profiling▌

Profile code execution to identify performance bottlenecks and optimize critical paths using data-driven approaches.

Profile and optimize Swift async/await code using Instruments.

Systematic approach to measuring, analyzing, and optimizing application performance. \n \n Covers Core Web Vitals (LCP, INP, CLS) with target thresholds and measurement stages from development through production \n Provides a 4-step profiling workflow: establish baseline, identify bottleneck, apply fix, validate improvement \n Includes tool selection guidance for specific problems (Lighthouse for page load, DevTools for runtime and memory, bundle analyzers for code size) \n Documents common runt

Comprehensive statistical and quality analysis of database tables with structured profiling output. \n \n Generates column-level statistics tailored to data type: min/max/percentiles for numeric columns, length metrics for strings, date ranges for timestamps \n Performs cardinality analysis to identify categorical vs. high-cardinality columns and detect skewed distributions \n Assesses data quality across five dimensions: completeness (NULL rates), uniqueness (duplicates), freshness (update time

CPU profiling identifies which functions consume most CPU time, enabling targeted optimization of expensive code paths.

iOS app performance problems fall into distinct categories, each with a specific diagnosis tool. This skill helps you choose the right tool, use it effectively, and interpret results correctly under pressure.

Measure, analyze, optimize - in that order.