25.1. Routine Vacuuming

25.1.1. Vacuuming Basics
25.1.2. Recovering Disk Space
25.1.3. Updating Planner Statistics
25.1.4. Updating the Visibility Map
25.1.5. Freezing tuples
25.1.6. The Autovacuum Daemon

PostgreSQL databases require periodic maintenance known as vacuuming. For many installations, it is sufficient to let vacuuming be performed by the autovacuum daemon, which is described in Section 25.1.6. You might need to adjust the autovacuuming parameters described there to obtain best results for your situation. Some database administrators will want to supplement or replace the daemon's activities with manually-managed VACUUM commands, which typically are executed according to a schedule by cron or Task Scheduler scripts. To set up manually-managed vacuuming properly, it is essential to understand the issues discussed in the next few subsections. Administrators who rely on autovacuuming may still wish to skim this material to help them understand and adjust autovacuuming.

25.1.1. Vacuuming Basics

PostgreSQL's VACUUM command has to process each table on a regular basis for several reasons:

  1. To recover or reuse disk space occupied by updated or deleted rows.
  2. To update data statistics used by the PostgreSQL query planner.
  3. To update the visibility map, which speeds up index-only scans.
  4. To protect against loss of very old data due to transaction ID wraparound or multixact ID wraparound.

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

There are two variants of VACUUM: standard VACUUM and VACUUM FULL. VACUUM FULL can reclaim more disk space but runs much more slowly. Also, the standard form of VACUUM can run in parallel with production database operations. (Commands such as SELECT, INSERT, UPDATE, and DELETE will continue to function normally, though you will not be able to modify the definition of a table with commands such as ALTER TABLE while it is being vacuumed.) VACUUM FULL requires an ACCESS 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 standard VACUUM and avoid VACUUM 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 — see Section 20.4.4.

25.1.2. Recovering Disk Space

In PostgreSQL, an UPDATE or DELETE 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, see Chapter 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 running VACUUM.

The standard form of VACUUM 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 standard VACUUMs often enough to avoid needing VACUUM FULL. The autovacuum daemon attempts to work this way, and in fact will never issue VACUUM 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. Although VACUUM 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 standard VACUUM runs are a better approach than infrequent VACUUM 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 that VACUUM 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 scheduled VACUUMs 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-wide VACUUM 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 to VACUUM each one; the program vacuumdb might be helpful.


Plain VACUUM may not be satisfactory when a table contains large numbers of dead row versions as a result of massive update or delete activity. If you have such a table and you need to reclaim the excess disk space it occupies, you will need to use VACUUM FULL, or alternatively CLUSTER or one of the table-rewriting variants of ALTER TABLE. These commands rewrite an entire new copy of the table and build new indexes for it. All these options require an ACCESS EXCLUSIVE lock. Note that they also temporarily use extra disk space approximately equal to the size of the table, since the old copies of the table and indexes can't be released until the new ones are complete.


If you have a table whose entire contents are deleted on a periodic basis, consider doing it with TRUNCATE rather than using DELETE followed by VACUUM. TRUNCATE removes the entire content of the table immediately, without requiring a subsequent VACUUM or VACUUM FULL to reclaim the now-unused disk space. The disadvantage is that strict MVCC semantics are violated.

25.1.3. Updating Planner Statistics

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 might degrade database performance.

The autovacuum daemon, if enabled, will automatically issue ANALYZE commands whenever the content of a table has changed sufficiently. However, administrators might prefer to rely on manually-scheduled ANALYZE operations, particularly if it is known that update activity on a table will not affect the statistics of interesting columns. The daemon schedules ANALYZE 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 run ANALYZE 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, 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 might 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, 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.


Although per-column tweaking of ANALYZE frequency might not be very productive, you might 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 might require a finer-grain data histogram than other columns. See ALTER TABLE SET STATISTICS, or change the database-wide default using the default_statistics_target configuration parameter.

Also, by default there is limited information available about the selectivity of functions. However, if you create a statistics object or an expression index that uses a function call, useful statistics will be gathered about the function, which can greatly improve query plans that use the expression index.


The autovacuum daemon does not issue ANALYZE commands for foreign tables, since it has no means of determining how often that might be useful. If your queries require statistics on foreign tables for proper planning, it's a good idea to run manually-managed ANALYZE commands on those tables on a suitable schedule.


The autovacuum daemon does not issue ANALYZE commands for partitioned tables. Inheritance parents will only be analyzed if the parent itself is changed - changes to child tables do not trigger autoanalyze on the parent table. If your queries require statistics on parent tables for proper planning, it is necessary to periodically run a manual ANALYZE on those tables to keep the statistics up to date.

