Debugging and Profiling

These libraries help you with Python development: the debugger enables you to step through code, analyze stack frames and set breakpoints etc., and the profilers run code and give you a detailed breakdown of execution times, allowing you to identify bottlenecks in your programs. Auditing events provide visibility into runtime behaviors that would otherwise require intrusive debugging or patching.

• Audit events table
• bdb — Debugger framework
• faulthandler — Dump the Python traceback
  • Dumping the traceback
  • Dumping the C stack
    • C Stack Compatibility
  • Fault handler state
  • Dumping the tracebacks after a timeout
  • Dumping the traceback on a user signal
  • Issue with file descriptors
  • Example
• pdb — The Python Debugger
  • Debugger Commands
• The Python Profilers
  • Introduction to the profilers
  • What Is Statistical Profiling?
  • Instant User’s Manual
  • Statistical Profiler Command Line Interface
  • profiling.sampling Module Reference
  • Deterministic Profiler Command Line Interface
  • profile and cProfile Module Reference
  • The Stats Class
  • What Is Deterministic Profiling?
  • Limitations
  • Calibration
  • Using a custom timer
• timeit — Measure execution time of small code snippets
  • Basic Examples
  • Python Interface
  • Command-Line Interface
  • Examples
• trace — Trace or track Python statement execution
  • Command-Line Usage
    • Main options
    • Modifiers
    • Filters
  • Programmatic Interface
• tracemalloc — Trace memory allocations
  • Examples
    • Display the top 10
    • Compute differences
    • Get the traceback of a memory block
    • Pretty top
      • Record the current and peak size of all traced memory blocks
  • API
    • Functions
    • DomainFilter
    • Filter
    • Frame
    • Snapshot
    • Statistic
    • StatisticDiff
    • Trace
    • Traceback