Best Practices for Postgres Performance

Written by Ana Tavares

As your data grows, tuning PostgreSQL performance is crucial to enhance the efficiency and speed of data retrieval, improving your application’s responsiveness.

If you’ve been struggling with slow PostgreSQL queries or a sluggish database overall, implementing a few PostgreSQL performance best practices will give you a speed boost and allow for better use of system resources, which can lower operational costs. Additionally, it will help you maintain system stability under heavy loads and ensure scalability as your PostgreSQL database grows over time. 

As the builders of Timescale, which is PostgreSQL—but faster—we’ve discussed PostgreSQL performance tuning extensively in some of our previous posts. In this article, we gather these helpful resources in one place so you don’t have to rummage through our archives (but feel free to if you want; you’ll find a few gems along the way). We divided them into general performance advice, query performance, and database management for added simplicity.

Postgres Performance Tuning Series

When tuning your PostgreSQL database, you'll need to assess several critical aspects affecting performance, from CPU power or memory and partitioning to tweaking your PostgreSQL parameters like shared_buffers or max_parallel_workers and optimizing indexes, to name a few.

Balance potential bottlenecks depending on your needs, as some of these aspects may influence each other and your database performance. Let’s have a quick look at them:

CPU and memory sizing

Proper sizing of CPU and memory is crucial for optimal PostgreSQL performance. It's about striking the right balance based on your specific workload and performance requirements. Not having enough CPU power or memory can slow down your database, while overprovisioning CPU can be costly.

Key parameters

If you change the settings on a new phone right after purchase, why wouldn't you do the same to your database? PostgreSQL settings can impact performance, and you can get more from your database by moving a few levers. We have covered these PostgreSQL key parameters in a previous article—including max_worker_processes, autovacuum_max_workers, shared_buffers, and many more.

Database indexes

If you’re dealing with vast datasets, optimizing database indexes—a mechanism that helps retrieve data efficiently (like a table of contents in a book)—is crucial for enhanced performance.

Designing database schema

A database schema is vital for PostgreSQL performance as it organizes data efficiently, enables effective indexing, and avoids redundancy. It also ensures data integrity by enforcing constraints and relationships between tables. Lastly, a good schema aids in query optimization and scalability, managing larger data volumes without performance degradation.

Partitioning design

Finally, the last piece of the puzzle: partitioning is crucial for PostgreSQL performance as it enhances query speed by narrowing down data to specific partitions, reducing the data scanned. It also aids in efficient data management by breaking large tables into smaller, manageable pieces, improving scalability and load balancing.

Improving Postgres Query Performance

Query design is equally important as database design for SQL systems performance. A well-structured query can retrieve data efficiently, making optimal use of system resources, while a poorly written query can degrade performance, even with an optimal database design.

Traditional database design emphasizes doing maximum work with minimum queries, but the design and architecture decisions should focus on performance optimization from the start. Query bottlenecks refer to situations where a query becomes a weak point in the system, slowing down data retrieval. Examples include inefficient use of indexes, resulting in full table scans instead of specific data retrieval, or queries that lock large amounts of data, preventing other operations from executing concurrently.

Here are some guides and tips for improved Postgres query performance:

• A Guide to PostgreSQL Views: A PostgreSQL materialized view runs the query and stores the results. In essence, the materialized view acts as a cache for the query. Caching is a common way to improve performance in all sorts of computing systems.

• Mastering PostgreSQL String Functions: A Key to Enhanced Performance and Improved Data Analysis: PostgreSQL string functions enable developers to execute complex operations within the database itself, minimizing data transfer between the database and the application. This improves query performance by leveraging the database server's typically more robust computational capabilities, thus making the process faster and more efficient.

• Improving DISTINCT Query Performance Up to 8,000x on PostgreSQL: Why are DISTINCT queries slow on PostgreSQL when they seem to ask an "easy" question? In this article, we explain why and show you how we’ve improved DISTINCT query performance by up to 8,000 times!

