sdks/python/apache_beam/utils/histogram.py - beam - Git at Google

 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
 # this work for additional information regarding copyright ownership.
 # The ASF licenses this file to You under the Apache License, Version 2.0
 # (the "License"); you may not use this file except in compliance with
 # the License.  You may obtain a copy of the License at
 #
 #    http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #

 import logging
 import math
 import threading
 from collections import Counter

 _LOGGER = logging.getLogger(__name__)


 class Histogram(object):
   """A histogram that supports estimated percentile with linear interpolation.

   This class is considered experimental and may break or receive backwards-
   incompatible changes in future versions of the Apache Beam SDK.
   """
   def __init__(self, bucket_type):
     self._lock = threading.Lock()
     self._bucket_type = bucket_type
     self._buckets = Counter()
     self._num_records = 0
     self._num_top_records = 0
     self._num_bot_records = 0

   def clear(self):
     with self._lock:
       self._buckets = Counter()
       self._num_records = 0
       self._num_top_records = 0
       self._num_bot_records = 0

   def copy(self):
     with self._lock:
       histogram = Histogram(self._bucket_type)
       histogram._num_records = self._num_records
       histogram._num_top_records = self._num_top_records
       histogram._num_bot_records = self._num_bot_records
       histogram._buckets = self._buckets.copy()
       return histogram

   def combine(self, other):
     if not isinstance(other,
                       Histogram) or self._bucket_type != other._bucket_type:
       raise RuntimeError('failed to combine histogram.')
     other_histogram = other.copy()
     with self._lock:
       histogram = Histogram(self._bucket_type)
       histogram._num_records = self._num_records + other_histogram._num_records
       histogram._num_top_records = (
           self._num_top_records + other_histogram._num_top_records)
       histogram._num_bot_records = (
           self._num_bot_records + other_histogram._num_bot_records)
       histogram._buckets = self._buckets + other_histogram._buckets
       return histogram

   def record(self, *args):
     for arg in args:
       self._record(arg)

   def _record(self, value):
     range_from = self._bucket_type.range_from()
     range_to = self._bucket_type.range_to()
     with self._lock:
       if value >= range_to:
         _LOGGER.warning('record is out of upper bound %s: %s', range_to, value)
         self._num_top_records += 1
       elif value < range_from:
         _LOGGER.warning(
             'record is out of lower bound %s: %s', range_from, value)
         self._num_bot_records += 1
       else:
         index = self._bucket_type.bucket_index(value)
         self._buckets[index] = self._buckets.get(index, 0) + 1
         self._num_records += 1

   def total_count(self):
     return self._num_records + self._num_top_records + self._num_bot_records

   def p99(self):
     return self.get_linear_interpolation(0.99)

   def p90(self):
     return self.get_linear_interpolation(0.90)

   def p50(self):
     return self.get_linear_interpolation(0.50)

   def get_percentile_info(self):
     def _format(f):
       if f == float('-inf'):
         return '<%s' % self._bucket_type.range_from()
       elif f == float('inf'):
         return '>=%s' % self._bucket_type.range_to()
       else:
         return str(int(round(f)))  # pylint: disable=round-builtin

     with self._lock:
       return (
           'Total count: %s, '
           'P99: %s, P90: %s, P50: %s' % (
               self.total_count(),
               _format(self._get_linear_interpolation(0.99)),
               _format(self._get_linear_interpolation(0.90)),
               _format(self._get_linear_interpolation(0.50))))

   def get_linear_interpolation(self, percentile):
     """Calculate percentile estimation based on linear interpolation.

     It first finds the bucket which includes the target percentile and
     projects the estimated point in the bucket by assuming all the elements
     in the bucket are uniformly distributed.

     Args:
       percentile: The target percentile of the value returning from this
         method. Should be a floating point number greater than 0 and less
         than 1.
     """
     with self._lock:
       return self._get_linear_interpolation(percentile)

   def _get_linear_interpolation(self, percentile):
     total_num_records = self.total_count()
     if total_num_records == 0:
       raise RuntimeError('histogram has no record.')

     index = 0
     record_sum = self._num_bot_records
     if record_sum / total_num_records >= percentile:
       return float('-inf')
     while index < self._bucket_type.num_buckets():
       record_sum += self._buckets.get(index, 0)
       if record_sum / total_num_records >= percentile:
         break
       index += 1
     if index == self._bucket_type.num_buckets():
       return float('inf')

     frac_percentile = percentile - (
         record_sum - self._buckets[index]) / total_num_records
     bucket_percentile = self._buckets[index] / total_num_records
     frac_bucket_size = frac_percentile * self._bucket_type.bucket_size(
         index) / bucket_percentile
     return (
         self._bucket_type.range_from() +
         self._bucket_type.accumulated_bucket_size(index) + frac_bucket_size)

   def __eq__(self, other):
     if not isinstance(other, Histogram):
       return False
     return (
         self._bucket_type == other._bucket_type and
         self._num_records == other._num_records and
         self._num_top_records == other._num_top_records and
         self._num_bot_records == other._num_bot_records and
         self._buckets == other._buckets)

