Prometheus Metrics Explained: Counters, Gauges, Histograms & Summaries This discussion is the first part of the basic monitoring series, an effort to eliminate confusion in monitoring for both beginners and experienced users.

Prometheus Monitoring: Instant Queries and Range Queries Explained Over the years, we’ve received many questions about MetricsQL/PromQL, even from experienced users—especially regarding range queries and instant queries. This article is basic but turns out to be really important to explain why your query behaves the way it does. This discussion is part of the basic monitoring series, an effort to eliminate confusion in monitoring for both beginners and experienced users.

Datadog Database Monitoring (DBM) Recommendations help you proactively optimize performance throughout your database fleet. DBM draws on a wide range of data sources in order to detect and provide actionable guidance on issues such as blocking queries, low disk space, and missing indexes. In this post, we’ll show you how DBM formulates targeted indexing recommendations to help you optimize database performance.

This year Grafana Mimir — the open source, horizontally scalable, multi-tenant time series database (TSDB) — will celebrate its third anniversary. Over the years, Mimir has become the go-to, Prometheus-compatible metrics backend within the open source community, with 29 maintainers and more than 4.6k GitHub stars. Since introducing Mimir, we’ve worked hard to deliver on our promise of making it the most scalable and performant open source TSDB in the world.

In the last few years, Postgres has experienced a meteoric rise in popularity. A relational database that not long ago was relatively unknown outside of academic circles has now eclipsed MySql as the most popular database for developers in the most recent StackOverflow user survey. Why has it achieved such impressive popularity with developers?