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23.1.1. Vacuuming Basics

23.1.2. Recovering Disk Space

23.1.3. Updating Planner Statistics

23.1.4. Updating The Visibility Map

23.1.5. Preventing Transaction ID Wraparound Failures

23.1.6. The Autovacuum Daemon

Postgres Pro'sVACUUM command has to process each table on a regular basis for several reasons:

Each of these reasons dictates performingVACUUM operations of varying frequency and scope, as explained in the following subsections.

There are two variants ofVACUUM: standardVACUUM andVACUUM FULL.VACUUM FULL can reclaim more disk space but runs much more slowly. Also, the standard form ofVACUUM can run in parallel with production database operations. (Commands such asSELECT,INSERT,UPDATE, andDELETE will continue to function normally, though you will not be able to modify the definition of a table with commands such asALTER TABLE while it is being vacuumed.)VACUUM FULL requires anACCESS EXCLUSIVE lock on the table it is working on, and therefore cannot be done in parallel with other use of the table. Generally, therefore, administrators should strive to use standardVACUUM and avoidVACUUM FULL.

VACUUM creates a substantial amount of I/O traffic, which can cause poor performance for other active sessions. There are configuration parameters that can be adjusted to reduce the performance impact of background vacuuming — seeSection 18.4.4.

InPostgres Pro, anUPDATE orDELETE of a row does not immediately remove the old version of the row. This approach is necessary to gain the benefits of multiversion concurrency control (MVCC, seeChapter 13): the row version must not be deleted while it is still potentially visible to other transactions. But eventually, an outdated or deleted row version is no longer of interest to any transaction. The space it occupies must then be reclaimed for reuse by new rows, to avoid unbounded growth of disk space requirements. This is done by runningVACUUM.

The standard form ofVACUUM removes dead row versions in tables and indexes and marks the space available for future reuse. However, it will not return the space to the operating system, except in the special case where one or more pages at the end of a table become entirely free and an exclusive table lock can be easily obtained. In contrast,VACUUM FULL actively compacts tables by writing a complete new version of the table file with no dead space. This minimizes the size of the table, but can take a long time. It also requires extra disk space for the new copy of the table, until the operation completes.

The usual goal of routine vacuuming is to do standardVACUUMs often enough to avoid needingVACUUM FULL. The autovacuum daemon attempts to work this way, and in fact will never issueVACUUM FULL. In this approach, the idea is not to keep tables at their minimum size, but to maintain steady-state usage of disk space: each table occupies space equivalent to its minimum size plus however much space gets used up between vacuum runs. AlthoughVACUUM FULL can be used to shrink a table back to its minimum size and return the disk space to the operating system, there is not much point in this if the table will just grow again in the future. Thus, moderately-frequent standardVACUUM runs are a better approach than infrequentVACUUM FULL runs for maintaining heavily-updated tables.

Some administrators prefer to schedule vacuuming themselves, for example doing all the work at night when load is low. The difficulty with doing vacuuming according to a fixed schedule is that if a table has an unexpected spike in update activity, it may get bloated to the point thatVACUUM FULL is really necessary to reclaim space. Using the autovacuum daemon alleviates this problem, since the daemon schedules vacuuming dynamically in response to update activity. It is unwise to disable the daemon completely unless you have an extremely predictable workload. One possible compromise is to set the daemon's parameters so that it will only react to unusually heavy update activity, thus keeping things from getting out of hand, while scheduledVACUUMs are expected to do the bulk of the work when the load is typical.

For those not using autovacuum, a typical approach is to schedule a database-wideVACUUM once a day during a low-usage period, supplemented by more frequent vacuuming of heavily-updated tables as necessary. (Some installations with extremely high update rates vacuum their busiest tables as often as once every few minutes.) If you have multiple databases in a cluster, don't forget toVACUUM each one; the programvacuumdb might be helpful.

ThePostgres Pro query planner relies on statistical information about the contents of tables in order to generate good plans for queries. These statistics are gathered by theANALYZE command, which can be invoked by itself or as an optional step inVACUUM. It is important to have reasonably accurate statistics, otherwise poor choices of plans might degrade database performance.

The autovacuum daemon, if enabled, will automatically issueANALYZE commands whenever the content of a table has changed sufficiently. However, administrators might prefer to rely on manually-scheduledANALYZE operations, particularly if it is known that update activity on a table will not affect the statistics of“interesting” columns. The daemon schedulesANALYZE strictly as a function of the number of rows inserted or updated; it has no knowledge of whether that will lead to meaningful statistical changes.

Tuples changed in partitions and inheritance children do not trigger analyze on the parent table. If the parent table is empty or rarely changed, it may never be processed by autovacuum, and the statistics for the inheritance tree as a whole won't be collected. It is necessary to runANALYZE on the parent table manually in order to keep the statistics up to date.

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 might 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, atimestamp 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 might receive changes just as often, but the statistical distribution of its values probably changes relatively slowly.

It is possible to runANALYZE 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, it is usually best to just analyze the entire database, because it is a fast operation.ANALYZE uses a statistically random sampling of the rows of a table rather than reading every single row.

Vacuum maintains avisibility map for each table to keep track of which pages contain only tuples that are known to be visible to all active transactions (and all future transactions, until the page is again modified). This has two purposes. First, vacuum itself can skip such pages on the next run, since there is nothing to clean up.

Second, it allowsPostgres Pro to answer some queries using only the index, without reference to the underlying table. SincePostgres Pro indexes don't contain tuple visibility information, a normal index scan fetches the heap tuple for each matching index entry, to check whether it should be seen by the current transaction. Anindex-only scan, on the other hand, checks the visibility map first. If it's known that all tuples on the page are visible, the heap fetch can be skipped. This is most useful on large data sets where the visibility map can prevent disk accesses. The visibility map is vastly smaller than the heap, so it can easily be cached even when the heap is very large.

Postgres Pro'sMVCC transaction semantics depend on being able to compare transaction ID (XID) numbers: a row version with an insertion XID greater 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) a cluster that runs for a long time (more than 4 billion transactions) would suffertransaction 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 output become invisible. In short, catastrophic data loss. (Actually the data is still there, but that's cold comfort if you cannot get at it.) To avoid this, it is necessary to vacuum every table in every database at least once every two billion transactions.

The reason that periodic vacuuming solves the problem is thatVACUUM will mark rows asfrozen, indicating that they were inserted by a transaction that committed sufficiently far in the past that the effects of the inserting transaction are certain to be visible to all current and future transactions. Normal XIDs are compared using modulo-2³² 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 row version has been created with a particular normal XID, the row version will appear to be“in the past” for the next two billion transactions, no matter which normal XID we are talking about. If the row version still exists after more than two billion transactions, it will suddenly appear to be in the future. To prevent this,Postgres Pro reserves a special XID,FrozenTransactionId, which does not follow the normal XID comparison rules and is always considered older than every normal XID. Frozen row versions are treated as if the inserting XID wereFrozenTransactionId, so that they will appear to be“in the past” to all normal transactions regardless of wraparound issues, and so such row versions will be valid until deleted, no matter how long that is.

vacuum_freeze_min_age controls how old an XID value has to be before rows bearing that XID will be frozen. Increasing this setting may avoid unnecessary work if the rows that would otherwise be frozen will soon be modified again, but decreasing this setting increases the number of transactions that can elapse before the table must be vacuumed again.

VACUUM uses thevisibility map to determine which pages of a table must be scanned. Normally, it will skip pages that don't have any dead row versions even if those pages might still have row versions with old XID values. Therefore, normalVACUUMs won't always freeze every old row version in the table. Periodically,VACUUM will perform anaggressive vacuum, skipping only those pages which contain neither dead rows nor any unfrozen XID or MXID values.vacuum_freeze_table_age controls whenVACUUM does that: all-visible but not all-frozen pages are scanned if the number of transactions that have passed since the last such scan is greater thanvacuum_freeze_table_age minusvacuum_freeze_min_age. Settingvacuum_freeze_table_age to 0 forcesVACUUM to use this more aggressive strategy for all scans.

The maximum time that a table can go unvacuumed is two billion transactions minus thevacuum_freeze_min_age value at the time of the last aggressive vacuum. If it were to go unvacuumed for longer than that, data loss could result. To ensure that this does not happen, autovacuum is invoked on any table that might contain unfrozen rows with XIDs older than the age specified by the configuration parameterautovacuum_freeze_max_age. (This will happen even if autovacuum is disabled.)

This implies that if a table is not otherwise vacuumed, autovacuum will be invoked on it approximately once everyautovacuum_freeze_max_age minusvacuum_freeze_min_age transactions. For tables that are regularly vacuumed for space reclamation purposes, this is of little importance. However, for static tables (including tables that receive inserts, but no updates or deletes), there is no need to vacuum for space reclamation, so it can be useful to try to maximize the interval between forced autovacuums on very large static tables. Obviously one can do this either by increasingautovacuum_freeze_max_age or decreasingvacuum_freeze_min_age.

