<reponame>zealoussnow/chromium
# Copyright 2021 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.
"""Parses traces into Python objects.

Takes a trace from chrome://tracing and returns a Python dict containing the
result. The functions in the file are not very useful on their own, but are
intended as helpers for the other scripts in this directory.

This parses an allocation profile generated by PartitionAlloc in the thread
cache. This will only give data on Chrome instances where the thread cache is
enabled, and PA_THREAD_CACHE_ALLOC_STATS is defined, that is non-official
builds.

To collect a profile:
- Build a non-official chrome version (Should be a release build for accurate
  reports; Debug builds have PA-E disabled)
- Collect a trace with the memory-infra category enabled (in chrome://tracing)
- Save it as json.gz, and load it here.
"""

import gzip
import json

from matplotlib import pylab as plt
from typing import Callable


def LoadTrace(filename: str) -> dict:
  """Loads a JSON trace, gzipped or not. This trace should be taken directly
  from Chrome (chrome://tracing).

  Args:
    filename: Filename, gzipped or not.

  Returns:
    A dictionary with the trace content. This is simply the content of the
    trace from |filename|, as a Python dictionary.
  """
  try:
    f = None
    if filename.endswith('.gz'):
      f = gzip.open(filename, 'r')
    else:
      f = open(filename, 'r')
    return json.load(f)
  finally:
    if f is not None:
      f.close()


def _GetAllocatorDumps(trace: dict) -> list:
  """Takes in a trace (as returned from |LoadTrace|), and returns the parts
  of it related to the allocators.

  Args:
    trace: Trace content as returned by LoadTrace.

  Returns:
    The parts of the trace related to allocator metrics. Note that these
    entries are taken as-is from the trace.

    Each entry of the return value has
    the following format. The important field here is 'args', but there are
    other unimportant ones as well (shown with ...):
    {
      'args': {
        'dumps': {
          'allocators': dict[str, dict],
          'allocator_graph': list[dict],
          'level_of_detail': 'detailed'
        }
      },
      ...
    }

    Of the fields listed above, we mainly care about 'allocators'. Each entry
    here corresponds to an allocator or a subset of the allocations for a given
    allocator. For example, we have an entry in 'allocators' with key
    'malloc/partitions/allocator/buckets/bucket_1024', which corresponds to the
    1024 byte bucket of PartitionAlloc's main malloc partition.

    Note that in the trace, we have entries for allocators besides just
    PartitionAlloc, but in these scripts, we will only be looking at entries
    for PartitionAlloc allocators.

    Each entry of 'allocators' has the
    following format:
    {
      'attrs': dict[str, dict],
      'guid': str
    }

    The contents of 'attrs' varies depending on which entry we are looking at,
    but generally contains all the relevant information we need for a
    particular allocator. The attributes we care about here are mainly:
    'allocated_objects_size', 'size', and 'slot_size'.

    Finally, each entry of 'attrs' has the following format:
    {
      'type': str,
      'units': str,
      'value': str
    }

    For example, in 'malloc/partitions/allocator/buckets/bucket_1024', 'attrs'
    has a 'allocated_objects_size' entry, which might look like so:
    {
      'type': 'scalar',
      'units': 'bytes',
      'value': 'ef8000'
    }

    The entry we care about here is 'value', which is a string representing a
    hexadecimal number.
  """
  events = trace['traceEvents']
  memory_infra_events = [
      e for e in events if e['cat'] == 'disabled-by-default-memory-infra'
  ]
  dumps = [
      e for e in memory_infra_events
      if e['name'] == 'periodic_interval' and e['args']['dumps']
      ['level_of_detail'] == 'detailed' and 'allocators' in e['args']['dumps']
  ]
  return dumps


def _ProcessNamesAndLabels(trace: dict) -> (dict, dict):
  """Get mappings of pid to name and pid to label.

  Args:
    trace: Trace content as returned by LoadTrace.

  Returns:
    A tuple containing two dicts. The first maps pids to names, the second maps
    pids to labels.
  """
  # Process names and labels.
  pid_to_name = {}
  pid_to_labels = {}

  metadata_events = [
      e for e in trace['traceEvents'] if e['cat'] == '__metadata'
  ]

  process_name_events = [
      e for e in metadata_events if e['name'] == 'process_name'
  ]
  for e in process_name_events:
    pid_to_name[e['pid']] = e['args']['name']

  process_labels_events = [
      e for e in metadata_events if e['name'] == 'process_labels'
  ]
  for e in process_labels_events:
    pid_to_labels[e['pid']] = e['args']['labels']

  return pid_to_name, pid_to_labels


def ParseTrace(trace: dict,
               compute_result: Callable[[dict, dict], None]) -> dict[int, dict]:
  """Parses a trace, and returns thread cache stats.

