Movatterモバイル変換

13.3.1. Table-Level Locks

13.3.2. Row-Level Locks

13.3.3. Page-Level Locks

13.3.4. Deadlocks

13.3.5. Advisory Locks

The list below shows the available lock modes and the contexts in which they are used automatically byPostgreSQL. You can also acquire any of these locks explicitly with the commandLOCK. Remember that all of these lock modes are table-level locks, even if the name contains the word“row”; the names of the lock modes are historical. To some extent the names reflect the typical usage of each lock mode — but the semantics are all the same. The only real difference between one lock mode and another is the set of lock modes with which each conflicts (seeTable 13.2). Two transactions cannot hold locks of conflicting modes on the same table at the same time. (However, a transaction never conflicts with itself. For example, it might acquireACCESS EXCLUSIVE lock and later acquireACCESS SHARE lock on the same table.) Non-conflicting lock modes can be held concurrently by many transactions. Notice in particular that some lock modes are self-conflicting (for example, anACCESS EXCLUSIVE lock cannot be held by more than one transaction at a time) while others are not self-conflicting (for example, anACCESS SHARE lock can be held by multiple transactions).

Once acquired, a lock is normally held until the end of the transaction. But if a lock is acquired after establishing a savepoint, the lock is released immediately if the savepoint is rolled back to. This is consistent with the principle thatROLLBACK cancels all effects of the commands since the savepoint. The same holds for locks acquired within aPL/pgSQL exception block: an error escape from the block releases locks acquired within it.

In addition to table-level locks, there are row-level locks, which are listed as below with the contexts in which they are used automatically byPostgreSQL. SeeTable 13.3 for a complete table of row-level lock conflicts. Note that a transaction can hold conflicting locks on the same row, even in different subtransactions; but other than that, two transactions can never hold conflicting locks on the same row. Row-level locks do not affect data querying; they block onlywriters and lockers to the same row. Row-level locks are released at transaction end or during savepoint rollback, just like table-level locks.

PostgreSQL doesn't remember any information about modified rows in memory, so there is no limit on the number of rows locked at one time. However, locking a row might cause a disk write, e.g.,SELECT FOR UPDATE modifies selected rows to mark them locked, and so will result in disk writes.

In addition to table and row locks, page-level share/exclusive locks are used to control read/write access to table pages in the shared buffer pool. These locks are released immediately after a row is fetched or updated. Application developers normally need not be concerned with page-level locks, but they are mentioned here for completeness.

The use of explicit locking can increase the likelihood ofdeadlocks, wherein two (or more) transactions each hold locks that the other wants. For example, if transaction 1 acquires an exclusive lock on table A and then tries to acquire an exclusive lock on table B, while transaction 2 has already exclusive-locked table B and now wants an exclusive lock on table A, then neither one can proceed.PostgreSQL automatically detects deadlock situations and resolves them by aborting one of the transactions involved, allowing the other(s) to complete. (Exactly which transaction will be aborted is difficult to predict and should not be relied upon.)

Note that deadlocks can also occur as the result of row-level locks (and thus, they can occur even if explicit locking is not used). Consider the case in which two concurrent transactions modify a table. The first transaction executes:

UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 11111;

This acquires a row-level lock on the row with the specified account number. Then, the second transaction executes:

UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 22222;UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 11111;

The firstUPDATE statement successfully acquires a row-level lock on the specified row, so it succeeds in updating that row. However, the secondUPDATE statement finds that the row it is attempting to update has already been locked, so it waits for the transaction that acquired the lock to complete. Transaction two is now waiting on transaction one to complete before it continues execution. Now, transaction one executes:

UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 22222;

Transaction one attempts to acquire a row-level lock on the specified row, but it cannot: transaction two already holds such a lock. So it waits for transaction two to complete. Thus, transaction one is blocked on transaction two, and transaction two is blocked on transaction one: a deadlock condition.PostgreSQL will detect this situation and abort one of the transactions.

The best defense against deadlocks is generally to avoid them by being certain that all applications using a database acquire locks on multiple objects in a consistent order. In the example above, if both transactions had updated the rows in the same order, no deadlock would have occurred. One should also ensure that the first lock acquired on an object in a transaction is the most restrictive mode that will be needed for that object. If it is not feasible to verify this in advance, then deadlocks can be handled on-the-fly by retrying transactions that abort due to deadlocks.

So long as no deadlock situation is detected, a transaction seeking either a table-level or row-level lock will wait indefinitely for conflicting locks to be released. This means it is a bad idea for applications to hold transactions open for long periods of time (e.g., while waiting for user input).

PostgreSQL provides a means for creating locks that have application-defined meanings. These are calledadvisory locks, because the system does not enforce their use — it is up to the application to use them correctly. Advisory locks can be useful for locking strategies that are an awkward fit for the MVCC model. For example, a common use of advisory locks is to emulate pessimistic locking strategies typical of so-called“flat file” data management systems. While a flag stored in a table could be used for the same purpose, advisory locks are faster, avoid table bloat, and are automatically cleaned up by the server at the end of the session.

There are two ways to acquire an advisory lock inPostgreSQL: at session level or at transaction level. Once acquired at session level, an advisory lock is held until explicitly released or the session ends. Unlike standard lock requests, session-level advisory lock requests do not honor transaction semantics: a lock acquired during a transaction that is later rolled back will still be held following the rollback, and likewise an unlock is effective even if the calling transaction fails later. A lock can be acquired multiple times by its owning process; for each completed lock request there must be a corresponding unlock request before the lock is actually released. Transaction-level lock requests, on the other hand, behave more like regular lock requests: they are automatically released at the end of the transaction, and there is no explicit unlock operation. This behavior is often more convenient than the session-level behavior for short-term usage of an advisory lock. Session-level and transaction-level lock requests for the same advisory lock identifier will block each other in the expected way. If a session already holds a given advisory lock, additional requests by it will always succeed, even if other sessions are awaiting the lock; this statement is true regardless of whether the existing lock hold and new request are at session level or transaction level.

Like all locks inPostgreSQL, a complete list of advisory locks currently held by any session can be found in thepg_locks system view.

Both advisory locks and regular locks are stored in a shared memory pool whose size is defined by the configuration variablesmax_locks_per_transaction andmax_connections. Care must be taken not to exhaust this memory or the server will be unable to grant any locks at all. This imposes an upper limit on the number of advisory locks grantable by the server, typically in the tens to hundreds of thousands depending on how the server is configured.

In certain cases using advisory locking methods, especially in queries involving explicit ordering andLIMIT clauses, care must be taken to control the locks acquired because of the order in which SQL expressions are evaluated. For example:

SELECT pg_advisory_lock(id) FROM foo WHERE id = 12345; -- okSELECT pg_advisory_lock(id) FROM foo WHERE id > 12345 LIMIT 100; -- danger!SELECT pg_advisory_lock(q.id) FROM(  SELECT id FROM foo WHERE id > 12345 LIMIT 100) q; -- ok

In the above queries, the second form is dangerous because theLIMIT is not guaranteed to be applied before the locking function is executed. This might cause some locks to be acquired that the application was not expecting, and hence would fail to release (until it ends the session). From the point of view of the application, such locks would be dangling, although still viewable inpg_locks.

The functions provided to manipulate advisory locks are described inSection 9.26.10.