Operations | Monitoring | ITSM | DevOps | Cloud

Today, virtually everyone who manages infrastructure or applications relies on logging to understand what is happening within their environments. But some teams do logging better than others. Although there is no one right – or wrong – approach to log management, there are a variety of logging mistakes that engineers commonly make when deciding what to log, how to log it, and how to work with their log data.

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.

Since we introduced AppScope in 2021, we’ve been relentlessly working towards the production-ready milestone. Last week we released AppScope 1.0. It’s been a long haul getting to this point. Not really sure if it took this long because we solved difficult problems, or if we’re just that slow. Someone told me that what we are doing would go a lot faster if we use a modern high-level language. Maybe … Can you imagine doing this in TypeScript? Yeah, me either.

Lambda is a top-rated compute service available on the AWS cloud service network. Its popularity largely derives from its ease of use, allowing users to run Lambda functions reliably without provisioning or managing servers. Lambda can be triggered manually or by any linked events in the AWS network, including DynamoDB streams, SQS, Kinesis, and more.

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.

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.

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?

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.