Скачать или смотреть How to Optimize Your Laravel Queries: Speeding Up Table Joins and Group By Sums

Discover how to increase the speed of your `Laravel` database queries that involve table joins and group by sums with these indexing techniques.
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This video is based on the question https://stackoverflow.com/q/64484438/ asked by the user 'Amandeep Singh' ( https://stackoverflow.com/u/1672885/ ) and on the answer https://stackoverflow.com/a/64484657/ provided by the user 'Tim Biegeleisen' ( https://stackoverflow.com/u/1863229/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Optimize Your Laravel Queries: Speeding Up Table Joins and Group By Sums

If you're using Laravel and find that your database queries are running slowly, you're not alone. One common scenario arises when using the query builder to join tables and perform aggregations like sums. Such queries can be resource-intensive and lead to frustratingly slow response times. In this guide, we’ll explore how to optimize a specific SQL query to enhance its performance significantly. Let's dive in!

Understanding the Problem

Let's look at the original query that is causing delays:

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While this query works as needed, it is evident that it could benefit from optimizations. The response time can be improved by addressing indexing and query structure as indicated by the query execution plan, which suggests that inefficient scans are being performed.

Key Issues Identified

Temp Table Usage: Query execution shows "Using temporary; Using filesort", which can largely slow down performance.

Lack of Indices: There are no indices on the columns used in the joins and where conditions, leading to slower data retrieval.

Selecting All Columns: Pulling all columns from one table can lead to unnecessary data processing, especially if that table has many columns.

Optimizing the Query

Step 1: Indexing for Performance

Creating appropriate indices can drastically increase the speed of your queries. Here are some indices that can be implemented based on the query structure:

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Explanation of the Indices

idx1: This multi-column index is essential as it covers the entire WHERE clause, allowing MySQL to eliminate many records early on. Including campaignId is crucial for quick linkage to related data during the joins.

idx2 and idx3: These are single-column indices that are specific to the join conditions in their respective tables. They enhance the lookup speed when MySQL is resolving the join operations.

Step 2: Reviewing Selected Columns

Consider whether it is necessary to select all columns from amz_ads_sp_campaigns. If the campaignId is not the primary key, the usage of amz_ads_sp_campaigns.* could lead to errors or inefficiencies. Instead, specify only the required columns to minimize data processed.

Step 3: Understanding SUM Constraints

Keep in mind that the SUM function inherently requires accessing every record to compute results. There isn't much scope for further reduction here; however, ensuring efficient data retrieval through indices can help limit the number of records processed.

Conclusion

Optimizing your Laravel database queries, especially those that involve complex joins and aggregations, is essential for ensuring a smooth user experience. By implementing targeted indexing strategies and refining the query structure, you can significantly reduce response times. Always keep an eye on execution plans, as they can be a valuable tool for identifying performance bottlenecks in your queries.

For more tips on Laravel performance optimization, stay tuned for our upcoming posts!