PostgreSQL, like any database software, requires that certain tasks be performed regularly to achieve optimum performance. The tasks discussed here are required, but they are repetitive in nature and can easily be automated using standard tools such as cron scripts or Windows’ Task Scheduler. It is the database administrator’s responsibility to set up appropriate scripts, and to check that they execute successfully.
One obvious maintenance task is the creation of backup copies of the data on a regular schedule. Without a recent backup, you have no chance of recovery after a catastrophe (disk failure, fire, mistakenly dropping a critical table, etc.).
The other main category of maintenance task is periodic “vacuuming” of the database. Closely related to this is updating the statistics that will be used by the query planner. Another task that might need periodic attention is log file management.
check_postgres is available for monitoring database health and reporting unusual conditions. check_postgres integrates with Nagios and MRTG, but can be run standalone too.
PostgreSQL is low-maintenance compared to some other database management systems. Nonetheless, appropriate attention to these tasks will go far towards ensuring a pleasant and productive experience with the system.
PostgreSQL databases require periodic maintenance known as vacuuming. For many installations, it is sufficient to let vacuuming be performed by the autovacuum daemon. 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 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
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 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.
Recovering Disk Space
In PostgreSQL, an
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: 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
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.
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.
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.
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.
Preventing Transaction ID Wraparound Failures
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 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, PostgreSQL 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 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 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 the visibility 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, normal
VACUUMs won’t always freeze every old row version in the table. Periodically,
VACUUM will perform an aggressive vacuum, skipping only those pages which contain neither dead rows nor any unfrozen XID or MXID values. vacuum_freeze_table_age controls when
VACUUM 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 than
vacuum_freeze_table_age to 0 forces
VACUUM to use this more aggressive strategy for all scans.
The maximum time that a table can go unvacuumed is two billion transactions minus the
vacuum_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 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
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
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 aggressive vacuuming.
The sole disadvantage of increasing
vacuum_freeze_table_age along with it) is that the
pg_commit_ts subdirectories of the database cluster will take more space, because it must store the commit status and (if
track_commit_timestamp is enabled) timestamp 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_xact can be expected to grow to about half a gigabyte and
pg_commit_ts to about 20GB. 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_commit_ts storage. (The default, 200 million transactions, translates to about 50MB of
pg_xact storage and about 2GB of
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_database. In particular, the
relfrozenxid column of a table’s
pg_class row contains the freeze cutoff XID that was used by the last aggressive
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;
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 every page of the table that might contain unfrozen XIDs is scanned. This happens when
relfrozenxid is more than
vacuum_freeze_table_age transactions old, when
FREEZE option is used, or when all pages that are not already all-frozen happen to require vacuuming to remove dead row versions. When
VACUUM scans every page in the table that is not already all-frozen, it should set
age(relfrozenxid) to a value just 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
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 eleven million transactions from the wraparound point:
WARNING: database "mydb" must be vacuumed within 10985967 transactions
HINT: To avoid a database shutdown, execute a database-wide VACUUM in that database.
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.
Multixacts And Wraparound
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. There is a separate storage area which holds the list of members in each multixact, which also uses a 32-bit counter and which must also be managed.
VACUUM scans any part of a table, it will replace any multixact ID it encounters which is older than vacuum_multixact_freeze_min_age by a different value, which can be the zero value, a single transaction ID, or a newer multixact ID. For each table,
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, an aggressive vacuum is forced. As discussed in the previous section, an aggressive vacuum means that only those pages which are known to be all-frozen will be skipped.
mxid_age() can be used on
relminmxid to find its age.
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, an aggressive vacuum scan will occur for any table whose multixact-age is greater than autovacuum_multixact_freeze_max_age. Aggressive vacuum scans will also occur progressively for all tables, starting with those that have the oldest multixact-age, if the amount of used member storage space exceeds the amount 50% of the addressable storage space. Both of these kinds of aggressive scans will occur even if autovacuum is nominally disabled.
The Autovacuum Daemon
PostgreSQL has an optional but highly recommended feature called autovacuum, whose purpose is to automate the execution of
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
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
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.
