![]() ![]() This can have some unpleasant consequences, so a superfluous lock isn't that bad, all things considered. rows that satisfied the query conditions as of the query snapshot will be locked, although they will not be returned if they were updated after the snapshot and no longer satisfy the query conditions. This behaviour is mentioned in the FOR UPDATE documentation: The check fails, and the row is filtered out of the result set, but the lock is still held. Detecting that the record has changed since its SELECT, it rechecks the data against its WHERE condition. Thread-1 runs the UPDATE / COMMIT and releases the lock.Thread-2 runs the SELECT and enters the lock's wait queue.Thread-1 runs the SELECT and acquires the record lock.You can run it with psycopg2 and pytest, remember to change the database setting, and run pip install pytest psycopg2 To reproduce the issue, I wrote a simple test here Release is waiting for SELECT FOR UPDATE, and SELECT FOR UPDATE is doing infinite loop while it's holding a lock for no reason. Somehow, even no locked = false row exists, it acquire lock anyway. Should only acquire the lock on target row only if the target row's locked is false. The strange part of it is, I thought SELECT id FROM my_locks WHERE locked = false AND id = '' FOR UPDATE This looks pretty straightforward, but it doesn't work. UPDATE my_locks SET locked = false WHERE id = '' UPDATE my_locks SET locked = true WHERE id = '' SELECT id FROM my_locks WHERE locked = false AND id = '' FOR UPDATE But if you do encounter lock wait timeouts or deadlocks, you might need to dig more deeply into the internals of the database’s execution plans with EXPLAIN, examine the schema and indexing, and perhaps even think about the physical ordering of the data to understand what’s causing the issues.I am building a locking system based on PostgreSQL, I have two methods, acquire and release. You normally don’t need to explicitly ask for data to be locked, and you generally won’t need to think a lot about the order of data access. The database typically takes care of all of this for you. Most of the time, you don’t need to worry about any of this. (Cycles only happen when some things are working one direction and others are going the opposite way.) If you’re operating on multiple pieces of data, try to arrange for it to be done in the same order.Lock it for as short a time as possible.The basic strategies for avoiding the wasteful work delays from lock waits and deadlocks are the same: Hopefully, the other users can proceed with their work now that this user’s locks have been forcibly cleared. The user selected as the victim will receive an error message saying there was a deadlock. When they find the loop, they know one of the users’ work needs to be aborted to let others go ahead. ![]() That’s why high-performance databases typically do lock cycle detection, looking for a loop in the chain of locks. (MySQL’s default lock wait timeout is 50 seconds.) More seriously, this will lead to a lot of long-lived locks, which increases the chance that lots and lots of other users will also try to access the same data and themselves get into lock waits or deadlocks. ![]() For one thing, if users have to wait for their lock requests to time out, everything will become really slow. Some databases resolve deadlocks through lock wait timeouts, but this is really wasteful. There’s a cycle in the locking: user A is waiting on B, and B is waiting on A. Then each of them tries to access each other’s locked data. For example, the first user and second user both lock some data. A deadlock happens when multiple lock waits happen in such a manner that none of the users can do any further work. Deadlocks are a related behavior, but they’re more complicated. This behavior keeps the second user from waiting forever, so they can stay productive by working on something else. The database will respond to the second user with an error message saying their lock wait was too long. If the first user doesn’t unlock the data, the second one will time out after a while. A lock wait timeout results when one user gets a lock on some data and holds it while another user tries to access it. Lock wait timeouts and deadlocks both arise from certain locking mechanisms. Without this locking, confusing and incorrect behaviors can happen. The locks ensure only one operation can alter a specific portion of the data at a time and serialize changes to further mitigate these race conditions. Most databases use locking strategies to make sure data stays consistent when multiple users (or connections, or processes) are reading and modifying the data concurrently. Inevitably, many of us will come across these phenomena at some point. What are these? And how are they different? If you use ACID transactional databases, you’ve probably heard of lock wait timeouts and deadlocks. ![]()
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