These days, the internal workings of Linux applications involve many different moving parts. Sometimes, it can be rather difficult to debug them when things go wrong or run slower than expected. Tracing an application’s execution is one way of understanding potential issues without diving into the source code. To this end, we wrote an app-tracing tool called etrace, designed to detect performance bottlenecks and runtime issues in snaps.

According to the Stackoverflow survey of 2019, Python programming language garnered 73.1% approval among developers. It ranks second to Rust and continues to dominate in Data Science and Machine Learning(ML). Python is a developers’ favorite. It is a high-level language known for its robustness and its core philosophy―simplicity over complexity. However, Python application’s performance is another story. Just like any other application, it has its share of performance issues.

To complement distributed tracing, runtime metrics, log analytics, Synthetic Monitoring, and Real User Monitoring, we’ve made another addition to the application developer’s toolkit to make troubleshooting performance issues even faster and simpler. Continuous Profiler is an always-on, production code profiler that enables you to analyze code-level performance across your entire environment, with minimal overhead.