How Can I Optimize a Slow MySQL SELECT Query to Improve Performance and Reduce Disk Space Usage?

Optimizing MySQL Select Queries for Performance and Disk Space Reduction

A slow MySQL query that takes several minutes to execute is a common performance issue faced by developers. In this scenario, the query retrieves data from three tables to display a web page. Upon investigation using the EXPLAIN command, it was found that the query was writing intermediate results to disk, resulting in a significant performance bottleneck.

To optimize the query, a comprehensive approach was taken:

Table Structure Analysis

The query used three tables: poster_data, poster_categories, and poster_prodcat. Poster_data contained information on individual posters, while poster_categories listed all categories (e.g., movies, art). Poster_prodcat held the poster IDs and the associated categories. The primary bottleneck was identified in poster_prodcat, which had over 17 million rows and a large number of results for the specific category being filtered (approximately 400,000).

Index Optimization

The EXPLAIN output revealed that the query lacked optimal indexing, leading to inefficient data access and slow execution. The primary issue was the absence of an index on the poster_prodcat.apcatnum column, which was used for filtering. Without an index, the MySQL optimizer resorted to a full table scan, resulting in excessive disk I/O and long execution times.

Query Rewriting

To address the performance issue, the query was rewritten using a more efficient approach:

• The three tables were joined using an INNER JOIN instead of the less efficient SELECT *.
• The WHERE clause was simplified to filter on the desired category directly, avoiding the need for a subquery.
• The ORDER BY clause was moved to the end of the query to prevent unnecessary sorting of large datasets.

Temporary Table Creation

To mitigate the disk space usage issue, the optimized query was further modified to create a temporary table that stored the intermediate results. This approach allowed the query to bypass writing data to disk, significantly improving performance.

Additional Optimizations

Apart from the primary optimizations, several additional measures were implemented to further enhance query performance:

• Limiting the results using the LIMIT clause: The original query had no limit, potentially returning a large number of results that were not required for the web page.
• Caching results: The temporary table was created with the MEMORY engine, which kept the data in memory for faster access.

Conclusion

By addressing the indexing, query structure, and temporary table usage, the original query was optimized to improve performance and reduce disk space usage. This optimization resulted in a significant reduction in execution time, making the web page generation much more responsive.

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