Operations | Monitoring | ITSM | DevOps | Cloud

Elasticsearch is a complex piece of software by itself, but complexity is further increased when you spin up multiple instances to form a cluster. This complexity comes with the risk of things going wrong. In this lesson, we’re going to explore some common Elasticsearch problems that you’re likely to encounter on your Elasticsearch journey.

Microservice architecture is widely popular. The ease of building and maintaining apps, scaling CI/CD pipelines, as well as the flexibility it offers when it comes to pivoting technologies are some of the main reasons companies like Uber and Netflix are all in on this approach. As the amount of services in a microservice architecture rises, complexity naturally also rises.

Continuous Integration and Continuous Delivery (CI/CD) delivers services fast, effectively, and accurately. In doing so, CI/CD pipelines have become the mainstay of effective DevOps. But this process needs accurate, timely, contextual data if it’s to operate effectively. This critical data comes in the form of logs and this article will guide you through optimizing logs for CI/CD.

Untangling a chaotic mess can be daunting. And that’s exactly the problem Alain Adler, the Head of Engineering at BetterHelp, faced.

As cloud providers and infrastructure technologies grow their support for Windows containers, developers who use the Windows ecosystem are more and more able to enjoy the benefits of containerization. It’s quicker and easier than ever to modernize and deploy applications that use Windows-specific frameworks like .NET. Plus, Windows developers can use orchestration services like Kubernetes, Amazon ECS, or Docker Swarm to manage the complexity that containerized environments introduce.

As organizations build out their serverless footprint, they might find themselves managing hundreds or thousands of individual components (e.g., Amazon S3 buckets, Amazon DynamoDB tables, AWS SQS queues) for just a single application. At the same time, performance issues can crop up at any of these points, which means that having access to detailed observability data from your serverless functions is crucial for effective troubleshooting.

Here at Raygun, improving performance is baked into our culture. In a previous blog post, we showed how we achieved a 12% performance lift by migrating Raygun’s API to .NET Core 3.1. In publishing this, a question was asked on Twitter as to why we still use Nginx as a proxy to the Raygun API application. Our response was that we thought this was the recommended approach from Microsoft. It turns out this has not been the case since the release of .NET Core 2.1.

After my last blog around sending Github Data to Splunk via Webhooks, I received a healthy amount of feedback that I want to address here. I learned that (unsurprisingly) a lot of customers are curious about, or dependant on, other cloud platforms out there. In fact, I heard directly from some customers who specifically cannot use any other cloud platforms than one in particular that was not highlighted in my last blog.

Simple, yet powerful metrics to determine the health and protection status of your Spectrum Protect environment in a single pane of glass.

Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.