Handling Postgres Text Array Growth: Strategies for Appending and Removing Elements

Learn how to effectively manage the growth of text arrays in `Postgres`, particularly in gaming applications. Strategies for optimization and best practices are discussed.
---
This video is based on the question https://stackoverflow.com/q/65722373/ asked by the user 'Sean Place' ( https://stackoverflow.com/u/15006461/ ) and on the answer https://stackoverflow.com/a/65722706/ provided by the user 'Gordon Seidoh Worley' ( https://stackoverflow.com/u/170876/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Appending + Removing Postgres Text Array elements. Resulting in massive table sizes

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Issue: Array Manipulation in PostgreSQL

When managing data in PostgreSQL, particularly with arrays, it's common to encounter challenges that can lead to significant disk space issues. For instance, consider a scenario in a turn-based game where you need to store logs of player actions in a text array for real-time debugging. The query you might use to append entries looks something like this:

[[See Video to Reveal this Text or Code Snippet]]

This query works effectively in prepending logs while attempting to keep them limited to a specified length. However, it often leads to unforeseen consequences, such as massive table sizes, which can balloon from around 150MB to multiple gigabytes. This raises questions: Is this just a typical PostgreSQL quirk, or is there an underlying issue with the approach?

Why Does Table Size Swell?

The unexpected growth of table sizes in this context is indeed an expected behavior when dealing with updates in PostgreSQL. Here’s why:

1. Row Rewrites

When you modify a column value—such as your text array—PostgreSQL performs a complete rewrite of the entire row. This means that even if you are only changing a small element of the array, the database still has to rewrite everything tied to that row. If your array is large or grows frequently, this can add up quickly in terms of row rewrites and disk space usage.

2. TOAST Storage

Additionally, PostgreSQL utilizes TOAST (The Oversized-Attribute Storage Technique) for storing large data types efficiently. This involves creating pointers to a separate table for larger pieces of data, rewriting these pointers as you update, which further contributes to additional data writes and consequently, space usage.

3. Space Management

The only way PostgreSQL cleans up unused space is through the VACUUM and AUTOVACUUM processes. If these are not run regularly, or if they are unable to keep up with your data changes, you may end up using all available disk space. This can significantly affect the performance and functionality of your database.

Addressing the Growing Table Size: Suggested Solutions

If you're facing issues with escalating table sizes, here are several strategies to consider:

1. Regular Vacuuming

Schedule regular VACUUM processes to help remove unused space.

Tune the autovacuum settings to ensure it runs frequently enough to keep disk usage in check.

2. Data Normalization

Consider normalizing your database schema to reduce the need for frequent updates to large columns.

For example, you could separate log entries into their own table, reducing the need to rewrite large arrays.

3. Utilizing Append-only Storage Systems

Look into storing log information in systems better optimized for append-only workloads.

Options include using Foreign Data Wrappers (FDWs) for offloading data to more suitable storage formats or files that manage append operations more efficiently.

4. Monitoring Disk Usage

Regularly monitor the growth of your database tables. Tools and queries exist within PostgreSQL to help track how much space is being consumed based on your operations.

5. Reviewing Update Patterns

Analyze when and how often you are executing updates. If possible, batch updates instead of executing them one at a time.

Conclusion: Optimize Your PostgreSQL Usage

Managing a PostgreSQL database, especially with arrays, requires careful planning and a good understanding of how PostgreSQL handles data storage. By adopting the strategies outlined above, you can minimize the ballooning of your table sizes, keep your database performant, and ensure a smooth experience both during development and runtime. Remember, regular