cpython/Lib/profiling/sampling/stack_collector.py

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import base64
import collections
import functools
import importlib.resources
import json
import linecache
import os
import sys
import sysconfig
from ._css_utils import get_combined_css
from .collector import Collector, extract_lineno
from .opcode_utils import get_opcode_mapping
from .string_table import StringTable
class StackTraceCollector(Collector):
def __init__(self, sample_interval_usec, *, skip_idle=False):
self.sample_interval_usec = sample_interval_usec
self.skip_idle = skip_idle
def collect(self, stack_frames, timestamps_us=None, skip_idle=False):
weight = len(timestamps_us) if timestamps_us else 1
for frames, thread_id in self._iter_stacks(stack_frames, skip_idle=skip_idle):
self.process_frames(frames, thread_id, weight=weight)
def process_frames(self, frames, thread_id, weight=1):
pass
class CollapsedStackCollector(StackTraceCollector):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.stack_counter = collections.Counter()
def process_frames(self, frames, thread_id, weight=1):
# Extract only (filename, lineno, funcname) - opcode not needed for collapsed stacks
# frame is (filename, location, funcname, opcode)
call_tree = tuple(
(f[0], extract_lineno(f[1]), f[2]) for f in reversed(frames)
)
self.stack_counter[(call_tree, thread_id)] += weight
def export(self, filename):
lines = []
for (call_tree, thread_id), count in self.stack_counter.items():
parts = [f"tid:{thread_id}"]
for file, line, func in call_tree:
# This is what pstats does for "special" frames:
if file == "~" and line == 0:
part = func
else:
part = f"{os.path.basename(file)}:{func}:{line}"
parts.append(part)
stack_str = ";".join(parts)
lines.append((stack_str, count))
lines.sort(key=lambda x: (-x[1], x[0]))
with open(filename, "w") as f:
for stack, count in lines:
f.write(f"{stack} {count}\n")
print(f"Collapsed stack output written to {filename}")
class FlamegraphCollector(StackTraceCollector):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.stats = {}
self._root = {"samples": 0, "children": {}, "threads": set()}
self._total_samples = 0
self._sample_count = 0 # Track actual number of samples (not thread traces)
self._func_intern = {}
self._string_table = StringTable()
self._all_threads = set()
# Thread status statistics (similar to LiveStatsCollector)
self.thread_status_counts = {
"has_gil": 0,
"on_cpu": 0,
"gil_requested": 0,
"unknown": 0,
"has_exception": 0,
"total": 0,
}
self.samples_with_gc_frames = 0
# Per-thread statistics
self.per_thread_stats = {} # {thread_id: {has_gil, on_cpu, gil_requested, unknown, has_exception, total, gc_samples}}
def collect(self, stack_frames, timestamps_us=None, skip_idle=False):
"""Override to track thread status statistics before processing frames."""
# Weight is number of timestamps (samples with identical stack)
weight = len(timestamps_us) if timestamps_us else 1
# Increment sample count by weight
self._sample_count += weight
# Collect both aggregate and per-thread statistics using base method
status_counts, has_gc_frame, per_thread_stats = self._collect_thread_status_stats(stack_frames)
# Merge aggregate status counts (multiply by weight)
for key in status_counts:
self.thread_status_counts[key] += status_counts[key] * weight
# Update aggregate GC frame count
if has_gc_frame:
self.samples_with_gc_frames += weight
# Merge per-thread statistics (multiply by weight)
for thread_id, stats in per_thread_stats.items():
if thread_id not in self.per_thread_stats:
self.per_thread_stats[thread_id] = {
"has_gil": 0,
"on_cpu": 0,
"gil_requested": 0,
"unknown": 0,
"has_exception": 0,
"total": 0,
"gc_samples": 0,
}
for key, value in stats.items():
self.per_thread_stats[thread_id][key] += value * weight
# Call parent collect to process frames
super().collect(stack_frames, timestamps_us, skip_idle=skip_idle)
def set_stats(self, sample_interval_usec, duration_sec, sample_rate,
error_rate=None, missed_samples=None, mode=None):
"""Set profiling statistics to include in flamegraph data."""
self.stats = {
"sample_interval_usec": sample_interval_usec,
"duration_sec": duration_sec,
"sample_rate": sample_rate,
"error_rate": error_rate,
"missed_samples": missed_samples,
"mode": mode
}
def export(self, filename):
flamegraph_data = self._convert_to_flamegraph_format()
# Debug output with string table statistics
num_functions = len(flamegraph_data.get("children", []))
total_time = flamegraph_data.get("value", 0)
string_count = len(self._string_table)
print(
f"Flamegraph data: {num_functions} root functions, total samples: {total_time}, "
f"{string_count} unique strings"
)
if num_functions == 0:
print(
"Warning: No functions found in profiling data. Check if sampling captured any data."
