blob: c5a868cbf5e1bc12280aa9462d9ffabc8b005bae [file] [log] [blame]
# Copyright (c) 2018 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Summarize the last ninja build, invoked with ninja's -C syntax.
This script is designed to be automatically run after each ninja build in
order to summarize the build's performance. Making build performance information
more visible should make it easier to notice anomalies and opportunities. To use
this script on Windows just set NINJA_SUMMARIZE_BUILD=1 and run autoninja.bat.
On Linux you can get autoninja to invoke this script using this syntax:
$ NINJA_SUMMARIZE_BUILD=1 autoninja -C out/Default/ chrome
You can also call this script directly using ninja's syntax to specify the
output directory of interest:
> python -C out/Default
Typical output looks like this:
>ninja -C out\debug_component base
ninja.exe -C out\debug_component base -j 960 -l 48 -d keeprsp
ninja: Entering directory `out\debug_component'
[1 processes, 1/1 @ 0.3/s : 3.092s ] Regenerating ninja files
[1 processes, 23/23 @ 0.9/s : 26.280s ] LINK(DLL) base.dll base.dll.lib
Longest build steps:
0.9 weighted s to build obj/base/base_jumbo_17.obj (5.0 s CPU time)
1.0 weighted s to build obj/base/base_jumbo_31.obj (13.1 s CPU time)
1.2 weighted s to build obj/base/base_jumbo_4.obj (14.7 s CPU time)
1.3 weighted s to build obj/base/base_jumbo_32.obj (15.0 s CPU time)
1.6 weighted s to build obj/base/base_jumbo_26.obj (17.1 s CPU time)
1.7 weighted s to build base.dll, base.dll.lib (1.7 s CPU time)
1.7 weighted s to build obj/base/base_jumbo_11.obj (15.9 s CPU time)
1.9 weighted s to build obj/base/base_jumbo_12.obj (18.5 s CPU time)
3.6 weighted s to build obj/base/base_jumbo_34.obj (20.1 s CPU time)
4.3 weighted s to build obj/base/base_jumbo_33.obj (22.3 s CPU time)
Time by build-step type:
0.1 s weighted time to generate 1 .c files (0.1 s CPU time)
0.1 s weighted time to generate 1 .stamp files (0.1 s CPU time)
0.2 s weighted time to generate 1 .h files (0.2 s CPU time)
1.7 s weighted time to generate 1 PEFile files (1.7 s CPU time)
24.3 s weighted time to generate 19 .obj files (233.4 s CPU time)
26.3 s weighted time (235.5 s CPU time, 9.0x parallelism)
23 build steps completed, average of 0.88/s
If no gn clean has been done then results will be for the last non-NULL
invocation of ninja. Ideas for future statistics, and implementations are
The "weighted" time is the elapsed time of each build step divided by the number
of tasks that were running in parallel. This makes it an excellent approximation
of how "important" a slow step was. A link that is entirely or mostly serialized
will have a weighted time that is the same or similar to its elapsed time. A
compile that runs in parallel with 999 other compiles will have a weighted time
that is tiny."""
from __future__ import print_function
import argparse
import errno
import os
import sys
# The number of long build times to report:
long_count = 10
# The number of long times by extension to report
long_ext_count = 5
class Target:
"""Represents a single line read for a .ninja_log file."""
def __init__(self, start, end):
"""Creates a target object by passing in the start/end times in seconds
as a float."""
self.start = start
self.end = end
# A list of targets, appended to by the owner of this object.
self.targets = []
self.weighted_duration = 0.0
def Duration(self):
"""Returns the task duration in seconds as a float."""
return self.end - self.start
def SetWeightedDuration(self, weighted_duration):
"""Sets the duration, in seconds, passed in as a float."""
self.weighted_duration = weighted_duration
def WeightedDuration(self):
"""Returns the task's weighted duration in seconds as a float.
