13.2. Transaction Isolation
The SQL standard defines four levels of transaction isolation. The most strict is Serializable, which is defined by the standard in a paragraph which says that any concurrent execution of a set of Serializable transactions is guaranteed to produce the same effect as running them one at a time in some order. The other three levels are defined in terms of phenomena, resulting from interaction between concurrent transactions, which must not occur at each level. The standard notes that due to the definition of Serializable, none of these phenomena are possible at that level. (This is hardly surprising -- if the effect of the transactions must be consistent with having been run one at a time, how could you see any phenomena caused by interactions?)
The phenomena which are prohibited at various levels are:
- dirty read
A transaction reads data written by a concurrent uncommitted transaction.
- nonrepeatable read
A transaction re-reads data it has previously read and finds that data has been modified by another transaction (that committed since the initial read).
- phantom read
A transaction re-executes a query returning a set of rows that satisfy a search condition and finds that the set of rows satisfying the condition has changed due to another recently-committed transaction.
- serialization anomaly
The result of successfully committing a group of transactions is inconsistent with all possible orderings of running those transactions one at a time.
The SQL standard and PostgreSQL-implemented transaction isolation levels are described in Table 13.1.
Table 13.1. Transaction Isolation Levels
|Isolation Level||Dirty Read||Nonrepeatable Read||Phantom Read||Serialization Anomaly|
|Read uncommitted||Allowed, but not in PG||Possible||Possible||Possible|
|Read committed||Not possible||Possible||Possible||Possible|
|Repeatable read||Not possible||Not possible||Allowed, but not in PG||Possible|
|Serializable||Not possible||Not possible||Not possible||Not possible|
In PostgreSQL, you can request any of the four standard transaction isolation levels, but internally only three distinct isolation levels are implemented, i.e., PostgreSQL's Read Uncommitted mode behaves like Read Committed. This is because it is the only sensible way to map the standard isolation levels to PostgreSQL's multiversion concurrency control architecture.
The table also shows that PostgreSQL's Repeatable Read implementation does not allow phantom reads. This is acceptable under the SQL standard because the standard specifies which anomalies must not occur at certain isolation levels; higher guarantees are acceptable. The behavior of the available isolation levels is detailed in the following subsections.
To set the transaction isolation level of a transaction, use the command SET TRANSACTION.
Some PostgreSQL data types and functions have special rules regarding transactional behavior. In particular, changes made to a sequence (and therefore the counter of a column declared using
serial) are immediately visible to all other transactions and are not rolled back if the transaction that made the changes aborts. See Section 9.17 and Section 8.1.4.
13.2.1. Read Committed Isolation Level
Read Committed is the default isolation level in PostgreSQL. When a transaction uses this isolation level, a
SELECT query (without a
FOR UPDATE/SHARE clause) sees only data committed before the query began; it never sees either uncommitted data or changes committed during query execution by concurrent transactions. In effect, a
SELECT query sees a snapshot of the database as of the instant the query begins to run. However,
SELECT does see the effects of previous updates executed within its own transaction, even though they are not yet committed. Also note that two successive
SELECT commands can see different data, even though they are within a single transaction, if other transactions commit changes after the first
SELECT starts and before the second
SELECT FOR UPDATE, and
SELECT FOR SHARE commands behave the same as
SELECT in terms of searching for target rows: they will only find target rows that were committed as of the command start time. However, such a target row might have already been updated (or deleted or locked) by another concurrent transaction by the time it is found. In this case, the would-be updater will wait for the first updating transaction to commit or roll back (if it is still in progress). If the first updater rolls back, then its effects are negated and the second updater can proceed with updating the originally found row. If the first updater commits, the second updater will ignore the row if the first updater deleted it, otherwise it will attempt to apply its operation to the updated version of the row. The search condition of the command (the
WHERE clause) is re-evaluated to see if the updated version of the row still matches the search condition. If so, the second updater proceeds with its operation using the updated version of the row. In the case of
SELECT FOR UPDATE and
SELECT FOR SHARE, this means it is the updated version of the row that is locked and returned to the client.
INSERT with an
ON CONFLICT DO UPDATE clause behaves similarly. In Read Committed mode, each row proposed for insertion will either insert or update. Unless there are unrelated errors, one of those two outcomes is guaranteed. If a conflict originates in another transaction whose effects are not yet visible to the
UPDATE clause will affect that row, even though possibly no version of that row is conventionally visible to the command.