)
return
html_content = self._create_flamegraph_html(flamegraph_data)
with open(filename, "w", encoding="utf-8") as f:
f.write(html_content)
print(f"Flamegraph saved to: {filename}")
@staticmethod
@functools.lru_cache(maxsize=None)
def _format_function_name(func):
filename, lineno, funcname = func
# Special frames like <GC> and <native> should not show file:line
if filename == "~" and lineno == 0:
return funcname
if len(filename) > 50:
parts = filename.split("/")
if len(parts) > 2:
filename = f".../{'/'.join(parts[-2:])}"
return f"{funcname} ({filename}:{lineno})"
def _convert_to_flamegraph_format(self):
if self._total_samples == 0:
return {
"name": self._string_table.intern("No Data"),
"value": 0,
"children": [],
"threads": [],
"strings": self._string_table.get_strings()
}
def convert_children(children, min_samples):
out = []
for func, node in children.items():
samples = node["samples"]
if samples < min_samples:
continue
# Intern all string components for maximum efficiency
filename_idx = self._string_table.intern(func[0])
funcname_idx = self._string_table.intern(func[2])
name_idx = self._string_table.intern(self._format_function_name(func))
child_entry = {
"name": name_idx,
"value": samples,
"children": [],
"filename": filename_idx,
"lineno": func[1],
"funcname": funcname_idx,
"threads": sorted(list(node.get("threads", set()))),
}
source = self._get_source_lines(func)
if source:
# Intern source lines for memory efficiency
source_indices = [self._string_table.intern(line) for line in source]
child_entry["source"] = source_indices
# Include opcode data if available
opcodes = node.get("opcodes", {})
if opcodes:
child_entry["opcodes"] = dict(opcodes)
# Recurse
child_entry["children"] = convert_children(
node["children"], min_samples
)
out.append(child_entry)
# Sort by value (descending) then by name index for consistent ordering
out.sort(key=lambda x: (-x["value"], x["name"]))
return out
# Filter out very small functions (less than 0.1% of total samples)
total_samples = self._total_samples
min_samples = max(1, int(total_samples * 0.001))
root_children = convert_children(self._root["children"], min_samples)
if not root_children:
return {
"name": self._string_table.intern("No significant data"),
"value": 0,
"children": [],
"strings": self._string_table.get_strings()
}
# Calculate thread status percentages for display
is_free_threaded = bool(sysconfig.get_config_var("Py_GIL_DISABLED"))
total_threads = max(1, self.thread_status_counts["total"])
thread_stats = {
"has_gil_pct": (self.thread_status_counts["has_gil"] / total_threads) * 100,
"on_cpu_pct": (self.thread_status_counts["on_cpu"] / total_threads) * 100,
"gil_requested_pct": (self.thread_status_counts["gil_requested"] / total_threads) * 100,
"has_exception_pct": (self.thread_status_counts["has_exception"] / total_threads) * 100,
"gc_pct": (self.samples_with_gc_frames / max(1, self._sample_count)) * 100,
"free_threaded": is_free_threaded,
**self.thread_status_counts
}
# Calculate per-thread statistics with percentages
per_thread_stats_with_pct = {}
total_samples_denominator = max(1, self._sample_count)
for thread_id, stats in self.per_thread_stats.items():
total = max(1, stats["total"])
per_thread_stats_with_pct[thread_id] = {
"has_gil_pct": (stats["has_gil"] / total) * 100,
"on_cpu_pct": (stats["on_cpu"] / total) * 100,
"gil_requested_pct": (stats["gil_requested"] / total) * 100,
"has_exception_pct": (stats["has_exception"] / total) * 100,
"gc_pct": (stats["gc_samples"] / total_samples_denominator) * 100,
**stats
}
# Build opcode mapping for JS
opcode_mapping = get_opcode_mapping()
# If we only have one root child, make it the root to avoid redundant level
if len(root_children) == 1:
main_child = root_children[0]
# Update the name to indicate it's the program root
old_name = self._string_table.get_string(main_child["name"])
new_name = f"Program Root: {old_name}"
main_child["name"] = self._string_table.intern(new_name)
main_child["stats"] = {
**self.stats,
"thread_stats": thread_stats,
"per_thread_stats": per_thread_stats_with_pct
}
main_child["threads"] = sorted(list(self._all_threads))
main_child["strings"] = self._string_table.get_strings()
main_child["opcode_mapping"] = opcode_mapping
return main_child
return {
"name": self._string_table.intern("Program Root"),
"value": total_samples,
"children": root_children,
"stats": {
**self.stats,
"thread_stats": thread_stats,
"per_thread_stats": per_thread_stats_with_pct
},
"threads": sorted(list(self._all_threads)),
"strings": self._string_table.get_strings(),
"opcode_mapping": opcode_mapping
}
def process_frames(self, frames, thread_id, weight=1):
"""Process stack frames into flamegraph tree structure.
