about them at some point. test3_i_md5_idx, here is the comparison of real bloat, estimation without This is the second part of my blog “ My Favorite PostgreSQL Extensions” wherein I had introduced you to two PostgreSQL extensions, postgres_fdw and pg_partman. As it is not really convenient for most of you to follow the updates on my I gave full command examples here so you can see the runtimes involved. This is actually the group_members table I used as the example in my previous post. But the rename is optional and can be done at any time later. Index bloat is the most common occurrence, so I’ll start with that. To be more precise PostgreSQL B-Tree implementation is based on Lehman & Yao Algorithm and B+-Trees. In case of B-Tree each … I think btree is used because it excels at the simple use case: what roes contain the following data? Different types of indexes have Different purposes, for example, the B-tree index was effectively used when a query involves the Range and equality operators and the hash index is effectively used when the equality There is a lot of work done in the coming version to make them faster. The fundamental indexing system PostgreSQL uses is called a B-tree, which is a type of index that is optimized for storage systems. I threw the ANALYZE calls in there just to ensure that the catalogs are up to date for any queries coming in during this rebuild. previous parts, stuffed with some interesting infos about these queries and But it isn't true that PostgreSQL cannot use B+ trees. If you’ve just got a plain old index (b-tree, gin or gist), there’s a combination of 3 commands that can clear up bloat with minimal downtime (depending on database activity). PostgreSQL B-Tree indexes are multi-level tree structures, where each level of the tree can be used as a doubly-linked list of pages. Tagged with bloat, json, monitoring, postgresql, tuning, The Journalist template by Lucian E. Marin — Built for WordPress, Removing A Lot of Old Data (But Keeping Some Recent). BTree indexes: And under the hood, creating a unique constraint will just create a unique index anyway. I have used table_bloat_check.sql and index_bloat_check.sql to identify table and index bloat respectively. I also made note of the fact that this script isn’t something that’s made for real-time monitoring of bloat status. When studying the Btree layout, I forgot about one small non-data area in index DROP CONSTRAINT […] call, which will require an exclusive lock, just like the RENAME above. Code simplification is always a good news :). Using the previous demo on PostgreSQL 9.5 reduced the number of cases in which btree index scans retain a pin on the last-accessed index page, which eliminates most cases of VACUUM getting stuck waiting for an index scan. This will take an exclusive lock on the table (blocks all reads and writes) and completely rebuild the table to new underlying files on disk. definitely help the bloat estimation accuracy. --This query run much faster than btree_bloat.sql, about 1000x faster.----This query is compatible with PostgreSQL 8.2 and after. ASC is the default. However, I felt that we needed several additional changes before the query is ready for me to use in our internal monitoring utilities, and thought I'd post our version here. for PostgreSQL), under the checks “table_bloat” and “btree_bloat”. About me PostgreSQL contributor since 2015 • Index-only scan for GiST • Microvacuum for GiST • B-tree INCLUDE clause • B-tree. This is is a small space on each pages reserved to the access method so it can in my Table bloat estimation query). The flat file size is only 25M. If anyone else has some handy tips for bloat cleanup, I’d definitely be interested in hearing them. I will NOT publish your email address. seems to me there’s no solution for 7.4. It's very easy to take for granted the statement CREATE INDEX ON some_table (some_column);as PostgreSQL does a lot of work to keep the index up-to-date as the values it stores are continuously inserted, updated, and deleted. Having less 25% free can put you in a precarious situation where you may have a whole lot of disk space you can free up, but not enough room to actually do any cleanup at all or without possibly impacting performance in big ways (Ex. Now we can write our set of commands to rebuild the index. While searching the disk is a linear operation, the index has do better than linear in order to be useful. The same logic has been ported to Hash indexes. However I think the big problem is that it relies on pg_class.relpages and reltuples which are only accurate just after VACUUM, only a sample-based estimate just after ANALYZE, and wrong at any other time (assuming the table has any movement). These bugs have the same results: very bad estimation. For table bloat, Depesz wrote some blog posts a while ago that are still relevant with some interesting methods of moving data around on disk. It’s showing disk space available instead of total usage, hence the line going the opposite direction, and db12 is a slave of db11. In PostgreSQL 11, Btree indexes have an optimization called "single page vacuum", which opportunistically removes dead index pointers from index pages, preventing a huge amount of index bloat, which would otherwise occur. Identifying Bloat! For very small tables this is likely your best option. In that case, it may just be better to take the outage to rebuild the primary key with the REINDEX command. However, that final ALTER INDEX call can block other sessions coming in that try to use the given table. The flat file size is only 25M. (thank you -E). If you’ve got tables that can’t really afford long outages, then things start getting tricky. All other pages are either leaf pages or internal pages. © 2010 - 2019: Jehan-Guillaume (ioguix) de Rorthais, current_database | schemaname | tblname | idxname | real_size | estimated_size | bloat_size | bloat_ratio | is_na, ------------------+------------+---------+-----------------+-----------+----------------+------------+----------------------------+-------, pagila | public | test | test_expression | 974848 | 335872 | 638976 | 65.5462184873949580 | f, current_database | schemaname | tblname | idxname | real_size | estimated_size | bloat_size | bloat_ratio | is_na, ------------------+------------+---------+-----------------+-----------+----------------+------------+------------------+-------, pagila | public | test | test_expression | 974848 | 851968 | 122880 | 12.6050420168067 | f, current_database | schemaname | tblname | idxname | real_size | estimated_size | bloat_size | bloat_ratio | is_na, ------------------+------------+---------+-----------------+-----------+----------------+------------+---------------------+-------, pagila | public | test3 | test3_i_md5_idx | 590536704 | 601776128 | -11239424 | -1.9032557881448805 | f, pagila | public | test3 | test3_i_md5_idx | 590536704 | 521535488 | 69001216 | 11.6844923495221052 | f, pagila | public | test3 | test3_i_md5_idx | 590536704 | 525139968 | 65396736 | 11.0741187731491 | f, https://gist.github.com/ioguix/dfa41eb0ef73e1cbd943, https://gist.github.com/ioguix/5f60e24a77828078ff5f, https://gist.github.com/ioguix/c29d5790b8b93bf81c27, https://wiki.postgresql.org/wiki/Index_Maintenance#New_query, https://wiki.postgresql.org/wiki/Show_database_bloat, https://github.com/zalando/PGObserver/commit/ac3de84e71d6593f8e64f68a4b5eaad9ceb85803. 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