![]() ![]() Look further in this post to learn how to create indexes for specific queries. We advise to create one index per unique query for better performance. However, when read query performance is a priority, as is the case with business analytics, it’s usually a well-working approach. It’s always a trade-off between storage space and query time, and a lot of indexes can introduce overhead for DML operations. Indexes in Postgres also store row identifiers or row addresses used to speed up the original table scans. They contain only specific columns of the table, so you can quickly find data based on the values in these columns. Indexes are materialized copies of your table. The ability to see indexes is the first step to learning PostgreSQL query optimization. The most important thing is that the EXPLAIN command will help you to understand if a specific index is used and how. ![]() We highly recommend you use EXPLAIN ANALYZE because there are a lot of cases when EXPLAIN shows a higher query cost, while the time to execute is actually less and vice versa. The difference is that EXPLAIN shows you query cost based on collected statistics about your database, and EXPLAIN ANALYZE actually runs it to show the processed time for every stage. Postgres has a cool extension to the well-known EXPLAIN command, which is called EXPLAIN ANALYZE. Although it doesn’t show the actual performance improvement, you will see that our tips solve the significant set of optimization problems and work well in real-world case scenarios. To keep it easy, we ran examples for this article on a test dataset. We use these techniques a lot to optimize our customers PostgreSQL databases with billions of data points during Cube.js deployments. It returns the execution plan generated by PostgreSQL query planner for a given statement. In this post, we share five simple but still powerful tips for PostgreSQL query optimization. EXPLAIN is very useful for understanding the performance of a Postgres query. As a result, their date range query sped up by 112x. At one point, we advised one of our customers that had a 10TB database to use a date-based multi-column index. The techniques below are useful primarily for analytics queries, although some of them could be helpful in OLTP cases as well.Ī single query optimization tip can boost your database performance by 100x. ![]()
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