Args:
frames: List of (filename, location, funcname, opcode) tuples in
leaf-to-root order. location is (lineno, end_lineno, col_offset, end_col_offset).
opcode is None if not gathered.
thread_id: Thread ID for this stack trace
weight: Number of samples this stack represents (for batched RLE)
"""
# Reverse to root->leaf order for tree building
self._root["samples"] += weight
self._total_samples += weight
self._root["threads"].add(thread_id)
self._all_threads.add(thread_id)
current = self._root
for filename, location, funcname, opcode in reversed(frames):
lineno = extract_lineno(location)
func = (filename, lineno, funcname)
func = self._func_intern.setdefault(func, func)
node = current["children"].get(func)
if node is None:
node = {"samples": 0, "children": {}, "threads": set(), "opcodes": collections.Counter()}
current["children"][func] = node
node["samples"] += weight
node["threads"].add(thread_id)
if opcode is not None:
node["opcodes"][opcode] += weight
current = node
def _get_source_lines(self, func):
filename, lineno, _ = func
try:
lines = []
start_line = max(1, lineno - 2)
end_line = lineno + 3
for line_num in range(start_line, end_line):
line = linecache.getline(filename, line_num)
if line.strip():
marker = "" if line_num == lineno else " "
lines.append(f"{marker}{line_num}: {line.rstrip()}")
return lines if lines else None
except Exception:
return None
def _create_flamegraph_html(self, data):
data_json = json.dumps(data)
template_dir = importlib.resources.files(__package__)
vendor_dir = template_dir / "_vendor"
assets_dir = template_dir / "_assets"
d3_path = vendor_dir / "d3" / "7.8.5" / "d3.min.js"
d3_flame_graph_dir = vendor_dir / "d3-flame-graph" / "4.1.3"
fg_css_path = d3_flame_graph_dir / "d3-flamegraph.css"
fg_js_path = d3_flame_graph_dir / "d3-flamegraph.min.js"
fg_tooltip_js_path = d3_flame_graph_dir / "d3-flamegraph-tooltip.min.js"
html_template = (template_dir / "_flamegraph_assets" / "flamegraph_template.html").read_text(encoding="utf-8")
css_content = get_combined_css("flamegraph")
js_content = (template_dir / "_flamegraph_assets" / "flamegraph.js").read_text(encoding="utf-8")
# Inline first-party CSS/JS
html_template = html_template.replace(
"<!-- INLINE_CSS -->", f"<style>\n{css_content}\n</style>"
)
html_template = html_template.replace(
"<!-- INLINE_JS -->", f"<script>\n{js_content}\n</script>"
)
gh-140727: Restructure profiling documentation for PEP 799 (#142373) * Add profiling module documentation structure PEP 799 introduces a new `profiling` package that reorganizes Python's profiling tools under a unified namespace. This commit adds the documentation structure to match: a main entry point (profiling.rst) that helps users choose between profilers, detailed docs for the tracing profiler (profiling-tracing.rst), and separated pstats documentation. The tracing profiler docs note that cProfile remains as a backward-compatible alias, so existing code continues to work. The pstats module gets its own page since it's used by both profiler types and deserves focused documentation. * Add profiling.sampling documentation The sampling profiler is new in Python 3.15 and works fundamentally differently from the tracing profiler. It observes programs from outside by periodically capturing stack snapshots, which means zero overhead on the profiled code. This makes it practical for production use where you can attach to live servers. The docs explain the key concepts (statistical vs deterministic profiling), provide quick examples upfront, document all output formats (pstats, flamegraph, gecko, heatmap), and cover the live TUI mode. The defaults table helps users understand what happens without any flags. * Wire profiling docs into the documentation tree Add the new profiling module pages to the Debugging and Profiling toctree. The order places the main profiling.rst entry point first, followed by the two profiler implementations, then pstats, and finally the deprecated profile module last. * Convert profile.rst to deprecation stub The pure Python profile module is deprecated in 3.15 and scheduled for removal in 3.17. Users should migrate to profiling.tracing (or use the cProfile alias which continues to work). The page now focuses on helping existing users migrate: it shows the old vs new import style, keeps the shared API reference since both modules have the same interface, and preserves the calibration docs for anyone still using the pure Python implementation during the transition period. * Update CLI module references for profiling restructure Point cProfile to profiling.tracing docs and add profiling.sampling to the list of modules with CLI interfaces. The old profile-cli label no longer exists after the documentation restructure. * Update whatsnew to link to profiling module docs Enable cross-references to the new profiling module documentation and update the CLI examples to use the current syntax with the attach subcommand. Also reference profiling.tracing instead of cProfile since that's the new canonical name.
