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MVCC (Multi-Version Concurrency Control) feature allows databases to provide concurrent access to data. This allows each SQL statement to see a snapshot of data as it was some time ago, regardless of the current state of the underlying data. This prevents statements from viewing inconsistent data produced by concurrent transactions performing updates on the same data rows, providing transaction isolation for each database session. To summarize “Readers don’t block writers and writers don’t block readers”

If you are coming from Oracle or MySQL background, you would be aware that during an update/delete ,DML activity will make changes to rows and use separate store called “Undo” to track the old image of data. If any other session want to get consistent image, then database uses undo to provide consistent snapshot of data. Like many other databases, PostgreSQL also supports MVCC but takes different approach to store the old changes.

In PostgreSQL, update or delete of a row (tuple in PostgreSQL) does not immediately remove the old version of the row. When you update a row, it will create a copy of the row with new changes and insert in the table. Then, it will update the old copy header to make it invisible for future transactions. Similarly for delete, it won’t delete the row but update metadata to make them invisible. Eventually, these old rows will no longer be required by transactions and will have to be cleaned up. This cleanup is handled by “Vacuum”. Note that apart from increasing the total database size, table or index bloat also impacts query performance as database need to process bigger objects.

=>vacuum table 'bloated_table';

If you run above command, it will remove dead tuples in tables and indexes and marks the space available for future reuse. But this will not release the space to operating system. As vacuum is manual approach, PostgreSQL has a background process called “Autovacuum” which takes care of this maintenance process automatically. Autovacuum process to delete rows is controlled by 2 parameters autovacuum_vacuum_threshold and autovacuum_vacuum_scale_factor (There are other parametes like autovacuum_freeze_max_age which can trigger autovacuum)

vacuum threshold = autovacuum_vacuum_threshold + autovacuum_vacuum_scale_factor * number of tuples

You can find this values by querying pg_settings. For RDS, autovacuum_vacuum_threshold is 50 rows and autovacuum_vacuum_scale_factor is 0.1 i.e 10% of the table size.

select name,setting from pg_settings where name in ('autovacuum_vacuum_threshold','autovacuum_vacuum_scale_factor');
               name              | setting
 --------------------------------+---------
  autovacuum_vacuum_scale_factor | 0.1
  autovacuum_vacuum_threshold    | 50

This means that if there is table with 100 M rows, you should have ~10M changes ( 50+0.1*100M ) before autovacuum is triggered. Additionally, there are limited number of autovacuum worker processes and if autovacuum is not tuned properly, table could have much higher dead rows. It’s advisable to reduce the scale factor to lower value, either at table level or at database level to prevent bloat. You would also need to tune the autovacuum process settings to improve the cleanup process. There is an excellent blog article titled “A Case Study of Tuning Autovacuum in Amazon RDS for PostgreSQL” on AWS database blog which helps to tune autovacuum.

If you wish to reclaim OS space, then you need to execute “Vacuum Full” which will compact tables by writing a complete new version of the table file with no dead tuples. But this comes at a cost. Vacuum full requires “Exclusive lock” on the table and blocks any DML on the table, leading to downtime. This can be problematic as large tables with multiple indexes can take really long time (7-8 hours) to rebuild.

Enter pg_repack !! This utility helps to perform Full vacuum without downtime by making use of trigger to take care of changes happening on parent table. Below table compares the internal working on Table vs Index rebuilds

Identifying Bloat!

I have used table_bloat_check.sql and index_bloat_check.sql to identify table and index bloat respectively. Below snippet displays output of table_bloat_check.sql query output. As per the results, this table is around 30GB and we have ~7.5GB of bloat.

databasename | schemaname | tablename | can_estimate | est_rows  | pct_bloat | mb_bloat | table_mb

--------------+------------+-----------+--------------+-----------+-----------+----------+-----------

 postgres     | askdba     | bloated   | t            | 547962000 |        25 |  7828.23 | 30877.930

Similarly, when we run index_bloat_check.sql query to identify index bloat , we see that pkey_bloated is 65G and has bloat of ~54GB.

database_name | schema_name |      table_name      |        index_name         | bloat_pct | bloat_mb |  index_mb  |  table_mb  | index_scans
 ---------------+-------------+----------------------+---------------------------+-----------+----------+------------+------------+-------------
  postgres      | askdba       | bloated              |  pkey_bloated              |        83 |    54125 |  65137.813 |  30877.930 |     3820674

Let’s use pg_repack to clear this bloat. To use pg_repack, you need to install extension and a client utility. RDS PostgreSQL supports pg_repack for installations having version of 9.6.3 and higher.Extension version will differ depending on your RDS PostgreSQL version. e.g RDS PostgreSQL 9.6.3 installs pg_repack 1.4.0 extension, whereas 9.6.11 installs pg_repack 1.4.3 extension.

To create extension, connect as master user for RDS database and run create extension command,

postgres=>  create extension pg_extension;
postgres=> \dx pg_repack
                                 List of installed extensions
    Name    | Version | Schema |                         Description
 -----------+---------+--------+--------------------------------------------------------------
  pg_repack | 1.4.3   | public | Reorganize tables in PostgreSQL databases with minimal locks

To install pg_repack client, download the tar bar from here and build the utility. You need to ensure that extension and client utility version matches. pg_repack provides option to perform full vacuum at table level, index level or table+index . If you want to perform vacuum full for table and associated indexes, then it can be done by using below statement

./pg_repack -h [rds-host-name] -p [dbPort] -d [dbname] -U [superuser] --table=[schema.tablename] --no-order -k

Password:

Password:

-k flag is important as RDS master user does not have Postgresql superuser role and omitting this option leads to error “ERROR: pg_repack failed with error: You must be a superuser to use pg_repack”

To run index-only repack, use following statement

./pg_repack -h [rds-host-name] -p [dbPort] -d [dbname] -U [superuser] --table=[schema.tablename] --only-indexes -k

Above statement will create new indexes and will drop the older indexes after all indexes are recreated. If you are performing this action on really big tables, it will take time and will slow down DML activity on the table as you will have 2*n-1 indexes before last one is created (n=number of indexes).Therefore, if there are multiple indexes on the table, it would be better to recreate index one by one using –index clause

./pg_repack -h [rds-host-name] -p [dbPort] -d [dbname] -U [superuser] --index=[schema.indexname] -k

In my scenario, I went with table+index vacuum option.After rebuild, actual table size reduction was 10% instead of 24% and for index , it was 75% instead of 85%. As you can see, there could be 10-20% variance between actual object size (post vacuum) vs estimated size. Therefore, it would be good to carry out testing in clone environment before committing on size savings.

Pg_repack creates the objects under repack schema and later moves it to the correct schema. To monitor the pg_repack session, use pg_stat_activity view

select * from pg_stat_activity where application_name='pg_repack'