Database bloat occurs in heap tables, append-optimized tables, indexes, and system catalogs and affects database performance and disk usage. You can detect database bloat and remove it from the database.

About Bloat

Database bloat is disk space that was used by a table or index and is available for reuse by the database but has not been reclaimed. Bloat is created when updating tables or indexes.

Because Greenplum Database heap tables use the PostgreSQL Multiversion Concurrency Control (MVCC) storage implementation, a deleted or updated row is logically deleted from the database, but a non-visible image of the row remains in the table. These deleted rows, also called expired rows, are tracked in a free space map. Running VACUUM marks the expired rows as free space that is available for reuse by subsequent inserts.

It is normal for tables that have frequent updates to have a small or moderate amount of expired rows and free space that will be reused as new data is added. But when the table is allowed to grow so large that active data occupies just a small fraction of the space, the table has become significantly bloated. Bloated tables require more disk storage and additional I/O that can slow down query execution.


It is very important to run VACUUM on individual tables after large UPDATE and DELETE operations to avoid the necessity of ever running VACUUM FULL.

Running the VACUUM command regularly on tables prevents them from growing too large. If the table does become significantly bloated, the VACUUM FULL command must be used to compact the table data.

If the free space map is not large enough to accommodate all of the expired rows, the VACUUM command is unable to reclaim space for expired rows that overflowed the free space map. The disk space may only be recovered by running VACUUM FULL, which locks the table, creates a new table, copies the table data to the new table, and then drops old table. This is an expensive operation that can take an exceptional amount of time to complete with a large table.


VACUUM FULL acquires an ACCESS EXCLUSIVE lock on tables. You should not run VACUUM FULL. If you run VACUUM FULL on tables, run it during a time when users and applications do not require access to the tables, such as during a time of low activity, or during a maintenance window.

Detecting Bloat

The statistics collected by the ANALYZE statement can be used to calculate the expected number of disk pages required to store a table. The difference between the expected number of pages and the actual number of pages is a measure of bloat. The gp_toolkit schema provides the gp_bloat_diag view that identifies table bloat by comparing the ratio of expected to actual pages. To use it, make sure statistics are up to date for all of the tables in the database, then run the following SQL:

gpadmin=# SELECT * FROM gp_toolkit.gp_bloat_diag;
 bdirelid | bdinspname | bdirelname | bdirelpages | bdiexppages |                bdidiag                
    21488 | public     | t1         |          97 |           1 | significant amount of bloat suspected
(1 row)

The results include only tables with moderate or significant bloat. Moderate bloat is reported when the ratio of actual to expected pages is greater than four and less than ten. Significant bloat is reported when the ratio is greater than ten.

The gp_toolkit.gp_bloat_expected_pages view lists the actual number of used pages and expected number of used pages for each database object.

gpadmin=# SELECT * FROM gp_toolkit.gp_bloat_expected_pages LIMIT 5;
 btdrelid | btdrelpages | btdexppages 
    10789 |           1 |           1
    10794 |           1 |           1
    10799 |           1 |           1
     5004 |           1 |           1
     7175 |           1 |           1
(5 rows)

The btdrelid is the object ID of the table. The btdrelpages column reports the number of pages the table uses; the btdexppages column is the number of pages expected. Again, the numbers reported are based on the table statistics, so be sure to run ANALYZE on tables that have changed.

Removing Bloat from Database Tables

The VACUUM command adds expired rows to the free space map so that the space can be reused. When VACUUM is run regularly on a table that is frequently updated, the space occupied by the expired rows can be promptly reused, preventing the table file from growing larger. It is also important to run VACUUM before the free space map is filled. For heavily updated tables, you may need to run VACUUM at least once a day to prevent the table from becoming bloated.


When a table is significantly bloated, it is better to run VACUUM before running ANALYZE. Analyzing a severely bloated table can generate poor statistics if the sample contains empty pages, so it is good practice to vacuum a bloated table before analyzing it.

When a table accumulates significant bloat, running the VACUUM command is insufficient. For small tables, running VACUUM FULL <table_name> can reclaim space used by rows that overflowed the free space map and reduce the size of the table file. However, a VACUUM FULL statement is an expensive operation that requires an ACCESS EXCLUSIVE lock and may take an exceptionally long and unpredictable amount of time to finish for large tables. You should run VACUUM FULL on tables during a time when users and applications do not require access to the tables being vacuumed, such as during a time of low activity, or during a maintenance window.

