So bloat is actually not always a bad thing and the nature of MVCC can lead to improved write performance on some tables. Used by queries that select from inheritance hierarchies. (the “C” in A.C.I.D). Read his blog for a summary of his performance findings, along with important conclusions on Intel Optane performance. I have tried VACUUM, REINDEX, VACUUM FULL ANALYZE with REINDEX, and even dump and restore. What happens when you perform a DELETE or an UPDATE of a row? # CREATE TABLE scott.employee (emp_id INT, emp_name VARCHAR(100), dept_id INT); # UPDATE scott.employee SET emp_name = 'avii'; # INSERT into scott.employee VALUES (1,'avi',2); # INSERT into scott.employee VALUES (2,'avi',2); # INSERT into scott.employee VALUES (3,'avi',2); # INSERT into scott.employee VALUES (4,'avi',2); # INSERT into scott.employee VALUES (5,'avi',2); # INSERT into scott.employee VALUES (6,'avi',2); # INSERT into scott.employee VALUES (7,'avi',2); # INSERT into scott.employee VALUES (8,'avi',2); # select xmin,xmax,cmin,cmax,* from scott.employee; # DELETE from scott.employee where emp_id = 4; # DELETE from scott.employee where emp_id = 5; # DELETE from scott.employee where emp_id = 6; # select oid from pg_class where relname = 'employee'; # CREATE TABLE scott.employee (emp_id int PRIMARY KEY, name varchar(20), dept_id int); # INSERT INTO scott.employee VALUES (generate_series(1,1000), 'avi', 1); # select relpages, relpages*8192 as total_bytes, pg_relation_size('scott.employee') as relsize. Hey Folks, Back with another post on PostgreSQL. This is related to some CPU manipulation optimisation. the fillfactor: this allows you to set up a ratio of free space to keep in your tables or indexes. After an UPDATE or DELETE, PostgreSQL keeps old versions of a table row around. If you have issued a ROLLBACK, or if the transaction got aborted, xmax remains at the transaction ID that tried to DELETE it (which is 655) in this case. We have a hidden column called ctid which is the physical location of the row version within its table. Our white paper, Why Choose PostgreSQL?, takes a look at the situations where PostgreSQL makes sense and when it does not. Catalogs can bloat because they are tables too. Index Bloat Based on check_postgres. Earlier, it occupied 6 pages (8KB each or as set to parameter : block_size). Only the future inserts can use this space. He has given several talks and trainings on PostgreSQL. Then old row versions don’t get deleted, and the table keeps growing. One nasty case of table bloat is PostgreSQL’s own system catalogs. Upon VACUUM, this space is not reclaimed to disk but can be re-used by future inserts on this table. Under certain circumstances, with autovacuum daemon not aggressive enough, for heavily-written tables bloat can be a problem that has to be taken care of by the DBA. The view always shows 375MB of bloat for the table. We discussed about xmin and xmax. That is the task of the autovacuum daemon. What are these hidden columns cmin and cmax ? And that is absolutely correct. VACUUM does an additional task. For more informations about these queries, see the following articles. On Terminal A : We open a transaction and delete a row without committing it. Consider the case when a table … * This query is compatible with PostgreSQL 9.0 and more */ SELECT current_database(), schemaname, tblname, bs * tblpages AS real_size, (tblpages-est_tblpages) * bs AS extra_size, CASE WHEN tblpages -est_tblpages > 0 the fillfactor: this allows you to set up a ratio of free space to keep in your tables or indexes. This is not a table that has frequent deletes, so I'm at a loss as to what is causing the bloat. This means, no transaction ID that has started before the ID 647, can see this row. Now, run ANALYZE on the table to update its statistics and see how many pages are allocated to the table after the above insert. Please note that VACUUM FULL is not an ONLINE operation. Bloat can also be efficiently managed by adjusting VACUUM settings per table, which marks dead tuple space available for reuse by subsequent queries. Indexes can get bloated too. However, this space is not reclaimed to filesystem after VACUUM. All the rows that are inserted and successfully committed in the past are marked as frozen, which indicates that they are visible to all the current and future transactions. These queries is for informational purposes only. Bloat Removal By Tuples Moving Autovacuum helps you remove bloat, reduce table disk usage, and update your table stats regularly for the query planner to run cost-effectively. Table Bloat. If you have a database that seems to be missing its performance marks, take a look at how often you’re running the autovacuum and analyze functions—those settings may be all you need to tweak. How often do you upgrade your database software version? Monitor the bloat of indexes as both an absolute value (number of bytes) and as a percentage. For Btree indexes, pick the correct query here depending to your PostgreSQL version. 