![]() ![]() Here you can see all the connections, queries and last activity time for each connection, so it should be easy to see if there are any hanging connections. Amazon Relational Database Services (RDS) is a managed database service that runs on familiar. | query_start | state_change | waiting | state |. Template1 => select * from pg_stat_activity ĭatid | datname |. ![]() To actually make sure that everything is OK with connections handling, run the following query: Each instance runs a flask application with several threads, so they can easily consume around 20 connections. In my case the higher connection consumption was not something unexpected, the Elastic Beanstalk scaled the application to 6 EC2 instances under load. So I decided to change the instance type to db.t2.small, it costs twice more, but it has 2GB RAM and default max_connections value is 60. once done - go to the db instance settings (Modify action) and set the new parameter group.īut for me it seems to be dangerous solution to use in production, because more connections will consume more memory and instead of connection limit errors you can end up with out of memory errors.using the default group as parent and then edit the max_connection value.The default value can be changed this way: The db.t1.micro instance has 1GB memory, and calculation like 1024*1024*1024/31457280 gives around 36, while actually it is 26. The max_connection setting can also be found in the RDS UI, under “Parameter Groups”, but the value there is set as, so it is complex to be sure about the actual value. That means we used 25 connections (22 + 3 reserved) out of 26. So we have 26 max connections and, as stated in comments (thanks, Johannes Schickling), there are also 3 connections reserved to superuser. ![]()
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