Removing Bloat from Append-Optimized Tables

Append-optimized tables are handled much differently than heap tables. Although append-optimized tables allow update, insert, and delete operations, these operations are not optimized and are not recommended with append-optimized tables. If you heed this advice and use append-optimized for load-once/read-many workloads, VACUUM on an append-optimized table runs almost instantaneously.

If you do run UPDATE or DELETE commands on an append-optimized table, expired rows are tracked in an auxiliary bitmap instead of the free space map. VACUUM is the only way to recover the space. Running VACUUM on an append-optimized table with expired rows compacts a table by rewriting the entire table without the expired rows. However, no action is performed if the percentage of expired rows in the table exceeds the value of the gp_appendonly_compaction_threshold configuration parameter, which is 10 (10%) by default. The threshold is checked on each segment, so it is possible that a VACUUM statement will compact an append-only table on some segments and not others. Compacting append-only tables can be deactivated by setting the gp_appendonly_compaction parameter to no.

Removing Bloat from Indexes

The VACUUM command only recovers space from tables. To recover the space from indexes, recreate them using the REINDEX command.

To rebuild all indexes on a table run REINDEX *table_name*;. To rebuild a particular index, run REINDEX *index_name*;. REINDEX sets the reltuples and relpages to 0 (zero) for the index, To update those statistics, run ANALYZE on the table after reindexing.

Removing Bloat from System Catalogs

Greenplum Database system catalog tables are heap tables and can become bloated over time. As database objects are created, altered, or dropped, expired rows are left in the system catalogs. Using gpload to load data contributes to the bloat since gpload creates and drops external tables. (Rather than use gpload, it is recommended to use gpfdist to load data.)

Bloat in the system catalogs increases the time require to scan the tables, for example, when creating explain plans. System catalogs are scanned frequently and if they become bloated, overall system performance is degraded.

It is recommended to run VACUUM on system catalog tables nightly and at least weekly. At the same time, running REINDEX SYSTEM on system catalog tables removes bloat from the indexes. Alternatively, you can reindex system tables using the reindexdb utility with the -s (--system) option. After removing catalog bloat, run ANALYZE to update catalog table statistics.

These are Greenplum Database system catalog maintenance steps.

  1. Perform a REINDEX on the system catalog tables to rebuild the system catalog indexes. This removes bloat in the indexes and improves VACUUM performance.


    When performing REINDEX on the system catalog tables, locking will occur on the tables and might have an impact on currently running queries. You can schedule the REINDEX operation during a period of low activity to avoid disrupting ongoing business operations.

  2. Perform a VACUUM on system catalog tables.

  3. Perform an ANALYZE on the system catalog tables to update the table statistics.

If you are performing system catalog maintenance during a maintenance period and you need to stop a process due to time constraints, run the Greenplum Database function pg_cancel_backend(<PID>) to safely stop a Greenplum Database process.

The following script runs REINDEX, VACUUM, and ANALYZE on the system catalogs.

SYSTABLES="' pg_catalog.' || relname || ';' from pg_class a, pg_namespace b \
where a.relnamespace=b.oid and b.nspname='pg_catalog' and a.relkind='r'"

reindexdb -s -d $DBNAME
psql -tc "SELECT 'VACUUM' || $SYSTABLES" $DBNAME | psql -a $DBNAME
analyzedb -a -s pg_catalog -d $DBNAME

If the system catalogs become significantly bloated, you must run VACUUM FULL during a scheduled downtime period. During this period, stop all catalog activity on the system; VACUUM FULL takes ACCESS EXCLUSIVE locks against the system catalog. Running VACUUM regularly on system catalog tables can prevent the need for this more costly procedure.

These are steps for intensive system catalog maintenance.

  1. Stop all catalog activity on the Greenplum Database system.
  2. Perform a VACUUM FULL on the system catalog tables. See the following Note.
  3. Perform an ANALYZE on the system catalog tables to update the catalog table statistics.

The system catalog table pg_attribute is usually the largest catalog table. If the pg_attribute table is significantly bloated, a VACUUM FULL operation on the table might require a significant amount of time and might need to be performed separately. The presence of both of these conditions indicate a significantly bloated pg_attribute table that might require a long VACUUM FULL time:

  • The pg_attribute table contains a large number of records.
  • The diagnostic message for pg_attribute is significant amount of bloat in the gp_toolkit.gp_bloat_diag view.

Parent topic: System Monitoring and Maintenance

check-circle-line exclamation-circle-line close-line
Scroll to top icon