25.1.4. Updating the Visibility Map

Vacuum maintains a visibility 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 allows PostgreSQL to answer some queries using only the index, without reference to the underlying table. Since PostgreSQL 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. An index-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.

25.1.5. Freezing tuples

VACUUM freezes a page's tuples (by processing the tuple header fields described in Section 73.6.1) as a way of avoiding long term dependencies on transaction status metadata referenced therein. Heap pages that only contain frozen tuples are suitable for long term storage. Larger databases are often mostly comprised of cold data that is modified very infrequently, plus a relatively small amount of hot data that is updated far more frequently. VACUUM applies a variety of techniques that allow it to concentrate most of its efforts on hot data. Managing the 32-bit Transaction ID address space

PostgreSQL's MVCC 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 the on-disk representation of transaction IDs is only 32-bits, the system is incapable of representing distances between any two XIDs that exceed about 2 billion transaction IDs.

One of the purposes of periodic vacuuming is to manage the Transaction Id address space. VACUUM will mark rows as frozen, 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. There is, in effect, an infinite distance between a frozen transaction ID and any unfrozen transaction ID. This allows the on-disk representation of transaction IDs to recycle the 32-bit address space efficiently.

To track the age of the oldest unfrozen XIDs in a database, VACUUM stores XID statistics in the system tables pg_class and pg_database. In particular, the relfrozenxid column of a table's pg_class row contains the oldest remaining unfrozen XID at the end of the most recent VACUUM. All rows inserted by transactions older than this cutoff XID are guaranteed to have been frozen. Similarly, the datfrozenxid column of a database's pg_database row is a lower bound on the unfrozen XIDs appearing in that database — it is just the minimum of the per-table relfrozenxid 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 age
FROM pg_class c
LEFT JOIN pg_class t ON c.reltoastrelid = t.oid
WHERE c.relkind IN ('r', 'm');

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. Managing the 32-bit MultiXactId address space

Multixact IDs are used to support row locking by multiple transactions. Since there is only limited space in a tuple header to store lock information, that information is encoded as a multiple transaction ID, or multixact ID for short, whenever there is more than one transaction concurrently locking a row. Information about which transaction IDs are included in any particular multixact ID is stored separately in the pg_multixact subdirectory, and only the multixact ID appears in the xmax field in the tuple header. Like transaction IDs, multixact IDs are implemented as a 32-bit counter and corresponding storage.

A separate relminmxid field can be advanced any time relfrozenxid is advanced. VACUUM manages the MultiXactId address space by implementing rules that are analogous to the approach taken with Transaction IDs. Many of the XID-based settings that influence VACUUM's behavior have direct MultiXactId analogs. A convenient way to examine information about the MultiXactId address space is to execute queries such as:

SELECT c.oid::regclass as table_name,
FROM pg_class c
WHERE c.relkind IN ('r', 'm');

SELECT datname, mxid_age(datminmxid) FROM pg_database; Lazy and eager freezing strategies

When VACUUM is configured to freeze more aggressively it will typically set the table's relfrozenxid and relminmxid fields to relatively recent values. However, there can be significant variation among tables with varying workload characteristics. There can even be variation in how relfrozenxid advancement takes place over time for the same table, across successive VACUUM operations. Sometimes VACUUM will be able to advance relfrozenxid and relminmxid by relatively many XIDs/MXIDs despite performing relatively little freezing work. On the other hand VACUUM can sometimes freeze many individual pages while only advancing relfrozenxid by as few as one or two XIDs (this is typically seen following bulk loading).


When the VACUUM command's VERBOSE parameter is specified, VACUUM prints various statistics about the table. This includes information about how relfrozenxid and relminmxid advanced, as well as information about how many pages were newly frozen. The same details appear in the server log when autovacuum logging (controlled by log_autovacuum_min_duration) reports on a VACUUM operation executed by autovacuum.

As a general rule, the design of VACUUM prioritizes stable and predictable performance characteristics over time, while still leaving some scope for freezing lazily when a lazy strategy is likely to avoid unnecessary work altogether. Tables whose heap relation on-disk size is less than vacuum_freeze_strategy_threshold at the start of VACUUM will have page freezing triggered based on lazy criteria. Freezing will only take place when one or more XIDs attain an age greater than vacuum_freeze_min_age, or when one or more MXIDs attain an age greater than vacuum_multixact_freeze_min_age.

Tables that are larger than vacuum_freeze_strategy_threshold will have VACUUM trigger freezing for any and all pages that are eligible to be frozen under the lazy criteria, as well as pages that VACUUM considers all visible pages. This is the eager freezing strategy. The design makes the soft assumption that larger tables will tend to consist of pages that will only need to be processed by VACUUM once. The overhead of freezing each page is expected to be slightly higher in the short term, but much lower in the long term, at least on average. Eager freezing also limits the accumulation of unfrozen pages, which tends to improve performance stability over time.

vacuum_freeze_min_age and vacuum_multixact_freeze_min_age also act as limits on the age of the final values that relfrozenxid and relminmxid can be set to. Note that lazy strategy VACUUMs don't necessarily have to advance either field by any amount, but may nevertheless advance each field frequently in practice.

