Operations | Monitoring | ITSM | DevOps | Cloud

Log messages help us to understand data flow through applications, as well as spot when and where errors are occurring. There are a lot of resources for how to store and view logs for applications running on traditional services, but Kubernetes breaks the existing model by running many applications per server and abstracting away most of the maintenance for your applications. In this blog post, we focus on log management for applications running in Kubernetes by reviewing the following topics.

Logging is probably not the first item to come to mind when most of us think about DevSecOps, a term that refers to the integration of security into DevOps processes, but it should be. Logging and log management play a critical role in helping to put DevSecOps principles into practice by ensuring that developers, IT operations staff, and security teams have the visibility and communication pipelines they need to prioritize security at all stages of the DevOps delivery cycle.

In this article, we are going to have a quick introduction to Logstash, a very popular application for collecting, processing and filtering log data – and see how it works. We will review plugins, installation and configuration of Logstash, we will briefly mention Beats, and also compare Logstash to other log collectors and review Logstash alternatives.

OpenTelemetry — the merger of OpenCensus and OpenTracing — appeared in May of 2019, led by companies like Omnition (now a part of Splunk), Google, Microsoft, and others who are pushing the curve on observability. OpenTelemetry is a project within the Cloud Native Computing Foundation (CNCF) that has gathered contributors and supporters far and wide, becoming one of the most active projects found in open source today. It’s currently #2 behind only Kubernetes!

Logz.io Infrastructure Monitoring just got some substantive upgrades to kick off the summer. The major change is the Grafana 7 update, specifically to version 7.0.3. This includes several new features. The Logz.io deployment will also include applying Grafana to logs. The Transform tab gives users the option to quickly add non-time series data to tables and then pair it with other data (time series or not) in your Metrics deployment.

Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.