Operations | Monitoring | ITSM | DevOps | Cloud

Running systems in production involves requirements for high availability, resilience and recovery from failure. When running cloud native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running.

Last week, we had an issue with one of our Kafka brokers. Don’t worry, it didn’t impact any customers. When monitoring things closely, you can often solve things before they impact a customer ;-). In today’s post, I’ll show you how we use AppSignal to dogfood our own issues. I’ll go through how we monitor the non-Ruby part of our stack and how we used AppSignal to detect and resolve the issue.

At LogicMonitor, we deal primarily with large quantities of time series data. Our backend infrastructure processes billions of metrics, events, and configurations daily. In previous blogs, we discussed our transition from monolith to microservice. We also explained why we chose Quarkus as our microservices framework for our Java-based microservices. In this blog we will cover.

Today, we dip our toes into collecting custom metrics with a standalone agent. We’ll be taking our own Kafka brokers and using the StatsD protocol to get the metrics into AppSignal. This post is for those with some experience in using monitoring tools, and who want to take monitoring to every corner of their architecture, or want to add their own metrics to their monitoring setup.

Everyone hates waiting in a queue. On the other hand, when you’re moving gigabytes of data around a cloud environment, message queues are your best friend. Enter Apache Kafka. Apache Kafka enables organisations to create message queues for large volumes of data. That’s about it – it does one simple but critical element of cloud-native strategies, really well.

Today, monolithic applications evolve to be too large to deal with as all the functionalities are placed in a single unit. Many enterprises are tasked with breaking them down into microservices architecture. At LogicMonitor we have a few legacy monolithic services. As business rapidly grew we had to scale up these services, as scaleout was not an option.

Kafka is a distributed streaming platform that acts as a publish-subscribe messaging queue by receiving data from various source systems and making it available to various systems and applications in real time. Key advantages for utilizing Kafka are that it provides durable storage, meaning the data stored within it cannot be easily tampered with, and it is highly scalable, so it can handle a large increase in users, workloads, and transactions when necessary.