In an earlier blog post, Log monitoring and unstructured log data, moving beyond tail -f, we talked about collecting and working with unstructured log data. We learned that it’s very easy to add data to the Elastic Stack. So far the only parsing we did was to extract the timestamp from this data, so older data gets backfilled correctly. We also talked about searching this unstructured data toward the end of the blog.
With the growing adoption of remote and distributed application development including micro-services, cloud-native applications, serverless, and more, it is becoming challenging more than ever before for developers to troubleshoot issues within a reasonable time, and that is a bottleneck. That in a sense contradicts the objectives of Agile and DevOps through fast feedback loops, continuous delivery, quick MTTR (mean time to resolution of defects), etc.
When working with log messages, it’s critical that the timestamp of the log message is accurate. Incorrect timestamps can cause problems when trying to find log messages at a specific date/time or may cause alerts to not function properly. A common cause of incorrect timestamps for log messages is a mismatch of time zones between the log source (device sending the log) and log destination (device receiving the log, such as Graylog).
We Just celebrated 10 year birthday to Prometheus last month. Prometheus was the second project to join the Cloud Native Computing Foundation after Kubernetes in 2016, and has quickly become the de-facto way to monitor Kubernetes workloads. The plug-and-play experience, just putting Prometheus server and starting to see metrics flowing in tagged with Kubernetes labels, was a compelling offer.
Grafana Loki is designed to be cost effective and easy to operate for DevOps and SRE teams, but running queries in Loki can be confusing for those who are new to it. Loki is a horizontally scalable, highly available, multi-tenant log aggregation system inspired by Prometheus. It doesn’t index the content of the logs, but rather a set of labels for each log stream.
Table of Contents Setting up and administering multiple servers for business and application purposes has become easier thanks to advancements in cloud technology. Today, enterprises are choosing to operate large numbers of servers both in the cloud and in their data centers to meet the ever-increasing demand. As a result of these changes, monitoring technologies have become crucial. In this post, we’ll explore the best server monitoring tools and software currently on the market.
Table of Contents When an application written for the Java Virtual Machine is running, it constantly creates new objects and puts them on the heap. Well, at least in the vast majority of the cases. Such objects can have a longer or shorter life, but at some point, they stopped being referenced from the code. Unlike languages like C/C++, we don’t have exact control over when the memory will be freed – freeing the memory is the garbage collector’s job.
Monitoring the performance of an application is not a strange concept to most developers. At one point or another, we’ve all had to do some performance debugging of our own. Usually, it happens when there’s a big issue affecting the user’s experience or cost implications. Only then do we make time to look at how the app performs in different scenarios.
In 2017, Just Eat Takeaway.com (JET) was transitioning from a scrappy startup to a surging scaleup. With a global customer base and workforce, the food delivery marketplace’s front line teams needed to scale the real-time monitoring of the platform. Their initial efforts looked like “NASA’s mission control with Grafana dashboards,” said Senior Technology Manager Alex Murray.
