Autonomous log monitoring for Kubernetes

November 18, 2019 | Gavin Cohen

Kubernetes makes it easy to deploy, manage and scale large distributed applications. But what happens when something goes wrong with an app? And how do you even know?

Kubernetes makes it easy to deploy, manage and scale large distributed applications. But what happens when something goes wrong with an app? And how do you even know? We hear variations on this all the time: “It was only when customers started complaining that we realized our service had degraded”, “A simple authentication problem stopped customers logging. It took six hours to resolve.”, and so on.

Read More

Using machine learning to shine a light inside the monitoring black box

October 24, 2019 | Ajay Singh

A widely prevalent application monitoring strategy today is sometimes described as “black box” monitoring. Black box monitoring focuses just on externally visible symptoms, including those that approximate the user experience. Black box monitoring is a good way to know when things are broken.

Read More

The hidden complexity of hiding complexity

October 22, 2019 | Gavin Cohen

Kubernetes and other orchestration tools use abstraction to hide complexity. Deploying, managing and scaling a distributed application are made easy. But what happens when something goes wrong? And, when it does, do you even know?

Kubernetes and other orchestration tools use abstraction to hide complexity. Deploying, managing and scaling a distributed application are made easy. But what happens when something goes wrong? And, when it does, do you even know?

Read More

Catching Faults Missed by APM and Monitoring tools

August 19, 2019 | Ajay Singh

As software gets more complex, it gets harder to test all possible failure modes within a reasonable time. Monitoring can catch known problems – albeit with pre-defined instrumentation. But it’s hard to catch new (unknown) software problems. 

A quick, free and easy way to find anomalies in your logs

 

Read More

Deploying into Production: The need for a Red Light

July 23, 2019 | Larry Lancaster

As scale and complexity grow, there are diminishing returns from pre-deployment testing. A test writer cannot envision the combinatoric explosion of coincidences that yield calamity. We must accept that deploying into production is the only definitive test.

Read More

Using ML to auto-learn changing log structures

July 14, 2019 | David Adamson

Software log messages are potential goldmines of information, but their lack of explicit structure makes them difficult to programmatically analyze. Tasks as common as accessing (or creating an alert on) a metric in a log message require carefully crafted regexes that can easily capture the wrong data by accident (or break silently because of changing log formats across software versions). But there’s an even bigger prize buried within logs – the possibility of using event patterns to learn what’s normal and what’s anomalous. 

Why understand log structure at all?

Read More

Featured Posts