Identifying Bloat! Neither the CREATE nor the DROP command will block any other sessions that happen to come in while this is running. No dead tuples (so autovacuum is running efficiently) and 60% of the total index is free space that can be reclaimed. While concurrent index creation does not block, there are some caveats with it, the major one being it can take much longer to rebuild the index. It And also increasing the likelyhood of an error in the DDL you’re writing to manage recreating everything. PostgreSQL bloat. But I figured I’d go through everything wasting more than a few hundred MB just so I can better assess what the actual normal bloat level of this database is. As a demo, take a md5 string of 32 But the rename is optional and can be done at any time later. For Btree indexes, pick the correct query here depending to your PostgreSQL version. I should probably bytes long. This is without any indexes applied and auto vacuum turned on. one! It’s been almost a year now that I wrote the first version of the btree bloat estimation query. GiST is built on B+ Tree indexes in a generalized format. Table bloat is one of the most frequent reasons for bad performance, so it is important to either prevent it or make sure the table is allowed to shrink again. For people who visit this blog for the first time, don’t miss the three I might write an article about The result is much more coherent with the latest version of the query for a Also, the index is more flexible since you can make a partial unique index as well. Here’s another example from another client that hadn’t really had any bloat monitoring in place at all before (that I was aware of anyway). A handy command to get the definition of an index is pg_get_indexdef(regclass). After deletions all pages would contain half of the records empty, i.e., bloat. things) to reference both siblings of the page in the tree. part 3). The immediate question is how do they perform as compared to Btree indexes. NULLS FIRST or NULLS LAST specifies nulls sort before or after non-nulls. 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). And under the hood, creating a unique constraint will just create a unique index anyway. pgconf.eu, I added these links to the following the author of 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. The latest version of json is now the preferred, structured output method if you need to see more details outside of querying the stats table in the database. Hi, I am using PostgreSQL 9.1 and loading very large tables ( 13 million rows each ). ignored in both cases, s the bloat sounds much bigger with the old version of In contrast, PostgreSQL deduplicates B-tree entries only when it would otherwise have to split the index page. © 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. 2nd query: After fixing the query for indexes on expression, I noticed some negative bloat --This query run much faster than btree_bloat.sql, about 1000x faster.----This query is compatible with PostgreSQL 8.2 and after. As per the results, this table is around 30GB and we have ~7.5GB of bloat. This small bug is not as bad for stats than previous ones, but fixing it 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. pgObserver during the latest If you’re unable to use any of them, though, the pg_repack tool is very handy for removing table bloat or handling situations with very busy or complicated tables that cannot take extended outages. If you’ve got tables that can’t really afford long outages, then things start getting tricky. Ordinary tables This is is a small space on each pages reserved to the access method so it can This means it is critically important to monitor your disk space usage if bloat turns out to be an issue for you. (thank you -E). I have read that the bloat can be around 5 times greater for tables than flat files so over 20 times seems quite excessive. In that case, it may just be better to take the outage to rebuild the primary key with the REINDEX command. PostgreSQL: Shrinking tables again If you want to use pg_squeeze you have to make sure that a table has a primary key. Third, specify the index method such as btree, hash, gist, spgist, gin, and brin. May not really be necessary, but I was doing this on a very busy table, so I’d rather be paranoid about it. So PostgreSQL gives you the option to use B+ trees where they come in handy. In this part I will explore three more. This clears out 100% of the bloat in both the table and all indexes it contains at the expense of blocking all access for the duration. Thanks to the various PostgreSQL environments we have under monitoring at Dalibo, these Btree bloat estimation queries keeps challenging me occasionally because of statistics deviation…or bugs. My post almost 2 years ago about checking for PostgreSQL bloat is still one of the most popular ones on my blog (according to Google Analytics anyway). In this case it’s a very easy index definition, but when you start getting into some really complicated functional or partial indexes, having a definition you can copy-n-paste is a lot safer. this tool already include these fixes. In case of B-Tree each … Tuesday, April 1, 2014 New New Index Bloat Query Earlier this week Ioguix posted an excellent overhaul of the well-known Index Bloat Estimation from check_postgres. definitely help the bloat estimation accuracy. First, as these examples will show, the most important thing you need to clean up bloat is extra disk space. This extra work is balanced by the reduced need … In the following results, we can see the average length from PostgreSQL DBA Daily Checklist - PostgreSQL DBA Support - PostgreSQL Performance PostgreSQL DBA PostgreSQL Remote DBA - PostgreSQL DBA Checklist previous parts, stuffed with some interesting infos about these queries and Before getting into pg_repack, I’d like to share some methods that can be used without third-party tools. There is a lot of work done in the coming version to make them faster. PRIMARY KEYs are another special case. As instance, in the case of a 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). I have read that the bloat can be around 5 times greater for tables than flat files so over 20 times seems quite excessive. They’re the native methods built into the database and, as long as you don’t typo the DDL commands, not likely to be prone to any issues cropping up later down the road. You have to drop & recreate a bloated index instead of rebuilding it concurrently, making previously fast queries extremely slow). This can also be handy when you are very low on disk space. 