Operations | Monitoring | ITSM | DevOps | Cloud

In this survey of over 200 CIOs in the US, the IDC analyses the critical role played by Log Analytics in any modern infrastructure.

Logging is critical for monitoring and troubleshooting your Node.js project. The open-source Winston logger helps take a load off our shoulders by making it easier to centralize, format, enrich, and distribute the logs to fit a particular need. Winston creates custom logger instances which can be configured to act as centralized logging entities. Essentially, the internal architecture of the module decouples the actual event logging from the implementation of the storage logic.

In this article, we’ll learn about the Elasticsearch flattened datatype which was introduced in order to better handle documents that contain a large or unknown number of fields. The lesson examples were formed within the context of a centralized logging solution, but the same principles generally apply. By default, Elasticsearch maps fields contained in documents automatically as they’re ingested.

One of the most common dashboards for metric visualization and alerting is, of course, Grafana. In addition to logs, we use metrics to ensure the stability and operational observability of our product. This document will describe some basic Grafana operations you can perform with the Coralogix-Grafana integration. We will use a generic Coralogix Grafana dashboard that has statistics and information based on logs. It was built to be portable across accounts.

For many companies today business success depends on quality efficient build, test and delivery processes. Development and deployment environments become complex very quickly, even for small and medium companies. A contributing factor to this complexity is companies’ adoption of microservices. This is where modern CI/CD solutions like CircleCI come in to help streamline these processes.

In this post we will cover some of the main use cases FluentD supports and provide example FluentD configurations for the different cases.

Are you building and deploying software manually and would like to change that? Are you interested in learning about building a Jenkins pipeline and better understand CI/CD and DevOps at the same time? In this first post, we will go over the fundamentals of how to design pipelines and how to implement them in Jenkins. Automation is the key to eliminating manual tasks and to reducing the number of errors while building, testing and deploying software.

In the context of logging, multiline logs happen when a single log is written as multiple lines in the log file. When logs are sent to 3rd party log monitoring platforms like Coralogix using standard shipping methods (e.g. Fluentd, Filebeat), which read log files line-by-line, every new line creates a new log entry, making these logs unreadable for the user.

Out of the four basic computing resources (storage, memory, compute, network), storage tends to be positioned as the foremost one to focus on for any architect optimizing an Elasticsearch cluster. Let’s take a closer look at a couple of interesting aspects in relation to the Elasticsearch storage optimization and let’s do some hands-on tests along the way to get actionable insights. The storage topic consists of two general perspectives.

Elasticsearch provides a powerful set of options for querying documents for various use cases so it’s useful to know which query to apply to a specific case. The following is a hands-on tutorial to help you take advantage of the most important queries that Elasticsearch has to offer. In this guide, you’ll learn 42 popular query examples with detailed explanations, but before we get started, here’s a summary of what the types of queries we’ll tackle.