Скачать или смотреть How to Optimize Your PostgreSQL UPDATE JOIN Queries for Better Performance

Discover how to rewrite your `PostgreSQL UPDATE JOIN` queries to avoid long execution times and improve performance significantly.
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This video is based on the question https://stackoverflow.com/q/62959739/ asked by the user 'Arli Chokoev' ( https://stackoverflow.com/u/10910480/ ) and on the answer https://stackoverflow.com/a/62959789/ provided by the user 'Gordon Linoff' ( https://stackoverflow.com/u/1144035/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Speeding Up Your PostgreSQL UPDATE JOIN Query

If you're working with PostgreSQL and relying on UPDATE JOIN statements, you may have noticed that some queries take an unexpectedly long time to run. One common issue is when the query seems to hang or run indefinitely, like when trying to update tags based on conditions from another table. In this post, we’ll explore a specific example and offer a succinct solution to help you optimize your query.

The Problem Explained

In our case, a user tried to append a text value 'accident' to an array field tags in the cards table based on the respective event_id from the cardaccidents table. The original query was structured as follows:

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

This query, although logically correct, ran extremely slow, taking over 7 minutes in some instances, while the corresponding JOIN without the UPDATE executed in just 16 milliseconds. This stark difference in execution time raised a question: "How can we speed up this query?"

Analyzing the Original Query

The key issue with the original query is its structure. Let's break it down:

Repeated References: In the given statement, cards is referenced twice—once in the UPDATE clause and again in the FROM clause through a JOIN. This approach can lead PostgreSQL to interpret it incorrectly, causing it to attempt to update all rows in cards multiple times.

Performance Implications: When the database expands the query based on the JOIN conditions, it can create a bloated intermediary result set, which drastically increases the execution time, leading to performance issues.

The Solution: Streamlining the Query

To optimize the UPDATE JOIN operation, we need to eliminate the redundancy in referencing the cards table. The ideal revised query looks like this:

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

Breakdown of the Improved Query

Single Reference to the Target Table: The cards table is now referenced only once, preventing repeated updates on each row.

Using a WHERE Clause: The WHERE clause effectively links the cardaccidents table to cards based on the event_id, providing a clear filtration for which records to update.

Expected Performance: If your SELECT queries on these tables are already fast, this revamped UPDATE method should retrieve and modify data without unnecessary delays, maintaining the efficiency of the overall operation.

Conclusion

In summary, the key to optimizing your PostgreSQL UPDATE JOIN queries lies in simplifying how tables are referenced in your SQL statements. With the right adjustments, as demonstrated, you can dramatically reduce execution times and enhance your database performance. Remember, always ensure that unnecessary repetitions are avoided in your queries to maintain efficiency.

By applying the solution provided, you should experience significantly improved performance with your PostgreSQL queries.

If you have any questions or need further clarification, feel free to reach out in the comments below!