• 8 Performance Improvements in Recent TimescaleDB Releases for Faster Query Analytics: Not to toot our own horn, but Timescale makes PostgreSQL faster for demanding workloads like time series, events, and analytics. But performance improvements come not only from big game-changing features but also the continuous focus on continual improvement. Read eight significant performance improvements to take your analytical queries to new heights (and speed).

• Implementing Constraint Exclusion for Faster Query Performance: Learn how we applied constraint exclusion at execution time to speed up Postgres performance.

• Using pg_stat_statements to Optimize Queries: To speed up your queries, you first need to know how they're performing. This article walks you through using pg_stat_statements (which is enabled in Timescale by default) to do just that.

• 13 Tips to Improve PostgreSQL Insert Performance: To enhance PostgreSQL insert performance, you’ll need to moderate your use of indexes, reconsider foreign key constraints, avoid unnecessary UNIQUE keys, use separate disks for Write-Ahead Log (WAL) and data, and deploy on performant disks. Each of these strategies can help optimize the speed at which your database ingests new data.

Postgres Database Management Tips

Now, on to database management. Want to learn how to ensure database performance, guarantee data integrity and security, and effectively manage your database for scalability and efficiency? We also have a few database management tips for that:

• One of the key suggestions is data normalization, which minimizes redundancy and bolsters data integrity, thereby making databases more efficient and reliable. Regular backups, paired with consistent testing, are also crucial to safeguard data and ensure successful restoration in the event of system failure or a data loss incident.

• Active monitoring of database performance, including performance metrics, query analysis, and database logs, is recommended for early detection of potential issues and performance optimization. Security measures against unauthorized access, like implementing user roles and permissions, regular security updates, and adhering to best practices, are vital for database protection.

• Planning for scalability from the outset by employing techniques like data partitioning and indexing is important to cater to increased load and larger datasets as your system expands. Writing efficient queries and optimizing them regularly can notably enhance database performance.

• Keeping your database management system updated with bug fixes, security patches, and performance improvements is essential for maintaining a robust, secure, and efficient database environment. Comprehensive documentation of database schema, stored procedures, triggers, and other elements is also key for efficient database management.

• Finally, strategies like data archiving and leveraging database caching mechanisms can significantly improve performance by reducing disk I/O, enhancing data retrieval speed, and moving rarely accessed data to secondary storage.

PgBouncer

A PostgreSQL connection pooler manages the connections between your application and your database. Instead of creating a new connection each time your application needs to query the database, the connection pooler maintains a pool of active connections that the application can reuse.

At Timescale, we built our implementation of connection pooling on PgBouncer, a self-described “lightweight connection pooler for PostgreSQL,” since it is a widely used tool by many in our community and performs its function well—you can read our advice on PgBouncer here.

“Wait, what does this have to do with performance?” you may be wondering. Adopting prepared statements can significantly enhance application performance by bypassing query parsing and analysis, thus removing a lot of overhead. When combined with a connection pooler and transaction mode, the performance of a Postgres database can be dramatically improved—we explain everything in this article on boosting Postgres performance with prepared statements and PgBouncer’s transaction mode.

TimescaleDB tips

But, the quickest way to boost Postgres performance is to create a Timescale account.  Compared to PostgreSQL, TimescaleDB can dramatically improve query performance by 1,000x or more, reduce storage utilization by 90 percent, and provide features essential for time-series and analytical applications.

You can leverage Timescale for your data analysis using hyperfunctions, a set of functions, procedures, and data types optimized for querying, aggregating, and analyzing time series. If you want to check their effectiveness, read this article on our largest dogfooding effort to date, where we use hyperfunctions to analyze and aggregate query information across our fleet of continuously running production databases. 

While most of the articles we gathered here include some Timescale-specific tips, we also have a summarized version to simplify your life, full of Timescale parameters you should know about (and tune) to maximize your performance. And if you find that some of these overlap with our general Postgres advice, that’s because Timescale is PostgreSQL, but faster.

Happy performance tuning!