The latest News and Information on Observabilty for complex systems and related technologies.
Today, I found a bug before I noticed it. Like, it was subtle, and so I wasn’t quite sure I saw it—maybe I hadn’t hit refresh yet? Later, I looked at the trace of my function and, boom, there was a clear bug. Here’s the function with the bug. It responds to a request to /win by saving a record of the win and returning the total of my winnings so far. Can you spot the problem in the TypeScript? It’s subtle. Now here’s a trace in Honeycomb: Now do you see the bug?
Elastic Observability 7.16 introduces curated data exploration views for ad hoc analysis and further extends visibility into complex and distributed systems with the general availability (GA) of dozens of prebuilt Elastic Agent data integrations, observability tooling for continuous integration and continuous delivery (CI/CD) pipelines, and a new native data source integration with Amazon Web Services (AWS) FireLens. These new features allow customers to.
I have a good sense of how to use traces to understand my system’s behavior within request/response cycles. What about multi-request processes? What about async tasks spawned within a request? Is there a higher-level or more holistic approach?
I can’t remember the last time I drove down highway 101 between San Francisco and the South Bay and didn’t see a billboard claiming to be the single tool to solve all of my data problems.
As one of the leading enterprise collaboration software globally, Microsoft Teams helps remote workers come together and stay productive. But while IT already has tools to monitor Teams call quality metrics, the pandemic shifted the organizational landscape with all of us working remotely from home. Or at least work in a hybrid way! So what does that mean for Teams monitoring now? The shift necessitates a newer Microsoft Teams monitoring strategy approach that combines synthetics with real user monitoring (RUM) to get a complete seamless digital experience.
When you send telemetry into Honeycomb, our infrastructure needs to buffer your data before processing it in our “retriever” columnar storage database. For the entirety of Honeycomb’s existence, we have used Apache Kafka to perform this buffering function in our observability pipeline.
