Concurrency Controlconcurrency
This chapter describes the behavior of the
PostgreSQL database system when two or
more sessions try to access the same data at the same time. The
goals in that situation are to allow efficient access for all
sessions while maintaining strict data integrity. Every developer
of database applications should be familiar with the topics covered
in this chapter.
IntroductionMVCCPostgreSQL provides a rich set of tools
for developers to manage concurrent access to data. Internally,
data consistency is maintained by using a multiversion
model (Multiversion Concurrency Control, MVCC).
This means that while querying a database each transaction sees
a snapshot of data (a database version)
as it was some
time ago, regardless of the current state of the underlying data.
This protects the transaction from viewing inconsistent data that
could be caused by (other) concurrent transaction updates on the same
data rows, providing transaction isolation
for each database session. MVCC, by eschewing
explicit locking methodologies of traditional database systems,
minimizes lock contention in order to allow for reasonable
performance in multiuser environments.
The main advantage of using the MVCC model of
concurrency control rather than locking is that in
MVCC locks acquired for querying (reading) data
do not conflict with locks acquired for writing data, and so
reading never blocks writing and writing never blocks reading.
Table- and row-level locking facilities are also available in
PostgreSQL for applications that cannot
adapt easily to MVCC behavior. However, proper
use of MVCC will generally provide better
performance than locks. In addition, application-defined advisory
locks provide a mechanism for acquiring locks that are not tied
to a single transaction.
Transaction Isolationtransaction isolation
The SQL standard defines four levels of
transaction isolation in terms of three phenomena that must be
prevented between concurrent transactions. These undesirable
phenomena are:
dirty read
dirty read
A transaction reads data written by a concurrent uncommitted transaction.
nonrepeatable read
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
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.
transaction isolation level
The four transaction isolation levels and the corresponding
behaviors are described in .
SQL Transaction Isolation Levels
Isolation Level
Dirty Read
Nonrepeatable Read
Phantom Read
Read uncommitted
Possible
Possible
Possible
Read committed
Not possible
Possible
Possible
Repeatable read
Not possible
Not possible
Possible
Serializable
Not possible
Not possible
Not possible
In PostgreSQL, you can request any of the
four standard transaction isolation levels. But internally, there are
only two distinct isolation levels, which correspond to the levels Read
Committed and Serializable. When you select the level Read
Uncommitted you really get Read Committed, and when you select
Repeatable Read you really get Serializable, so the actual
isolation level might be stricter than what you select. This is
permitted by the SQL standard: the four isolation levels only
define which phenomena must not happen, they do not define which
phenomena must happen. The reason that PostgreSQL>
only provides two isolation levels is that this is the only
sensible way to map the standard isolation levels to the multiversion
concurrency control architecture. 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 .
Read Committed Isolation Leveltransaction isolation levelread committedRead 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 during execution of the first SELECT.
UPDATE, DELETE, 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.
Because of the above rule, 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
website.hits equaling 9 and
10:
BEGIN;
UPDATE website SET hits = hits + 1;
-- run from another session: DELETE FROM website WHERE hits = 10;
COMMIT;
The 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 10> but
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.
Serializable Isolation Leveltransaction isolation levelserializable
The Serializable isolation level provides the strictest transaction
isolation. This level emulates serial transaction execution,
as if transactions had been executed one after another, serially,
rather than concurrently. However, applications using this level must
be prepared to retry transactions due to serialization failures.
When a transaction is using the serializable level,
a SELECT query 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 different from Read Committed in that
a query in a serializable transaction
sees a snapshot as of the start of the transaction>,
not as of the start
of the current query 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.
(This behavior can be ideal for reporting applications.)
UPDATE, DELETE, 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
serializable 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 serializable 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 serializable transaction will be rolled back with the message
ERROR: could not serialize access due to concurrent update
because a serializable transaction cannot modify or lock rows changed by
other transactions after the serializable 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 Serializable mode provides a rigorous guarantee that each
transaction sees a wholly consistent view of the database. However,
the application has to be prepared to retry transactions when concurrent
updates make it impossible to sustain the illusion of serial execution.
Since the cost of redoing complex transactions can be significant,
serializable mode is recommended only when updating transactions contain logic
sufficiently complex that they might give wrong answers in Read
Committed mode. Most commonly, Serializable mode is necessary when
a transaction executes several successive commands that must see
identical views of the database.
Serializable Isolation versus True Serializabilityserializabilitypredicate locking
The intuitive meaning (and mathematical definition) of
serializable> execution is that any two successfully committed
concurrent transactions will appear to have executed strictly serially,
one after the other — although which one appeared to occur first might
not be predictable in advance. It is important to realize that forbidding
the undesirable behaviors listed in
is not sufficient to guarantee true serializability, and in fact
PostgreSQL's Serializable mode does
not guarantee serializable execution in this sense>. 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 class> = 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
class> = 1>. Then both transactions commit. None of
the listed undesirable behaviors have occurred, yet we have a result
that could not have occurred in either order serially. 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.
