It is very difficult to enforce business rules regarding data integrity using Read Committed transactions because the view of the data is shifting with each statement, and even a single statement may not restrict itself to the statement's snapshot if a write conflict occurs.
While a Repeatable Read transaction has a stable view of the data throughout its execution, there is a subtle issue with using MVCC snapshots for data consistency checks, involving something known as read/write conflicts. If one transaction writes data and a concurrent transaction attempts to read the same data (whether before or after the write), it cannot see the work of the other transaction. The reader then appears to have executed first regardless of which started first or which committed first. If that is as far as it goes, there is no problem, but if the reader also writes data which is read by a concurrent transaction there is now a transaction which appears to have run before either of the previously mentioned transactions. If the transaction which appears to have executed last actually commits first, it is very easy for a cycle to appear in a graph of the order of execution of the transactions. When such a cycle appears, integrity checks will not work correctly without some help.
As mentioned in Section 13.2.3, Serializable transactions are just Repeatable Read transactions which add nonblocking monitoring for dangerous patterns of read/write conflicts. When a pattern is detected which could cause a cycle in the apparent order of execution, one of the transactions involved is rolled back to break the cycle.
If the Serializable transaction isolation level is used for all writes and for all reads which need a consistent view of the data, no other effort is required to ensure consistency. Software from other environments which is written to use serializable transactions to ensure consistency should “just work” in this regard in PostgreSQL.
When using this technique, it will avoid creating an unnecessary burden
for application programmers if the application software goes through a
framework which automatically retries transactions which are rolled
back with a serialization failure. It may be a good idea to set
default_transaction_isolation to serializable.
It would also be wise to take some action to ensure that no other
transaction isolation level is used, either inadvertently or to
subvert integrity checks, through checks of the transaction isolation
level in triggers.
See Section 13.2.3 for performance suggestions.
This level of integrity protection using Serializable transactions does not yet extend to hot standby mode (Section 27.4). Because of that, those using hot standby may want to use Repeatable Read and explicit locking on the primary.
When non-serializable writes are possible,
to ensure the current validity of a row and protect it against
concurrent updates one must use SELECT FOR UPDATE,
SELECT FOR SHARE, or an appropriate LOCK
TABLE statement. (SELECT FOR UPDATE
and SELECT FOR SHARE lock just the
returned rows against concurrent updates, while LOCK
TABLE locks the whole table.) This should be taken into
account when porting applications to
PostgreSQL from other environments.
Also of note to those converting from other environments is the fact
that SELECT FOR UPDATE does not ensure that a
concurrent transaction will not update or delete a selected row.
To do that in PostgreSQL you must actually
update the row, even if no values need to be changed.
SELECT FOR UPDATE temporarily blocks
other transactions from acquiring the same lock or executing an
UPDATE or DELETE which would
affect the locked row, but once the transaction holding this lock
commits or rolls back, a blocked transaction will proceed with the
conflicting operation unless an actual UPDATE of
the row was performed while the lock was held.
Global validity checks require extra thought under
non-serializable MVCC.
For example, a banking application might wish to check that the sum of
all credits in one table equals the sum of debits in another table,
when both tables are being actively updated. Comparing the results of two
successive SELECT sum(...) commands will not work reliably in
Read Committed mode, since the second query will likely include the results
of transactions not counted by the first. Doing the two sums in a
single repeatable read transaction will give an accurate picture of only the
effects of transactions that committed before the repeatable read transaction
started — but one might legitimately wonder whether the answer is still
relevant by the time it is delivered. If the repeatable read transaction
itself applied some changes before trying to make the consistency check,
the usefulness of the check becomes even more debatable, since now it
includes some but not all post-transaction-start changes. In such cases
a careful person might wish to lock all tables needed for the check,
in order to get an indisputable picture of current reality. A
SHARE mode (or higher) lock guarantees that there are no
uncommitted changes in the locked table, other than those of the current
transaction.
Note also that if one is relying on explicit locking to prevent concurrent
changes, one should either use Read Committed mode, or in Repeatable Read
mode be careful to obtain
locks before performing queries. A lock obtained by a
repeatable read transaction guarantees that no other transactions modifying
the table are still running, but if the snapshot seen by the
transaction predates obtaining the lock, it might predate some now-committed
changes in the table. A repeatable read transaction's snapshot is actually
frozen at the start of its first query or data-modification command
(SELECT, INSERT,
UPDATE, or DELETE), so
it is possible to obtain locks explicitly before the snapshot is
frozen.