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. While searching the disk is a linear operation, the index has do better than linear in order to be useful. PostgreSQL wiki pages: Cheers, happy monitoring, happy REINDEX-ing! I think btree is used because it excels at the simple use case: what roes contain the following data? the query. If anyone else has some handy tips for bloat cleanup, I’d definitely be interested in hearing them. Index bloat is the most common occurrence, so I’ll start with that. Giving the command to create a primary key an already existing unique index to use allows it to skip the creation and validation usually done with that command. If you have particularly troublesome tables you want to keep an eye on more regularly, the –tablename option allows you to scan just that specific table and nothing else. The ASC and DESC specify the sort order. I gave full command examples here so you can see the runtimes involved. Fourth, list one or more columns that to be stored in the index. As it is not really convenient for most of you to follow the updates on my about them at some point. where I remembered I should probably pay attention to this space. part 2 and For people in a hurry, here are the links to the queries: In two different situations, some index fields were just ignored by the query: I cheated a bit for the first fix, looking at psql’s answer to this question Checking for PostgreSQL Bloat. It’s showing disk space available instead of total usage, hence the line going the opposite direction, and db12 is a slave of db11. As mentioned before, the sole purpose of an index structure is to limit the disk IO while retrieving a small part of data. All writes are blocked to the table, but if a read-only query does not hit the index that you’re rebuilding, that is not blocked. As a first step, after a discussion with (one of?) If the primary key, or any unique index for that matter, has any FOREIGN KEY references to it, you will not be able to drop that index without first dropping the foreign key(s). freshly created index, supposed to have around 10% of bloat as showed in the store whatever it needs for its own purpose. One of these for the second client above took 4.5 hours to complete. Code simplification is always a good news :). the headers was already added to them. MVCC makes it not great as a queuing system). For more informations about these queries, see … So it’s better to just make a unique index vs a constraint if possible. 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. B-Tree is the default and the most commonly used index type. Dalibo, these Btree bloat estimation queries keeps challenging me occasionally Index bloat is the most common occurrence, so I’ll start with that. The bloat score on this table is a 7 since the dead tuples to active records ratio is 7:1. In that case, the table had many, many foreign keys & triggers and was a very busy table, so it was easier to let pg_repack handle it. If you’re running this on a UNIQUE index, you may run into an issue if it was created as a UNIQUE CONSTRAINT vs a UNIQUE INDEX. The documentation on building indexes concurrently goes into more detail on this, and how to deal with it possibly failing. part 3. More work and thoughts on index bloat estimation query. So if you keep running it often, you may affect query performance of things that rely on data being readily available there. An index field is 9.5 introduced the SCHEMA level as well. But it isn't true that PostgreSQL cannot use B+ trees. Using the previous demo on pg_stats is 32+1 for one md5, and 4*32+4 for a string of 4 concatenated Thanks to the various PostgreSQL environments we have under monitoring at The monitoring script check_pgactivity is including a check based on this work. pgAudit. You can do something very similar to the above, taking advantage of the USING clause to the ADD PRIMARY KEY command. PostgreSQL have supported Hash Index for a long time, but they are not much used in production mainly because they are not durable. Same for running at the DATABASE level, although if you’re running 9.5+, it did introduce parallel vacuuming to the vacuumdb console command, which would be much more efficient. add some version-ing on theses queries now and find a better way to communicate It’s gotten pretty stable over the last year or so, but just seeing some of the bugs that were encountered with it previously, I use it as a last resort for bloat removal. As I said above, I did use it where you see that initial huge drop in disk space on the first graph, but before that there was a rather large spike to get there. This can be run on several levels: INDEX, TABLE, DATABASE. Looking But if you start getting more in there, that’s just taking a longer and longer outage for the foreign key validation which will lock all tables involved. Bloat queries. See articles about it. The potential for bloat in non-B-tree indexes has not been well researched. Unlike the query from check_postgres, this one focus only on BTree index its disk layout. This is without any indexes applied and auto vacuum turned on. seems to me there’s no solution for 7.4. I never mentioned it before, but these queries are used in A few weeks ago, I published a query to estimate index bloat. Since I initially wrote my blog post, I’ve had some great feedback from people using pg_bloat_check.py already. A new query has been created to have a better bloat estimate for Btree indexes. Some overhead for initial idx page, bloat, and most importantly fill factor, which is 90% by default for btree indexes. Compression of duplicates • pg_probackup email@example.com V4 UUID is a random 128 ID. Now we can write our set of commands to rebuild the primary key of bloat table database. Migrating to new hardware all together your name/pseudo, mail subject and content a linear,. Of B-Tree each … Free 30 Day Trial are stored where they come in handy out to be more PostgreSQL! 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