Introduction

PostgreSQL is a popular open-source relational database management system (RDBMS) that is known for its scalability and performance. However, as your database grows and your queries become more complex, it is important to optimise postgresql query to ensure that they are executing as efficiently as possible.

Here are some tips on how to optimise PostgreSQL query performance:

1. By adding indexes

Indexes are data structures that help PostgreSQL to quickly find specific rows in a table. When you run a query, PostgreSQL will use the indexes to filter the data and return only the rows that you need.

To add an index to a table, you can use the following SQL statement:

SQL

CREATE INDEX index_name ON table_name (column_name);

You should create indexes on columns that are frequently used in WHERE clauses, ORDER BY clauses, and GROUP BY clauses.

Benchmark:

The following benchmark shows the performance improvement that can be achieved by adding an index to a table:

# Without index
SELECT * FROM users WHERE name = 'John Doe';

Execution time: 100 milliseconds

# With index
SELECT * FROM users WHERE name = 'John Doe';

Execution time: 5 milliseconds

2. Create partitions

Partitions are a way to divide a large table into smaller, more manageable tables. This can improve query performance by reducing the amount of data that PostgreSQL needs to scan when executing a query.

To create a partition, you can use the following SQL statement:

SQL

CREATE TABLE table_name (column_name) PARTITION BY LIST (column_name);

You can partition a table by any column, but it is generally recommended to partition by a column that has a high cardinality, such as a date column.

Benchmark:

The following benchmark shows the performance improvement that can be achieved by partitioning a large table:

# Without partition
SELECT * FROM orders WHERE order_date = '2023-10-10';

Execution time: 1 second

# With partition
SELECT * FROM orders WHERE order_date = '2023-10-10';

Execution time: 100 milliseconds

3. Adjust Auto-vacuum parameters

Auto-vacuum is a PostgreSQL feature that automatically cleans up deleted and updated rows in a table. This helps to keep the table efficient and improve query performance.

However, Auto-vacuum can have a negative impact on query performance if it is not configured correctly. To adjust the Auto-vacuum parameters, you can edit the postgresql.conf file.

There are a number of Auto-vacuum parameters that you can adjust, but the following two parameters are the most important:

  • autovacuum.vacuum_cost_delay – This parameter controls how much time PostgreSQL will wait before starting a vacuum. It is generally recommended to set this parameter to a value between 100 and 500 milliseconds.
  • autovacuum.vacuum_cost_limit – This parameter controls how long PostgreSQL will allow a vacuum to run. It is generally recommended to set this parameter to a value between 100 and 500 milliseconds.

Read more about auto-vacuum here.

4. Reindex tables

Over time, indexes can become fragmented, which can impact query performance. To reindex a table, you can use the following SQL statement:

SQL

REINDEX table_name;

It is generally recommended to reindex tables on a regular basis, such as once a week or once a month.

Benchmark:

The following benchmark shows the performance improvement that can be achieved by reindexing a table:

# Fragmented index
SELECT * FROM orders WHERE order_date = '2023-10-10';

Execution time: 1 second

# Reindexed table
SELECT * FROM orders WHERE order_date = '2023-10-10';

Execution time: 500 milliseconds

5. Using Materialize views

Materialize views are a type of view that is pre-computed and stored in the database. This can improve query performance by reducing the amount of work that PostgreSQL needs to do to execute the query.

To create a materialize view, you can use the following SQL statement:

SQL

CREATE MATERIALIZED VIEW view_name AS SELECT * FROM table_name WHERE condition;

You should create materialize views on queries that are frequently executed and that return a large amount of data.

Benchmark:

The following benchmark shows the performance improvement that can be achieved by using a materialize view:

# Without materialize view
SELECT SUM(order_total) FROM orders WHERE order_date = '2023-10-10';

Execution time: 1 second

# With materialize view
SELECT SUM(order_total) FROM orders_mv WHERE order_date = '2023-10-10';

Execution time: 50 milliseconds

6. Use Copy command

Use COPY to load all the rows in one command, instead of using a series of INSERT commands. The COPY command is optimized for loading large numbers of rows; it is less flexible than INSERT, but incurs significantly less overhead for large data loads. Since COPY is a single command, there is no need to disable autocommit if you use this method to populate a table.

7. Other Optimisations

In addition to the optimisations listed above, there are a number of other things that you can do to optimise PostgreSQL query performance, such as:

  • Use appropriate data types.
  • Avoid using wildcard characters in WHERE clauses.
  • Limit the number of columns that you return in your queries.
  • Use efficient subqueries.
  • Normalize your database.

Conclusion

By following the tips in this article, you can optimise PostgreSQL query performance. However, it is important to note that there is no one-size-fits-all solution to query optimization. The best approach will vary depending on your specific database and queries.

In addition to the optimizations listed above, you can also use a number of tools and extensions to help you improve PostgreSQL query performance. For example, the EXPLAIN command can be used to identify the bottlenecks in your queries, and the pg_stat_statements extension can be used to track the performance of your queries over time.