25.1.6. The Autovacuum Daemon

PostgreSQL has an optional but highly recommended feature called autovacuum, whose purpose is to automate the execution of VACUUM and ANALYZE 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 unless track_counts is set to true. 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 the autovacuum launcher, which is in charge of starting autovacuum worker processes for all databases. The launcher will distribute the work across time, attempting to start one worker within each database every autovacuum_naptime seconds. (Therefore, if the installation has N databases, a new worker will be launched every autovacuum_naptime/N seconds.) A maximum of autovacuum_max_workers worker processes are allowed to run at the same time. If there are more than autovacuum_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 execute VACUUM and/or ANALYZE 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 towards max_connections or superuser_reserved_connections limits. Triggering thresholds

Tables whose relfrozenxid value is more than autovacuum_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 last VACUUM 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 is autovacuum_vacuum_threshold, the vacuum scale factor is autovacuum_vacuum_scale_factor, and the number of tuples is pg_class.reltuples.

The table is also vacuumed if the number of tuples inserted since the last vacuum has exceeded the defined insert threshold, which is defined as:

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

where the vacuum insert base threshold is autovacuum_vacuum_insert_threshold, and vacuum insert scale factor is autovacuum_vacuum_insert_scale_factor. Such vacuums may allow portions of the table to be marked as all visible and also allow tuples to be frozen. The number of obsolete tuples and the number of inserted tuples are obtained from the cumulative statistics system; it is a semi-accurate count updated by each UPDATE, DELETE and INSERT operation. (It is only semi-accurate because some information might be lost under heavy load.)

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 last ANALYZE. Anti-wraparound autovacuum

If no relfrozenxid-advancing VACUUM is issued on the table before autovacuum_freeze_max_age is reached, an anti-wraparound autovacuum will soon be launched against the table. This reliably advances relfrozenxid when there is no other reason for VACUUM to run, or when a smaller table had VACUUM operations that lazily opted not to advance relfrozenxid.

An anti-wraparound autovacuum will also be triggered for any table whose multixact-age is greater than autovacuum_multixact_freeze_max_age. However, if the storage occupied by multixacts members exceeds 2GB, anti-wraparound vacuum might occur more often than this.

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 forty million transactions from the wraparound point:

WARNING:  database "mydb" must be vacuumed within 39985967 transactions
HINT:  To avoid a database shutdown, execute a database-wide VACUUM in that database.

(A manual VACUUM should fix the problem, as suggested by the hint; but note that the VACUUM must be performed by a superuser, else it will fail to process system catalogs and thus not be able to advance the database's datfrozenxid.) If these warnings are ignored, the system will shut down and refuse to start any new transactions once there are fewer than three 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 three-million-transaction safety margin exists to let the administrator recover by manually executing the required VACUUM commands. It is usually sufficient to allow autovacuum to finish against the table with the oldest relfrozenxid and/or relminmxid value. The wraparound failsafe mechanism controlled by vacuum_failsafe_age and vacuum_multixact_failsafe_age will typically trigger before warning messages are first emitted. This happens dynamically, in any antiwraparound autovacuum worker that is tasked with advancing very old table ages. It will also happen during manual VACUUM operations.

The shutdown mode is not enforced in single-user mode, which can be useful in some disaster recovery scenarios. See the postgres reference page for details about using single-user mode. Limitations

Partitioned tables are not processed by autovacuum. Statistics should be collected by running a manual ANALYZE when it is first populated, and again whenever the distribution of data in its 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 from postgresql.conf, but it is possible to override them (and many other autovacuum control parameters) on a per-table basis; see Storage 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. See Section 20.10 for more details on the global settings.

When multiple workers are running, the autovacuum cost delay parameters (see Section 20.4.4) 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-table autovacuum_vacuum_cost_delay or autovacuum_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 the SHARE UPDATE EXCLUSIVE lock held by autovacuum, lock acquisition will interrupt the autovacuum. For conflicting lock modes, see Table 13.2. However, if the autovacuum is running to prevent transaction ID wraparound (i.e., the autovacuum query name in the pg_stat_activity view ends with (to prevent wraparound)), the autovacuum is not automatically interrupted.


Regularly running commands that acquire locks conflicting with a SHARE UPDATE EXCLUSIVE lock (e.g., ANALYZE) can effectively prevent autovacuums from ever completing.