  Args:
    trace: As returned by LoadTrace()
    compute_result: function taking |result_for_pid| (which it modifies
      in-place), and |allocators|. It should add any results we'd like to graph
      into |result_for_pid['data']|, as an |np.array|.

  Returns:
    {pid -> {'name': str, 'labels': str, 'data': np.array}.
    Where the data array contains the data we would like to graph. For example,
    it may contain 'size' and 'fragmentation' columns, which is what
    |PlotProcessFragmentation| expects.
  """
  dumps = _GetAllocatorDumps(trace)
  pid_to_name, pid_to_labels = _ProcessNamesAndLabels(trace)

  result = {}
  for dump in dumps:
    pid = dump['pid']
    allocators = dump['args']['dumps']['allocators']

    # The browser process also has global dumps, we do not care about these.
    # These dumps are also returned from |_GetAllocatorDumps|, but the
    # corresponding metadata for them is not returned from
    # |_ProcessNamesAndLabels|. We have to special-case things _somewhere_ to
    # exclude these dumps, and this seems like the best spot.
    if 'global' in allocators:
      continue

    # We only use the last dump for a given pid, overwritting all previous
    # ones.
    result[pid] = {
        'name': pid_to_name[pid],
        'labels': pid_to_labels.get(pid, '')
    }
    compute_result(result[pid], allocators)

  return result


def PlotProcessFragmentation(title, data, output):
  """Plots the Fragmentation vs size for a single process.

  Args:
    title: Title of the graph
    data: Data to plot. Should contain 'size' and 'fragmentation' entries.
    output: Filename to save the result to.
  """
  plt.figure(figsize=(16, 8))
  plt.title(title)
  plt.stem(data['data']['size'], data['data']['fragmentation'])
  plt.xscale('log', base=2)
  plt.yscale('linear')
  plt.ylim(ymin=0, ymax=100)
  plt.xlabel('Size (log)')
  plt.ylabel('Fragmentation (%)')
  plt.savefig(output, bbox_inches='tight')
  plt.close()


def PlotProcessWaste(title, data, output):
  """Plots the Unused memory vs size for a single process.

  Args:
    title: Title of the graph
    data: Data to plot. Should contain 'size' and 'unused' entries.
    output: Filename to save the result to.
  """
  plt.figure(figsize=(16, 8))
  plt.title(title)
  plt.xscale('log', base=2)
  plt.yscale('log', base=2)
  plt.stem(data['data']['size'][data['data']['unused'] != 0],
           data['data']['unused'][data['data']['unused'] != 0])
  plt.ylim(ymin=1, ymax=2**20)
  plt.xlabel('Size (log)')
  plt.ylabel('Unused Size (log)')
  plt.savefig(output, bbox_inches='tight')
  plt.close()


def GetAllocatorAttr(attrs: dict, name: str) -> str:
  return int(attrs[name]['value'], base=16)

# Copyright 2021 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.
"""Parses traces into Python objects.

Takes a trace from chrome://tracing and returns a Python dict containing the
result. The functions in the file are not very useful on their own, but are
intended as helpers for the other scripts in this directory.

This parses an allocation profile generated by PartitionAlloc in the thread
cache. This will only give data on Chrome instances where the thread cache is
enabled, and PA_THREAD_CACHE_ALLOC_STATS is defined, that is non-official
builds.

To collect a profile:
- Build a non-official chrome version (Should be a release build for accurate
  reports; Debug builds have PA-E disabled)
- Collect a trace with the memory-infra category enabled (in chrome://tracing)
- Save it as json.gz, and load it here.
"""

import gzip
import json

from matplotlib import pylab as plt
from typing import Callable


def LoadTrace(filename: str) -> dict:
  """Loads a JSON trace, gzipped or not. This trace should be taken directly
  from Chrome (chrome://tracing).

  Args:
    filename: Filename, gzipped or not.

  Returns:
    A dictionary with the trace content. This is simply the content of the
    trace from |filename|, as a Python dictionary.
  """
  try:
    f = None
    if filename.endswith('.gz'):
      f = gzip.open(filename, 'r')
    else:
      f = open(filename, 'r')
    return json.load(f)
  finally:
    if f is not None:
      f.close()


def _GetAllocatorDumps(trace: dict) -> list:
  """Takes in a trace (as returned from |LoadTrace|), and returns the parts
  of it related to the allocators.

  Args:
    trace: Trace content as returned by LoadTrace.

  Returns:
    The parts of the trace related to allocator metrics. Note that these
    entries are taken as-is from the trace.

    Each entry of the return value has
    the following format. The important field here is 'args', but there are
    other unimportant ones as well (shown with ...):
    {
      'args': {
        'dumps': {
          'allocators': dict[str, dict],
          'allocator_graph': list[dict],
          'level_of_detail': 'detailed'
        }
      },
      ...
    }

    Of the fields listed above, we mainly care about 'allocators'. Each entry
    here corresponds to an allocator or a subset of the allocations for a given
    allocator. For example, we have an entry in 'allocators' with key
    'malloc/partitions/allocator/buckets/bucket_1024', which corresponds to the
    1024 byte bucket of PartitionAlloc's main malloc partition.

