EBPF

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. ...

eBPF being a promising technology is no news. And C is the defacto choice for writing eBPF programs. The act of writing C programs in an error-prone process. Even the eBPF verifier makes life a lot easier; it is still possible to write unsafe programs and make trivial mistakes that elude the compiler but are detected by the verifier in the load time, which are preventable with compile-time checks. It is where Rust comes in. Rust is a language designed for safety. Recently the Rust compiler gained the ability to compile to the eBPF virtual machine, and Rust became an official language for Linux. We discover more and more use cases where eBPF can be helpful. We find more efficient ways to build safe eBPF programs that are parallel to these developments. We will demonstrate how we made applications combined with Rust in the data plane for more safety and Go in the control plane for a higher development pace to target Kubernetes for security, observability and performance tuning. ...

Profiling has long been part of the Go developer’s toolbox to analyze the resource usage of a running process. But do you ever wonder how profilers built? In this talk, I will bring eBPF (a promising Kernel technology) and Go together to build a profiler for understanding Go code at runtime. Profiling has long been part of the developer’s toolbox to analyze the resource usage of a running process. Go users are very familiar with the concept thanks to state-of-art Go tooling. 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, which is called continuous profiling. Through continuous profiling, the systematic collection of profiles, entirely new workflows suddenly become possible. ...