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
  • Command-line interface
  • Debugger commands
• profiling Python profilers
  • Introduction to profiling
  • Choosing a profiler
    • When to use statistical sampling
    • When to use deterministic tracing
  • Quick start
    • Statistical profiling
    • Deterministic profiling
  • Understanding profile output
  • Legacy compatibility
    • profiling.tracing Deterministic profiler
      • What is deterministic profiling?
      • Command-line interface
      • Programmatic usage examples
        • Basic profiling
        • Using the Profile class
      • Module reference
      • Using a custom timer
      • Limitations
    • profiling.sampling Statistical profiler
      • What is statistical profiling?
        • How time is estimated
        • When to use a different approach
      • Quick examples
      • Commands
        • The run command
        • The attach command
        • The dump command
        • The replay command
        • Profiling in production
        • Platform requirements
        • Version compatibility
      • Sampling configuration
        • Sampling rate and duration
        • Thread selection
        • Blocking mode
        • Special frames
        • Opcode-aware profiling
        • Real-time statistics
        • Subprocess profiling
        • Sampling efficiency
      • Profiling modes
        • Wall-clock mode
        • CPU mode
        • Comparing wall-clock and CPU profiles
        • GIL mode
        • Exception mode
      • Output formats
        • pstats format
        • Collapsed stacks format
        • Flame graph format
          • Differential flame graphs
        • Gecko format
        • Heatmap format
        • Binary format
      • Record and replay workflow
      • Live mode
        • Keyboard commands
      • Async-aware profiling
        • Async modes
        • Task markers and stack reconstruction
        • Option restrictions
      • Command-line interface
        • Global options
        • Dump options
        • Sampling options
        • Mode options
        • Output options
        • pstats display options
        • Run command options
• pstats Statistics for profilers
  • Reading and displaying profile data
    • Working with statistics
    • Filtering output
    • Analyzing call relationships
    • Combining multiple profiles
  • The Stats class
  • Command-line interface
• 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