Needless to say that such an explosion does have a real impact on performance. We have recently seen cases, where a table containing only a couple of millions of rows has exploded to the staggering size of 4.5 TB. At some point the situation is not under control anymore and the tables in your database will simply explode. Of course a larger table will make VACUUM take longer and longer and longer. Your tables will simply grow beyond proportion. In this case all ingredients for disaster are there: The long reads will make sure that VACUUM cannot do its job and remove dead rows in time, which in turn will lead to table bloat. ![]() Long reads reading some other tables in your database.However, there are also some very special cases, which users should be aware of – imagine the following situation: In most cases autovacuum is exactly what you want. However, there are cases, in which things can turn out to be a problem. In 90% of all cases autovacuum does a perfect job and cleans out tables just the way it should be. Running VACUUM on a table manually will therefore “feel” faster than autovacuum, which is of course only related to the default configuration. Note that those automatic VACUUM processes are not running at full speed to ensure that running transactions are not affected. In case something has to be done, a process is launched to cleanup a table. The autovacuum daemon periodically checks (usually once a minute) if tables are in need of VACUUM. VACUUM is very important and to make life as simple as possible, PostgreSQL does a lot of vacuum by itself. One way to take care of long transaction is to simply kill them if they are not active for a certain period of time: i dle_in_transaction_session_timeout can be pretty helpful in this case. Therefore keeping a close eye on long transactions is crucial. It is also worth pointing out that VACUUM does NOT necessarily shrink the table on disk anymore but simply marks space in a way that it can be reused later on. Rule: Long transactions can delay cleanup and cause table bloat. In short: Even when you are trying to run VACUUM like crazy, it does not necessarily reclaim space because long running transactions will delay the cleanup. The important thing in this example is: The first VACUUM is not allowed to remove dead rows already because there are still transactions, which can see those rows, which are about to be deleted. Let us take a look at an example: Connection 1 VACUUM: The art of cleaning dead rowsĪs stated already DELETE does not actually remove old rows. The same applies to DELETE: If your disk is full, deleting 100 million rows will not automatically return space to the filesystem because some concurrent transactions might still use the data. PostgreSQL has to ensure that the row is still there. Note that the second SELECT in the second connection will still see the old row. Let us assume that the table will contain a single row (id = 16): Connection 1 Why is that important? Here is one more example. UPDATE has to copy the row and ensure that both versions are there to handle transactions properly. ![]() The idea is simple: UPDATE is not allowed to destroy the old version of the row because otherwise ROLLBACK would not work. But why is that the case? Here is an example: After an UPDATE the old as well as the new row will be in your table. The most important thing beginners have to keep in mind is: Internally UPDATE will duplicate a row. So it can make sense to take a step back and take a look at the broader picture: Here is our beginners guide on UPDATE and autovacuum in PostgreSQL. Therefore people moving from Oracle to PostgreSQL might be surprised. Compared to other databases such as Oracle, PostgreSQL’s way of handling UPDATE and storage in general is quite different. Looking at the type of PostgreSQL support requests, we have received recently, it is striking to see, how many of them are basically related to autovacuum and UPDATE in particular. Autovacuum table bloat update and upgrade vacuum
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