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
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, which can reduce the work required in subsequent vacuums. For tables which receive
INSERT operations but no or almost no
DELETE operations, it may be beneficial to lower the table’s autovacuum_freeze_min_age as this may allow tuples to be frozen by earlier vacuums. The number of obsolete tuples and the number of inserted tuples are obtained from the statistics collector; it is a semi-accurate count updated by each
INSERT 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, an aggressive vacuum is performed 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
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. 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.
When multiple workers are running, the autovacuum 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 per-table
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. 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.
In some situations it is worthwhile to rebuild indexes periodically with the REINDEX command or a series of individual rebuilding steps.
B-tree index pages that have become completely empty are reclaimed for re-use. However, there is still a possibility of inefficient use of space: if all but a few index keys on a page have been deleted, the page remains allocated. Therefore, a usage pattern in which most, but not all, keys in each range are eventually deleted will see poor use of space. For such usage patterns, periodic reindexing is recommended.
The potential for bloat in non-B-tree indexes has not been well researched. It is a good idea to periodically monitor the index’s physical size when using any non-B-tree index type.
Also, for B-tree indexes, a freshly-constructed index is slightly faster to access than one that has been updated many times because logically adjacent pages are usually also physically adjacent in a newly built index. (This consideration does not apply to non-B-tree indexes.) It might be worthwhile to reindex periodically just to improve access speed.
REINDEX can be used safely and easily in all cases. This command requires an
ACCESS EXCLUSIVE lock by default, hence it is often preferable to execute it with its
CONCURRENTLY option, which requires only a
SHARE UPDATE EXCLUSIVE lock.
Log File Maintenance
It is a good idea to save the database server’s log output somewhere, rather than just discarding it via
/dev/null. The log output is invaluable when diagnosing problems. However, the log output tends to be voluminous (especially at higher debug levels) so you won’t want to save it indefinitely. You need to rotate the log files so that new log files are started and old ones removed after a reasonable period of time.
If you simply direct the stderr of
postgres into a file, you will have log output, but the only way to truncate the log file is to stop and restart the server. This might be acceptable if you are using PostgreSQL in a development environment, but few production servers would find this behavior acceptable.
A better approach is to send the server’s stderr output to some type of log rotation program. There is a built-in log rotation facility, which you can use by setting the configuration parameter
postgresql.conf. You can also use this approach to capture the log data in machine readable CSV (comma-separated values) format.
Alternatively, you might prefer to use an external log rotation program if you have one that you are already using with other server software. For example, the rotatelogs tool included in the Apache distribution can be used with PostgreSQL. One way to do this is to pipe the server’s stderr output to the desired program. If you start the server with
pg_ctl, then stderr is already redirected to stdout, so you just need a pipe command, for example:
pg_ctl start | rotatelogs /var/log/pgsql_log 86400
You can combine these approaches by setting up logrotate to collect log files produced by PostgreSQL built-in logging collector. In this case, the logging collector defines the names and location of the log files, while logrotate periodically archives these files. When initiating log rotation, logrotate must ensure that the application sends further output to the new file. This is commonly done with a
postrotate script that sends a
SIGHUP signal to the application, which then reopens the log file. In PostgreSQL, you can run
pg_ctl with the
logrotate option instead. When the server receives this command, the server either switches to a new log file or reopens the existing file, depending on the logging configuration.
Another production-grade approach to managing log output is to send it to syslog and let syslog deal with file rotation. To do this, set the configuration parameter
syslog (to log to syslog only) in
postgresql.conf. Then you can send a
SIGHUP signal to the syslog daemon whenever you want to force it to start writing a new log file. If you want to automate log rotation, the logrotate program can be configured to work with log files from syslog.
On many systems, however, syslog is not very reliable, particularly with large log messages; it might truncate or drop messages just when you need them the most. Also, on Linux, syslog will flush each message to disk, yielding poor performance. (You can use a “
-” at the start of the file name in the syslog configuration file to disable syncing.)
Note that all the solutions described above take care of starting new log files at configurable intervals, but they do not handle deletion of old, no-longer-useful log files. You will probably want to set up a batch job to periodically delete old log files. Another possibility is to configure the rotation program so that old log files are overwritten cyclically.
pgBadger is an external project that does sophisticated log file analysis. check_postgres provides Nagios alerts when important messages appear in the log files, as well as detection of many other extraordinary conditions.