PostgreSQL's VACUUM command
must be run on a regular basis for several reasons:
-
To recover disk space occupied by updated or deleted rows.
-
To update data statistics used by the PostgreSQL
query planner.
-
To protect against loss of very old data due to transaction ID
wraparound.
The frequency and scope of VACUUMs performed for each of these
reasons will vary depending on the needs of each installation. Therefore, database
administrators must understand these issues and develop an appropriate maintenance strategy.
This section concentrates on explaining the high-level issues; for details about command
syntax and so on, see the VACUUM command reference page.
Beginning in PostgreSQL 7.2, the standard form of VACUUM can run in parallel with normal database operations (selects,
inserts, updates, deletes, but not changes to table schemas). Routine vacuuming is therefore
not nearly as intrusive as it was in prior releases, and it's not as critical to try to
schedule it at low-usage times of day.
In normal PostgreSQL operation, an UPDATE
or DELETE of a row does not immediately remove the old tuple (version of the row). This approach is necessary to gain the
benefits of multiversion concurrency control (see the PostgreSQL 7.3 User's Guide):
the tuple must not be deleted while it is still potentially visible to other transactions.
But eventually, an outdated or deleted tuple is no longer of interest to any transaction.
The space it occupies must be reclaimed for reuse by new tuples, to avoid infinite growth
of disk space requirements. This is done by running VACUUM.
Clearly, a table that receives frequent updates or deletes will need to be vacuumed
more often than tables that are seldom updated. It may be useful to set up periodic cron tasks that vacuum only selected tables, skipping tables
that are known not to change often. This is only likely to be helpful if you have both
large heavily-updated tables and large seldom-updated tables --- the extra cost of
vacuuming a small table isn't enough to be worth worrying about.
The standard form of VACUUM is best used with the goal of
maintaining a fairly level steady-state usage of disk space. The standard form finds old
tuples and makes their space available for re-use within the table, but it does not try
very hard to shorten the table file and return disk space to the operating system. If you
need to return disk space to the operating system you can use VACUUM
FULL --- but what's the point of releasing disk space that will only have to be
allocated again soon? Moderately frequent standard VACUUMs are a
better approach than infrequent VACUUM FULLs for maintaining
heavily-updated tables.
Recommended practice for most sites is to schedule a database-wide VACUUM
once a day at a low-usage time of day, supplemented by more frequent vacuuming of
heavily-updated tables if necessary. (If you have multiple databases in an installation,
don't forget to vacuum each one; the vacuumdb script may be
helpful.) Use plain VACUUM, not VACUUM FULL,
for routine vacuuming for space recovery.
VACUUM FULL is recommended for cases where you know you have
deleted the majority of tuples in a table, so that the steady-state size of the table can
be shrunk substantially with VACUUM FULL's more aggressive
approach.
If you have a table whose contents are deleted completely every so often, consider
doing it with TRUNCATE rather than using DELETE
followed by VACUUM.
The PostgreSQL query planner relies on statistical
information about the contents of tables in order to generate good plans for queries.
These statistics are gathered by the ANALYZE command, which can
be invoked by itself or as an optional step in VACUUM. It is
important to have reasonably accurate statistics, otherwise poor choices of plans may
degrade database performance.
As with vacuuming for space recovery, frequent updates of statistics are more useful
for heavily-updated tables than for seldom-updated ones. But even for a heavily-updated
table, there may be no need for statistics updates if the statistical distribution of the
data is not changing much. A simple rule of thumb is to think about how much the minimum
and maximum values of the columns in the table change. For example, a timestamp
column that contains the time of row update will have a constantly-increasing maximum
value as rows are added and updated; such a column will probably need more frequent
statistics updates than, say, a column containing URLs for pages accessed on a website.
The URL column may receive changes just as often, but the statistical distribution of its
values probably changes relatively slowly.
It is possible to run ANALYZE on specific tables and even just
specific columns of a table, so the flexibility exists to update some statistics more
frequently than others if your application requires it. In practice, however, the
usefulness of this feature is doubtful. Beginning in PostgreSQL
7.2, ANALYZE is a fairly fast operation even on large tables,
because it uses a statistical random sampling of the rows of a table rather than reading
every single row. So it's probably much simpler to just run it over the whole database
every so often.
Tip: Although per-column tweaking of ANALYZE
frequency may not be very productive, you may well find it worthwhile to do per-column
adjustment of the level of detail of the statistics collected by ANALYZE.
Columns that are heavily used in WHERE clauses and have
highly irregular data distributions may require a finer-grain data histogram than
other columns. See ALTER TABLE SET STATISTICS.
Recommended practice for most sites is to schedule a database-wide ANALYZE
once a day at a low-usage time of day; this can usefully be combined with a nightly VACUUM. However, sites with relatively slowly changing table
statistics may find that this is overkill, and that less-frequent ANALYZE
runs are sufficient.
