How to use Sparse Index in PostgreSQL 15?

How to use Sparse Index in PostgreSQL 15?


Sparse indexes are used in PostgreSQL when the table contains many null or empty values in the indexed column. These indexes can greatly reduce the size of the index and improve performance by not indexing null or empty values, resulting in faster queries and less disk space usage. They are particularly useful when dealing with large columns with many null or empty values and when the queries are looking for non-null values.
 
It's recommended to use sparse index when the indexed columns have a high percentage of NULL values or when the table is frequently updated with the new values.
 
Implementing a sparse index in PostgreSQL 15 can be done by using the CREATE INDEX command and specifying the WHERE clause to indicate the conditions under which the index should be used. For example, to create a sparse index on a column named "price" in a table named "products" that only includes rows where the "price" is greater than 100, you would use the following command:
 
This will create an index on the "price" column, but only for the rows where the "price" is greater than 100, which can help to improve performance and reduce storage space. It's important to note that the sparse index will not cover all queries, so it's important to test the performance with and without the sparse index before deciding to use it.
 

Conclusion

Sparse indexes may not be recommended in PostgreSQL when the table has a high percentage of non-NULL values in the indexed column. In this case, a regular index would be more efficient. Additionally, sparse indexes may not be recommended for columns with high cardinality or high update rates, as this can lead to fragmentation and decreased performance. It is also not recommended for use with small tables or for queries that return a high percentage of the table's rows, as the index will likely be larger than the table itself and not provide much of a performance benefit.

About Shiv Iyer 72 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.

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