perfetto-sql

Translating natural language into Perfetto SQL involves converting intent-based questions into syntactically valid queries for the trace_processor engine. This allows you to extract slice, thread, and memory performance data from local trace files without writing complex join logic manually.

Yes, you can analyze Android app startup times and CPU frequency by leveraging Perfetto's standard library modules. The generated queries utilize these modules to extract robust performance metrics directly from your system trace data.

To identify UI jank causes in an Android system trace, you can query thread states and slice durations matching specific patterns like RenderThread. This isolates the exact scheduling delays and execution durations causing the performance issues.

Yes, Perfetto SQL supports idempotent query execution against local trace files. This ensures that running the same generated queries repeatedly yields consistent results without side effects during your performance data extraction.

You can extract thread scheduling and memory performance data by stating your data intent in natural language. The system handles the schema-aware analysis and generates the necessary complex SQL join logic automatically.

You need schema-aware analysis for Perfetto trace files when extracting specific performance metrics like CPU frequency or app startup times. It ensures the generated SQL accurately targets the trace_processor engine's available tables and columns.