Anomaly detection for monitoring has been a trending topic in recent years. And while the math behind it is fascinating, too much of the discussion has revolved around histograms, moving averages and standard deviations. More discussion needs to happen around its practical applications, and for that reason, this practical guide to anomaly detection will attempt to provide an actionable overview of current off-the-shelf anomaly detection tools.
One of the first things we do right after installing Nagios, is set up email notifications. Without that, how would you know when something went wrong?
In many ways, incident management for devops is similar to typical issue tracking processes: it facilitates coordination and collaboration of daily tasks. For this reason, tools such as Jira, Zendesk, and even email are often used as solutions for incident management. But incident management faces one unique challenge that makes it different from other issue tracking processes. In addition to human-operated workflows, incident management also relies heavily on machine-driven workflows. Unfortunately, traditional issue trackers and ticketing systems cannot accommodate for this with their current product mechanics.
Many alerts place an unnecessary burden on Ops teams instead of helping them to solve issues. The main problem is that most alerts are not actionable enough:
Few things damage productivity as much as waiting. Waiting forces us to context switch, disrupts our creative momentum and eliminates our ability to experiment. Whether we are deploying a new service or troubleshooting a problem, waiting puts a heavy tax on efficient work.