gh-137122: Improve the profiling section in the 3.15 what's new document

Author: pablogsal

Doc/whatsnew/3.15.rst

@@ -96,94 +96,60 @@ performance issues in production environments.
 Key features include:
 
 * **Zero-overhead profiling**: Attach to any running Python process without
-  affecting its performance
-* **No code modification required**: Profile existing applications without restart
-* **Real-time statistics**: Monitor sampling quality during data collection
-* **Multiple output formats**: Generate both detailed statistics and flamegraph data
-* **Thread-aware profiling**: Option to profile all threads or just the main thread
-
-Profile process 1234 for 10 seconds with default settings:
-
-.. code-block:: shell
-
-  python -m profiling.sampling 1234
-
-Profile with custom interval and duration, save to file:
-
-.. code-block:: shell
-
-  python -m profiling.sampling -i 50 -d 30 -o profile.stats 1234
-
-Generate collapsed stacks for flamegraph:
-
-.. code-block:: shell
-
-  python -m profiling.sampling --collapsed 1234
-
-Profile all threads and sort by total time:
-
-.. code-block:: shell
-
-  python -m profiling.sampling -a --sort-tottime 1234
-
-The profiler generates statistical estimates of where time is spent:
-
-.. code-block:: text
-
-  Real-time sampling stats: Mean: 100261.5Hz (9.97µs) Min: 86333.4Hz (11.58µs) Max: 118807.2Hz (8.42µs) Samples: 400001
-  Captured 498841 samples in 5.00 seconds
-  Sample rate: 99768.04 samples/sec
-  Error rate: 0.72%
-  Profile Stats:
-        nsamples   sample%   tottime (s)    cumul%   cumtime (s)  filename:lineno(function)
-        43/418858       0.0         0.000      87.9         4.189  case.py:667(TestCase.run)
-      3293/418812       0.7         0.033      87.9         4.188  case.py:613(TestCase._callTestMethod)
-    158562/158562      33.3         1.586      33.3         1.586  test_compile.py:725(TestSpecifics.test_compiler_recursion_limit.<locals>.check_limit)
-    129553/129553      27.2         1.296      27.2         1.296  ast.py:46(parse)
-        0/128129       0.0         0.000      26.9         1.281  test_ast.py:884(AST_Tests.test_ast_recursion_limit.<locals>.check_limit)
-          7/67446       0.0         0.000      14.2         0.674  test_compile.py:729(TestSpecifics.test_compiler_recursion_limit)
-          6/60380       0.0         0.000      12.7         0.604  test_ast.py:888(AST_Tests.test_ast_recursion_limit)
-          3/50020       0.0         0.000      10.5         0.500  test_compile.py:727(TestSpecifics.test_compiler_recursion_limit)
-          1/38011       0.0         0.000       8.0         0.380  test_ast.py:886(AST_Tests.test_ast_recursion_limit)
-          1/25076       0.0         0.000       5.3         0.251  test_compile.py:728(TestSpecifics.test_compiler_recursion_limit)
-      22361/22362       4.7         0.224       4.7         0.224  test_compile.py:1368(TestSpecifics.test_big_dict_literal)
-          4/18008       0.0         0.000       3.8         0.180  test_ast.py:889(AST_Tests.test_ast_recursion_limit)
-        11/17696       0.0         0.000       3.7         0.177  subprocess.py:1038(Popen.__init__)
-      16968/16968       3.6         0.170       3.6         0.170  subprocess.py:1900(Popen._execute_child)
-          2/16941       0.0         0.000       3.6         0.169  test_compile.py:730(TestSpecifics.test_compiler_recursion_limit)
-
-  Legend:
-    nsamples: Direct/Cumulative samples (direct executing / on call stack)
-    sample%: Percentage of total samples this function was directly executing
-    tottime: Estimated total time spent directly in this function
-    cumul%: Percentage of total samples when this function was on the call stack
-    cumtime: Estimated cumulative time (including time in called functions)
-    filename:lineno(function): Function location and name
-
-  Summary of Interesting Functions:
-
-  Functions with Highest Direct/Cumulative Ratio (Hot Spots):
-    1.000 direct/cumulative ratio, 33.3% direct samples: test_compile.py:(TestSpecifics.test_compiler_recursion_limit.<locals>.check_limit)
-    1.000 direct/cumulative ratio, 27.2% direct samples: ast.py:(parse)
-    1.000 direct/cumulative ratio, 3.6% direct samples: subprocess.py:(Popen._execute_child)
-
-  Functions with Highest Call Frequency (Indirect Calls):
-    418815 indirect calls, 87.9% total stack presence: case.py:(TestCase.run)
