How to avoid a mutating table happening in PostgreSQL?

In PostgreSQL, a mutating table error occurs when a row-level trigger tries to modify the same table that the trigger is defined on. This occurs because the database does not allow a query to modify a table that the query is currently reading or writing. When a row-level trigger tries to modify the table, it conflicts with the database’s rules, which can cause unpredictable behaviour or data inconsistencies. Mutating table errors are common in scenarios where a trigger is defined on a table that is referenced by the trigger’s query.

How to avoid a mutating table happening in PostgreSQL?

A mutating table error in PostgreSQL happens when you try to perform an operation on a table that is currently being modified. This can occur when you have triggers, views, or functions that modify the same table they are accessing. To avoid a mutating table error, you can use one of the following approaches:

  1. Use a temporary table: Instead of directly modifying the table, create a temporary table and modify it. Once the modifications are complete, you can copy the data from the temporary table back to the original table.
  2. Use a stored procedure: By encapsulating your code in a stored procedure, you can ensure that all modifications to the table are performed in a single transaction. This can help to avoid a mutating table error.
  3. Avoid triggers that modify the same table: Triggers can be a common cause of mutating table errors. If possible, try to avoid using triggers that modify the same table.
  4. Use a transaction: Wrap all the operations in a transaction so that they are executed atomically.
  5. Use constraint checks: Define constraints on the table to avoid the risk of inconsistent data.

Troubleshooting a mutating table happening in PostgreSQL

Here’s an example of a SQL script that can be used to monitor for mutating tables in PostgreSQL:

To use the function, you can simply call it like this:

This will return a list of tables and their associated triggers that are potentially affected by the mutating table issue. It’s important to note that this function only identifies triggers that might cause issues and does not necessarily indicate that there is an actual problem with the database. It’s up to the user to analyze the results and take appropriate action if necessary.

About Shiv Iyer 455 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.