If you want to learn more about Citus on Microsoft Azure, read this post about Hyperscale (Citus) on Azure Database for PostgreSQL.
Postgres under the covers in simplified terms is one giant append only log. When you insert a new record that gets appended, but the same happens for deletes and updates. For a delete a record is just flagged as invisible, it’s not actually removed from disk immediately. For updates the old record is flagged as invisible and a new record is written. What then later happens is Postgres comes through and looks at all records that are invisible and actually frees up the disk storage. This process is known as vacuum.
There are a couple of key levels to
VACUUM within Postgres:
VACUUM ANALYZE- This one is commonly run when you’ve recently loaded data into your database and want Postgres to update it’s statistics about the data
VACUUM FULL- This will take a lock during the operation, but will scan the full table and reclaim all the space it can from dead tuples.
Postgres also has a mechanism for regularly freeing up unused space known as
autovacuum. Autovacuum we’ve previously discussed in an earlier post. We do highly recommend you tune autovacuum appropriately to prevent your bloat ballooning. But a good question remains: Do I have bloat on my Postgres database?
This query is useful to periodically monitor your bloat. How often you should check is dependent on your application, but every month or two is a reasonable timeframe.
WITH constants AS ( -- define some constants for sizes of things -- for reference down the query and easy maintenance SELECT current_setting('block_size')::numeric AS bs, 23 AS hdr, 8 AS ma ), no_stats AS ( -- screen out table who have attributes -- which dont have stats, such as JSON SELECT table_schema, table_name, n_live_tup::numeric as est_rows, pg_table_size(relid)::numeric as table_size FROM information_schema.columns JOIN pg_stat_user_tables as psut ON table_schema = psut.schemaname AND table_name = psut.relname LEFT OUTER JOIN pg_stats ON table_schema = pg_stats.schemaname AND table_name = pg_stats.tablename AND column_name = attname WHERE attname IS NULL AND table_schema NOT IN ('pg_catalog', 'information_schema') GROUP BY table_schema, table_name, relid, n_live_tup ), null_headers AS ( -- calculate null header sizes -- omitting tables which dont have complete stats -- and attributes which aren't visible SELECT hdr+1+(sum(case when null_frac <> 0 THEN 1 else 0 END)/8) as nullhdr, SUM((1-null_frac)*avg_width) as datawidth, MAX(null_frac) as maxfracsum, schemaname, tablename, hdr, ma, bs FROM pg_stats CROSS JOIN constants LEFT OUTER JOIN no_stats ON schemaname = no_stats.table_schema AND tablename = no_stats.table_name WHERE schemaname NOT IN ('pg_catalog', 'information_schema') AND no_stats.table_name IS NULL AND EXISTS ( SELECT 1 FROM information_schema.columns WHERE schemaname = columns.table_schema AND tablename = columns.table_name ) GROUP BY schemaname, tablename, hdr, ma, bs ), data_headers AS ( -- estimate header and row size SELECT ma, bs, hdr, schemaname, tablename, (datawidth+(hdr+ma-(case when hdr%ma=0 THEN ma ELSE hdr%ma END)))::numeric AS datahdr, (maxfracsum*(nullhdr+ma-(case when nullhdr%ma=0 THEN ma ELSE nullhdr%ma END))) AS nullhdr2 FROM null_headers ), table_estimates AS ( -- make estimates of how large the table should be -- based on row and page size SELECT schemaname, tablename, bs, reltuples::numeric as est_rows, relpages * bs as table_bytes, CEIL((reltuples* (datahdr + nullhdr2 + 4 + ma - (CASE WHEN datahdr%ma=0 THEN ma ELSE datahdr%ma END) )/(bs-20))) * bs AS expected_bytes, reltoastrelid FROM data_headers JOIN pg_class ON tablename = relname JOIN pg_namespace ON relnamespace = pg_namespace.oid AND schemaname = nspname WHERE pg_class.relkind = 'r' ), estimates_with_toast AS ( -- add in estimated TOAST table sizes -- estimate based on 4 toast tuples per page because we dont have -- anything better. also append the no_data tables SELECT schemaname, tablename, TRUE as can_estimate, est_rows, table_bytes + ( coalesce(toast.relpages, 0) * bs ) as table_bytes, expected_bytes + ( ceil( coalesce(toast.reltuples, 0) / 4 ) * bs ) as expected_bytes FROM table_estimates LEFT OUTER JOIN pg_class as toast ON table_estimates.reltoastrelid = toast.oid AND toast.relkind = 't' ), table_estimates_plus AS ( -- add some extra metadata to the table data -- and calculations to be reused -- including whether we cant estimate it -- or whether we think it might be compressed SELECT current_database() as databasename, schemaname, tablename, can_estimate, est_rows, CASE WHEN table_bytes > 0 THEN table_bytes::NUMERIC ELSE NULL::NUMERIC END AS table_bytes, CASE WHEN expected_bytes > 0 THEN expected_bytes::NUMERIC ELSE NULL::NUMERIC END AS expected_bytes, CASE WHEN expected_bytes > 0 AND table_bytes > 0 AND expected_bytes <= table_bytes THEN (table_bytes - expected_bytes)::NUMERIC ELSE 0::NUMERIC END AS bloat_bytes FROM estimates_with_toast UNION ALL SELECT current_database() as databasename, table_schema, table_name, FALSE, est_rows, table_size, NULL::NUMERIC, NULL::NUMERIC FROM no_stats ), bloat_data AS ( -- do final math calculations and formatting select current_database() as databasename, schemaname, tablename, can_estimate, table_bytes, round(table_bytes/(1024^2)::NUMERIC,3) as table_mb, expected_bytes, round(expected_bytes/(1024^2)::NUMERIC,3) as expected_mb, round(bloat_bytes*100/table_bytes) as pct_bloat, round(bloat_bytes/(1024::NUMERIC^2),2) as mb_bloat, table_bytes, expected_bytes, est_rows FROM table_estimates_plus ) -- filter output for bloated tables SELECT databasename, schemaname, tablename, can_estimate, est_rows, pct_bloat, mb_bloat, table_mb FROM bloat_data -- this where clause defines which tables actually appear -- in the bloat chart -- example below filters for tables which are either 50% -- bloated and more than 20mb in size, or more than 25% -- bloated and more than 1GB in size WHERE ( pct_bloat >= 50 AND mb_bloat >= 20 ) OR ( pct_bloat >= 25 AND mb_bloat >= 1000 ) ORDER BY pct_bloat DESC;
The above query will give you something that looks like this:
databasename | schemaname | tablename | can_estimate | est_rows | pct_bloat | mb_bloat | table_mb ---------------+------------+---------------+--------------+----------+-----------+-----------+----------- djsdcv27d8mqq | public | github_events | t | 1820360 | 29 | 9830.65 | 33970.547 djsdcv27d8mqq | public | github_users | t | 952920 | 28 | 4750.80 | 17100.852 (2 rows)
The amount of bloat you have is relative to the size of your database. If you’re running a smaller database with only 20 GB of data total this amount may be noticable and useful to clean up up, but you also may be able to skate by for a while. If you are at several hundred GB of data stored within have bloat that is 100 GB or more and a significant percentage of your database in that case it can be really worth cleaning up.
If you do find yourself with significant bloat you have a few options: