When Logz.io was founded in 2015, we set out to simplify logging with the ELK Stack by delivering Elasticsearch and Kibana as a managed cloud service. But logs only tell part of the story – DevOps teams also need metric and trace data to better monitor the health and performance of their environment and quickly pinpoint the root cause of new problems. Importantly, using multiple tools to collect and analyze this data adds complexity and extra work.
Game development is an entirely different beast to other industries. Marketing, development, and release are more tightly interwoven than in other sectors, with a lot of pressure to meet community-anticipated milestones and launch. As such, it’s important to have game engine logging and monitoring pipelines set up for your projects. In other platforms, version upgrades and roll-outs tend to be sudden, with no definitive date set.
In this post, we will discuss some key considerations and strategies to collect and analyze your AWS Lambda logs. This will include 1) what to know about logging Lambda functions, 2) how to ship log data to a centralized logging solution, and 3) how to search and visualize log data on monitoring dashboards.
The aim of this article is to demonstrate how you can instrument a Java application using Opentelementry and Jaeger. In this example, we will be instrumenting our Java application using OpenTelemetry and the OpenTelemetry Java client, and the tracing data will be exported and visualized using Jaeger. We will use the Logz.io Jaeger backend as it is compatible with common tracing standards like Zipkin, OpenTelemetry, and OpenTracing.
Previously, I wrote a Beginner’s Guide to Jaeger + OpenTracing Instrumentation for Go providing guidance on manually instrumenting Go services. This is useful for cases where we want fine-grained tracing of specific functions. However, what if all we want is to trace a service’s inbound and outbound calls with little to no additional code?
OpenSearch has been a buzz in DevOps over the first half of 2021. The project is moving forward, but understandably there are a lot of questions. This article will address some of those frequently asked questions, and will be updated to address more over time.
Last week Elastic.co started locking down its Beats OSS shippers such that they will not be able to send data to Elasticsearch 7.10 or earlier open source distros, or Non-Elastic distros of Elasticsearch. If you weren’t watching closely this might have slipped under your radar. Embedded within the Beats 7.13 minor release that was published over the weekend, a release note advised of a breaking change in which “Beats may not be sending data to some distributions of Elasticsearch”.
What an exciting episode of OpenObservability Talks it was! On May 27, I hosted Kyle Davis, Senior Developer Advocate for OpenSearch at AWS, for a chat about the OpenSearch project, where it stands and where it’s heading. I wanted to share with you some interesting insights from our chat. You’re more than welcome to check out the full episode.
Personally, I’ve always wanted to contribute to an open-source project, but never found a way to incorporate it with my day-to-day work. Occasionally, I’d muster up the courage to clone a project I liked, seeking a good entry point to add some new feature or handle some issue. I thought that all I needed was to make a small contribution and everything else would just flow into place.
Logz.io is always looking to improve the user experience when it comes to Kubernetes and monitoring your K8s architecture. We’ve taken another step with that, adding OpenTelemetry instrumentation with Helm charts. We have made Helm charts available before, previously with editions suitable for Metricbeat and for Prometheus operators.