PostgreSQL's MVCC transaction semantics depend on
being able to compare transaction ID (XID) numbers: a tuple with
an insertion XID newer than the current transaction's XID is "in
the future" and should not be visible to the current transaction. But since
transaction IDs have limited size (32 bits at this writing) an installation that runs for
a long time (more than 4 billion transactions) will suffer transaction
ID wraparound: the XID counter wraps around to zero, and all of a sudden transactions
that were in the past appear to be in the future --- which means their outputs become
invisible. In short, catastrophic data loss. (Actually the data is still there, but that's
cold comfort if you can't get at it.)
Prior to PostgreSQL 7.2, the only defense against XID
wraparound was to re-initdb at least every 4 billion
transactions. This of course was not very satisfactory for high-traffic sites, so a better
solution has been devised. The new approach allows an installation to remain up
indefinitely, without initdb or any sort of restart. The price is
this maintenance requirement: every table in
the database must be vacuumed at least once every billion transactions.
In practice this isn't an onerous requirement, but since the consequences of failing to
meet it can be complete data loss (not just wasted disk space or slow performance), some
special provisions have been made to help database administrators keep track of the time
since the last VACUUM. The remainder of this section gives the
details.
The new approach to XID comparison distinguishes two special XIDs, numbers 1 and 2 (BootstrapXID and FrozenXID). These two XIDs
are always considered older than every normal XID. Normal XIDs (those greater than 2) are
compared using modulo-231 arithmetic. This means that for every normal XID,
there are two billion XIDs that are "older" and two
billion that are "newer"; another way to say it is
that the normal XID space is circular with no endpoint. Therefore, once a tuple has been
created with a particular normal XID, the tuple will appear to be "in
the past" for the next two billion transactions, no matter which normal XID we
are talking about. If the tuple still exists after more than two billion transactions, it
will suddenly appear to be in the future. To prevent data loss, old tuples must be
reassigned the XID FrozenXID sometime before they reach the
two-billion-transactions-old mark. Once they are assigned this special XID, they will
appear to be "in the past" to all normal transactions
regardless of wraparound issues, and so such tuples will be good until deleted, no matter
how long that is. This reassignment of XID is handled by VACUUM.
VACUUM's normal policy is to reassign FrozenXID
to any tuple with a normal XID more than one billion transactions in the past. This policy
preserves the original insertion XID until it is not likely to be of interest anymore (in
fact, most tuples will probably live and die without ever being "frozen").
With this policy, the maximum safe interval between VACUUMs of
any table is exactly one billion transactions: if you wait longer, it's possible that a
tuple that was not quite old enough to be reassigned last time is now more than two
billion transactions old and has wrapped around into the future --- i.e., is lost to you.
(Of course, it'll reappear after another two billion transactions, but that's no help.)
Since periodic VACUUMs are needed anyway for the reasons
described earlier, it's unlikely that any table would not be vacuumed for as long as a
billion transactions. But to help administrators ensure this constraint is met, VACUUM stores transaction ID statistics in the system table pg_database. In particular, the datfrozenxid
field of a database's pg_database row is updated at the
completion of any database-wide vacuum operation (i.e., VACUUM
that does not name a specific table). The value stored in this field is the freeze cutoff
XID that was used by that VACUUM command. All normal XIDs older
than this cutoff XID are guaranteed to have been replaced by FrozenXID
within that database. A convenient way to examine this information is to execute the query
SELECT datname, age(datfrozenxid) FROM pg_database;
The age column measures the number of transactions from the
cutoff XID to the current transaction's XID.
With the standard freezing policy, the age column will start
at one billion for a freshly-vacuumed database. When the age
approaches two billion, the database must be vacuumed again to avoid risk of wraparound
failures. Recommended practice is to vacuum each database at least once every
half-a-billion (500 million) transactions, so as to provide plenty of safety margin. To
help meet this rule, each database-wide VACUUM automatically
delivers a warning if there are any pg_database entries showing
an age of more than 1.5 billion transactions, for example:
play=# vacuum;
WARNING: Some databases have not been vacuumed in 1613770184 transactions.
Better vacuum them within 533713463 transactions,
or you may have a wraparound failure.
VACUUM
VACUUM with the FREEZE option uses a
more aggressive freezing policy: tuples are frozen if they are old enough to be considered
good by all open transactions. In particular, if a VACUUM FREEZE
is performed in an otherwise-idle database, it is guaranteed that all tuples in that database will be frozen. Hence, as long as
the database is not modified in any way, it will not need subsequent vacuuming to avoid
transaction ID wraparound problems. This technique is used by initdb
to prepare the template0 database. It should also be used to
prepare any user-created databases that are to be marked datallowconn
= false in pg_database, since there
isn't any convenient way to vacuum a database that you can't connect to. Note that VACUUM's automatic warning message about unvacuumed databases will
ignore pg_database entries with datallowconn
= false, so as to avoid giving false warnings about these
databases; therefore it's up to you to ensure that such databases are frozen correctly.