10 Postgres Index Best Practices

Indexes are an important tool for making your Postgres database performant. But with great power comes great responsibility, and it’s important to use indexes wisely. In this article, we’ll discuss 10 best practices for using indexes in Postgres. By following these best practices, you can ensure that your database is performant and your indexes are being used effectively.

1. Use the right index for your query

If you use the wrong index, your query will be slow. If you use the right index, your query will be fast. It’s really that simple.

The key to using the right index is understanding how your query works. You need to know what columns you’re querying on and in what order. Once you know that, you can look at the indexes that are available and choose the one that best matches your query.

If you’re not sure which index to use, you can always ask the Postgres optimizer. The optimizer is a built-in tool that will analyze your query and recommend the best index to use. To use the optimizer, simply add the EXPLAIN keyword to your query. For example:

EXPLAIN SELECT * FROM my_table WHERE my_column = ‘some value’;

This will output the plan that the optimizer has chosen for your query. From there, you can decide if you want to use a different index.

2. Avoid indexes on low cardinality columns

If a column has very few distinct values, an index on that column will be very small and not offer much of a performance boost. In fact, in some cases, an index on a low cardinality column can actually slow down performance because the index itself needs to be scanned for every query.

It’s generally best to only index columns that have a high number of distinct values (a high cardinality). This ensures that the index will be large enough to offer a significant performance boost, without being so large that it becomes a hindrance.

3. Index only frequently used columns

If you index every column in a table, the indexes will take up too much space and slow down write operations. On the other hand, if you don’t index any columns, read performance will suffer. The sweet spot is somewhere in between, where you index only the columns that are most often used in queries.

To figure out which columns to index, you can use the Postgres query planner. The query planner will show you which columns are being used in queries and how often they’re being used. Based on this information, you can decide which columns to index.

4. Don’t use too many indexes

If you have too many indexes, your database will actually slow down because every time you insert, update, or delete data, all of those indexes need to be updated as well. So if you have a table with a million rows and 10 indexes, every time you insert a row, you’re actually inserting 11 rows – one in the table and then one in each index.

This can really slow down your database over time, so it’s important to only create indexes when they are absolutely necessary. You can use the EXPLAIN command in Postgres to help you determine which indexes are being used and which ones aren’t.

5. Keep indexes small and simple

The smaller an index is, the less disk space it will take up. This is important because indexes can quickly become large, especially on tables with many rows.

Second, small indexes are faster to scan. When Postgres needs to find data using an index, it will need to scan the entire index to find the matching values. The larger the index, the longer this will take.

Finally, small indexes are easier to maintain. As data changes, indexes need to be updated to reflect those changes. The more complex an index is, the more difficult and time-consuming it will be to keep it up-to-date.

6. Monitor performance after creating an index

When you add an index to a table, it can speed up queries by allowing Postgres to more quickly find the data it needs. However, indexes also take up space and can slow down writes if they’re not used judiciously.

It’s important to monitor performance after creating an index to make sure that it’s actually helping queries run faster. If you see no improvement, or if query times get worse, you may want to remove the index.

You can use the EXPLAIN command in Postgres to see how an index is being used. This can be helpful in understanding why an index isn’t providing the performance boost you were hoping for.

7. Update statistics after adding or removing an index

If you don’t update statistics after adding or removing an index, the query optimizer won’t have accurate information about the distribution of values in the indexed columns. This can lead to sub-optimal query plans, and ultimately, poor performance.

Fortunately, updating statistics is easy to do. Just run the ANALYZE command on the table after adding or removing an index. For example:

ANALYZE mytable;

This will update the statistics for all columns in the table. If you only want to update statistics for a specific column, you can use the COLUMNS option:

ANALYZE mytable COLUMNS mycolumn;

8. Consider using partial indexes

Partial indexes only index a subset of rows in a table, which can be useful when:

– The indexed columns are only used for a specific query or queries.
– The index is only used by a small number of rows.
– The index is only used by a small number of queries.

Creating a partial index can save space and improve performance because the index will be smaller and more targeted.

9. Choose a good fill factor

A fill factor is the percentage of space that’s filled with data on each page. A lower fill factor means there’s more free space on each page, which can be useful if you’re frequently inserting or updating data. However, it also means that each index entry is larger, which can make queries slower.

A higher fill factor means there’s less free space on each page, which can make queries faster but can also make inserts and updates slower. The sweet spot is usually around 90%, but it can vary depending on your workload.

You can set the fill factor when you create an index, and you can also change it later with the ALTER INDEX command.

10. Vacuum regularly to avoid bloat

Bloat is when an index or table has more pages than it needs to store the data. This can happen for a number of reasons, but most often it’s due to updates or deletes that haven’t been vacuumed yet. When this happens, queries can slow down because the database has to scan more pages.

To avoid bloat, you should vacuum your database regularly. You can do this manually with the VACUUM command, or you can set up automatic vacuuming with the autovacuum daemon.

Automatic vacuuming is the recommended approach, as it will keep your database clean without you having to remember to do it manually.