DevOps と IT 業務｜リソースセンター

Application monitoring is a well-established discipline that dates back decades and remains a pillar of software management strategies today. However, as software environments and architectures have evolved, monitoring techniques have needed to evolve along with them. That’s why many teams today rely on distributed tracing to glean insights that they can’t gather from application monitoring alone. Distributed tracing provides a deeper level of visibility into complex distributed environments than application monitoring can achieve.

Telegraf is a server-based agent for collecting all kinds of metrics for further processing. It’s a piece of software that you can install anywhere in your infrastructure and it will read metrics from specified sources – typically application logs, events, or data outputs.

In a recent experiment with my colleagues, I polled them about the following: “What would they do if the lights went out as you worked at night?” Besides identifying the funny and who-you-want-in-case-of-an-emergency responses, most of my colleagues checked to see if the problem might be broader than their own home.

When migrating to Kubernetes and re-architecting your applications into containers, logging is a critical piece to consider. The twelve-factor app methodology has a section dedicated to logging and outlines the importance of not worrying about routing and storage of your logs. As a best practice, applications running in containers should rely 100% on standard output (STDOUT). Unfortunately, getting logs from applications that do not write to STDOUT is non-trivial and has many things to consider.

It’s essential to choose the right tool for the job. I have an old, sturdy screwdriver that I use for lots of odd DIY jobs around my house, like cleaning gutters, opening paint cans, and general maintenance on my lawnmower. However, when I’m performing an upgrade on my computer, a large, rusty screwdriver isn’t the best tool to remove the screws anchoring my motherboard.

If your team falls into the majority of organizations that use NGINX – which remains the world’s most popular Web server – to host websites and Web applications, monitoring NGINX usage, performance, and transactions is critical for maintaining a positive end-user experience.

We are excited to join AWS for the launch of Amazon CloudWatch Metric Streams; a fully managed, scalable, and low latency service that streams Amazon CloudWatch metrics to partners via Amazon Kinesis Data Firehose. AWS and Sumo Logic customers can now leverage AWS Kinesis Firehose for Metrics Source for streaming CloudWatch metrics into their Sumo Logic accounts, to help simplify the monitoring and troubleshooting of AWS infrastructure, services, and applications.

Modern systems look very different than they did years ago. For the most part, development organizations have moved away from building traditional monoliths towards the development of containerized applications running across a highly-distributed infrastructure. While this change has made systems inherently more resilient, the increase in overall complexity has made it more important (and more challenging) to effectively identify and address problems at their root cause when issues occur.

Microservices and serverless are both important topics in the world of cloud-native computing. Yet, although serverless functions and microservices architectures often go hand-in-hand, they’re distinct technologies that fill different roles in modern software environments.

Cloud-native and serverless come hand in hand. One of the initial motivations to move business workflows to the cloud was related to cutting costs related to provisioning infrastructure and elasticity that on-demand allocation of resources is offering. The serverless approach takes this to the next level, where infrastructure is provisioned only for the time of code execution, and the whole stack below the executed code, including application components, OS, and hardware (of course) is provided by the cloud vendor. No surprise this approach takes more and more traction, although it’s nothing new.

Over a year ago we decided to invest heavily in Application Observability, understanding the modern observability platform must unite logs, metrics, and traces in one analytics layer to better serve reliability use cases. We have also advocated a modern trend to acquire tracing data via open source industry standards like OpenTelemetry without vendor lock-in.