Operations | Monitoring | ITSM | DevOps | Cloud

Logs Monitoring plays an important role in analyzing, troubleshooting and alerting on problems. Organizations have lots of data in logs that should be mined to get valuable insights on users, applications and systems behavior. Real time alerting from logs monitoring does help in identifying the problems early. It also provides security related insights when someone is trying to break into your organization.

As Stephen Marsland once said, “if data had mass, the earth would be a black hole.” A vast part of the immense amount of structured and unstructured data that we call “Big Data” is nothing but machine-originated log data. Logs are generated for a lot of different purposes – from security to debugging and troubleshooting. They constitute a gold mine of useful information and actionable insights if properly stored, managed, and analyzed.

Last week I was at Monitorama and it was an amazing time! Not only where the talks fantastic, but I also had the opportunity to join Liz Fong-Jones from Honeycomb.io for a podcast with The New Stack to dig a little deeper into some of the topics in monitoring and site reliability engineering.

What are the essential things to monitor in your infrastructure? Sure, CPU utilization, memory usage, and IO throughput. However, once you notice a significant load somewhere in your infrastructure you want to know what is causing it, and that typically boils down to needing to find the process that’s using too much CPU or memory or that’s doing disk or network IO like there’s no tomorrow.

Pulling in data exposed via API is not one of the most common use cases for ELK Stack users but it is definitely one I’ve come across in the past. Developers wrapping their database services with REST API, for example, might be interested in analyzing this data for business intelligence purposes. Whatever the reason, the ELK Stack offers some easy ways to integrate with this API. One of these methods is the Logstash HTTP poller input plugin.