To guarantee true mathematical serializability, it is necessary for
a database system to enforce predicate locking>, which
means that a transaction cannot insert or modify a row that would
have matched the WHERE> condition of a query in another concurrent
transaction. For example, once transaction A has executed the query
SELECT ... WHERE class = 1>, a predicate-locking system
would forbid transaction B from inserting any new row with class 1
until A has committed.
Essentially, a predicate-locking system prevents phantom reads
by restricting what is written, whereas MVCC prevents them by
restricting what is read.
Such a locking system is complex to
implement and extremely expensive in execution, since every session must
be aware of the details of every query executed by every concurrent
transaction. And this large expense is mostly wasted, since in
practice most applications do not do the sorts of things that could
result in problems. (Certainly the example above is rather contrived
and unlikely to represent real software.) For these reasons,
PostgreSQL does not implement predicate
locking.
In cases where the possibility of non-serializable execution
is a real hazard, problems can be prevented by appropriate use of
explicit locking. Further discussion appears in the following
sections.
Explicit LockinglockPostgreSQL provides various lock modes
to control concurrent access to data in tables. These modes can
be used for application-controlled locking in situations where
MVCC does not give the desired behavior. Also,
most PostgreSQL commands automatically
acquire locks of appropriate modes to ensure that referenced
tables are not dropped or modified in incompatible ways while the
command executes. (For example, TRUNCATE> cannot safely be
executed concurrently with other operations on the same table, so it
obtains an exclusive lock on the table to enforce that.)
To examine a list of the currently outstanding locks in a database
server, use the
pg_locks
system view. For more information on monitoring the status of the lock
manager subsystem, refer to .
Table-Level LocksLOCK
The list below shows the available lock modes and the contexts in
which they are used automatically by
PostgreSQL. You can also acquire any
of these locks explicitly with the command .
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 (see ).
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 acquire
ACCESS EXCLUSIVE lock and later acquire
ACCESS 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,
an ACCESS EXCLUSIVE lock cannot be held by more than one
transaction at a time) while others are not self-conflicting (for example,
an ACCESS SHARE lock can be held by multiple transactions).
Table-level lock modesACCESS SHARE
Conflicts with the ACCESS EXCLUSIVE lock
mode only.
The SELECT command acquires a lock of this mode on
referenced tables. In general, any query that only reads> a table
and does not modify it will acquire this lock mode.
ROW SHARE
Conflicts with the EXCLUSIVE and
ACCESS EXCLUSIVE lock modes.
The SELECT FOR UPDATE and
SELECT FOR SHARE commands acquire a
lock of this mode on the target table(s) (in addition to
ACCESS SHARE locks on any other tables
that are referenced but not selected
).
ROW EXCLUSIVE
Conflicts with the SHARE, SHARE ROW
EXCLUSIVE, EXCLUSIVE, and
ACCESS EXCLUSIVE lock modes.
The commands UPDATE,
DELETE, and INSERT
acquire this lock mode on the target table (in addition to
ACCESS SHARE locks on any other referenced
tables). In general, this lock mode will be acquired by any
command that modifies data> in a table.
SHARE UPDATE EXCLUSIVE
Conflicts with the SHARE UPDATE EXCLUSIVE,
SHARE, SHARE ROW
EXCLUSIVE, EXCLUSIVE, and
ACCESS EXCLUSIVE lock modes.
This mode protects a table against
concurrent schema changes and VACUUM> runs.
Acquired by VACUUM (without ),
ANALYZE>, and CREATE INDEX CONCURRENTLY>.
SHARE
Conflicts with the ROW EXCLUSIVE,
SHARE UPDATE EXCLUSIVE, SHARE ROW
EXCLUSIVE, EXCLUSIVE, and
ACCESS EXCLUSIVE lock modes.
This mode protects a table against concurrent data changes.
Acquired by CREATE INDEX
(without ).
SHARE ROW EXCLUSIVE
Conflicts with the ROW EXCLUSIVE,
SHARE UPDATE EXCLUSIVE,
SHARE, SHARE ROW
EXCLUSIVE, EXCLUSIVE, and
ACCESS EXCLUSIVE lock modes.
Acquired by CREATE TRIGGER,
CREATE RULE (except for ON SELECT>
rules) and in some cases ALTER TABLE.
EXCLUSIVE
Conflicts with the ROW SHARE, ROW
EXCLUSIVE, SHARE UPDATE
EXCLUSIVE, SHARE, SHARE
ROW EXCLUSIVE, EXCLUSIVE, and
ACCESS EXCLUSIVE lock modes.
This mode allows only concurrent ACCESS SHARE locks,
i.e., only reads from the table can proceed in parallel with a
transaction holding this lock mode.
This lock mode is not automatically acquired on user tables by any
PostgreSQL command. However it is
acquired on certain system catalogs in some operations.
ACCESS EXCLUSIVE
Conflicts with locks of all modes (ACCESS
SHARE, ROW SHARE, ROW
EXCLUSIVE, SHARE UPDATE
EXCLUSIVE, SHARE, SHARE
ROW EXCLUSIVE, EXCLUSIVE, and
ACCESS EXCLUSIVE).
