In LogStream 3.0, we introduced a framework that provides a way for LogStream customers to build, reuse, and share configuration modules – including pipelines, lookups, data samples, and knowledge objects – called Packs. While each Pack has its own “context” containing custom pipelines, routes, lookups, variables, etc., it still retains access to built-in LogStream configuration that is shipped with the product.

Whether you’re investigating an issue or simply exploring your data, the ability to perform advanced log analytics is key to uncovering patterns and insights. Datadog Log Management makes it easy to centralize your log data, which you can then manipulate and analyze to answer complex questions.

We were able to leverage the Node.js VM module to execute arbitrary JavaScript code with its own set of globals, separate from the running process’ globals.

We know customers and users today demand new features to be frequently released to their favorite apps. Plus they expect any bugs or issues hindering a great user experience to be fixed—and fast. Here we're going to cover new capabilities built to help you keep up with the business by measuring how well your team works in small batches and identifying previously invisible cross-team dependencies in your development and delivery processes.

In Cribl LogStream 3.0, we introduced Packs! Packs are self-contained bundles of configurations that allow users to solve full use cases with minimal setup/configuration on their part.

In this article, you’ll learn how to understand and debug the memory usage of a Node.js application and use monitoring tools to get a complete insight into what is happening with the heap memory and garbage collection. Here’s what you’ll get by the end of this tutorial. Memory leaks often go unnoticed. This is why I suggest using a tool to keep track of historical data of garbage collection cycles and to notify you if the heap memory usage starts spiking uncontrollably.

IoT has rapidly moved from a fringe technology to a mainstream collection of techniques, protocols, and applications that better enable you to support and monitor a highly distributed, complex system. One of the most critical challenges to overcome is processing an ever-growing stream of analytics data, from IoT security data to business insights, coming from each device. Many protocols have been implemented for this, but could logs provide a powerful option for IoT data and IoT monitoring?

The final stage of the popular Software Development Lifecycle after planning, analysis, design, and implementation is maintenance. This is where a full-fledged application running in production is constantly looked after and taken care of. Bugs, bottlenecks, slow database queries, security loopholes, and other issues are discovered and fixed before deploying the updated code. Log records and Application Performance Monitoring (APM) tools play a crucial role in software development maintenance.

All the applications that you’re trying to execute require memory. It doesn’t matter if the application was developed using assembly language. Or if you used a low-level programming language like C or a language compiled to a bytecode like Java. Running the application requires memory for the code itself, the variables, and the data that the code processes. Depending on your usage, the memory requirements will vary.

Logging is an essential component of many applications. Every application has a different logging technique. You may prefer certain logging implementations, but you must also consider what to log, when to log, how much to log, and how to control logging. System administrators and developers, particularly the support team, benefit greatly from a well-designed logging system. For both the support team and the developers, logs save a lot of time.