The latest News and Information on Log Management, Log Analytics and related technologies.
This article is part 2 of a four-part series of articles about Elasticsearch performance monitoring. Part 1 explains what Elasticsearch is and how it works, while in this part, we’re going to look at Elasticsearch’s capabilities and potential use cases, and how to check its status. We’ll identify key metrics that you need to monitor to maintain the health and performance of your Elasticsearch cluster.
Breaking down larger, monolithic software, services, and applications into microservices has become a standard practice for developers. While this solves many issues, it also creates new ones. Architectures composed of microservices create their own unique challenges. In this article, we are going to break down some of the most common. More specifically, we are going to assess how observability-based solutions can overcome many of these obstacles.
More and more employers are looking for people experienced in building and running Kubernetes-based systems, so it’s a great time to start learning how to take advantage of the new technology. Elasticsearch consists of multiple nodes working together, and Kubernetes can automate the process of creating these nodes and taking care of the infrastructure for us, so running ELK on Kubernetes can be a good options in many scenarios.
What are logs? In computing, when generally speaking of “log”, we refer to information belonging to a more or less low level reported by the operating system or a specific application that helps identify what is being done, including errors, problems or minor warnings, and when that happens, it indicates the date and time. In some cases, the source, the user, the IP address and other interesting fields from the point of view of what has happened can be identified.
This post is part of a blog series highlighting how we embrace the solutions and features of the Elastic Stack to support our business and drive customer success. The Elastic InfoSec Security Engineering team is responsible for deploying and managing InfoSec's infrastructure and tools. At Elastic, speed, scale, and relevance is our DNA and leveraging the power of the Elastic Stack is the heart of InfoSec.
LogQL, the Loki query language, is heavily inspired by Prometheus PromQL. However, when it comes to filtering logs and finding the needle in the haystack, the query language is very specific to Loki. In this article we’ll give you all the tips to create fast filter queries that can filter terabytes of data in seconds. In Loki there are three types of filters that you can use.
When approached by stakeholders in their organization, few security teams can confidently demonstrate that logging and alerting capabilities are working as expected. Organizations have become more distributed and reliant on cloud offerings for use cases such as identity and access management, user productivity, and file storage. Meanwhile, adversaries have extended their operational capabilities in cloud environments.
Monitoring and analytics have been an issue for Serverless systems since they were invented. While it’s easy to attach an agent like NewRelic or DataDog to a server or container, function monitoring requires a different approach. Serverless applications, where logic is distributed over a large number of functions, attaching agents or wrappers leads to cost increase and development overhead.
