The latest News and Information on Log Management, Log Analytics and related technologies.
If you’re a software developer, then you understand how vital application logging is in software development and a critical part of logging is something called logging levels. Log entries generally contain essential information—such as a timestamp, a message, and sometimes additional stuff like an exception’s stack trace. Those pieces of information are useful because they allow someone reading the log entry to understand how the application behaved in production.
Kafka and the ELK Stack — usually these two are part of the same architectural solution, Kafka acting as a buffer in front of Logstash to ensure resiliency. This article explores a different combination — using the ELK Stack to collect and analyze Kafka logs. As explained in a previous post, Kafka plays a key role in our architecture. As such, we’ve constructed a monitoring system to ensure data is flowing through the pipelines as expected.
Let’s start with the happy ending — after a long search, we managed to identify a Netty memory leak in one of our log listeners and were able to troubleshoot and fix the issue on time before the service crashed.
To quote the timeless Ren and Stimpy jingle, “everyone needs a log.” It’s true — everyone does need a log, especially developers. Despite the fact that no developer looks forward to combing through logs, they’re incredibly useful when unexpected performance issues, like one we recently had with file uploads, need some investigation.
In October of last year, I joined Sumo Logic to lead sales and go-to-market functions with the goal of successfully launching our newly established Japan region in Tokyo. The launch was highly received by our customers, partners, prospects and peers in the Japanese market and everyone walked away from the event optimistic about the future and hungry for more!
