Postgresql copy table11/10/2022 So as I feared, the things that fail that filter might be rarer than other things, but they are sitting right in the way. You are currently removing 855 rows using a filter on col3 and col4, in order to find 10 rows which pass that filter. In this process, only col4 is updated which can either be set to zero since it's been consumed or a number lower than it's current value. We're looking at 10 entries at a time from the database and if need be there might be a subsequent same query on the database to fetch the next valid entries. It's like a bag with a capacity C which we're trying to fill with col4 values only taking entries which are valid ( col3 is 'f' and col4 > 0). I can give an idea what we're trying to achieve here. Postgresql copy table update#It's very difficult to tell the number of rows affected during update but it will not be more than 20 for a particular transaction. No, we don't need to update a billion rows at a time. The performance looks more than acceptable since we're on a larger machine but this was taking more than 1 second on the smaller one. Sharding would be our last resort but we want to exhaust all our options before moving to this solution.Įdit: Attaching the query plans (1st when there's no data in buffer and the second is the immediate subsequent query hit). Due to the nature of the application, we can't partition the table unless it's 6 months old or more. We also want suggestions on building a sustainable solution. The database went live for the production use exactly 3 months back. P.S.: The data growth is about 30 million entries per day so this is going to go worse. Sharing a few Postgres db parameters (currently on the bigger machine): effective_cache_size: 130GB But increasing shared_buffer to more than 40% of RAM always led to queries getting extremely slow and we always had to revert it back to the previous value. We've tried multiple memory tuning in the previous machine so as to fully utilize the RAM. We want to be using the smaller machine by doing any changes in parameters so that this query can run under some tolerable latency limit. All in all, this machine is heavily underutilized. The problem is that the machine which we're now using is running at ~5% CPU load and there's 184 GB of RAM still unused. This certainly has to do with the higher shared_buffer now available for postgres to keep the hot referenced rows in cache, we're assuming. On the larger machine we're observing that even the read IOPS has now fallen to ~5000 and the machine now consumes overall IOPS of around 6000 at peak times and the query time has halved precisely. We upgraded to a larger machine with exactly double the size since we could no longer provision IOPS more than 20000 on the prior machine. We recently faced an issue where the read IOPS started hitting 20000 and the application got too slow. The autovacuum is configured in such a way that it runs every couple of days on this table. We started with a provisioned IOPS of about 3000 and kept increasing it with hopes that with an aggressive autovacuum and data localization it will stabilize at some value. Up until now we were using a 16 vCPU and 128 GB machine with io1 storage type with a provisioned IOPS of 20000 for hosting (it's hosted on AWS RDS). This leads to the queries waiting on DataFileRead. The problem arises when the database needs to do a lot of disk seeks when there's a miss from buffer. To serve this query, there is an index on the table ON (col1, col2, col5) and the query uses this index. The query looks something like this: select id, col4 from table where col1=$1 and col2=$2 and col3='f' and col4>0 order by col5 limit 10 There is one query that we need to do in order to find a set of valid rows from the data set and do updates on them. The total size of the table (including index) stands at 500 GB. We've a very large table with more than 2.2 billion rows at present on Postgres 12.5.
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