Weighted_duration takes the elapsed time of the task and divides it
by how many other tasks were running at the same time. Thus, it
represents the approximate impact of this task on the total build time,
with serialized or serializing steps typically ending up with much
longer weighted durations.
weighted_duration should always be the same or shorter than duration.
# Allow for modest floating-point errors
epsilon = 0.000002
if (self.weighted_duration > self.Duration() + epsilon):
print('%s > %s?' % (self.weighted_duration, self.Duration()))
assert(self.weighted_duration <= self.Duration() + epsilon)
return self.weighted_duration
def DescribeTargets(self):
"""Returns a printable string that summarizes the targets."""
if len(self.targets) == 1:
return self.targets[0]
# Some build steps generate dozens of outputs - handle them sanely.
# It's a bit odd that if there are three targets we return all three
# but if there are more than three we just return two, but this works
# well in practice.
elif len(self.targets) > 3:
return '(%d items) ' % len(self.targets) + (
', '.join(self.targets[:2]) + ', ...')
return ', '.join(self.targets)
# Copied with some modifications from ninjatracing
def ReadTargets(log, show_all):
"""Reads all targets from .ninja_log file |log_file|, sorted by duration.
The result is a list of Target objects."""
header = log.readline()
assert header == '# ninja log v5\n', \
'unrecognized ninja log version %r' % header
targets_dict = {}
last_end_seen = 0.0
for line in log:
parts = line.strip().split('\t')
if len(parts) != 5:
# If ninja.exe is rudely halted then the .ninja_log file may be
# corrupt. Silently continue.
start, end, _, name, cmdhash = parts # Ignore restat.
# Convert from integral milliseconds to float seconds.
start = int(start) / 1000.0
end = int(end) / 1000.0
if not show_all and end < last_end_seen:
# An earlier time stamp means that this step is the first in a new
# build, possibly an incremental build. Throw away the previous
# data so that this new build will be displayed independently.
# This has to be done by comparing end times because records are
# written to the .ninja_log file when commands complete, so end
# times are guaranteed to be in order, but start times are not.
targets_dict = {}
target = None
if cmdhash in targets_dict:
target = targets_dict[cmdhash]
if not show_all and (target.start != start or target.end != end):
# If several builds in a row just run one or two build steps then
# the end times may not go backwards so the last build may not be
# detected as such. However in many cases there will be a build step
# repeated in the two builds and the changed start/stop points for
# that command, identified by the hash, can be used to detect and
# reset the target dictionary.
targets_dict = {}
target = None
if not target:
targets_dict[cmdhash] = target = Target(start, end)
last_end_seen = end
return targets_dict.values()
def GetExtension(target):
"""Return the file extension that best represents a target.
For targets that generate multiple outputs it is important to return a
consistent 'canonical' extension. Ultimately the goal is to group build steps
by type."""
for output in target.targets:
# Normalize all mojo related outputs to 'mojo'.
if output.count('.mojom') > 0:
extension = 'mojo'
# Not a true extension, but a good grouping.
if output.endswith('type_mappings'):
extension = 'type_mappings'
extension = os.path.splitext(output)[1]
if len(extension) == 0:
extension = '(no extension found)'
if extension in ['.pdb', '.dll', '.exe']:
extension = 'PEFile (linking)'
# Make sure that .dll and .exe are grouped together and that the
# .dll.lib files don't cause these to be listed as libraries
if extension in ['.so', '.TOC']:
extension = '.so (linking)'
# Attempt to identify linking, avoid identifying as '.TOC'
return extension
def SummarizeEntries(entries):
"""Print a summary of the passed in list of Target objects."""