    Note that in the trace, we have entries for allocators besides just
    PartitionAlloc, but in these scripts, we will only be looking at entries
    for PartitionAlloc allocators.

    Each entry of 'allocators' has the
    following format:
    {
      'attrs': dict[str, dict],
      'guid': str
    }

    The contents of 'attrs' varies depending on which entry we are looking at,
    but generally contains all the relevant information we need for a
    particular allocator. The attributes we care about here are mainly:
    'allocated_objects_size', 'size', and 'slot_size'.

    Finally, each entry of 'attrs' has the following format:
    {
      'type': str,
      'units': str,
      'value': str
    }

    For example, in 'malloc/partitions/allocator/buckets/bucket_1024', 'attrs'
    has a 'allocated_objects_size' entry, which might look like so:
    {
      'type': 'scalar',
      'units': 'bytes',
      'value': 'ef8000'
    }

    The entry we care about here is 'value', which is a string representing a
    hexadecimal number.
  """
  events = trace['traceEvents']
  memory_infra_events = [
      e for e in events if e['cat'] == 'disabled-by-default-memory-infra'
  ]
  dumps = [
      e for e in memory_infra_events
      if e['name'] == 'periodic_interval' and e['args']['dumps']
      ['level_of_detail'] == 'detailed' and 'allocators' in e['args']['dumps']
  ]
  return dumps


def _ProcessNamesAndLabels(trace: dict) -> (dict, dict):
  """Get mappings of pid to name and pid to label.

  Args:
    trace: Trace content as returned by LoadTrace.

  Returns:
    A tuple containing two dicts. The first maps pids to names, the second maps
    pids to labels.
  """
  # Process names and labels.
  pid_to_name = {}
  pid_to_labels = {}

  metadata_events = [
      e for e in trace['traceEvents'] if e['cat'] == '__metadata'
  ]

  process_name_events = [
      e for e in metadata_events if e['name'] == 'process_name'
  ]
  for e in process_name_events:
    pid_to_name[e['pid']] = e['args']['name']

  process_labels_events = [
      e for e in metadata_events if e['name'] == 'process_labels'
  ]
  for e in process_labels_events:
    pid_to_labels[e['pid']] = e['args']['labels']

  return pid_to_name, pid_to_labels


def ParseTrace(trace: dict,
               compute_result: Callable[[dict, dict], None]) -> dict[int, dict]:
  """Parses a trace, and returns thread cache stats.

  Args:
    trace: As returned by LoadTrace()
    compute_result: function taking |result_for_pid| (which it modifies
      in-place), and |allocators|. It should add any results we'd like to graph
      into |result_for_pid['data']|, as an |np.array|.

  Returns:
    {pid -> {'name': str, 'labels': str, 'data': np.array}.
    Where the data array contains the data we would like to graph. For example,
    it may contain 'size' and 'fragmentation' columns, which is what
    |PlotProcessFragmentation| expects.
  """
  dumps = _GetAllocatorDumps(trace)
  pid_to_name, pid_to_labels = _ProcessNamesAndLabels(trace)

  result = {}
  for dump in dumps:
    pid = dump['pid']
    allocators = dump['args']['dumps']['allocators']

    # The browser process also has global dumps, we do not care about these.
    # These dumps are also returned from |_GetAllocatorDumps|, but the
    # corresponding metadata for them is not returned from
    # |_ProcessNamesAndLabels|. We have to special-case things _somewhere_ to
    # exclude these dumps, and this seems like the best spot.
    if 'global' in allocators:
      continue

    # We only use the last dump for a given pid, overwritting all previous
    # ones.
    result[pid] = {
        'name': pid_to_name[pid],
        'labels': pid_to_labels.get(pid, '')
    }
    compute_result(result[pid], allocators)

  return result


def PlotProcessFragmentation(title, data, output):
  """Plots the Fragmentation vs size for a single process.

  Args:
    title: Title of the graph
    data: Data to plot. Should contain 'size' and 'fragmentation' entries.
    output: Filename to save the result to.
  """
  plt.figure(figsize=(16, 8))
  plt.title(title)
  plt.stem(data['data']['size'], data['data']['fragmentation'])
  plt.xscale('log', base=2)
  plt.yscale('linear')
  plt.ylim(ymin=0, ymax=100)
  plt.xlabel('Size (log)')
  plt.ylabel('Fragmentation (%)')
  plt.savefig(output, bbox_inches='tight')
  plt.close()


def PlotProcessWaste(title, data, output):
  """Plots the Unused memory vs size for a single process.

