The network is foundational to distributed application environments. A distributed application has multiple microservices, each running in a set of pods often located on different nodes. Problem areas in a distributed application can be in network layer connectivity (think network flow logs), or application resources unavailability (think metrics), or component unavailability (think tracing).
The aggregations framework has been part of Elasticsearch since version 1.0, and through the years it has seen optimizations, fixes, and even a few overhauls. Since the Elasticsearch 7.0 release, quite a few new aggregations have been added to Elasticsearch like the rare_terms, top_metrics or auto_date_histogram aggregation. In this blog post we will explore a few of those and take a closer look at what they can do for you.
In our latest post we’re covering a range of the different kinds of problems and threats data analysis can help protect your business from. We’ve brought together some of our favourite experts working in big data, cybersecurity and tech to shed light on some of the practical applications of using data analysis for protecting your operations.
In today’s world, with many microservices fuelling hundreds of components, the failure of just one piece can cause a crash for the whole system. For example, a lack of memory in one component can cause a database failure. This database failure could be the reason for authentication problems for particular users, causing those users to not be able to login. And of course, finding the core problem manually can be complex and time-consuming.
The ability to monitor your Elastic Cloud deployment is critical for helping ensure its health, performance, and security. Our Elastic Observability solution provides unified visibility across your entire ecosystem — including your Elastic Cloud deployments. Elastic Observability allows you to bring your logs, metrics, and APM traces together at scale in a single stack so you can monitor and react to events happening anywhere in your environment.
Logs are an important part of your application and logging shouldn’t be one of the last things you think of. You should configure your log system, formatter, and style as soon as you start the development of your app. Also, do your best to document the process and share how it works with the rest of your team. In this article, we’re going to demonstrate how logs work in Elixir. We’ll jump into Elixir’s Logger module, which brings a lot of power to logging features.
If you’re reading this article, you’re most likely looking for a simple one-stop-shop way to understand logs. I’m sorry to be the one to tell you this, but logs are not simple enough to deal with easily. In fact, as you start approaching this topic on a practical level you’ll quickly realize how complex and annoying it truly is.
Log scaling is something that should be top of mind for organizations seeking to future-proof their logging solutions. Logging requirements will grow through use, particularly if not maintained or utilized effectively. There are barriers to successful log scaling, and in this post we’ll be discussing storage volume problems; increased load on the ELK stack, the amount of ‘noise’ generated by a growing ELK stack, and the pains of managing burgeoning clusters of nodes.
Monitoring Jenkins is a serious challenge. Logging is often overlooked, but it provides a wealth of information about the health of your Jenkins instance. The following are some approaches to generating informative logging to these issues, that can help to monitor and provide suitable explanations of where the problems lie; even identifying what the possible solutions are.
