Routine Vacuuming

PostgreSQL 7.3.4 Documentation
Prev | Chapter 8. Routine Database Maintenance Tasks | Next

# 8.2. Routine Vacuuming

PostgreSQL's `VACUUM` command must be run on a regular basis for several reasons:

1. To recover disk space occupied by updated or deleted rows.

2. To update data statistics used by the PostgreSQL query planner.

3. To protect against loss of very old data due to transaction ID wraparound.

The frequency and scope of `VACUUM`s performed for each of these reasons will vary depending on the needs of each installation. Therefore, database administrators must understand these issues and develop an appropriate maintenance strategy. This section concentrates on explaining the high-level issues; for details about command syntax and so on, see the `VACUUM` command reference page.

Beginning in PostgreSQL 7.2, the standard form of `VACUUM` can run in parallel with normal database operations (selects, inserts, updates, deletes, but not changes to table schemas). Routine vacuuming is therefore not nearly as intrusive as it was in prior releases, and it's not as critical to try to schedule it at low-usage times of day.

## 8.2.1. Recovering disk space

In normal PostgreSQL operation, an `UPDATE` or `DELETE` of a row does not immediately remove the old tuple (version of the row). This approach is necessary to gain the benefits of multiversion concurrency control (see the PostgreSQL 7.3.4 User's Guide): the tuple must not be deleted while it is still potentially visible to other transactions. But eventually, an outdated or deleted tuple is no longer of interest to any transaction. The space it occupies must be reclaimed for reuse by new tuples, to avoid infinite growth of disk space requirements. This is done by running `VACUUM`.

Clearly, a table that receives frequent updates or deletes will need to be vacuumed more often than tables that are seldom updated. It may be useful to set up periodic cron tasks that vacuum only selected tables, skipping tables that are known not to change often. This is only likely to be helpful if you have both large heavily-updated tables and large seldom-updated tables --- the extra cost of vacuuming a small table isn't enough to be worth worrying about.

The standard form of `VACUUM` is best used with the goal of maintaining a fairly level steady-state usage of disk space. The standard form finds old tuples and makes their space available for re-use within the table, but it does not try very hard to shorten the table file and return disk space to the operating system. If you need to return disk space to the operating system you can use `VACUUM FULL` --- but what's the point of releasing disk space that will only have to be allocated again soon? Moderately frequent standard `VACUUM`s are a better approach than infrequent `VACUUM FULL`s for maintaining heavily-updated tables.

Recommended practice for most sites is to schedule a database-wide `VACUUM` once a day at a low-usage time of day, supplemented by more frequent vacuuming of heavily-updated tables if necessary. (If you have multiple databases in an installation, don't forget to vacuum each one; the `vacuumdb` script may be helpful.) Use plain `VACUUM`, not `VACUUM FULL`, for routine vacuuming for space recovery.

`VACUUM FULL` is recommended for cases where you know you have deleted the majority of tuples in a table, so that the steady-state size of the table can be shrunk substantially with `VACUUM FULL`'s more aggressive approach.

If you have a table whose contents are deleted completely every so often, consider doing it with `TRUNCATE` rather than using `DELETE` followed by `VACUUM`.

## 8.2.2. 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 may degrade database performance.

As with vacuuming for space recovery, frequent updates of statistics are more useful for heavily-updated tables than for seldom-updated ones. But even for a heavily-updated table, there may be no need for statistics updates if the statistical distribution of the data is not changing much. A simple rule of thumb is to think about how much the minimum and maximum values of the columns in the table change. For example, a `timestamp` column that contains the time of row update will have a constantly-increasing maximum value as rows are added and updated; such a column will probably need more frequent statistics updates than, say, a column containing URLs for pages accessed on a website. The URL column may receive changes just as often, but the statistical distribution of its values probably changes relatively slowly.

It is possible to run `ANALYZE` on specific tables and even just specific columns of a table, so the flexibility exists to update some statistics more frequently than others if your application requires it. In practice, however, the usefulness of this feature is doubtful. Beginning in PostgreSQL 7.2, `ANALYZE` is a fairly fast operation even on large tables, because it uses a statistical random sampling of the rows of a table rather than reading every single row. So it's probably much simpler to just run it over the whole database every so often.

> Tip: Although per-column tweaking of `ANALYZE` frequency may not be very productive, you may well find it worthwhile to do per-column adjustment of the level of detail of the statistics collected by `ANALYZE`. Columns that are heavily used in WHERE clauses and have highly irregular data distributions may require a finer-grain data histogram than other columns. See `ALTER TABLE SET STATISTICS`.

Recommended practice for most sites is to schedule a database-wide `ANALYZE` once a day at a low-usage time of day; this can usefully be combined with a nightly `VACUUM`. However, sites with relatively slowly changing table statistics may find that this is overkill, and that less-frequent `ANALYZE` runs are sufficient.