2025-12-09 12:55:04 +00:00
png_path = assets_dir / "tachyon-logo.png"
b64_logo = base64.b64encode(png_path.read_bytes()).decode("ascii")
# Let CSS control size; keep markup simple
gh-140727: Restructure profiling documentation for PEP 799 (#142373) * Add profiling module documentation structure PEP 799 introduces a new `profiling` package that reorganizes Python's profiling tools under a unified namespace. This commit adds the documentation structure to match: a main entry point (profiling.rst) that helps users choose between profilers, detailed docs for the tracing profiler (profiling-tracing.rst), and separated pstats documentation. The tracing profiler docs note that cProfile remains as a backward-compatible alias, so existing code continues to work. The pstats module gets its own page since it's used by both profiler types and deserves focused documentation. * Add profiling.sampling documentation The sampling profiler is new in Python 3.15 and works fundamentally differently from the tracing profiler. It observes programs from outside by periodically capturing stack snapshots, which means zero overhead on the profiled code. This makes it practical for production use where you can attach to live servers. The docs explain the key concepts (statistical vs deterministic profiling), provide quick examples upfront, document all output formats (pstats, flamegraph, gecko, heatmap), and cover the live TUI mode. The defaults table helps users understand what happens without any flags. * Wire profiling docs into the documentation tree Add the new profiling module pages to the Debugging and Profiling toctree. The order places the main profiling.rst entry point first, followed by the two profiler implementations, then pstats, and finally the deprecated profile module last. * Convert profile.rst to deprecation stub The pure Python profile module is deprecated in 3.15 and scheduled for removal in 3.17. Users should migrate to profiling.tracing (or use the cProfile alias which continues to work). The page now focuses on helping existing users migrate: it shows the old vs new import style, keeps the shared API reference since both modules have the same interface, and preserves the calibration docs for anyone still using the pure Python implementation during the transition period. * Update CLI module references for profiling restructure Point cProfile to profiling.tracing docs and add profiling.sampling to the list of modules with CLI interfaces. The old profile-cli label no longer exists after the documentation restructure. * Update whatsnew to link to profiling module docs Enable cross-references to the new profiling module documentation and update the CLI examples to use the current syntax with the attach subcommand. Also reference profiling.tracing instead of cProfile since that's the new canonical name.
2025-12-09 12:55:04 +00:00
logo_html = f'<img src="data:image/png;base64,{b64_logo}" alt="Tachyon logo"/>'
html_template = html_template.replace("<!-- INLINE_LOGO -->", logo_html)
html_template = html_template.replace(
"<!-- PYTHON_VERSION -->", f"{sys.version_info.major}.{sys.version_info.minor}"
)
d3_js = d3_path.read_text(encoding="utf-8")
fg_css = fg_css_path.read_text(encoding="utf-8")
fg_js = fg_js_path.read_text(encoding="utf-8")
fg_tooltip_js = fg_tooltip_js_path.read_text(encoding="utf-8")
html_template = html_template.replace(
"<!-- INLINE_VENDOR_D3_JS -->",
f"<script>\n{d3_js}\n</script>",
)
html_template = html_template.replace(
"<!-- INLINE_VENDOR_FLAMEGRAPH_CSS -->",
f"<style>\n{fg_css}\n</style>",
)
html_template = html_template.replace(
"<!-- INLINE_VENDOR_FLAMEGRAPH_JS -->",
f"<script>\n{fg_js}\n</script>",
)
html_template = html_template.replace(
"<!-- INLINE_VENDOR_FLAMEGRAPH_TOOLTIP_JS -->",
f"<script>\n{fg_tooltip_js}\n</script>",
)
# Replace the placeholder with actual data
html_content = html_template.replace(
"{{FLAMEGRAPH_DATA}}", data_json
)
return html_content