-    415519 indirect calls, 87.9% total stack presence: case.py:(TestCase._callTestMethod)
-    159470 indirect calls, 33.5% total stack presence: test_compile.py:(TestSpecifics.test_compiler_recursion_limit)
-
-  Functions with Highest Call Magnification (Cumulative/Direct):
-    12267.9x call magnification, 159470 indirect calls from 13 direct: test_compile.py:(TestSpecifics.test_compiler_recursion_limit)
-    10581.7x call magnification, 116388 indirect calls from 11 direct: test_ast.py:(AST_Tests.test_ast_recursion_limit)
-    9740.9x call magnification, 418815 indirect calls from 43 direct: case.py:(TestCase.run)
-
-The profiler automatically identifies performance bottlenecks through statistical
-analysis, highlighting functions with high CPU usage and call frequency patterns.
+  affecting its performance. Ideal for production debugging where you can't afford
+  to restart or slow down your application.
+
+* **No code modification required**: Profile existing applications without restart.
+  Simply point the profiler at a running process by PID and start collecting data.
+
+* **Flexible target modes**:
+
+  * Profile running processes by PID (``attach``) - attach to already-running applications
+  * Run and profile scripts directly (``run``) - profile from the very start of execution
+  * Execute and profile modules (``run -m``) - profile packages run as ``python -m module``
+
+* **Multiple profiling modes**: Choose what to measure based on your performance investigation:
+
+  * **Wall-clock time** (``--mode wall``, default): Measures real elapsed time including I/O,
+    network waits, and blocking operations. Use this to understand where your program spends
+    calendar time, including when waiting for external resources.
+  * **CPU time** (``--mode cpu``): Measures only active CPU execution time, excluding I/O waits
+    and blocking. Use this to identify CPU-bound bottlenecks and optimize computational work.
+  * **GIL-holding time** (``--mode gil``): Measures time spent holding Python's Global Interpreter
+    Lock. Use this to identify which threads dominate GIL usage in multi-threaded applications.
+
+* **Thread-aware profiling**: Option to profile all threads (``-a``) or just the main thread,
+  essential for understanding multi-threaded application behavior.
+
+* **Multiple output formats**: Choose the visualization that best fits your workflow:
+
+  * ``--pstats``: Detailed tabular statistics compatible with :mod:`pstats`. Shows function-level
+    timing with direct and cumulative samples. Best for detailed analysis and integration with
+    existing Python profiling tools.
+  * ``--collapsed``: Generates collapsed stack traces (one line per stack). This format is
+    specifically designed for creating flamegraphs with external tools like Brendan Gregg's
+    FlameGraph scripts or speedscope.
+  * ``--flamegraph``: Generates a self-contained interactive HTML flamegraph using D3.js.
+    Opens directly in your browser for immediate visual analysis. Flamegraphs show the call
+    hierarchy where width represents time spent, making it easy to spot bottlenecks at a glance.
+  * ``--gecko``: Generates Gecko Profiler format compatible with Firefox Profiler
+    (https://profiler.firefox.com). Upload the output to Firefox Profiler for advanced
+    timeline-based analysis with features like stack charts, markers, and network activity.
+  * ``--heatmap``: Generates an interactive HTML heatmap visualization with line-level sample
+    counts. Creates a directory with per-file heatmaps showing exactly where time is spent
+    at the source code level.
+
+* **Live interactive mode**: Real-time TUI profiler with a top-like interface (``--live``).
+  Monitor performance as your application runs with interactive sorting and filtering.
+
+* **Async-aware profiling**: Profile async/await code with task-based stack reconstruction
+  (``--async-aware``). See which coroutines are consuming time, with options to show only
+  running tasks or all tasks including those waiting.
 
 This capability is particularly valuable for debugging performance issues in
 production systems where traditional profiling approaches would be too intrusive.
 
-  .. seealso:: :pep:`799` for further details.
+.. seealso:: :pep:`799` for further details.
 
 (Contributed by Pablo Galindo and László Kiss Kollár in :gh:`135953`.)