Скачать или смотреть How to Make Postgresql INDEX Cover ORDER BY Function Expressions Effectively

Learn how to create efficient indexes in PostgreSQL that can improve query performance and handle `ORDER BY` function expressions effectively.
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This video is based on the question https://stackoverflow.com/q/62446910/ asked by the user 'Niels Kristian' ( https://stackoverflow.com/u/1002814/ ) and on the answer https://stackoverflow.com/a/62450976/ provided by the user 'jjanes' ( https://stackoverflow.com/u/1721239/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

When working with PostgreSQL, optimizing your queries is crucial for maintaining performance, especially when dealing with large datasets. A common challenge is ensuring that your indexes not only speed up filtering conditions but also effectively support ORDER BY clauses, particularly those with complex expressions.

In this guide, we will explore a real-world scenario where the need for advanced indexing arises and how to successfully implement it. We will address the problem presented and offer detailed solutions, making it easier for you to enhance your queries using PostgreSQL.

The Problem

Consider a set of queries aimed at retrieving car data based on specific filtering conditions, with a focus on ordering based on calculated cases. Here’s an overview of the situation:

The queries feature consistent filtering conditions (such as sales_state, is_disabled, and more) but include various dynamic parameters.

There is a notable use of ORDER BY that relies on complex expressions based on columns like featuring_score and au_rating.

Sample Query:

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The user has attempted to create composite indexes to speed up the execution but hasn’t observed substantial improvements.

Proposed Solutions

1. Rethink Indexing Strategy

Given that some of the filtering criteria vary and are less commonly used, a leading column in the index may not yield the best performance. Consider the following options:

Single-Column Indexes: Instead of composite indexes, you can create multiple single-column indexes. All should include the same WHERE conditions for consistency. This allows PostgreSQL to optimize their usage via a BitmapAnd technique.

Partial Indexes: Given the fixed conditions, you can focus on creating partial indexes tailored to frequently filtered datasets.

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2. Use GiST Indexes for Range Queries

If your filtering criteria involve range queries or IN lists, consider using a multicolumn GiST index. However, be cautious, as building these indexes may require considerable resources and time.

3. Manage Unnecessary Rows

If you have a significant number of rows that don't meet your criteria (i.e., images_count > 0 AND sales_state = 'onsale'), think about:

Archiving or Deleting: Rows that significantly clutter the dataset without being queried can be archived or removed to enhance performance.

Partitioning: Another effective strategy is to partition your data, separating rows that meet certain conditions from those that do not.

4. Fine-Tuning Queries

Experiment with the query itself:

Modify your ORDER BY clause by testing simplified versions of the query with less stringent conditions to see how results vary.

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5. Database Version Upgrade

Consider upgrading to a newer version of PostgreSQL (if possible). Later versions have introduced performance-oriented features, such as incremental sorting, which may optimize your indexes potentially unnoticed before.

Conclusion

Optimizing indexes in PostgreSQL, especially with complex filtering and ordering criteria, can significantly enhance your query performance. By considering a combination of indexing strategies, managing your data effectively, and continually testing and refining your queries, you can achieve a performant and efficient database setup.

For any in-depth analysis of your queries and their performance, tools like EXPLAIN (ANALYZE, BUFFERS) can provide insights into how your indexes are being utilized.

By incorporating these strategies into your PostgreSQL usage, you can leverage the system’s capabilities to maintain swift query responses while handling compl