The effective maximum forvacuum_freeze_table_age is 0.95 *autovacuum_freeze_max_age; a setting higher than that will be capped to the maximum. A value higher thanautovacuum_freeze_max_age wouldn't make sense because an anti-wraparound autovacuum would be triggered at that point anyway, and the 0.95 multiplier leaves some breathing room to run a manualVACUUM before that happens. As a rule of thumb,vacuum_freeze_table_age should be set to a value somewhat belowautovacuum_freeze_max_age, leaving enough gap so that a regularly scheduledVACUUM or an autovacuum triggered by normal delete and update activity is run in that window. Setting it too close could lead to anti-wraparound autovacuums, even though the table was recently vacuumed to reclaim space, whereas lower values lead to more frequent aggressive vacuuming.

The sole disadvantage of increasingautovacuum_freeze_max_age (andvacuum_freeze_table_age along with it) is that thepg_xact andpg_commit_ts subdirectories of the database cluster will take more space, because it must store the commit status and (iftrack_commit_timestamp is enabled) timestamp of all transactions back to theautovacuum_freeze_max_age horizon. The commit status uses two bits per transaction, so ifautovacuum_freeze_max_age is set to its maximum allowed value of two billion,pg_xact can be expected to grow to about half a gigabyte andpg_commit_ts to about 20GB. If this is trivial compared to your total database size, settingautovacuum_freeze_max_age to its maximum allowed value is recommended. Otherwise, set it depending on what you are willing to allow forpg_xact andpg_commit_ts storage. (The default, 200 million transactions, translates to about 50MB ofpg_xact storage and about 2GB ofpg_commit_ts storage.)

One disadvantage of decreasingvacuum_freeze_min_age is that it might causeVACUUM to do useless work: freezing a row version is a waste of time if the row is modified soon thereafter (causing it to acquire a new XID). So the setting should be large enough that rows are not frozen until they are unlikely to change any more.

To track the age of the oldest unfrozen XIDs in a database,VACUUM stores XID statistics in the system tablespg_class andpg_database. In particular, therelfrozenxid column of a table'spg_class row contains the freeze cutoff XID that was used by the last aggressiveVACUUM for that table. All rows inserted by transactions with XIDs older than this cutoff XID are guaranteed to have been frozen. Similarly, thedatfrozenxid column of a database'spg_database row is a lower bound on the unfrozen XIDs appearing in that database — it is just the minimum of the per-tablerelfrozenxid values within the database. A convenient way to examine this information is to execute queries such as:

SELECT c.oid::regclass as table_name,       greatest(age(c.relfrozenxid),age(t.relfrozenxid)) as ageFROM pg_class cLEFT JOIN pg_class t ON c.reltoastrelid = t.oidWHERE c.relkind IN ('r', 'm');SELECT datname, age(datfrozenxid) FROM pg_database;

Theage column measures the number of transactions from the cutoff XID to the current transaction's XID.

VACUUM normally only scans pages that have been modified since the last vacuum, butrelfrozenxid can only be advanced when every page of the table that might contain unfrozen XIDs is scanned. This happens whenrelfrozenxid is more thanvacuum_freeze_table_age transactions old, whenVACUUM'sFREEZE option is used, or when all pages that are not already all-frozen happen to require vacuuming to remove dead row versions. WhenVACUUM scans every page in the table that is not already all-frozen, it should setage(relfrozenxid) to a value just a little more than thevacuum_freeze_min_age setting that was used (more by the number of transactions started since theVACUUM started). If norelfrozenxid-advancingVACUUM is issued on the table untilautovacuum_freeze_max_age is reached, an autovacuum will soon be forced for the table.

If for some reason autovacuum fails to clear old XIDs from a table, the system will begin to emit warning messages like this when the database's oldest XIDs reach eleven million transactions from the wraparound point:

WARNING:  database "mydb" must be vacuumed within 10985967 transactionsHINT:  To avoid a database shutdown, execute a database-wide VACUUM in that database.

(A manualVACUUM should fix the problem, as suggested by the hint; but note that theVACUUM must be performed by a superuser, else it will fail to process system catalogs and thus not be able to advance the database'sdatfrozenxid.) If these warnings are ignored, the system will shut down and refuse to start any new transactions once there are fewer than 1 million transactions left until wraparound:

ERROR:  database is not accepting commands to avoid wraparound data loss in database "mydb"HINT:  Stop the postmaster and vacuum that database in single-user mode.

The 1-million-transaction safety margin exists to let the administrator recover without data loss, by manually executing the requiredVACUUM commands. However, since the system will not execute commands once it has gone into the safety shutdown mode, the only way to do this is to stop the server and start the server in single-user mode to executeVACUUM. The shutdown mode is not enforced in single-user mode. See thepostgres reference page for details about using single-user mode.