INSERT with an
ON CONFLICT DO NOTHING clause may have insertion not proceed for a row due to the outcome of another transaction whose effects are not visible to the
INSERT snapshot. Again, this is only the case in Read Committed mode.
MERGE allows the user to specify various combinations of
DELETE subcommands. A
MERGE command with both
UPDATE subcommands looks similar to
INSERT with an
ON CONFLICT DO UPDATE clause but does not guarantee that either
UPDATE will occur. If
MERGE attempts an
DELETE and the row is concurrently updated but the join condition still passes for the current target and the current source tuple, then
MERGE will behave the same as the
DELETE commands and perform its action on the updated version of the row. However, because
MERGE can specify several actions and they can be conditional, the conditions for each action are re-evaluated on the updated version of the row, starting from the first action, even if the action that had originally matched appears later in the list of actions. On the other hand, if the row is concurrently updated or deleted so that the join condition fails, then
MERGE will evaluate the condition's
NOT MATCHED actions next, and execute the first one that succeeds. If
MERGE attempts an
INSERT and a unique index is present and a duplicate row is concurrently inserted, then a uniqueness violation error is raised;
MERGE does not attempt to avoid such errors by restarting evaluation of
Because of the above rules, it is possible for an updating command to see an inconsistent snapshot: it can see the effects of concurrent updating commands on the same rows it is trying to update, but it does not see effects of those commands on other rows in the database. This behavior makes Read Committed mode unsuitable for commands that involve complex search conditions; however, it is just right for simpler cases. For example, consider updating bank balances with transactions like:
BEGIN; UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 12345; UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 7534; COMMIT;
If two such transactions concurrently try to change the balance of account 12345, we clearly want the second transaction to start with the updated version of the account's row. Because each command is affecting only a predetermined row, letting it see the updated version of the row does not create any troublesome inconsistency.
More complex usage can produce undesirable results in Read Committed mode. For example, consider a
DELETE command operating on data that is being both added and removed from its restriction criteria by another command, e.g., assume
website is a two-row table with
BEGIN; UPDATE website SET hits = hits + 1; -- run from another session: DELETE FROM website WHERE hits = 10; COMMIT;
DELETE will have no effect even though there is a
website.hits = 10 row before and after the
UPDATE. This occurs because the pre-update row value
9 is skipped, and when the
UPDATE completes and
DELETE obtains a lock, the new row value is no longer
11, which no longer matches the criteria.
Because Read Committed mode starts each command with a new snapshot that includes all transactions committed up to that instant, subsequent commands in the same transaction will see the effects of the committed concurrent transaction in any case. The point at issue above is whether or not a single command sees an absolutely consistent view of the database.
The partial transaction isolation provided by Read Committed mode is adequate for many applications, and this mode is fast and simple to use; however, it is not sufficient for all cases. Applications that do complex queries and updates might require a more rigorously consistent view of the database than Read Committed mode provides.
13.2.2. Repeatable Read Isolation Level
The Repeatable Read isolation level only sees data committed before the transaction began; it never sees either uncommitted data or changes committed during transaction execution by concurrent transactions. (However, the query does see the effects of previous updates executed within its own transaction, even though they are not yet committed.) This is a stronger guarantee than is required by the SQL standard for this isolation level, and prevents all of the phenomena described in Table 13.1 except for serialization anomalies. As mentioned above, this is specifically allowed by the standard, which only describes the minimum protections each isolation level must provide.
This level is different from Read Committed in that a query in a repeatable read transaction sees a snapshot as of the start of the first non-transaction-control statement in the transaction, not as of the start of the current statement within the transaction. Thus, successive
SELECT commands within a single transaction see the same data, i.e., they do not see changes made by other transactions that committed after their own transaction started.
Applications using this level must be prepared to retry transactions due to serialization failures.
SELECT FOR UPDATE, and
SELECT FOR SHARE commands behave the same as
SELECT in terms of searching for target rows: they will only find target rows that were committed as of the transaction start time. However, such a target row might have already been updated (or deleted or locked) by another concurrent transaction by the time it is found. In this case, the repeatable read transaction will wait for the first updating transaction to commit or roll back (if it is still in progress). If the first updater rolls back, then its effects are negated and the repeatable read transaction can proceed with updating the originally found row. But if the first updater commits (and actually updated or deleted the row, not just locked it) then the repeatable read transaction will be rolled back with the message
ERROR: could not serialize access due to concurrent update
because a repeatable read transaction cannot modify or lock rows changed by other transactions after the repeatable read transaction began.
