2020 might be a year many of us want to forget, but this year, we also unveiled a variety of new products and features worth remembering. For the Logz.io team, 2020 was a year full of innovation as we worked to continuously improve our product and complete our unified observability vision. We also launched a variety of new capabilities for Logz.io Log Management, Infrastructure Monitoring, Cloud SIEM, and Distributed Tracing, that make our product faster, smarter, and more cost-efficient.

As we move into the next year, we are hoping for a return to relative normalcy. That goes for our personal lives, social lives, and professional lives. Professionals have acclimated to the situation, or now see a return to relative normalcy just around the corner with the development of vaccines. And despite the wait, different industries and the DevOps community in particular have persisted.

Prometheus is an essential element in DevOps. It has one of the largest contributor communities in the open source world and is the favorite database for time-series metrics data. It is also part of Logz.io’s complete observability package, which integrates open source grafana, Jaeger, and ELK Stack into one platform. It’s a favorite for system and container monitoring. This tutorial will show you how Prometheus works with Docker and your local machine.

There is more to Distributed Tracing with Jaeger than just capturing machine data as with metrics, or tailing log files. To start, you should read this primer. In this article, I will walk you through the initial principles you’ll need before executing anything within your codebase. This is going to focus on Node.js, as slight differences and concerns exist for browser applications.

Load balancing is an element of most popular web applications. The reason for this is simple: Load balancers maintain application scalability and sustainability. It’s nearly impossible to imagine a modern application handling continuous traffic or periodic traffic spikes while relying only on a single running server’s capacity. As a result, load balancers have become a critical part of software development.

This post aims to provide a very simple beginner’s guide to Jaeger + OpenTracing instrumentation for Go applications (the terms “application” and “service” is used interchangeably in this document) via a working example. If you are new to instrumentation, I recommend that you first read this post for a practical introduction to instrumentation for Jaeger and OpenTracing. You can also get more info on using logs in Go.

Every journey in distributed tracing starts with instrumenting an application to emit or extract trace data from each service as they execute. There are many ways to instrument, including the use of SDKs and pre-configured frameworks, and many protocols for transmitting the trace data to the analysis tool.

At Logz.io, we are always keeping a close eye on the most widely-used and favorite open source monitoring tools among the developer community. This is why we announced the Early Access for our Infrastructure Monitoring product based on open source grafana at this time last year. After a successful GA and a year of strong adoption among new and existing customers, I’m thrilled to announce a huge milestone for the product: Early Access for Prometheus-as-a-service!