PostgreSQL Query Optimization -Tips and Tricks

What is PostgreSQL?

PostgreSQL is a powerful, open source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads. PostgreSQL has earned a strong reputation for its proven architecture, reliability, data integrity, robust feature set, extensibility, and the dedication of the open source community behind the software to consistently deliver performant and innovative solutions.

PostgreSQL comes with many features aimed to help developers build applications, administrators to protect data integrity and build fault-tolerant environments, and help you manage your data no matter how big or small the dataset. In addition to being free and open source, PostgreSQL is highly extensible. For example, you can define your own data types, build out custom functions, even write code from different programming languages without recompiling your database.

Writing Efficient Queries:

1. Use SELECT statements to limit results

If you need only a subset of data from a table, consider using a SELECT statement. This will limit the amount of data returned by the query, making it more efficient. For example:

This query selects only the first name and last name columns for actor id less than 100.

2.Avoid using wildcard characters:

Using wildcard characters like * may be convenient, but it can slow down complex queries. Instead, explicitly specify the columns you need in your SELECT statement as shown in the below example.

Use,

Instead of,

Lets look at another example. Suppose you have a query that searches for customer whose email address ends with “@sakilacustomer.org”:

This query will perform a sequential scan of the entire “customer ” table, which can be slow if the table is large. To optimize this query, you can rewrite it like this:

You can see a noticeable improvement in both planning and execution time.

3.Indexes:

Use indexes to speed up query execution. Indexes allow PostgreSQL to find data more quickly by creating a separate data structure that can be searched efficiently. Choosing the right indexes can significantly improve query performance.

An index serves as an additional access structure that enables the rapid location and retrieval of specific data. In the absence of an index, a query would need to search the entire table to find the desired information. By indexing columns commonly used in WHERE or JOIN clauses, PostgreSQL can swiftly locate the desired data.

For instance, the following picture depicts the query execution plan without any indexes.

The execution plan displayed in this image depicts the impact of adding an index on the email column. The addition of the index has resulted in a noticeable improvement in both Planning Time and Execution Time.

4. Use INNER JOINs

INNER JOINs are used to combine data from multiple tables. They are more efficient than using multiple SELECT statements and then combining the results in code. For example:

This query selects the first name column from the customer table and the amount column from the payment table based on the customer id columns in both the tables, respectively.

PostgreSQL Query Optimization

PostgreSQL query optimization refers to the process of improving the performance and efficiency of SQL queries executed on a PostgreSQL database. To optimize PostgreSQL queries, you can:

• Analyze and adjust various components of the database system, including the query planner, indexing, caching, and server configuration.

• Read and understand PostgreSQL execution plans.

• Distinguish between short queries and long queries.

• Choose the right optimization technique for each query type.

• Identify indexes that will improve query performance.

Here are some techniques and best practices for optimizing PostgreSQL queries:

1.EXPLAIN Command:

Use the EXPLAIN command to analyze query execution plans and identify potential performance bottlenecks. This command shows how the query is being executed and provides information about the indexes, join algorithms, and sort operations used.

2.Optimize SELECT Clause

Efficiency issues in queries can be addressed by using specific column names instead of the * argument in SELECT statements. Additionally, it is important to execute the HAVING clause after filtering the data with the SELECT statement, as SELECT acts as a filter. To illustrate, replacing * with 'store id' in GROUP BY can improve the query performance. You can see the change in execution plans in both the queries.

3.Avoid using subqueries

Subqueries can be useful, but they can also degrade query performance. If possible, try to rewrite subqueries as JOINs. For example:

This query selects the title column from the books table and the name column from the authors table based on the author_id and id columns, respectively. It then filters the results based on the continent column in the countries table. This subquery can be replaced with an INNER JOIN:

This query achieves the same result without using a subquery.

4.WHERE Clause

To optimize the WHERE clause, we can use OR instead of IN when there is no index on the filtering column. The IN statement matches a column value to a list of values, and technically, it should execute faster than OR. However, in some cases, using OR instead of IN may not improve performance significantly, but it is a way to test if the query can possibly run faster.

5.Use UNION ALL instead of UNION

UNION and UNION ALL are used to combine results from multiple SELECT statements. UNION removes duplicates, while UNION ALL does not. If you know there are no duplicates, use UNION ALL, as it is faster than UNION. For example:

This query combines the name column from the customers table and the vendors table, without removing duplicates.

6.Use LIMIT and OFFSET

If you only need a subset of data from a table, use the LIMIT and OFFSET keywords. LIMIT limits the number of rows returned, while OFFSET skips a certain number of rows. For example:

This query selects the name column from the users table starting from the 6th row and returning only 10 rows.

7.Optimize Joins

To enhance query performance, optimizing joins is essential as they are expensive operations. Substituting joins with subqueries whenever possible is one way to optimize them. Additionally, using the JOIN ON syntax instead of the WHERE clause can help the optimizer produce better execution plans. Filtering records of large tables before joining them with other tables can lead to substantial performance improvements.

8.Use stored procedures

Stored procedures are precompiled database objects that can be executed repeatedly. They can be implemented in any language supported by PostgreSQL, such as PL/pgSQL or Python. Using stored procedures can speed up complex operations by reducing network traffic and CPU overhead. For example:

This creates a stored procedure that selects the name column from the users table based on the id column.

Some additional factors that should be taken into consideration when designing a Postgres query for improved performance:

• Temporary tables can slow down execution but can also avoid the need for ORDER BY operations.

• Some operations may prevent the query from using indexes, so it's important to understand these peculiarities.

• The order of tables in the FROM statement can affect JOIN ordering, particularly when joining more than five tables.

• Index-only scans are faster than full table scans, but index access can be slower when query selectivity is high.

• Views can result in inefficient queries.

• Use DISTINCT only when necessary.

• Minimize the use of subqueries, especially correlated subqueries.

• Long queries are not helped by indexes, but instead can be optimized by minimizing the number of full table scans and reducing the result size as soon as possible.

• Materialized views are useful for speeding up execution time if having fully up-to-date data is not a priority for the query

Conclusion

Writing and optimizing PostgreSQL queries can seem overwhelming at first, but with these techniques, you can improve performance and streamline your database operations. The use of diagnostic tools such as EXPLAIN and EXPLAIN ANALYZE can assist in identifying likely problems. Remember to use SELECT statements to limit results, avoid using wildcard characters, use indexes, use INNER JOINs, use EXPLAIN to analyze query execution plans, avoid using subqueries, use UNION ALL instead of UNION, use LIMIT and OFFSET to limit results, and use stored procedures to speed up complex operations.

Happy querying!