considering the special space and estimation considering it: This is only an approximative 5% difference for the estimated size of this particular index. 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. 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. Dalibo, these Btree bloat estimation queries keeps challenging me occasionally This can be run on several levels: INDEX, TABLE, DATABASE. 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 part 3. Make sure to pick the correct one for your PostgreSQL version. I gave full command examples here so you can see the runtimes involved. this tool already include these fixes. This is is a small space on each pages reserved to the access method so it can Thanks to the various PostgreSQL environments we have under monitoring at previous parts, stuffed with some interesting infos about these queries and 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. 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. However, that final ALTER INDEX call can block other sessions coming in that try to use the given table. Since it’s doing full scans on both tables and indexes, this has the potential to force data out of shared buffers. gists, I keep writing here about my work on these queries. Unlike the query from check_postgres, this one focus only on BTree index its disk layout. Since I initially wrote my blog post, I’ve had some great feedback from people using pg_bloat_check.py already. As a followup to my previous post on checking for bloat, I figured I’d share some methods for actually cleaning up bloat once you find it. 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. 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. See the PostgreSQL documentation for more information the bloat itself: this is the extra space not needed by the table or the index to keep your rows. 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). wrong. After the DROP command, your bloat has been cleaned up. New repository for bloat estimation queries. I never mentioned it before, but these queries are used in pgAudit. If it is, you may want to re-evaluate how you’re using PostgreSQL (Ex. where I remembered I should probably pay attention to this space. See articles about it. For more informations about these queries, see … It’s best to run it maybe once a month or once a week at most during off-peak hours. DROP CONSTRAINT […] call, which will require an exclusive lock, just like the RENAME above. Leaf pages are the pages on the lowest level of the tree. 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. 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. The ASC and DESC specify the sort order. for PostgreSQL), under the checks “table_bloat” and “btree_bloat”. It’s been almost a year now that I wrote the first version of the btree bloat estimation query. It have no opaque data, so no special space (good, I ‘ll not have to fix this bug one! --This query run much faster than btree_bloat.sql, about 1000x faster.----This query is compatible with PostgreSQL 8.2 and after. 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. Monitoring your bloat in Postgres Postgres under the covers in simplified terms is one giant append only log. So if you keep running it often, you may affect query performance of things that rely on data being readily available there. In Robert M. Wysocki's latest Write Stuff article, he looks at the wider aspects of monitoring and managing the bloat in PostgreSQL.. PostgreSQL's MVCC model provides excellent support for running multiple transactions operating on the same data set. PostgreSQL supports the B-tree, hash, GiST, and GIN index methods. 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. 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). The difference between B-Trees and B+-Trees is the way keys are stored. The concurrent index creation took quite a while (about 46 minutes), but everything besides the analyze commands was sub-second. The above graph (y-axis terabytes) shows my recent adventures in bloat cleanup after using this new scan, and validates that what is reported by pg_bloat_check.py is actually bloat. ASC is the default. In case of B-Tree each … Specifying a primary key or a unique within a CREATE TABLE statement causes PostgreSQL to create B-Tree indexes. It’s showing disk space available instead of total usage, hence the line going the opposite direction, and db12 is a slave of db11. Now, with the next version of PostgreSQL, they will be durable. ignored in both cases, s the bloat sounds much bigger with the old version of things) to reference both siblings of the page in the tree. the author of bug took me back on this doc page reinsertion into the bloated V4 index reduces the bloating (last point in the expectation list). But it isn't true that PostgreSQL cannot use B+ trees. Now, it may turn out that some of these objects will have their bloat return to their previous values quickly again and those could be candidates for exclusion from the regular report. When you insert a new record that gets appended, but the same happens for deletes and updates. I’ve gotten several bugs fixed as well as adding some new features with version 2.1.0 being the latest available as of this blog post. (thank you -E). Index bloat is the most common occurrence, so I’ll start with that. B-tree index bloat estimation for PostgreSQL 8.0 to 8.1 - btree_bloat-8.0-8.1.sql 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. In this version of the query, I am computing and adding the headers length of varlena types (text, bytea, etc) to the statistics(see Btree index, this “special space” is 16 bytes long and used (among other Third, specify the index method such as btree, hash, gist, spgist, gin, and brin. Typically, it just seems to work. in my Table bloat estimation query). May not really be necessary, but I was doing this on a very busy table, so I’d rather be paranoid about it. PostgreSQL uses btree by default. For Btree indexes, pick the correct query here depending to your PostgreSQL version. This is without any indexes applied and auto vacuum turned on. If anyone else has some handy tips for bloat cleanup, I’d definitely be interested in hearing them. Over the next week or so I worked through roughly 80 bloated objects to recover about 270GB of disk space. 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). Some overhead for initial idx page, bloat, and most importantly fill factor, which is 90% by default for btree indexes. I will NOT publish your email address. In both this graph and the one below, there were no data purges going on and each of the significant line changes coincided exactly with a bloat cleanup session. When running on the INDEX level, things are a little more flexible. 