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Observability

The latest News and Information on Observabilty for complex systems and related technologies.

Budget Planning for Next-generation APM and Observability

If you’re trying to evaluate and understand the ROI of building an observability practice and carve out a budget for it, you’re not alone. You’ve probably got some monitoring and metrics capability already, but that’s proving to not be enough–how can you empower your teams as your environment becomes too complex for the basics? And how much will that cost?

Linux Kernel Observability through eBPF

Recent Linux kernel releases are coming weaponized with built-in instrumentation framework that has its roots in what historically was approached as BPF (Berkeley Packet Filter) – a very efficient network packet filtering mechanism which aims to avoid unnecessary user space allocations and operate on packet’s data directly in kernel land. The most familiar application of BPF powers is related to filter expressions used in tcpdump tool.

Introducing container observability with eBPF and Sysdig.

Today we’ve announced that we’ve officially added eBPF instrumentation to extend container observability with Sysdig monitoring, security and forensics solutions. eBPF – extended Berkeley Packet Filter – is a Linux-native in-kernel virtual machine that enables secure, low-overhead tracing for application performance and event observability and analysis.

How Fluentd compares to LogDNA

Observing modern applications is challenging. Microservices allow for applications that are not only more distributed but are made up of a number of different languages, frameworks, and backend services. DevOps teams have far greater flexibility in where and how they deploy applications,but when it comes time to collect logs, this flexibility can quickly become a hurdle.

How Much Should My Observability Stack Cost?

What should one pay for observability? How much observability is enough? How much is too much, or is there such a thing? Is it better to pay for one product that claims (dubiously) to do everything, or twenty products that are each optimized to do a different part of the problem super well? It’s almost enough to make a busy engineer say “Screw it, I’m spinning up Nagios”. (Hey, I said almost.)

"Observability": Just a Fancy Word for "Monitoring"? A Journey From What to Why

Too often, monitoring is a never-ending arms race. We keep adding more monitoring in response to new problems, but the cycle never seems to end. Humans, (the business), drive new changes, which cause new problems, and need more, new monitoring. And that’s where real, useful observability may be able to help finally identify root cause and break the cycle of reactive monitoring for novel issues.

Cutting-Edge Observability Tools into a Single Platform

Sematext provides a single pane of glass and machine learning powered alerts for logs, metrics, traces and user experience data. Sematext Cloud provides advanced monitoring, logging and tracing for all Docker platforms such as Docker EE, Kubernetes, GKE, AWS ECS, and IBM Cloud. Sematext’s new monitoring agent leverages the powerful eBPF Linux kernel observability functionality and uses the Kubernetes API to enrich the container and cluster level metrics.

Honeycomb and Rookout: An Integration That Finds the Dots to Connect

You probably know that Honeycomb is the most flexible observability tool around. Its powerful high-cardinality search makes working with real raw data quick and easy. But as you may have learned through hard experience, fetching those dots can still be quite a challenge.

Observability-Driven Development

TDD is table stakes for any good team, but it’s not enough: these days you need ODD: Observability-Driven Development (and Design). Observability should be baked into every step of your software development process, from conception to maintenance period. No pull request should ever be accepted without being able to answer the question, "how will you know if this works?".

Is observability good for our brain? How about post-mortems?

Your software stack likely consists of web servers, search engines, queues, databases, etc. Each part of your stack emits its own metrics and logs. Depending on the size of your team and structure, different team members might have permissions to look at one set of data, but not the other. Some data is needed for troubleshooting and can be discarded after just a few days, while more important data might need to be kept for months for legal or capacity planning purposes.