shaka-packager/tools/metrics/histograms/extract_histograms.py

369 lines
12 KiB
Python

# Copyright 2013 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.
"""Extract histogram names from the description XML file.
For more information on the format of the XML file, which is self-documenting,
see histograms.xml; however, here is a simple example to get you started. The
XML below will generate the following five histograms:
HistogramTime
HistogramEnum
HistogramEnum_Chrome
HistogramEnum_IE
HistogramEnum_Firefox
<histogram-configuration>
<histograms>
<histogram name="HistogramTime" units="milliseconds">
<summary>A brief description.</summary>
<details>This is a more thorough description of this histogram.</details>
</histogram>
<histogram name="HistogramEnum" enum="MyEnumType">
<summary>This histogram sports an enum value type.</summary>
</histogram>
</histograms>
<enums>
<enum name="MyEnumType">
<summary>This is an example enum type, where the values mean little.</summary>
<int value="1" label="FIRST_VALUE">This is the first value.</int>
<int value="2" label="SECOND_VALUE">This is the second value.</int>
</enum>
</enums>
<fieldtrials>
<fieldtrial name="BrowserType">
<group name="Chrome"/>
<group name="IE"/>
<group name="Firefox"/>
<affected-histogram name="HistogramEnum"/>
</fieldtrial>
</fieldtrials>
</histogram-configuration>
"""
import copy
import logging
import xml.dom.minidom
MAX_FIELDTRIAL_DEPENDENCY_DEPTH = 5
class Error(Exception):
pass
def JoinChildNodes(tag):
return ''.join([c.toxml() for c in tag.childNodes]).strip()
def NormalizeAttributeValue(s):
"""Normalizes an attribute value (which might be wrapped over multiple lines)
by replacing each whitespace sequence with a single space.
Args:
s: The string to normalize, e.g. ' \n a b c\n d '
Returns:
The normalized string, e.g. 'a b c d'
"""
return ' '.join(s.split())
def NormalizeAllAttributeValues(node):
"""Recursively normalizes all tag attribute values in the given tree.
Args:
node: The minidom node to be normalized.
Returns:
The normalized minidom node.
"""
if node.nodeType == xml.dom.minidom.Node.ELEMENT_NODE:
for a in node.attributes.keys():
node.attributes[a].value = NormalizeAttributeValue(
node.attributes[a].value)
for c in node.childNodes: NormalizeAllAttributeValues(c)
return node
def _ExpandHistogramNameWithFieldTrial(group_name, histogram_name, fieldtrial):
"""Creates a new histogram name based on the field trial group.
Args:
group_name: The name of the field trial group. May be empty.
histogram_name: The name of the histogram. May be of the form
Group.BaseName or BaseName
field_trial: The FieldTrial XML element.
Returns:
A string with the expanded histogram name.
Raises:
Error if the expansion can't be done.
"""
if fieldtrial.hasAttribute('separator'):
separator = fieldtrial.getAttribute('separator')
else:
separator = '_'
if fieldtrial.hasAttribute('ordering'):
ordering = fieldtrial.getAttribute('ordering')
else:
ordering = 'suffix'
if ordering not in ['prefix', 'suffix']:
logging.error('ordering needs to be prefix or suffix, value is %s' %
ordering)
raise Error()
if not group_name:
return histogram_name
if ordering == 'suffix':
return histogram_name + separator + group_name
# For prefixes, the group_name is inserted between the "cluster" and the
# "remainder", e.g. Foo.BarHist expanded with gamma becomes Foo.gamma_BarHist.
sections = histogram_name.split('.')
if len(sections) <= 1:
logging.error(
'Prefix Field Trial expansions require histogram names which include a '
'dot separator. Histogram name is %s, and Field Trial is %s' %
(histogram_name, fieldtrial.getAttribute('name')))
raise Error()
cluster = sections[0] + '.'
remainder = '.'.join(sections[1:])
return cluster + group_name + separator + remainder
def ExtractHistograms(filename):
"""Compute the histogram names and descriptions from the XML representation.
Args:
filename: The path to the histograms XML file.
Returns:
{ 'histogram_name': 'histogram_description', ... }
Raises:
Error if the file is not well-formatted.
"""
# Slurp in histograms.xml
raw_xml = ''
with open(filename, 'r') as f:
raw_xml = f.read()
# Parse the XML into a tree
tree = xml.dom.minidom.parseString(raw_xml)
NormalizeAllAttributeValues(tree)
histograms = {}
have_errors = False
# Load the enums.
enums = {}
last_name = None
for enum in tree.getElementsByTagName("enum"):
if enum.getAttribute('type') != 'int':
logging.error('Unknown enum type %s' % enum.getAttribute('type'))
have_errors = True
continue
name = enum.getAttribute('name')
if last_name is not None and name.lower() < last_name.lower():
logging.error('Enums %s and %s are not in alphabetical order'
% (last_name, name))
have_errors = True
last_name = name
if name in enums:
logging.error('Duplicate enum %s' % name)
have_errors = True
continue
last_int_value = None
enum_dict = {}
enum_dict['name'] = name
enum_dict['values'] = {}
for int_tag in enum.getElementsByTagName("int"):
value_dict = {}
int_value = int(int_tag.getAttribute('value'))
if last_int_value is not None and int_value < last_int_value:
logging.error('Enum %s int values %d and %d are not in numerical order'
% (name, last_int_value, int_value))
have_errors = True
last_int_value = int_value
if int_value in enum_dict['values']:
logging.error('Duplicate enum value %d for enum %s' % (int_value, name))
have_errors = True
continue
value_dict['label'] = int_tag.getAttribute('label')
value_dict['summary'] = JoinChildNodes(int_tag)
enum_dict['values'][int_value] = value_dict
summary_nodes = enum.getElementsByTagName("summary")
if len(summary_nodes) > 0:
enum_dict['summary'] = JoinChildNodes(summary_nodes[0])
