Consider the following question: Why do most teams face pressure to rethink traditional logging and observability approaches? Asking this question to most engineers would likely result in answers centered on the challenges posed by microservices apps. Because microservices are more complex than monoliths and involve more moving parts, they require more sophisticated, granular log collection, correlation, and analysis.
All too often, the Kelverion team hear that businesses are trying to deliver more services with less resource, all whilst meeting tight Service Level Agreements. There are many automation use cases to relieve this pressure, and this article explores the six use case examples driving automation adoption within service management.
When your internet connection times out and you can't access a specific webpage, it's one of the most annoying errors. The message "The webpage not available" ERR_CONNECTION_TIMED_OUT will appear on the screen. This error usually occurs when there is an issue with the internet connection and the website does not load. The name of this error can be seen in the notification on your screen.
We all know the importance of cataloging, organizing, and breaking down the data in your logs. That process, parsing, makes information easier to find and simplifies subsequent analysis. Now, with Logz.io’s upgraded self-parsing tool, custom parsing rules, and log organization is easier than ever. What’s important is parsing that data out correctly. The better parsed, the easier to query.
Version 2.4 improves its e2e tests, revamps how logging in the HAProxy Data Plane API works, adds support for namespace filtering in Consul Service Discovery, improves runtime capabilities for maps and ACLs, adds server-template support and adds log_targets to global and defaults sections.
On Oct. 5, we hosted the first Grafana Virtual Meetup for an EMEA-based audience. Each Grafana meetup features “bite-sized” presentations from our user community and members of the Grafana Labs team. We want to provide opportunities (even virtually!) for members of our community to connect with one another and share what they’re working on or have learned.
Whether you are moving your applications to the cloud or modernizing them using Kubernetes, observing cloud-based workloads is more challenging than observing traditional deployments. When monitoring on-prem monoliths, operations teams had full visibility over the entire stack and full control over how/what telemetry data is collected (from infrastructure to platform to application data).
Here’s a common situation that plagues many development teams. You run an application through your CI/CD pipeline and all of the tests pass, which is great. But when you deploy it to a live target environment the application just does not function as expected. You can’t always predict what will happen when your application is pushed live. The solution?
