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 "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

        * 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
