Deadlock error in INSERT statement

Question

We've got a web-based application. There is a time-bound database operation (INSERTs and UPDATEs) in the application which takes more time to complete, hence this particular flow has been changed into Java Thread so it will not wait (block) for the complete database operation to be completed.

My problem is, if more than 1 user come across this particular flow, I'm facing the following error thrown by PostgreSQL:

org.postgresql.util.PSQLException: ERROR: deadlock detected
  Detail: Process 13560 waits for ShareLock on transaction 3147316424; blocked by process 13566.
Process 13566 waits for ShareLock on transaction 3147316408; blocked by process 13560.

Above error is consistently thrown in INSERT statements.

Additional Information: 1) I have PRIMARY KEY defined in this table. 2) There are FOREIGN KEY references in this table. 3) Separate database connection is passed to each Java Thread.

Technologies Web Server: Tomcat v6.0.10 Java v1.6.0 Servlet Database: PostgreSQL v8.2.3 Connection Management: pgpool II

Answer 1

Your problem, probably, is the insert command is trying to lock one or both index and the indexes is locked for the other tread.

One common mistake is lock resources in different order on each thread. Check the orders and try to lock the resources in the same order in all threads.

Answer 2

Deadlock explained:
In a nutshell, what is happening is that a particular SQL statement (INSERT or other) is waiting on another statement to release a lock on a particular part of the database, before it can proceed. Until this lock is released, the first SQL statement, call it "statement A" will not allow itself to access this part of the database to do its job (= regular lock situation). But... statement A has also put a lock on another part of the database to ensure that no other users of the database access (for reading, or modifiying/deleting, depending on the type of lock). Now... the second SQL statement, is itself in need of accessing the data section marked by the lock of Statement A. That is a DEAD LOCK : both Statement will wait, ad infinitum, on one another.

The remedy...

This would require to know the specific SQL statement these various threads are running, and looking in there if there is a way to either:

a) removing some of the locks, or changing their types.
   For example, maybe the whole table is locked, whereby only a given row, or
   a page thereof would be necessary.
b) preventing multiple of these queries to be submitted at a given time.
   This would be done by way of semaphores/locks (aka MUTEX) at the level of the
   multi-threading logic.

Beware that the "b)" approach, if not correctly implemented may just move the deadlock situation from within SQL to within the program/threads logic. The key would be to only create one mutex to be obtained first by any thread which is about to run one of these deadlock-prone queries.

Answer 3

One way to cope with deadlocks is to have a retry mechanism that waits for a random interval and tries to run the transaction again. The random interval is necessary so that the colliding transactions don't continuously keep bumping into each other, causing what is called a live lock - something even nastier to debug. Actually most complex applications will need such a retry mechanism sooner or later when they need to handle transaction serialization failures.

Of course if you are able to determine the cause of the deadlock it's usually much better to eliminate it or it will come back to bite you. For almost all cases, even when the deadlock condition is rare, the little bit of throughput and coding overhead to get the locks in deterministic order or get more coarse-grained locks is worth it to avoid the occasional large latency hit and the sudden performance cliff when scaling concurrency.

When you are consistently getting two INSERT statements deadlocking it's most likely an unique index insert order issue. Try for example the following in two psql command windows:

Thread A           | Thread B
BEGIN;             | BEGIN;
                   | INSERT uniq=1;
INSERT uniq=2;     | 
                   | INSERT uniq=2; 
                   |   block waiting for thread A to commit or rollback, to
                   |   see if this is an unique key error.
INSERT uniq=1;     |
   blocks waiting  |
   for thread B,   |
     DEADLOCK      | 
                   V

Usually the best course of action to resolve this is to figure out the parent objects that guard all such transactions. Most applications have one or two of primary entities, such as users or accounts, that are good candidates for this. Then all you need is for every transaction to get the locks on the primary entity it touches via SELECT ... FOR UPDATE. Or if touches several, get locks on all of them but in the same order every time (order by primary key is a good choice).

Answer 4

What PostgreSQL does here is covered in the documentation on Explicit Locking. The example in the "Deadlocks" section shows what you're probably doing. The part you may not have expected is that when you UPDATE something, that acquires a lock on that row that continues until the transaction involved ends. If you have multiple clients all doing updates of more than one thing at once, you'll inevitably end up with deadlocks unless you go out of your way to prevent them.

If you have multiple things that take out implicit locks like UPDATE, you should wrap the whole sequence in BEGIN/COMMIT transaction blocks, and make sure you're consistent about the order they acquire locks (even the implicit ones like what UPDATE grabs) at everywhere. If you need to update something in table A then table B, and one part of the app does A then B while the other does B then A, you're going to deadlock one day. Two UPDATEs against the same table are similarly destined to fail unless you can enforce some ordering of the two that's repeatable among clients. Sorting by primary key once you have the set of records to update and always grabbing the "lower" one first is a common strategy.

It's less likely your INSERTs are to blame here, those are much harder to get into a deadlocked situation, unless you violate a primary key as Ants already described.

What you don't want to do is try and duplicate locking in your app, which is going to turn into a giant scalability and reliability mess (and will likely still result in database deadlocks). If you can't work around this within the confines of the standard database locking methods, consider using either the advisory lock facility or explicit LOCK TABLE to enforce what you need instead. That will save you a world of painful coding over trying to push all the locks onto the client side. If you have multiple updates against a table and can't enforce the order they happen in, you have no choice but to lock the whole table while you execute them; that's the only route that doesn't introduce a potential for deadlock.