When an application receives this error message, it should abort the current transaction and retry the whole transaction from the beginning. The second time through, the transaction will see the previously-committed change as part of its initial view of the database, so there is no logical conflict in using the new version of the row as the starting point for the new transaction's update.
Note that only updating transactions might need to be retried; read-only transactions will never have serialization conflicts.
The Repeatable Read mode provides a rigorous guarantee that each transaction sees a completely stable view of the database. However, this view will not necessarily always be consistent with some serial (one at a time) execution of concurrent transactions of the same level. For example, even a read-only transaction at this level may see a control record updated to show that a batch has been completed but not see one of the detail records which is logically part of the batch because it read an earlier revision of the control record. Attempts to enforce business rules by transactions running at this isolation level are not likely to work correctly without careful use of explicit locks to block conflicting transactions.
The Repeatable Read isolation level is implemented using a technique known in academic database literature and in some other database products as Snapshot Isolation. Differences in behavior and performance may be observed when compared with systems that use a traditional locking technique that reduces concurrency. Some other systems may even offer Repeatable Read and Snapshot Isolation as distinct isolation levels with different behavior. The permitted phenomena that distinguish the two techniques were not formalized by database researchers until after the SQL standard was developed, and are outside the scope of this manual. For a full treatment, please see [berenson95].
Prior to PostgreSQL version 9.1, a request for the Serializable transaction isolation level provided exactly the same behavior described here. To retain the legacy Serializable behavior, Repeatable Read should now be requested.
13.2.3. Serializable Isolation Level
The Serializable isolation level provides the strictest transaction isolation. This level emulates serial transaction execution for all committed transactions; as if transactions had been executed one after another, serially, rather than concurrently. However, like the Repeatable Read level, applications using this level must be prepared to retry transactions due to serialization failures. In fact, this isolation level works exactly the same as Repeatable Read except that it also monitors for conditions which could make execution of a concurrent set of serializable transactions behave in a manner inconsistent with all possible serial (one at a time) executions of those transactions. This monitoring does not introduce any blocking beyond that present in repeatable read, but there is some overhead to the monitoring, and detection of the conditions which could cause a serialization anomaly will trigger a serialization failure.
As an example, consider a table
mytab, initially containing:
class | value -------+------- 1 | 10 1 | 20 2 | 100 2 | 200
Suppose that serializable transaction A computes:
SELECT SUM(value) FROM mytab WHERE class = 1;
and then inserts the result (30) as the
value in a new row with
= 2. Concurrently, serializable transaction B computes:
SELECT SUM(value) FROM mytab WHERE class = 2;
and obtains the result 300, which it inserts in a new row with
= 1. Then both transactions try to commit. If either transaction were running at the Repeatable Read isolation level, both would be allowed to commit; but since there is no serial order of execution consistent with the result, using Serializable transactions will allow one transaction to commit and will roll the other back with this message:
ERROR: could not serialize access due to read/write dependencies among transactions
This is because if A had executed before B, B would have computed the sum 330, not 300, and similarly the other order would have resulted in a different sum computed by A.
When relying on Serializable transactions to prevent anomalies, it is important that any data read from a permanent user table not be considered valid until the transaction which read it has successfully committed. This is true even for read-only transactions, except that data read within a deferrable read-only transaction is known to be valid as soon as it is read, because such a transaction waits until it can acquire a snapshot guaranteed to be free from such problems before starting to read any data. In all other cases applications must not depend on results read during a transaction that later aborted; instead, they should retry the transaction until it succeeds.
To guarantee true serializability PostgreSQL uses predicate locking, which means that it keeps locks which allow it to determine when a write would have had an impact on the result of a previous read from a concurrent transaction, had it run first. In PostgreSQL these locks do not cause any blocking and therefore can not play any part in causing a deadlock. They are used to identify and flag dependencies among concurrent Serializable transactions which in certain combinations can lead to serialization anomalies. In contrast, a Read Committed or Repeatable Read transaction which wants to ensure data consistency may need to take out a lock on an entire table, which could block other users attempting to use that table, or it may use
SELECT FOR UPDATE or
SELECT FOR SHARE which not only can block other transactions but cause disk access.
