Apache SkyWalking– the APM tool for distributed systems– has historically focused on providing observability around tracing and metrics, but service performance is often affected by the host. The newest release, SkyWalking 8.4.0, introduces a new feature for monitoring virtual machines. Users can easily detect possible problems from the dashboard– for example, when CPU usage is overloaded, when there’s not enough memory or disk space, or when the network status is unhealthy, etc.
The Apache SkyWalking team today announced the 8.4 release is generally available. This release fills the gap between all previous versions of SkyWalking and the logging domain area. The release also advances SkyWalking’s capabilities for infrastructure observability, starting with virtual machine monitoring.
If you are looking for a more efficient solution to observe your service mesh instead of using a Mixer-based solution, this is exactly what you need.
SkyWalking, a top-level Apache project, is the open source APM and observability analysis platform that is solving the problems of 21st-century systems that are increasingly large, distributed, and heterogenous. It’s built for the struggles system admins face today: To identify and locate needles in a haystack of interdependent services, to get apples-to-apples metrics across polyglot apps, and to get a complete and meaningful view of performance.
SkyWalking is a holistic platform that can observe microservices on or off a mesh, and can provide consistent monitoring with a lightweight payload.
Let’s take a look at how SkyWalking evolved to address the problem of observability at scale, and grew from a pure tracing system to a feature-rich observability platform that is now used to analyze deployments that collect tens of billions of traces per day.
Features in SkyWalking 8.1: SpringSleuth metrics, endpoint dependency detection, Kafka transport traces and metrics
Apache SkyWalking, the observability platform, and open-source application performance monitor (APM) project, today announced the general availability of its 8.1 release that extends its functionalities and provides a transport layer to maintain the lightweight of the platform that observes data continuously.
This post originally appears on The New Stack
This post introduces a way to automatically profile code in production with Apache SkyWalking. We believe the profile method helps reduce maintenance and overhead while increasing the precision in root cause analysis.
Apache SkyWalking, the observability analysis and application performance monitoring (APM) tool, shattered its own performance record with its recent 6.1 release.
Designed especially for microservices, cloud native and container based architecture, SkyWalking provides distributed tracing, service mesh telemetry analysis, metric aggregation and workload visualization.
Following SkyWalking’s integration with Istio and Envoy-based Service Mesh at the end of 2018, our colleague, Hongtao Gao, set a performance baseline with his blog post SkyWalking performance in Service Mesh scenario.
Using an 8 CPU, 16GB VM test environment, SkyWalking was found to support 25K telemetry data per second, or 100K data per second in a 3-node cluster using elasticsearch as storage.