As an application performance monitoring tool for distributed systems, Apache SkyWalking observes metrics, logs, traces, and events in the service mesh.
SkyWalking OAP’s dataflow processing architecture boasts high performance and is capable of dealing with massive data traffic in real-time. However, storing, updating, and querying massive amounts of data poses a great challenge to its backend storage system.
Introducing performance analysis in production with SkyWalking Rover.
Apache SkyWalking observes metrics, logs, traces, and events for services deployed into the service mesh. When troubleshooting, SkyWalking error analysis can be an invaluable tool helping to pinpoint where an error occurred. However, performance problems are more difficult: It’s often impossible to locate the root cause of performance problems with pre-existing observation data. To move beyond the status quo, dynamic debugging and troubleshooting are essential service performance tools. In this article, we’ll discuss how to use eBPF technology to improve the profiling feature in SkyWalking and analyze the performance impact in the service mesh.
A critical vulnerability (CVE-2021-44228, CVSS score 10) was identified in the Java logging library Apache Log4j 2. Apache Log4j2 2.14.1 and below are susceptible to a remote code execution vulnerability where a remote attacker can leverage this vulnerability to take full control of a vulnerable machine.
Apache Log4j is used in many Java-based applications, making this vulnerability potentially affecting lots of organizations. As we continue to gain a deeper understanding of the impact of this threat, we will publish technical information to help you detect, investigate, and mitigate attacks. We will provide updates with more information and protection details as they become available.
Update at 2021-12-14: New, related CVE-2021-45046 has been disclosed and mitigations are included in this post.
SkyWalking has over 500 contributors based on GitHub repository statistics. This is a remarkable achievement for the whole diverse community.
Web application performance affects the retention rate of users. If a page load time is too long, the user will give up. So we need to monitor the web application to understand performance and ensure that servers are stable, available and healthy. Apache SkyWalking is an application performance monitoring (APM) tool designed especially for cloud native and container-based architectures. Its skywalking-client-js is a lightweight, client-side JavaScript exception, performance and tracing library.
Want to observe a service mesh that extends to virtual machines? A new analyzer in Apache SkyWalking — the APM system designed especially for microservices, cloud native and container-based architectures — leverages Envoy’s metadata exchange mechanism to work in Kubernetes, VM, or hybrid environments.
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.
The observability problem for modern DevOps is familiar: As enterprises move to microservices, containerization, multi-language RPC frameworks, and service meshes, there’s an increasing need for users to understand a highly complex, distributed architecture and the dependencies between applications. Apache SkyWalking, an application performance monitor (APM) and observability platform, is an open source project that addresses this need — with or without a service mesh.
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.
