Strategies for Improving Postgres JOIN Performance

Neon lights representing two Postgres tables joining each other.

Written by Team Timescale

As builders of a tool that allows you to scale PostgreSQL to handle terabytes of data successfully, we know the challenges of managing large (and ever-growing) PostgreSQL tables. In conversations with our users, other developers, and PostgreSQL enthusiasts like ourselves, the topic of JOINs in the context of large PostgreSQL tables often pops up.

As a part of our library of PostgreSQL tips—like our articles on navigating large PostgreSQL tables with partitioning and reducing table bloat in PostgreSQL—this article will cover strategies to improve JOIN performance. We will look at three strategies informed by common questions from our users:

  1. Caching JOIN results: I regularly join multiple PostgreSQL tables. Can I cache this JOIN result somewhere to avoid constantly computing it?

  2. JOINs vs. duplicate columns: When dealing with large datasets, is it better to run queries over a highly normalized schema with many JOINs or to denormalize by duplicating columns to avoid JOINs?

  3. Large table JOINs: How do you speed up JOINs involving two large tables?

First, we’ll recap the basics of PostgreSQL joins, and then we’ll jump right into answering these questions. 

What Is a JOIN in a Relational Database?

A JOIN is a method to combine rows from two or more tables based on matching values in a related column between them, providing a way to query data spread across multiple tables in a relational manner.

Common types of JOINs

  • Inner Join: Returns records that have matching values in both tables. Any rows in the joined tables that did not have matching rows in the other table are not included in the results. 

  • Left Join (or Left Outer Join): Returns all records from the left table and matched records from the right table. If there is no match, a NULL value is returned for all columns from the right table.

  • Right Join (or Right Outer Join): Returns all records from the right table and matched records from the left table. If there is no match, a NULL value is returned for all columns from the left table.

  • Full Join (or Full Outer Join): Combines the results of both left and right outer joins. The unmatched records from both tables are also included. A NULL value is returned for all columns from the unmatched table if there is no match.

Strategies for JOINs in a PostgreSQL Database 

Strategy 1: Using materialized views to cache JOIN results

This strategy was inspired by the question: "I JOIN multiple PostgreSQL tables together regularly. Can I cache this JOIN result somewhere to avoid constantly computing it?"

In PostgreSQL, system-level caching already occurs, reducing the need for user-level caching. However, if you're looking to improve performance for regular JOIN operations, consider using a materialized view.

Creating a materialized view involves calculating and storing the results from your query. Therefore, when you query the materialized view, it accesses this already computed data, bypassing the need to perform the JOIN operation. However, it's important to note that storing this pre-computed data requires disk space.

For example, consider a large transaction table and a table that stores the products in each transaction. You want to build a table that records each user's products they have purchased more than once. The query for this is involved, and the tables in question are large, so it takes a long time to compute upfront. However, if you materialize this query, the resulting table is stored and can be queried without recomputing the large join. 

The only limitation of materialized views is that they don't automatically update when the underlying data changes. To keep the view current, refresh it manually or automate the process. For instance, if you have a table tracking sales, and it's part of a materialized view, any changes such as insert/update/delete in this table require a refresh of the view:

REFRESH MATERIALIZED VIEW sales;

For automation, you can use a scheduling tool such as Linux crontab, pg_cron, or pg_timetable extension, which can schedule regular refreshes.

If you are working with TimescaleDB, you can use User Defined Actions to run functions and procedures. For example, you could use the add_job() function to schedule a function that recomputes a materialized view:

add_job(‘materialized_view_refresh’, ‘1h’);

You may also consider using Timescale continuous aggregates, which are good for caching simple time-grouped aggregate queries and joining with a small table. However, beyond this, continuous aggregates don’t currently have much support for joins.

Although PostgreSQL's shared_buffers and the operating system's caching mechanisms are available, using materialized views or continuous aggregates can offer improvements for repeated JOIN operations. 

Strategy 2: Evaluating normalization

This strategy answers the question: "When dealing with large datasets, is it better to run queries over a highly normalized schema with many JOINs or to denormalize by duplicating columns to avoid JOINs?"

The choice between a normalized and a denormalized schema for large datasets is not one-size-fits-all; it depends on your application's specific requirements and constraints.

Denormalization involves adding redundancy to a database schema to improve application performance. This strategy typically decreases the total number of tables and simplifies the complexity of joining tables, which is advantageous as too many joins can hinder query processing speed.

However, denormalization comes with its own set of challenges. It often results in tables with wider rows, meaning fewer rows per page, potentially more disk fetches, and a higher memory footprint. It also makes insert/update/delete more involved to keep redundant data aligned. 

This approach can be particularly advantageous for read-heavy databases where data is not frequently updated, as the overhead of maintaining data consistency across redundant data is less of a concern.

Normalization organizes data by breaking down a complex table into smaller, more manageable tables and establishing relationships among them. This approach effectively reduces data redundancy and minimizes disk space usage. 

If the data involved in JOIN operations is cached, the overhead associated with JOINs in a normalized schema is significantly reduced. This is because the repeated disk I/O operations are minimized. However, If a normalized schema frequently requires disk access for join operations, denormalization can offer performance benefits by reducing the number of JOINs at the expense of data redundancy (only if it lessens disk fetches). Normalized schemas are generally better for write-heavy operations due to less redundancy.

In practice, joining large data sets can be costly, but it's rare to join the entire contents of two large tables. Queries are typically designed to use only essential rows from each table, keeping the resultset small. For large queries with multiple joins, it's effective to start with joining smaller tables, progressively moving to larger ones to keep the in-memory data set minimal. It's often beneficial to conduct performance testing with realistic data and queries to make an informed decision.

Strategy 3: Optimizing large table JOINs

This strategy answers the question: "How do you speed up JOINs involving two large tables?"

When dealing with JOIN operations between two large tables, several strategies can be employed to enhance performance:

  • Use smaller data types to reduce memory and disk space usage, thereby speeding up JOINs. Choose types that best fit the nature of your data. Ensure that the keys used for joining have identical data types, thereby avoiding the need for type casting during the join process.

  • Simplify your queries and limit the amount of data processed in JOINs using WHERE to achieve better performance. Create appropriate indexes on both tables, particularly on columns used in the JOIN condition. Only fetch the columns you need, as this can minimize the data load during the JOIN. Being specific with columns lets you use index-only scans, which avoid loading all the data from an indexed table.

  • Ensure adequate RAM to reduce the need for disk access. Tune PostgreSQL settings like shared_buffers for caching and work_mem for memory-intensive operations like sorting and JOINs algorithms used by PostgreSQL. Additionally, using faster storage, like SSDs, can significantly improve performance for disk-intensive JOINS. Sufficient CPU power can also be used to enable parallel scans and parallel index scans on large tables, which can significantly speed up JOINs.

  • Utilize PostgreSQL’s declarative partitioning. Check query performance with partition-wise joins, which can process data more efficiently by focusing on relevant partitions, potentially fitting in memory and reducing resource usage. Note that a partition-wise join is only applicable when the join condition includes all the partition keys. Partition keys must have the same data type along with corresponding sets of child partitions that match one-to-one relationships.

  •  Tune autovacuum settings like autovacuum_vacuum_scale_factor and autovacuum_freeze_max_age to keep table bloat to a minimum. 

By applying these strategies, you can significantly improve the efficiency of JOIN operations between large tables in PostgreSQL. Remember, the effectiveness of each method can vary depending on your specific dataset and use case, so it’s often beneficial to test different approaches to find the optimal solution for your scenario.