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 23.1.6. 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.
PostgreSQL's VACUUM command has to process each table on a regular basis for several reasons:
To recover or reuse disk space occupied by updated or deleted rows.
To update data statistics used by the PostgreSQL query planner.
To update the visibility map, which speeds up index-only scans.
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 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 18.4.4.
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 vacuumings. 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.
Tip: 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 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.
Tip: 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.
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.
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.
Tip: 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 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.
Tip: 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.
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 noticeable 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.
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 transaction IDs have limited size (32 bits) a cluster that runs for a long time (more than 4 billion transactions) would 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 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 that VACUUM will mark rows as frozen, indicating that they were inserted by a transaction which committed sufficiently far in the past that the effects of the inserting transaction is certain to be visible, from an MVCC perspective, to all current and future transactions. PostgreSQL reserves a special XID, FrozenTransactionId, which does not follow the normal XID comparison rules and is always considered older than every normal XID. 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, frozen row versions are treated as if the inserting XID were FrozenTransactionId, 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 its row version 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 normally skips pages that don't have any dead row versions, but those pages might still have row versions with old XID values. To ensure all old row versions have been frozen, a scan of the whole table is needed. vacuum_freeze_table_age controls when VACUUM does that: a whole table sweep is forced if the table hasn't been fully scanned for vacuum_freeze_table_age minus vacuum_freeze_min_age transactions. Setting it to 0 forces VACUUM to always scan all pages, effectively ignoring the visibility map.
The maximum time that a table can go unvacuumed is two billion transactions minus the vacuum_freeze_min_age value at the time VACUUM last scanned the whole table. 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 parameter autovacuum_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 every autovacuum_freeze_max_age minus vacuum_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 increasing autovacuum_freeze_max_age or decreasing vacuum_freeze_min_age.
The effective maximum for vacuum_freeze_table_age is 0.95 * autovacuum_freeze_max_age; a setting higher than that will be capped to the maximum. A value higher than autovacuum_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 manual VACUUM before that happens. As a rule of thumb, vacuum_freeze_table_age should be set to a value somewhat below autovacuum_freeze_max_age, leaving enough gap so that a regularly scheduled VACUUM 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 whole-table scans.
The sole disadvantage of increasing autovacuum_freeze_max_age (and vacuum_freeze_table_age along with it) is that the pg_clog subdirectory of the database cluster will take more space, because it must store the commit status of all transactions back to the autovacuum_freeze_max_age horizon. The commit status uses two bits per transaction, so if autovacuum_freeze_max_age is set to its maximum allowed value of two billion, pg_clog can be expected to grow to about half a gigabyte. If this is trivial compared to your total database size, setting autovacuum_freeze_max_age to its maximum allowed value is recommended. Otherwise, set it depending on what you are willing to allow for pg_clog storage. (The default, 200 million transactions, translates to about 50MB of pg_clog storage.)
One disadvantage of decreasing vacuum_freeze_min_age is that it might cause VACUUM 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 tables pg_class and pg_database. In particular, the relfrozenxid column of a table's pg_class row contains the freeze cutoff XID that was used by the last whole-table VACUUM for that table. All rows inserted by transactions with XIDs 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.
VACUUM normally only scans pages that have been modified since the last vacuum, but relfrozenxid can only be advanced when the whole table is scanned. The whole table is scanned when relfrozenxid is more than vacuum_freeze_table_age transactions old, when VACUUM's FREEZE option is used, or when all pages happen to require vacuuming to remove dead row versions. When VACUUM scans the whole table, after it's finished age(relfrozenxid) should be a little more than the vacuum_freeze_min_age setting that was used (more by the number of transactions started since the VACUUM started). If no whole-table-scanning VACUUM is issued on the table until autovacuum_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 ten million transactions from the wraparound point:
WARNING: database "mydb" must be vacuumed within 177009986 transactions HINT: To avoid a database shutdown, execute a database-wide VACUUM in "mydb".
(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 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 required VACUUM 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 execute VACUUM. The shutdown mode is not enforced in single-user mode. See the postgres reference page for details about using single-user mode.
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, all of which requires careful aging management, storage cleanup, and wraparound handling.
During a VACUUM table scan, either partial or of the whole table, any multixact ID older than vacuum_multixact_freeze_min_age is replaced by a different value, which can be the zero value, a single transaction ID, or a newer multixact ID. For each table, pg_class.relminmxid stores the oldest possible multixact ID still appearing in any tuple of that table. If this value is older than vacuum_multixact_freeze_table_age, a whole-table scan is forced. Whole-table VACUUM scans, regardless of what causes them, enable advancing the value for that table. Eventually, as all tables in all databases are scanned and their oldest multixact values are advanced, on-disk storage for older multixacts can be removed.
As a safety device, a whole-table vacuum scan will occur for any table whose multixact-age is greater than autovacuum_multixact_freeze_max_age. This will occur even if autovacuum is nominally disabled.
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 used to monitor autovacuum 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 became 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.
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 number of obsolete tuples is obtained from the statistics collector; it is a semi-accurate count updated by each UPDATE and DELETE operation. (It is only semi-accurate because some information might be lost under heavy load.) If the relfrozenxid value of the table is more than vacuum_freeze_table_age transactions old, the whole table is scanned to freeze old tuples and advance relfrozenxid, 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 last ANALYZE.
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 on a table-by-table basis; see Storage Parameters for more information. If a setting has been changed via storage parameters, that value is used; otherwise the global settings are used. See Section 18.10 for more details on the global settings.
Besides the base threshold values and scale factors, there are six more autovacuum parameters that can be set for each table via storage parameters. The first parameter, autovacuum_enabled, can be set to false to instruct the autovacuum daemon to skip that particular table entirely. In this case autovacuum will only touch the table if it must do so to prevent transaction ID wraparound. Another two parameters, autovacuum_vacuum_cost_delay and autovacuum_vacuum_cost_limit, are used to set table-specific values for the cost-based vacuum delay feature (see Section 18.4.4). autovacuum_freeze_min_age, autovacuum_freeze_max_age and autovacuum_freeze_table_age are used to set values for vacuum_freeze_min_age, autovacuum_freeze_max_age and vacuum_freeze_table_age respectively.
When multiple workers are running, the cost delay parameters 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 autovacuum_vacuum_cost_delay or autovacuum_vacuum_cost_limit have been set are not considered in the balancing algorithm.