  Args:
    title: Title of the graph
    data: Data to plot. Should contain 'size' and 'unused' entries.
    output: Filename to save the result to.
  """
  plt.figure(figsize=(16, 8))
  plt.title(title)
  plt.xscale('log', base=2)
  plt.yscale('log', base=2)
  plt.stem(data['data']['size'][data['data']['unused'] != 0],
           data['data']['unused'][data['data']['unused'] != 0])
  plt.ylim(ymin=1, ymax=2**20)
  plt.xlabel('Size (log)')
  plt.ylabel('Unused Size (log)')
  plt.savefig(output, bbox_inches='tight')
  plt.close()


def GetAllocatorAttr(attrs: dict, name: str) -> str:
  return int(attrs[name]['value'], base=16)
# Copyright 2021 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. Parses traces into Python objects.  Takes a trace from chrome://tracing and returns a Python dict containing the result. The functions in the file are not very useful on their own, but are intended as helpers for the other scripts in this directory.  This parses an allocation profile generated by PartitionAlloc in the thread cache. This will only give data on Chrome instances where the thread cache is enabled, and PA_THREAD_CACHE_ALLOC_STATS is defined, that is non-official builds.  To collect a profile: - Build a non-official chrome version (Should be a release build for accurate   reports; Debug builds have PA-E disabled) - Collect a trace with the memory-infra category enabled (in chrome://tracing) - Save it as json.gz, and load it here. Loads a JSON trace, gzipped or not. This trace should be taken directly   from Chrome (chrome://tracing).    Args:     filename: Filename, gzipped or not.    Returns:     A dictionary with the trace content. This is simply the content of the     trace from |filename|, as a Python dictionary. Takes in a trace (as returned from |LoadTrace|), and returns the parts   of it related to the allocators.    Args:     trace: Trace content as returned by LoadTrace.    Returns:     The parts of the trace related to allocator metrics. Note that these     entries are taken as-is from the trace.      Each entry of the return value has     the following format. The important field here is 'args', but there are     other unimportant ones as well (shown with ...):     {       'args': {         'dumps': {           'allocators': dict[str, dict],           'allocator_graph': list[dict],           'level_of_detail': 'detailed'         }       },       ...     }      Of the fields listed above, we mainly care about 'allocators'. Each entry     here corresponds to an allocator or a subset of the allocations for a given     allocator. For example, we have an entry in 'allocators' with key     'malloc/partitions/allocator/buckets/bucket_1024', which corresponds to the     1024 byte bucket of PartitionAlloc's main malloc partition.      Note that in the trace, we have entries for allocators besides just     PartitionAlloc, but in these scripts, we will only be looking at entries     for PartitionAlloc allocators.      Each entry of 'allocators' has the     following format:     {       'attrs': dict[str, dict],       'guid': str     }      The contents of 'attrs' varies depending on which entry we are looking at,     but generally contains all the relevant information we need for a     particular allocator. The attributes we care about here are mainly:     'allocated_objects_size', 'size', and 'slot_size'.      Finally, each entry of 'attrs' has the following format:     {       'type': str,       'units': str,       'value': str     }      For example, in 'malloc/partitions/allocator/buckets/bucket_1024', 'attrs'     has a 'allocated_objects_size' entry, which might look like so:     {       'type': 'scalar',       'units': 'bytes',       'value': 'ef8000'     }      The entry we care about here is 'value', which is a string representing a     hexadecimal number. Get mappings of pid to name and pid to label.    Args:     trace: Trace content as returned by LoadTrace.    Returns:     A tuple containing two dicts. The first maps pids to names, the second maps     pids to labels. # Process names and labels. Parses a trace, and returns thread cache stats.    Args:     trace: As returned by LoadTrace()     compute_result: function taking |result_for_pid| (which it modifies       in-place), and |allocators|. It should add any results we'd like to graph       into |result_for_pid['data']|, as an |np.array|.    Returns:     {pid -> {'name': str, 'labels': str, 'data': np.array}.     Where the data array contains the data we would like to graph. For example,     it may contain 'size' and 'fragmentation' columns, which is what     |PlotProcessFragmentation| expects. # The browser process also has global dumps, we do not care about these. # These dumps are also returned from |_GetAllocatorDumps|, but the # corresponding metadata for them is not returned from # |_ProcessNamesAndLabels|. We have to special-case things _somewhere_ to # exclude these dumps, and this seems like the best spot. # We only use the last dump for a given pid, overwritting all previous # ones. Plots the Fragmentation vs size for a single process.    Args:     title: Title of the graph     data: Data to plot. Should contain 'size' and 'fragmentation' entries.     output: Filename to save the result to. Plots the Unused memory vs size for a single process.    Args:     title: Title of the graph     data: Data to plot. Should contain 'size' and 'unused' entries.     output: Filename to save the result to.