the planner has accurate data.

✅ **Rebuild indexes concurrently when needed**
 Use `REINDEX CONCURRENTLY` to avoid locking tables during index rebuilds.

✅ **Benchmark before and after**
 Use `EXPLAIN ANALYZE` to confirm that indexes are actually improving query plans.



## Timescale and Index Optimization Made Simple

If you’re using [Timescale](https://docs.timescale.com/), good news: much of this work is handled automatically.

- [**Hypertables**](https://docs.timescale.com/use-timescale/latest/hypertables/about-hypertables/) automatically create indexes on the time column.
- [`**CREATE INDEX**`](https://docs.timescale.com/api/latest/hypertable/create_index/) applies to each chunk, optimizing for partitioned workloads.
- **Composite indexes** (e.g., `ON (time, device_id)`) are supported and encouraged for time-series queries.
- **Sparse data?** Use partial indexes like `WHERE column IS NOT NULL` for leaner indexes.
- **Data retention**: Dropping old chunks (and their indexes) is fast and efficient.

You can inspect indexes visually in the Timescale Cloud console and tweak performance without getting lost in the weeds.

> Curious how hypertables preserve your indexes? [Check out this post for a deep dive](https://www.timescale.com/blog/database-indexes-in-postgresql-and-timescale-cloud-your-questions-answered/).



## Conclusion: Make Indexing Work *for* You

PostgreSQL indexes are powerful—but only if used and maintained properly. With the right monitoring tools (`pg_stat_user_indexes`, `pg_stat_statements`), server tuning, and indexing strategy, you can dramatically improve query performance.

And with [Timescale](https://console.cloud.timescale.com/signup), many of these complexities are abstracted away—so you can focus on building, not babysitting your database.

**Learn more about how to improve PostgreSQL performance:**

- [PostgreSQL Performance Tuning: Key Parameters](https://www.timescale.com/learn/postgresql-performance-tuning-key-parameters)
- [PostgreSQL Performance Tuning: Optimizing Database Indexes](https://www.timescale.com/learn/postgresql-performance-tuning-optimizing-database-indexes)
- [PostgreSQL Performance Tuning: How to Size Your Database](https://www.timescale.com/learn/postgresql-performance-tuning-how-to-size-your-database)
- [How to Reduce Bloat in Large PostgreSQL Tables](https://www.timescale.com/learn/how-to-reduce-bloat-in-large-postgresql-tables)