Predicate locks in PostgreSQL, like in most other database systems, are based on data actually accessed by a transaction. These will show up in the
pg_locks system view with a
SIReadLock. The particular locks acquired during execution of a query will depend on the plan used by the query, and multiple finer-grained locks (e.g., tuple locks) may be combined into fewer coarser-grained locks (e.g., page locks) during the course of the transaction to prevent exhaustion of the memory used to track the locks. A
READ ONLY transaction may be able to release its SIRead locks before completion, if it detects that no conflicts can still occur which could lead to a serialization anomaly. In fact,
READ ONLY transactions will often be able to establish that fact at startup and avoid taking any predicate locks. If you explicitly request a
SERIALIZABLE READ ONLY DEFERRABLE transaction, it will block until it can establish this fact. (This is the only case where Serializable transactions block but Repeatable Read transactions don't.) On the other hand, SIRead locks often need to be kept past transaction commit, until overlapping read write transactions complete.
Consistent use of Serializable transactions can simplify development. The guarantee that any set of successfully committed concurrent Serializable transactions will have the same effect as if they were run one at a time means that if you can demonstrate that a single transaction, as written, will do the right thing when run by itself, you can have confidence that it will do the right thing in any mix of Serializable transactions, even without any information about what those other transactions might do, or it will not successfully commit. It is important that an environment which uses this technique have a generalized way of handling serialization failures (which always return with an SQLSTATE value of '40001'), because it will be very hard to predict exactly which transactions might contribute to the read/write dependencies and need to be rolled back to prevent serialization anomalies. The monitoring of read/write dependencies has a cost, as does the restart of transactions which are terminated with a serialization failure, but balanced against the cost and blocking involved in use of explicit locks and
SELECT FOR UPDATE or
SELECT FOR SHARE, Serializable transactions are the best performance choice for some environments.
While PostgreSQL's Serializable transaction isolation level only allows concurrent transactions to commit if it can prove there is a serial order of execution that would produce the same effect, it doesn't always prevent errors from being raised that would not occur in true serial execution. In particular, it is possible to see unique constraint violations caused by conflicts with overlapping Serializable transactions even after explicitly checking that the key isn't present before attempting to insert it. This can be avoided by making sure that all Serializable transactions that insert potentially conflicting keys explicitly check if they can do so first. For example, imagine an application that asks the user for a new key and then checks that it doesn't exist already by trying to select it first, or generates a new key by selecting the maximum existing key and adding one. If some Serializable transactions insert new keys directly without following this protocol, unique constraints violations might be reported even in cases where they could not occur in a serial execution of the concurrent transactions.
For optimal performance when relying on Serializable transactions for concurrency control, these issues should be considered:
Declare transactions as
READ ONLYwhen possible.
Control the number of active connections, using a connection pool if needed. This is always an important performance consideration, but it can be particularly important in a busy system using Serializable transactions.
Don't put more into a single transaction than needed for integrity purposes.
Don't leave connections dangling “idle in transaction” longer than necessary. The configuration parameter idle_in_transaction_session_timeout may be used to automatically disconnect lingering sessions.
Eliminate explicit locks,
SELECT FOR UPDATE, and
SELECT FOR SHAREwhere no longer needed due to the protections automatically provided by Serializable transactions.
When the system is forced to combine multiple page-level predicate locks into a single relation-level predicate lock because the predicate lock table is short of memory, an increase in the rate of serialization failures may occur. You can avoid this by increasing max_pred_locks_per_transaction, max_pred_locks_per_relation, and/or max_pred_locks_per_page.
A sequential scan will always necessitate a relation-level predicate lock. This can result in an increased rate of serialization failures. It may be helpful to encourage the use of index scans by reducing random_page_cost and/or increasing cpu_tuple_cost. Be sure to weigh any decrease in transaction rollbacks and restarts against any overall change in query execution time.
The Serializable isolation level is implemented using a technique known in academic database literature as Serializable Snapshot Isolation, which builds on Snapshot Isolation by adding checks for serialization anomalies. Some differences in behavior and performance may be observed when compared with other systems that use a traditional locking technique. Please see [ports12] for detailed information.