gecko-dev/toolkit/components/telemetry/histogram_tools.py

216 lines
7.8 KiB
Python

# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import math
# For compatibility with Python 2.6
try:
from collections import OrderedDict
except ImportError:
from simplejson import OrderedDict
import simplejson as json
else:
import json
def table_dispatch(kind, table, body):
"""Call body with table[kind] if it exists. Raise an error otherwise."""
if kind in table:
return body(table[kind])
else:
raise BaseException, "don't know how to handle a histogram of kind %s" % kind
class DefinitionException(BaseException):
pass
def check_numeric_limits(dmin, dmax, n_buckets):
if type(dmin) != int:
raise DefinitionException, "minimum is not a number"
if type(dmax) != int:
raise DefinitionException, "maximum is not a number"
if type(n_buckets) != int:
raise DefinitionException, "number of buckets is not a number"
def linear_buckets(dmin, dmax, n_buckets):
check_numeric_limits(dmin, dmax, n_buckets)
ret_array = [0] * n_buckets
dmin = float(dmin)
dmax = float(dmax)
for i in range(1, n_buckets):
linear_range = (dmin * (n_buckets - 1 - i) + dmax * (i - 1)) / (n_buckets - 2)
ret_array[i] = int(linear_range + 0.5)
return ret_array
def exponential_buckets(dmin, dmax, n_buckets):
check_numeric_limits(dmin, dmax, n_buckets)
log_max = math.log(dmax);
bucket_index = 2;
ret_array = [0] * n_buckets
current = dmin
ret_array[1] = current
for bucket_index in range(2, n_buckets):
log_current = math.log(current)
log_ratio = (log_max - log_current) / (n_buckets - bucket_index)
log_next = log_current + log_ratio
next_value = int(math.floor(math.exp(log_next) + 0.5))
if next_value > current:
current = next_value
else:
current = current + 1
ret_array[bucket_index] = current
return ret_array
always_allowed_keys = ['kind', 'description', 'cpp_guard']
class Histogram:
"""A class for representing a histogram definition."""
def __init__(self, name, definition):
"""Initialize a histogram named name with the given definition.
definition is a dict-like object that must contain at least the keys:
- 'kind': The kind of histogram. Must be one of 'boolean', 'flag',
'enumerated', 'linear', or 'exponential'.
- 'description': A textual description of the histogram.
The key 'cpp_guard' is optional; if present, it denotes a preprocessor
symbol that should guard C/C++ definitions associated with the histogram."""
self.verify_attributes(name, definition)
self._name = name
self._description = definition['description']
self._kind = definition['kind']
self._cpp_guard = definition.get('cpp_guard')
self._extended_statistics_ok = definition.get('extended_statistics_ok', False)
self.compute_bucket_parameters(definition)
table = { 'boolean': 'BOOLEAN',
'flag': 'FLAG',
'enumerated': 'LINEAR',
'linear': 'LINEAR',
'exponential': 'EXPONENTIAL' }
table_dispatch(self.kind(), table,
lambda k: self._set_nsITelemetry_kind(k))
def name(self):
"""Return the name of the histogram."""
return self._name
def description(self):
"""Return the description of the histogram."""
return self._description
def kind(self):
"""Return the kind of the histogram.
Will be one of 'boolean', 'flag', 'enumerated', 'linear', or 'exponential'."""
return self._kind
def nsITelemetry_kind(self):
"""Return the nsITelemetry constant corresponding to the kind of
the histogram."""
return self._nsITelemetry_kind
def _set_nsITelemetry_kind(self, kind):
self._nsITelemetry_kind = "nsITelemetry::HISTOGRAM_%s" % kind
def low(self):
"""Return the lower bound of the histogram. May be a string."""
return self._low
def high(self):
"""Return the high bound of the histogram. May be a string."""
return self._high
def n_buckets(self):
"""Return the number of buckets in the histogram. May be a string."""
return self._n_buckets
def cpp_guard(self):
"""Return the preprocessor symbol that should guard C/C++ definitions
associated with the histogram. Returns None if no guarding is necessary."""
return self._cpp_guard
def extended_statistics_ok(self):
"""Return True if gathering extended statistics for this histogram
is enabled."""
return self._extended_statistics_ok
def ranges(self):
"""Return an array of lower bounds for each bucket in the histogram."""
table = { 'boolean': linear_buckets,
'flag': linear_buckets,
'enumerated': linear_buckets,
'linear': linear_buckets,
'exponential': exponential_buckets }
return table_dispatch(self.kind(), table,
lambda p: p(self.low(), self.high(), self.n_buckets()))
def compute_bucket_parameters(self, definition):
table = {
'boolean': Histogram.boolean_flag_bucket_parameters,
'flag': Histogram.boolean_flag_bucket_parameters,
'enumerated': Histogram.enumerated_bucket_parameters,
'linear': Histogram.linear_bucket_parameters,
'exponential': Histogram.exponential_bucket_parameters
}
table_dispatch(self.kind(), table,
lambda p: self.set_bucket_parameters(*p(definition)))
def verify_attributes(self, name, definition):
global always_allowed_keys
general_keys = always_allowed_keys + ['low', 'high', 'n_buckets']
table = {
'boolean': always_allowed_keys,
'flag': always_allowed_keys,
'enumerated': always_allowed_keys + ['n_values'],
'linear': general_keys,
'exponential': general_keys + ['extended_statistics_ok']
}
table_dispatch(definition['kind'], table,
lambda allowed_keys: Histogram.check_keys(name, definition, allowed_keys))
@staticmethod
def check_keys(name, definition, allowed_keys):
for key in definition.iterkeys():
if key not in allowed_keys:
raise KeyError, '%s not permitted for %s' % (key, name)
def set_bucket_parameters(self, low, high, n_buckets):
def try_to_coerce_to_number(v):
try:
return eval(v, {})
except:
return v
self._low = try_to_coerce_to_number(low)
self._high = try_to_coerce_to_number(high)
self._n_buckets = try_to_coerce_to_number(n_buckets)
@staticmethod
def boolean_flag_bucket_parameters(definition):
return (1, 2, 3)
@staticmethod
def linear_bucket_parameters(definition):
return (definition.get('low', 1),
definition['high'],
definition['n_buckets'])
@staticmethod
def enumerated_bucket_parameters(definition):
n_values = definition['n_values']
return (1, n_values, "%s+1" % n_values)
@staticmethod
def exponential_bucket_parameters(definition):
return (definition.get('low', 1),
definition['high'],
definition['n_buckets'])
def from_file(filename):
"""Return an iterator that provides a sequence of Histograms for
the histograms defined in filename.
"""
with open(filename, 'r') as f:
histograms = json.load(f, object_pairs_hook=OrderedDict)
for (name, definition) in histograms.iteritems():
yield Histogram(name, definition)