# Create a list that is in order by time stamp and has entries for the
# beginning and ending of each build step (one time stamp may have multiple
# entries due to multiple steps starting/stopping at exactly the same time).
# Iterate through this list, keeping track of which tasks are running at all
# times. At each time step calculate a running total for weighted time so
# that when each task ends its own weighted time can easily be calculated.
task_start_stop_times = []
earliest = -1
latest = 0
total_cpu_time = 0
for target in entries:
if earliest < 0 or target.start < earliest:
earliest = target.start
if target.end > latest:
latest = target.end
total_cpu_time += target.Duration()
task_start_stop_times.append((target.start, 'start', target))
task_start_stop_times.append((target.end, 'stop', target))
length = latest - earliest
weighted_total = 0.0
# Now we have all task start/stop times sorted by when they happen. If a
# task starts and stops on the same time stamp then the start will come
# first because of the alphabet, which is important for making this work
# correctly.
# Track the tasks which are currently running.
running_tasks = {}
# Record the time we have processed up to so we know how to calculate time
# deltas.
last_time = task_start_stop_times[0][0]
# Track the accumulated weighted time so that it can efficiently be added
# to individual tasks.
last_weighted_time = 0.0
# Scan all start/stop events.
for event in task_start_stop_times:
time, action_name, target = event
# Accumulate weighted time up to now.
num_running = len(running_tasks)
if num_running > 0:
# Update the total weighted time up to this moment.
last_weighted_time += (time - last_time) / float(num_running)
if action_name == 'start':
# Record the total weighted task time when this task starts.
running_tasks[target] = last_weighted_time
if action_name == 'stop':
# Record the change in the total weighted task time while this task ran.
weighted_duration = last_weighted_time - running_tasks[target]
weighted_total += weighted_duration
del running_tasks[target]
last_time = time
assert(len(running_tasks) == 0)
# Warn if the sum of weighted times is off by more than half a second.
if abs(length - weighted_total) > 500:
print('Discrepancy!!! Length = %.3f, weighted total = %.3f' % (
length, weighted_total))
# Print the slowest build steps (by weighted time).
print(' Longest build steps:')
entries.sort(key=lambda x: x.WeightedDuration())
for target in entries[-long_count:]:
print(' %8.1f weighted s to build %s (%.1f s CPU time)' % (
target.DescribeTargets(), target.Duration()))
# Sum up the time by file extension/type of the output file
count_by_ext = {}
time_by_ext = {}
weighted_time_by_ext = {}
# Scan through all of the targets to build up per-extension statistics.
for target in entries:
extension = GetExtension(target)
time_by_ext[extension] = time_by_ext.get(extension, 0) + target.Duration()
weighted_time_by_ext[extension] = weighted_time_by_ext.get(extension,
0) + target.WeightedDuration()
count_by_ext[extension] = count_by_ext.get(extension, 0) + 1
print(' Time by build-step type:')
# Copy to a list with extension name and total time swapped, to (time, ext)
weighted_time_by_ext_sorted = sorted((y, x) for (x, y) in
# Print the slowest build target types (by weighted time):
for time, extension in weighted_time_by_ext_sorted[-long_ext_count:]:
print(' %8.1f s weighted time to generate %d %s files '
'(%1.1f s CPU time)' % (time, count_by_ext[extension],
extension, time_by_ext[extension]))
print(' %.1f s weighted time (%.1f s CPU time, %1.1fx parallelism)' % (
length, total_cpu_time,
total_cpu_time * 1.0 / length))
print(' %d build steps completed, average of %1.2f/s' % (
len(entries), len(entries) / (length)))
def main():
log_file = '.ninja_log'
parser = argparse.ArgumentParser()
parser.add_argument('-C', dest='build_directory',
help='Build directory.')
help="specific ninja log file to analyze.")
args, _extra_args = parser.parse_known_args()
if args.build_directory:
log_file = os.path.join(args.build_directory, log_file)
if args.log_file:
log_file = args.log_file
with open(log_file, 'r') as log:
entries = ReadTargets(log, False)
except IOError:
print('Log file %r not found, no build summary created.' % log_file)
return errno.ENOENT
if __name__ == '__main__':