Lets look again at metrics. This time from the perspective of their definition and usability. It was not easy for me to understand types of metrics and how to read them. Which metrics are useful and therefore should be paid attention to? This blog post will focus on Dropwizard metrics with a sample application for a bit of practice.
Viewing Metrics in Datadog
This is the last post in the Datadog series. Previous two: PostgreSQL Metrics in Datadog and Sending Metrics to Datadog with Java showed a need to report data and how to do it. This one will focus on how those metrics can be observed and used.
Sending Metrics to Datadog with Java
In my previous post I drew an idea of sending PostgreSQL metrics to Datadog using Java code. This post will reveal implementation details of the Send action described previously.
How does Datadog collect metrics?
There are two basic ways of collecting and sending data:
- Use Datadog agent
- Collect and send manually
PostgreSQL Metrics in Datadog
I currently work on a project were AWS RDS PostgreSQL is used as a data storage. Since Postgres is using a mechanism called Multiversion Concurrency Control – MVCC, an UPDATE or DELETE command does not remove old versions of a row immediately. These are left on a disk, waiting to be collected and cleaned by a vacuum process. Vacuum can be automated and autovacuum serves that purpose.
Once autovacuum is configured, how do I know it works as expected? Is it triggered when I expect it to be?