## 8.2.3. Preventing transaction ID wraparound failures

PostgreSQL's MVCC transaction semantics depend on being able to compare transaction ID (XID) numbers: a tuple with an insertion XID newer than the current transaction's XID is "in the future" and should not be visible to the current transaction. But since transaction IDs have limited size (32 bits at this writing) an installation that runs for a long time (more than 4 billion transactions) will suffer transaction ID wraparound: the XID counter wraps around to zero, and all of a sudden transactions that were in the past appear to be in the future --- which means their outputs become invisible. In short, catastrophic data loss. (Actually the data is still there, but that's cold comfort if you can't get at it.)

Prior to PostgreSQL 7.2, the only defense against XID wraparound was to re-`initdb` at least every 4 billion transactions. This of course was not very satisfactory for high-traffic sites, so a better solution has been devised. The new approach allows an installation to remain up indefinitely, without `initdb` or any sort of restart. The price is this maintenance requirement: every table in the database must be vacuumed at least once every billion transactions.

In practice this isn't an onerous requirement, but since the consequences of failing to meet it can be complete data loss (not just wasted disk space or slow performance), some special provisions have been made to help database administrators keep track of the time since the last `VACUUM`. The remainder of this section gives the details.

The new approach to XID comparison distinguishes two special XIDs, numbers 1 and 2 (BootstrapXID and FrozenXID). These two XIDs are always considered older than every normal XID. Normal XIDs (those greater than 2) are compared using modulo-231 arithmetic. This means that for every normal XID, there are two billion XIDs that are "older" and two billion that are "newer"; another way to say it is that the normal XID space is circular with no endpoint. Therefore, once a tuple has been created with a particular normal XID, the tuple will appear to be "in the past" for the next two billion transactions, no matter which normal XID we are talking about. If the tuple still exists after more than two billion transactions, it will suddenly appear to be in the future. To prevent data loss, old tuples must be reassigned the XID FrozenXID sometime before they reach the two-billion-transactions-old mark. Once they are assigned this special XID, they will appear to be "in the past" to all normal transactions regardless of wraparound issues, and so such tuples will be good until deleted, no matter how long that is. This reassignment of XID is handled by `VACUUM`.

`VACUUM`'s normal policy is to reassign FrozenXID to any tuple with a normal XID more than one billion transactions in the past. This policy preserves the original insertion XID until it is not likely to be of interest anymore (in fact, most tuples will probably live and die without ever being "frozen"). With this policy, the maximum safe interval between `VACUUM`s of any table is exactly one billion transactions: if you wait longer, it's possible that a tuple that was not quite old enough to be reassigned last time is now more than two billion transactions old and has wrapped around into the future --- i.e., is lost to you. (Of course, it'll reappear after another two billion transactions, but that's no help.)

Since periodic `VACUUM`s are needed anyway for the reasons described earlier, it's unlikely that any table would not be vacuumed for as long as a billion transactions. But to help administrators ensure this constraint is met, `VACUUM` stores transaction ID statistics in the system table `pg_database`. In particular, the `datfrozenxid` field of a database's `pg_database` row is updated at the completion of any database-wide vacuum operation (i.e., `VACUUM` that does not name a specific table). The value stored in this field is the freeze cutoff XID that was used by that `VACUUM` command. All normal XIDs older than this cutoff XID are guaranteed to have been replaced by FrozenXID within that database. A convenient way to examine this information is to execute the query

    SELECT datname, age(datfrozenxid) FROM pg_database;

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

With the standard freezing policy, the age column will start at one billion for a freshly-vacuumed database. When the age approaches two billion, the database must be vacuumed again to avoid risk of wraparound failures. Recommended practice is to vacuum each database at least once every half-a-billion (500 million) transactions, so as to provide plenty of safety margin. To help meet this rule, each database-wide `VACUUM` automatically delivers a warning if there are any `pg_database` entries showing an age of more than 1.5 billion transactions, for example:

    play=# vacuum;
    WARNING:  Some databases have not been vacuumed in 1613770184 transactions.
            Better vacuum them within 533713463 transactions,
            or you may have a wraparound failure.
    VACUUM

`VACUUM` with the `FREEZE` option uses a more aggressive freezing policy: tuples are frozen if they are old enough to be considered good by all open transactions. In particular, if a `VACUUM FREEZE` is performed in an otherwise-idle database, it is guaranteed that all tuples in that database will be frozen. Hence, as long as the database is not modified in any way, it will not need subsequent vacuuming to avoid transaction ID wraparound problems. This technique is used by `initdb` to prepare the `template0` database. It should also be used to prepare any user-created databases that are to be marked datallowconn = false in `pg_database`, since there isn't any convenient way to vacuum a database that you can't connect to. Note that `VACUUM`'s automatic warning message about unvacuumed databases will ignore `pg_database` entries with datallowconn = false, so as to avoid giving false warnings about these databases; therefore it's up to you to ensure that such databases are frozen correctly.

Prev | Home | Next
Routine Database Maintenance Tasks | Up | Routine Reindexing