x-profile-performance

Automated performance profiling detects your project's runtime, selects the appropriate profiler, and runs a session to identify CPU, memory, and I/O bottlenecks. It outputs a flamegraph or structured report to guide your optimization efforts.

Yes, you can generate a flamegraph for Python or Go applications. The profiling workflow detects the language runtime and automatically selects the appropriate profiler, such as py-spy for Python or pprof for Go, to produce the output.

The best way to profile a Java application in production is using an automated workflow that detects the Java runtime and configures a Java Flight Recorder (JFR) profiling session to generate a flamegraph or structured report for locating hotspots.

Yes, automated profiling works with Node.js and Rust projects. The workflow automatically detects the tech stack, selects the appropriate profiler, and configures the session to identify I/O, memory, and CPU hotspots across these environments.

After locating performance hotspots, the profiling workflow generates actionable recommendations linked to a performance-engineering knowledge pack. This provides cross-stack improvement guidance directly alongside the generated flamegraphs and reports.

No, you do not need to manually configure profiler settings. The workflow implements language and runtime detection, automatically selects the correct profiler for your stack, and configures the profiling session duration and output format.