Parallel Processing in PostgreSQL: Setup, How It Works, and Use Cases

PostgreSQL’s parallel processing allows queries to run faster by distributing work across multiple CPU cores. This feature is essential for large datasets and complex queries, providing significant performance improvements. In this article, we’ll explore how to set up parallel query execution, understand its inner workings, and identify cases where parallel processing is most beneficial.

Setting Up Parallel Processing

PostgreSQL enables parallel query execution by default, but a few configurations can optimize it further. You can adjust these parameters in your postgresql.conf file:

• max_parallel_workers_per_gather: Limits the number of workers that can be assigned to each parallel query.
• max_worker_processes: Sets the total number of background processes PostgreSQL can use, including parallel workers.
• max_parallel_workers: Caps the total number of workers across all queries.

Example configuration:

max_parallel_workers_per_gather = 4
max_worker_processes = 8
max_parallel_workers = 8        

Once these settings are applied and the database is restarted, PostgreSQL will start using parallel workers for queries that qualify.

How It Works

Parallel queries operate using Gather and Gather Merge nodes. These nodes divide the query plan and assign portions to parallel workers:

• Gather: Workers read and process data independently, with results combined in any order.
• Gather Merge: Workers process sorted data, with the leader merging results in the correct order.

The planner determines whether to use parallelism based on query complexity, available workers, and cost-benefit analysis.

Examples

Consider a query scanning a large table:

SELECT * FROM large_table WHERE condition;        

With parallel processing enabled, PostgreSQL divides the table scan across multiple workers, drastically reducing the time to retrieve results.

When to Use Parallel Processing

Parallel processing is ideal for:

• Large table scans: Queries that read extensive data benefit greatly from worker distribution.
• Complex joins: Queries involving multiple joins or aggregate functions can be significantly sped up.
• Data-intensive analytics: Heavy analytical workloads, like reporting queries, often experience better performance with parallelism.

Conclusion

Parallel processing in PostgreSQL is a powerful tool for speeding up data-heavy queries. By configuring the right settings and understanding its use cases, you can optimize performance and reduce query times for large datasets and complex operations.