enums[name] = enum_dict
# Process the histograms. The descriptions can include HTML tags.
last_name = None
for histogram in tree.getElementsByTagName("histogram"):
name = histogram.getAttribute('name')
if last_name is not None and name.lower() < last_name.lower():
logging.error('Histograms %s and %s are not in alphabetical order'
% (last_name, name))
have_errors = True
last_name = name
if name in histograms:
logging.error('Duplicate histogram definition %s' % name)
have_errors = True
continue
histograms[name] = {}
# Find <summary> tag.
summary_nodes = histogram.getElementsByTagName("summary")
if len(summary_nodes) > 0:
histograms[name]['summary'] = JoinChildNodes(summary_nodes[0])
else:
histograms[name]['summary'] = 'TBD'
# Find <obsolete> tag.
obsolete_nodes = histogram.getElementsByTagName("obsolete")
if len(obsolete_nodes) > 0:
reason = JoinChildNodes(obsolete_nodes[0])
histograms[name]['obsolete'] = reason
# Handle units.
if histogram.hasAttribute('units'):
histograms[name]['units'] = histogram.getAttribute('units')
# Find <details> tag.
details_nodes = histogram.getElementsByTagName("details")
if len(details_nodes) > 0:
histograms[name]['details'] = JoinChildNodes(details_nodes[0])
# Handle enum types.
if histogram.hasAttribute('enum'):
enum_name = histogram.getAttribute('enum')
if not enum_name in enums:
logging.error('Unknown enum %s in histogram %s' % (enum_name, name))
have_errors = True
else:
histograms[name]['enum'] = enums[enum_name]
# Process the field trials and compute the combinations with their affected
# histograms.
last_name = None
for fieldtrial in tree.getElementsByTagName("fieldtrial"):
name = fieldtrial.getAttribute('name')
if last_name is not None and name.lower() < last_name.lower():
logging.error('Field trials %s and %s are not in alphabetical order'
% (last_name, name))
have_errors = True
last_name = name
# Field trials can depend on other field trials, so we need to be careful.
# Make a temporary copy of the list of field trials to use as a queue.
# Field trials whose dependencies have not yet been processed will get
# relegated to the back of the queue to be processed later.
reprocess_queue = []
def GenerateFieldTrials():
for f in tree.getElementsByTagName("fieldtrial"): yield 0, f
for r, f in reprocess_queue: yield r, f
for reprocess_count, fieldtrial in GenerateFieldTrials():
# Check dependencies first
dependencies_valid = True
affected_histograms = fieldtrial.getElementsByTagName('affected-histogram')
for affected_histogram in affected_histograms:
histogram_name = affected_histogram.getAttribute('name')
if not histogram_name in histograms:
# Base histogram is missing
dependencies_valid = False
missing_dependency = histogram_name
break
if not dependencies_valid:
if reprocess_count < MAX_FIELDTRIAL_DEPENDENCY_DEPTH:
reprocess_queue.append( (reprocess_count + 1, fieldtrial) )
continue
else:
logging.error('Field trial %s is missing its dependency %s'
% (fieldtrial.getAttribute('name'),
missing_dependency))
have_errors = True
continue
name = fieldtrial.getAttribute('name')
groups = fieldtrial.getElementsByTagName('group')
group_labels = {}
for group in groups:
group_labels[group.getAttribute('name')] = group.getAttribute('label')
last_histogram_name = None
for affected_histogram in affected_histograms:
histogram_name = affected_histogram.getAttribute('name')
if (last_histogram_name is not None
and histogram_name.lower() < last_histogram_name.lower()):
logging.error('Affected histograms %s and %s of field trial %s are not '
'in alphabetical order'
% (last_histogram_name, histogram_name, name))
have_errors = True
last_histogram_name = histogram_name
base_description = histograms[histogram_name]
with_groups = affected_histogram.getElementsByTagName('with-group')
if len(with_groups) > 0:
histogram_groups = with_groups
else:
histogram_groups = groups
for group in histogram_groups:
group_name = group.getAttribute('name')
try:
new_histogram_name = _ExpandHistogramNameWithFieldTrial(
group_name, histogram_name, fieldtrial)
if new_histogram_name != histogram_name:
histograms[new_histogram_name] = copy.deepcopy(
histograms[histogram_name])
group_label = group_labels.get(group_name, '')
if not 'fieldtrial_groups' in histograms[new_histogram_name]:
histograms[new_histogram_name]['fieldtrial_groups'] = []
histograms[new_histogram_name]['fieldtrial_groups'].append(group_name)
if not 'fieldtrial_names' in histograms[new_histogram_name]:
histograms[new_histogram_name]['fieldtrial_names'] = []
histograms[new_histogram_name]['fieldtrial_names'].append(name)
if not 'fieldtrial_labels' in histograms[new_histogram_name]:
histograms[new_histogram_name]['fieldtrial_labels'] = []
histograms[new_histogram_name]['fieldtrial_labels'].append(
group_label)
except Error:
have_errors = True
if have_errors:
logging.error('Error parsing %s' % filename)
raise Error()
return histograms
def ExtractNames(histograms):
return sorted(histograms.keys())