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. check_pgactivity (a nagios plugin If you can afford several shorter outages on a given table, or the index is rather small, this is the best route to take for bloat cleanup. The CONCURRENTLY flag to the CREATE INDEX command allows an index to be built without blocking any reads or writes to the table. A single metapage is stored in a fixed position at the start of the first segment file of the index. I also made note of the fact that this script isn’t something that’s made for real-time monitoring of bloat status. closer to the statistic values because of this negative bloat, I realized that This is me first fixing one small, but very bloated index followed by running a pg_repack to take care of both table and a lot of index bloat. Once you’ve gotten the majority of your bloat issues cleaned up after your first few times running the script and see how bad things may be, bloat shouldn’t get out of hand that quickly that you need to run it that often. Taking the “text” type as example, PostgreSQL adds a one byte header to the And since index bloat is primarily where I see the worst problems, it solves most cases (the second graph above was all index bloat). I’ve been noticing that the query used in v1.x of my pg_bloat_check.py script ... kfiske@prod=# CREATE INDEX concurrently ON group_members USING btree (user_id); CREATE INDEX Time: 5308849.412 ms 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. There are several built-in ways to deal with bloat in PostgreSQL, but all of them are far from universal solutions. The latest version of 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. This can also be handy when you are very low on disk space. store whatever it needs for its own purpose. Fourth, list one or more columns that to be stored in the index. json is now the preferred, structured output method if you need to see more details outside of querying the stats table in the database. The flat file size is only 25M. To be more precise PostgreSQL B-Tree implementation is based on Lehman & Yao Algorithm and B+-Trees. 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). Ordinary tables No dead tuples (so autovacuum is running efficiently) and 60% of the total index is free space that can be reclaimed. The next option is to use the REINDEX command. You have to drop & recreate a bloated index instead of rebuilding it concurrently, making previously fast queries extremely slow). PRIMARY KEYs are another special case. The previous However, the equivalent database table is 548MB. Now we can write our set of commands to rebuild the index. For a delete a record is just flagged … The result is much more coherent with the latest version of the query for a A new query has been created to have a better bloat estimate for Btree indexes. The potential for bloat in non-B-tree indexes has not been well researched. One of these for the second client above took 4.5 hours to complete. freshly created index, supposed to have around 10% of bloat as showed in the because of statistics deviation…or bugs. part 3). While searching the disk is a linear operation, the index has do better than linear in order to be useful. Also, if you’re running low on disk space, you may not have enough room for pg_repack since it requires rebuilding the entire table and all indexes in secondary tables before it can remove the original bloated table. Code simplification is always a good news :). However, the equivalent database table is 548MB. First, as these examples will show, the most important thing you need to clean up bloat is extra disk space. In that case, it may just be better to take the outage to rebuild the primary key with the REINDEX command. Before getting into pg_repack, I’d like to share some methods that can be used without third-party tools. 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. 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. md5, supposed to be 128 byte long: After removing this part of the query, stats for test3_i_md5_idx are much better: This is a nice bug fix AND one complexity out of the query. The flat file size is only 25M. In the following results, we can see the average length from As instance, in the case of a the query. After deletions all pages would contain half of the records empty, i.e., bloat. Note: I only publish your name/pseudo, mail subject and content. PostgreSQL DBA Daily Checklist - PostgreSQL DBA Support - PostgreSQL Performance PostgreSQL DBA PostgreSQL Remote DBA - PostgreSQL DBA Checklist Btree bloat query - part 4. About me PostgreSQL contributor since 2015 • Index-only scan for GiST • Microvacuum for GiST • B-tree INCLUDE clause • B-tree. 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. However, a pro… As mentioned before, the sole purpose of an index structure is to limit the disk IO while retrieving a small part of data. The bloat score on this table is a 7 since the dead tuples to active records ratio is 7:1. If you can afford the outage, it’s the easiest, most reliable method available. But they are marked specially in the catalog and some applications specifically look for them. 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. – Erwin Brandstetter Dec 9 at 21:46 I was This is without any indexes applied and auto vacuum turned on. When studying the Btree layout, I forgot about one small non-data area in index All other pages are either leaf pages or internal pages. PostgreSQL wiki pages: Cheers, happy monitoring, happy REINDEX-ing! The Index method Or type can be selected via the USING method. I have read that the bloat can be around 5 times greater for tables than flat files so over 20 times seems quite excessive. And under the hood, creating a unique constraint will just create a unique index anyway. test3_i_md5_idx, here is the comparison of real bloat, estimation without 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. But the rename is optional and can be done at any time later. (11 replies) Hi, I am using PostgreSQL 9.1 and loading very large tables ( 13 million rows each ). Running it on the TABLE level has the same consequence of likely locking the entire table for the duration, so if you’re going that route, you might as well just run a VACUUM FULL. CREATE INDEX statements without the USING clause will also create B-Tree indexes: And also increasing the likelyhood of an error in the DDL you’re writing to manage recreating everything. pgconf.eu, I added these links to the following Using the previous demo on A handy command to get the definition of an index is pg_get_indexdef(regclass). 2nd query: After fixing the query for indexes on expression, I noticed some negative bloat In this part I will explore three more. GiST is built on B+ Tree indexes in a generalized format. 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Check_Pgactivity is including a check based on this doc page where I remembered should! B-Tree each … Free 30 Day Trial made for real-time monitoring of bloat more precise PostgreSQL B-Tree index more! Is 90 % by default for Btree indexes, pick the correct query here depending to your PostgreSQL.. Only on Btree index its disk layout uses is called a B-Tree hash. The index has do better than linear in order to be an for! The name, the index is more flexible since you can see the involved... It has to do an ALTER table [.. ] it seems to me ’! The definition of an error in the DDL you ’ ve had some great feedback from using! Pay attention to this space more flexible the next option is postgresql btree bloat just make a partial unique vs!