Observability

Profiling Python with eBPF: A New Frontier in Performance Analysis Profiling Python applications can be challenging, especially in scenarios involving high-performance requirements or complex workloads. Existing tools often require code instrumentation, making them impractical for certain use cases. Enter eBPF (Extended Berkeley Packet Filter)—a revolutionary Linux technology—and the open-source project Parca, which together are reshaping the landscape of Python profiling. In this post, I’ll explore how eBPF enables continuous profiling, discuss challenges like stack unwinding in Python, and demonstrate the power of modern profiling tools. ...

Observability is crucial for understanding how your application operates in real-time. Among various observability signals—such as logs, traces, and continuous profiling—metrics play a significant role. They provide sampled measurements throughout the system, essential for ensuring service quality, improving performance, scalability, debuggability, security, and enabling real-time, actionable alerting. Building observable applications begins with proper instrumentation. While the Prometheus ecosystem offers tools that simplify this process, there are still numerous opportunities for mistakes or misuse. ...

The CNCF Incubated Thanos project with the large open-source community continues to push boundaries regarding observability and monitoring using Prometheus-based metrics. Together with the Prometheus community, it improves the metric story for Kubernetes clusters and beyond. Things like improved performance, better scalability, debuggability, security, metrics backfilling and query QoS is only the tip of the iceberg. As we know, observability nowadays comes in many flavours. Bunching them together is not a trivial side, given many shapes and collection points. Aside from metrics, we have logs, traces or even continuous profiling. In this talk, Kemal and Bartek, Thanos maintainers, after a quick overview of Thanos, will explain how Thanos can be integrated with those non-metric observability signals. The audience will learn an example, end-to-end ways to correlate multiple observability backends with Thanos for enhanced observability and monitoring experience. ...

For years Google has consistently been able to cut down multiple percentage points in their fleet-wide resource usage every quarter, using techniques described in their “Google-Wide Profiling” paper. Ad-hoc profiling has long been part of the developer’s toolbox to analyze CPU and memory usage of a running process, however, through continuous profiling, the systematic collection of profiles, entirely new workflows suddenly become possible. Matthias and Kemal will start this talk with an introduction to profiling with Go and demonstrate via Conprof - an open-source continuous profiling project - how continuous profiling allows for an unprecedented fleet-wide understanding of code at runtime. Attendees will learn how to continuously profile Go code to help guide building robust, reliable, and performant software and reduce cloud spend systematically. ...

For years Google has consistently been able to cut down multiple percentage points in their fleet-wide resource usage every quarter, using techniques described in their “Google-Wide Profiling” paper. Ad-hoc profiling has long been part of the developer’s toolbox to analyze the CPU and memory usage of a running process. However, through continuous profiling, and the systematic collection of profiles, entirely new workflows suddenly become possible. The presenter will start this talk with an introduction to profiling applications, and demonstrate how one can practice it using open-source continuous profiling tools, and how continuous profiling allows for an unprecedented fleet-wide understanding of code at production runtime. ...