Postgres Pro has an optional but highly recommended feature calledautovacuum, whose purpose is to automate the execution ofVACUUM andANALYZE commands. When enabled, autovacuum checks for tables that have had a large number of inserted, updated or deleted tuples. These checks use the statistics collection facility; therefore, autovacuum cannot be used unlesstrack_counts is set totrue. In the default configuration, autovacuuming is enabled and the related configuration parameters are appropriately set.

The“autovacuum daemon” actually consists of multiple processes. There is a persistent daemon process, called theautovacuum launcher, which is in charge of startingautovacuum worker processes for all databases. The launcher will distribute the work across time, attempting to start one worker within each database everyautovacuum_naptime seconds. (Therefore, if the installation hasN databases, a new worker will be launched everyautovacuum_naptime/N seconds.) A maximum ofautovacuum_max_workers worker processes are allowed to run at the same time. If there are more thanautovacuum_max_workers databases to be processed, the next database will be processed as soon as the first worker finishes. Each worker process will check each table within its database and executeVACUUM and/orANALYZE as needed.log_autovacuum_min_duration can be set to monitor autovacuum workers' activity.

If several large tables all become eligible for vacuuming in a short amount of time, all autovacuum workers might become occupied with vacuuming those tables for a long period. This would result in other tables and databases not being vacuumed until a worker becomes available. There is no limit on how many workers might be in a single database, but workers do try to avoid repeating work that has already been done by other workers. Note that the number of running workers does not count towardsmax_connections orsuperuser_reserved_connections limits.

Tables whoserelfrozenxid value is more thanautovacuum_freeze_max_age transactions old are always vacuumed (this also applies to those tables whose freeze max age has been modified via storage parameters; see below). Otherwise, if the number of tuples obsoleted since the lastVACUUM exceeds the“vacuum threshold”, the table is vacuumed. The vacuum threshold is defined as:

vacuum threshold = vacuum base threshold + vacuum scale factor * number of tuples

where the vacuum base threshold isautovacuum_vacuum_threshold, the vacuum scale factor isautovacuum_vacuum_scale_factor, and the number of tuples ispg_class.reltuples. The number of obsolete tuples is obtained from the statistics collector; it is a semi-accurate count updated by eachUPDATE andDELETE operation. (It is only semi-accurate because some information might be lost under heavy load.) If therelfrozenxid value of the table is more thanvacuum_freeze_table_age transactions old, an aggressive vacuum is performed to freeze old tuples and advancerelfrozenxid; otherwise, only pages that have been modified since the last vacuum are scanned.

For analyze, a similar condition is used: the threshold, defined as:

analyze threshold = analyze base threshold + analyze scale factor * number of tuples

is compared to the total number of tuples inserted, updated, or deleted since the lastANALYZE.

Partitioned tables do not directly store tuples and consequently are not processed by autovacuum. (Autovacuum does process table partitions just like other tables.) Unfortunately, this means that autovacuum does not runANALYZE on partitioned tables, and this can cause suboptimal plans for queries that reference partitioned table statistics. You can work around this problem by manually runningANALYZE on partitioned tables when they are first populated, and again whenever the distribution of data in their partitions changes significantly.

Temporary tables cannot be accessed by autovacuum. Therefore, appropriate vacuum and analyze operations should be performed via session SQL commands.

The default thresholds and scale factors are taken frompostgresql.conf, but it is possible to override them (and many other autovacuum control parameters) on a per-table basis; seeStorage Parameters for more information. If a setting has been changed via a table's storage parameters, that value is used when processing that table; otherwise the global settings are used. SeeSection 18.10 for more details on the global settings.

When multiple workers are running, the autovacuum cost delay parameters (seeSection 18.4.4) are“balanced” among all the running workers, so that the total I/O impact on the system is the same regardless of the number of workers actually running. However, any workers processing tables whose per-tableautovacuum_vacuum_cost_delay orautovacuum_vacuum_cost_limit storage parameters have been set are not considered in the balancing algorithm.

Autovacuum workers generally don't block other commands. If a process attempts to acquire a lock that conflicts with theSHARE UPDATE EXCLUSIVE lock held by autovacuum, lock acquisition will interrupt the autovacuum. For conflicting lock modes, seeTable 13.2. However, if the autovacuum is running to prevent transaction ID wraparound (i.e., the autovacuum query name in thepg_stat_activity view ends with(to prevent wraparound)), the autovacuum is not automatically interrupted.