This mode guarantees that the
holder is the only transaction accessing the table in any way.
Acquired by the DROP TABLE,
TRUNCATE, REINDEX,
CLUSTER, and VACUUM FULL
commands, as well as most variants of ALTER TABLE>.
This is also the default lock mode for LOCK TABLE
statements that do not specify a mode explicitly.
Only an ACCESS EXCLUSIVE lock blocks a
SELECT (without )
statement.
Once acquired, a lock is normally held till end of 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 that ROLLBACK> cancels all effects of the
commands since the savepoint. The same holds for locks acquired within a
PL/pgSQL> exception block: an error escape from the block
releases locks acquired within it.
Row-Level Locks
In addition to table-level locks, there are row-level locks, which
can be exclusive or shared locks. An exclusive row-level lock on a
specific row is automatically acquired when the row is updated or
deleted. The lock is held until the transaction commits or rolls
back, just like table-level locks. Row-level locks do
not affect data querying; they block only writers to the same
row.
To acquire an exclusive row-level lock on a row without actually
modifying the row, select the row with SELECT FOR
UPDATE. Note that once the row-level lock is acquired,
the transaction can update the row multiple times without
fear of conflicts.
To acquire a shared row-level lock on a row, select the row with
SELECT FOR SHARE. A shared lock does not prevent
other transactions from acquiring the same shared lock. However,
no transaction is allowed to update, delete, or exclusively lock a
row on which any other transaction holds a shared lock. Any attempt
to do so will block until the shared lock(s) have been released.
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.
Deadlocksdeadlock
The use of explicit locking can increase the likelihood of
deadlocks>, 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 first UPDATE statement successfully
acquires a row-level lock on the specified row, so it succeeds in
updating that row. However, the second UPDATE
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).
Advisory LockslockadvisoryPostgreSQL provides a means for
creating locks that have application-defined meanings. These are
called advisory 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. Once acquired, an
advisory lock is held until explicitly released or the session ends.
Unlike standard locks, advisory locks 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. The same lock can be acquired multiple times by
its owning process: for each lock request there must be a corresponding
unlock request before the lock is actually released. (If a session
already holds a given lock, additional requests will always succeed, even
if other sessions are awaiting the lock.) Like all locks in
PostgreSQL, a complete list of advisory
locks currently held by any session can be found in the
pg_locks
system view.
Advisory locks are allocated out of a shared memory pool whose size
is defined by the configuration variables
and
.
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.
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 MVCC bloat, and are automatically
cleaned up by the server at the end of the session.
In certain cases using this advisory locking method, especially in queries
involving explicit ordering and LIMIT> 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; -- ok
SELECT 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 the
LIMIT> 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 in
pg_locks.
The functions provided to manipulate advisory locks are described in
.
Data Consistency Checks at the Application Level
Because readers in PostgreSQL
do not lock data, regardless of
transaction isolation level, data read by one transaction can be
overwritten by another concurrent transaction. In other words,
if a row is returned by SELECT it doesn't mean that
the row is still current at the instant it is returned (i.e., sometime
after the current query began). The row might have been modified or
deleted by an already-committed transaction that committed after
the SELECT started.
Even if the row is still valid now>, it could be changed or
deleted
before the current transaction does a commit or rollback.
Another way to think about it is that each
transaction sees a snapshot of the database contents, and concurrently
executing transactions might very well see different snapshots. So the
whole concept of now is somewhat ill-defined anyway.
This is not normally
a big problem if the client applications are isolated from each other,
but if the clients can communicate via channels outside the database
then serious confusion might ensue.
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.
Global validity checks require extra thought under 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 serializable transaction will give an accurate picture of only the
effects of transactions that committed before the serializable transaction
started — but one might legitimately wonder whether the answer is still
relevant by the time it is delivered. If the serializable 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 Serializable
mode be careful to obtain
locks before performing queries. A lock obtained by a
serializable 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 serializable 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.
Locking and Indexesindexlocks
Though PostgreSQL
provides nonblocking read/write access to table
data, nonblocking read/write access is not currently offered for every
index access method implemented
in PostgreSQL.
The various index types are handled as follows:
B-tree and GiST indexes
Short-term share/exclusive page-level locks are used for
read/write access. Locks are released immediately after each
index row is fetched or inserted. These index types provide
the highest concurrency without deadlock conditions.
Hash indexes
Share/exclusive hash-bucket-level locks are used for read/write
access. Locks are released after the whole bucket is processed.
Bucket-level locks provide better concurrency than index-level
ones, but deadlock is possible since the locks are held longer
than one index operation.
GIN indexes
Short-term share/exclusive page-level locks are used for
read/write access. Locks are released immediately after each
index row is fetched or inserted. But note that insertion of a
GIN-indexed value usually produces several index key insertions
per row, so GIN might do substantial work for a single value's
insertion.
Currently, B-tree indexes offer the best performance for concurrent
applications; since they also have more features than hash
indexes, they are the recommended index type for concurrent
applications that need to index scalar data. When dealing with
non-scalar data, B-trees are not useful, and GiST or GIN indexes should
be used instead.