When working with parallel procedures on Snowflake, deadlocks can occur when multiple processes are waiting for each other to release a resource that they are holding. Deadlocks can be avoided while still using parallelism by following these tips:
- Use transaction control commands like COMMIT and ROLLBACK: When multiple processes access the same resource, it’s essential to ensure that only one process can access the resource at a time. Transaction control commands like COMMIT and ROLLBACK help ensure this by making sure that only one process can commit its changes to the resource at a time. This way, other processes don’t need to wait for the resource to be released.
- Optimize queries by minimizing the use of large tables, using appropriate join types, and ensuring that queries are well-indexed: Large tables can cause bottlenecks in the query performance, and poorly written queries can also lead to deadlocks. Optimizing your queries can help reduce the risk of deadlocks by minimizing the time required to access resources. It’s essential to use appropriate join types and ensure that your queries are well-indexed to improve performance.
- Use optimistic locking to allow multiple processes to access the same resource simultaneously but ensure that only one process can make changes at a time: Optimistic locking allows multiple processes to access a resource at the same time, but only one process can modify the resource at any given time. Other processes can still read the resource, but they cannot modify it until the current process has finished its changes. This way, deadlocks can be avoided while still allowing parallelism.
- Understand which DML commands lock a table and which ones don’t: It’s important to understand which DML (Data Manipulation Language) commands lock a table and which ones don’t. INSERT, UPDATE, and DELETE commands lock the table, while SELECT does not. By understanding which commands lock a table, you can avoid deadlocks by avoiding the use of locking commands when they are not needed.
By following these tips, you can reduce the likelihood of deadlocks occurring and ensure that your Snowflake environment runs smoothly and efficiently, with the benefits of parallelism.