- Concurrency issues can be difficult to recognize and often get overlooked, leading to hard-to-debug bugs.
- It's tempting to dismiss concurrency issues due to perceived low likelihood, but they can still crop up unexpectedly under high load.
- Locking is a common approach to handling concurrency, but locks can be overused and lead to performance issues.
- The database is the lowest common denominator for coordinating locks across multiple processes and servers.
- The "ask for forgiveness" (EAFP) approach, where you try an operation and handle exceptions, is often more Pythonic than checking conditions in advance.
- In PostgreSQL, when an exception occurs within a transaction, it can block further commands until the transaction ends, requiring special handling.
- Using SELECT FOR UPDATE can lock rows to prevent race conditions, but this can also cause performance issues with high concurrency.
- Incrementing counters directly in the database, using an F expression, can avoid race conditions without the need for explicit locking.
- Combining database-level updates with RETURNING to immediately fetch the updated object can optimize the process further.
- The key is to keep concurrency issues in mind, avoid dismissing them due to perceived low likelihood, and use the most appropriate concurrency control mechanisms for the specific situation.