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. This UNDO segment contains the past image of a row, to help database achieve consistency. This way, concurrent sessions that want to read the row don’t have to wait. Create a table and insert some sample records. Avinash Vallarapu joined Percona in the month of May 2018. Subscribe now and we'll send you an update every Friday at 1pm ET. Note: the behavior may change depending on the isolation levels you choose, would be discussed later in another blog post. What is table bloat in the first place? As you see in the above log, the transaction ID was 646 for the command => select txid_current(). Thus, the immediate INSERT statement got a transaction ID 647. You can use queries on the PostgreSQL Wiki related to Show Database Bloat and Index Bloat to determine how much bloat you have, and from there, do a bit of performance … If you observe the above output log, you see cmin and cmax values incrementing for each insert. In simple terms, PostgreSQL maintains both the past image and the latest image of a row in its own Table. It means, UNDO is maintained within each table. And this is done through versioning. VACUUM scans the pages for dead tuples and marks them to the freespace map … Now, let’s DELETE 5 records from the table. When a table is bloated, Postgres’s ANALYZE tool calculates poor/inaccurate information that the query planner uses. A very large bloat factor on a table or index can lead to poor performance for some queries, as Postgres will plan them without considering the bloat. In order to understand how these versions are maintained within each table, you should understand the hidden columns of a table (especially xmin) in PostgreSQL. PostgreSQL implements transactions using a technique called MVCC. The mechanics of MVCC make it obvious why VACUUM exists and the rate of changes in databases nowadays makes a good case for the existence of autovacuum daemon. Now, we may get a hint that, every row of PostgreSQL table has a version number. /*reltuples::bigint, relpages::bigint, otta,*/, /*ituples::bigint, ipages::bigint, iotta,*/, -- very rough approximation, assumes all cols, https://wiki.postgresql.org/index.php?title=Show_database_bloat&oldid=26028. cmin : The command identifier within the inserting transaction. You cannot read from or write to the table while VACUUM FULL is in progress. Thus, PostgreSQL runs VACUUM on such Tables. the bloat itself: this is the extra space not needed by the table or the index to keep your rows. For example: is it an issue if my largest table has just 100K rows after one year? In order to understand that better, we need to know about VACUUM in PostgreSQL. See the following log to understand how the cmin and cmax values change through inserts and deletes in a transaction. Make sure to pick the correct one for your PostgreSQL version. Let’s see the following log to understand the xmin more. Whenever a query requests for rows, the PostgreSQL instance loads these pages into the memory and dead rows causes expensive disk I/O during data loading. Bloat seriously affect the PostgreSQL query performance, In PostgreSQL tables and indexes are stored as array of fixed-size pages ( usually 8KB in size). The records are physically ordered on the disk based on the primary key index. This time related with table fragmentation (Bloating in PG) on how to identify it and fix it using Vacuuming.. As per the results, this table is around 30GB and we have ~7.5GB of bloat. In PostgreSQL table bloat has been a primary concern since the original MVCC model was conceived. To obtain more accurate information about database bloat, please refer to the pgstattuple or pg_freespacemap contrib modules. The view always shows 375MB of bloat for the table. From time to time there are news/messages about bloated tables in postgres and a thereby decreased performance of the database. Want to get weekly updates listing the latest blog posts? These deleted records are retained in the same table to serve any of the older transactions that are still accessing them. After VACUUM, it has released 3 pages to filesystem. Bloat can also be efficiently managed by adjusting VACUUM settings per table, which marks dead tuple space available for reuse by subsequent queries. As explained earlier, if there are pages with no more live tuples after the high water mark, the subsequent pages can be flushed away to the disk by VACUUM. The postgres-wiki contains a view (extracted from a script of the bucardo project) to check for bloat in your database here For a quick reference you can check your table/index sizes regularly and check the no. It never causes exclusive locks on tables. Therefore we have decided to do a series of blog posts discussing this issue in more detail. Bloated indexes can slow down inserts and reduce lookup performance. Removing the bloat from tables like this can actually cause decreased performance because instead of re-using the space that VACUUM marks as available, Postgres has to again allocate more pages to that object from disk first before the data can be added. If you are an Oracle DBA reading this blog post, you may quickly recollect the error ORA-01555 snapshot too old . Also, you can observe here that t_xmax is set to the transaction ID that has deleted them. So, lets manually vacuum our test table and see what happens: Now, let's look at our heapagain: After vacuuming, tuples 5, 11, and 12 are now freed up for reuse. In this part I will explore three more. However if empty pages at the end of tables are removed and space returned to the operating system. When you describe a table, you would only see the columns you have added, like you see in the following log. What this error means isâyou may have a smaller undo_retention or not a huge UNDO segment that could retain all the past images (versions) needed by the existing or old transactions. Under certain circumstances, with autovacuum daemon not aggressive enough, for heavily-written tables bloat can be a problem that has to be taken care of by the DBA. As seen in the above examples, every such record that has been deleted but is still taking some space is called a dead tuple. xmin : The transaction ID(xid) of the inserting transaction for this row version. Let’s observe the following log to understand that better. After an UPDATE or DELETE, PostgreSQL keeps old versions of a table row around. Can you please explain Transaction ID Wraparound in PSQL in a detail ? In the above example, you see that the number of pages still remain same after deleting half the records from the table. VACUUM scans the pages for dead tuples and marks them to the freespace map (FSM). Let us see the following log to understand what happens to those dead tuples after a VACUUM. I have a table in a Postgres 8.2.15 database. As we discussed earlier, through the hidden columns in PostgreSQL for every table, we understand that there are multiple versions of rows maintained within each table. The pageinspect module provides functions that allow you to inspect the contents of database pages at a low level, which is useful for debugging purposes. This explains why vacuum or autovacuum is so important. percona=# VACUUM ANALYZE percona; VACUUM percona=# SELECT t_xmin, t_xmax, tuple_data_split('percona'::regclass, t_data, t_infomask, t_infomask2, t_bits) FROM heap_page_items(get_raw_page('percona', 0)); t_xmin | t_xmax | tuple_data_split ——–+——–+——————————- | | | | 3825 | 0 | {"\\x03000000","\\x09617669"} (3 rows), percona=# SELECT * FROM bt_page_items('percona_id_index', 1); itemoffset | ctid | itemlen | nulls | vars | data ————+——-+———+——-+——+————————- 1 | (0,3) | 16 | f | f | 03 00 00 00 00 00 00 00 (1 row), Hello Avi, its good explanation. The operation to clear out obsolete row versions is called vacuum. This can also be handy when you are very low on disk space. Also note that before version 9.5, data types that are not analyzable, like xml, will make a table look bloated as the space needed for those columns is not accounted for. See the PostgreSQL documentation for more information. However, how do you know when it makes sense to use it over another database? If the table does become significantly bloated, the VACUUM FULL statement (or an alternative procedure) must be used to compact the file. With the above example, you should now understand that every tuple has an xmin that is assigned the txid that inserted it. Doesn’t this increase the size of a table continuously? Large and heavily updated database tables in PostgreSQL often suffer from two issues: table and index bloat, which means they occupy way more disk space and memory than actually required;; corrupted indexes, which means query planner can't generate efficient query execution plans for them and as a result DB performance degrades over time. Bloat queries. ; To help developers and database … You may not have to worry about that with PostgreSQL. 3. As we discussed earlier, an UPDATE of 10 records has generated 10 dead tuples. Upon update, a new row version is inserted. I have used table_bloat_check.sql and index_bloat_check.sql to identify table and index bloat respectively. 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. The user had a huge table, almost 1TB in size, with one of the columns recording the data-creation time. Even if you ROLLBACK, the values remain the same. Now that we understand the hidden columns xmin and xmax, let’s observe what happens after a DELETE or an UPDATE in PostgreSQL. For example: is it an issue of my largest table has just 100K rows after one year? –> is there a query to check dead tuples are beyond the high water mark or not? Unfortunately I am finding a table to have bloat which can't be reclaimed. So my first question to those of you who have been using Postgres for ages: how much of a problem is table bloat and XID wrap-around in practice? There is a common misconception that autovacuum slows down the database because it causes a lot of I/O. In the first case, it is understandable that there are no more live tuples after the 3rd page. Implementation of MVCC (Multi-Version Concurrency Control) in PostgreSQL is different and special when compared with other RDBMS. Before the DELETE is committed, the xmax of the row version changes to the ID of the transaction that has issued the DELETE. Some of them have gathered tens of gigabytes of data over the years. See the PostgreSQL documentation for more information. We would be submitting a blog post on it soon and then add a comment with the link. Very nice explanation. Monitoring your bloat in Postgres Postgres under the covers in simplified terms is one giant append only log. Table Bloat Across All Tables. One of the common needs for a REINDEX is when indexes become bloated due to either sparse deletions or use of VACUUM FULL (with pre 9.0 versions). Usually you don’t have to worry about that, but sometimes something goes wrong. In the above log, you might notice that the dead tuples are removed and the space is available for re-use. In the above log, you see that the VACUUM has reclaimed half the space to filesystem. Each relation apart from hash indexes has an FSM stored in a separate file called
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