   def __hash__(self):
     return hash((
         self._bucket_type,
         self._num_records,
         self._num_top_records,
         self._num_bot_records,
         frozenset(self._buckets.items())))


 class BucketType(object):
   def range_from(self):
     """Lower bound of a starting bucket."""
     raise NotImplementedError

   def range_to(self):
     """Upper bound of an ending bucket."""
     raise NotImplementedError

   def num_buckets(self):
     """The number of buckets."""
     raise NotImplementedError

   def bucket_index(self, value):
     """Get the bucket array index for the given value."""
     raise NotImplementedError

   def bucket_size(self, index):
     """Get the bucket size for the given bucket array index."""
     raise NotImplementedError

   def accumulated_bucket_size(self, end_index):
     """Get the accumulated bucket size from bucket index 0 until endIndex.

     Generally, this can be calculated as
     `sigma(0 <= i < endIndex) getBucketSize(i)`. However, a child class could
     provide better optimized calculation.
     """
     raise NotImplementedError


 class LinearBucket(BucketType):
   def __init__(self, start, width, num_buckets):
     """Create a histogram with linear buckets.

     Args:
       start: Lower bound of a starting bucket.
       width: Bucket width. Smaller width implies a better resolution for
         percentile estimation.
       num_buckets: The number of buckets. Upper bound of an ending bucket is
         defined by start + width * numBuckets.
     """
     self._start = start
     self._width = width
     self._num_buckets = num_buckets

   def range_from(self):
     return self._start

   def range_to(self):
     return self._start + self._width * self._num_buckets

   def num_buckets(self):
     return self._num_buckets

   def bucket_index(self, value):
     return math.floor((value - self._start) / self._width)

   def bucket_size(self, index):
     return self._width

   def accumulated_bucket_size(self, end_index):
     return self._width * end_index

   def __eq__(self, other):
     if not isinstance(other, LinearBucket):
       return False
     return (
         self._start == other._start and self._width == other._width and
         self._num_buckets == other._num_buckets)

   def __hash__(self):
     return hash((self._start, self._width, self._num_buckets))
	#
	# Licensed to the Apache Software Foundation (ASF) under one or more
	# contributor license agreements. See the NOTICE file distributed with
	# this work for additional information regarding copyright ownership.
	# The ASF licenses this file to You under the Apache License, Version 2.0
	# (the "License"); you may not use this file except in compliance with
	# the License. You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	#

	import logging
	import math
	import threading
	from collections import Counter

	_LOGGER = logging.getLogger(__name__)


	class Histogram(object):
	"""A histogram that supports estimated percentile with linear interpolation.

	This class is considered experimental and may break or receive backwards-
	incompatible changes in future versions of the Apache Beam SDK.
	"""
	def __init__(self, bucket_type):
	self._lock = threading.Lock()
	self._bucket_type = bucket_type
	self._buckets = Counter()
	self._num_records = 0
	self._num_top_records = 0
	self._num_bot_records = 0

	def clear(self):
	with self._lock:
	self._buckets = Counter()
	self._num_records = 0
	self._num_top_records = 0
	self._num_bot_records = 0

	def copy(self):
	with self._lock:
	histogram = Histogram(self._bucket_type)
	histogram._num_records = self._num_records
	histogram._num_top_records = self._num_top_records
	histogram._num_bot_records = self._num_bot_records
	histogram._buckets = self._buckets.copy()
	return histogram

	def combine(self, other):
	if not isinstance(other,
	Histogram) or self._bucket_type != other._bucket_type:
	raise RuntimeError('failed to combine histogram.')
	other_histogram = other.copy()
	with self._lock:
	histogram = Histogram(self._bucket_type)
	histogram._num_records = self._num_records + other_histogram._num_records
	histogram._num_top_records = (
	self._num_top_records + other_histogram._num_top_records)
	histogram._num_bot_records = (
	self._num_bot_records + other_histogram._num_bot_records)
	histogram._buckets = self._buckets + other_histogram._buckets
	return histogram

	def record(self, *args):
	for arg in args:
	self._record(arg)

	def _record(self, value):
	range_from = self._bucket_type.range_from()
	range_to = self._bucket_type.range_to()
	with self._lock:
	if value >= range_to:
	_LOGGER.warning('record is out of upper bound %s: %s', range_to, value)
	self._num_top_records += 1
	elif value < range_from:
	_LOGGER.warning(
	'record is out of lower bound %s: %s', range_from, value)
	self._num_bot_records += 1
	else:
	index = self._bucket_type.bucket_index(value)
	self._buckets[index] = self._buckets.get(index, 0) + 1
	self._num_records += 1

	def total_count(self):
	return self._num_records + self._num_top_records + self._num_bot_records

	def p99(self):
	return self.get_linear_interpolation(0.99)

	def p90(self):
	return self.get_linear_interpolation(0.90)

	def p50(self):
	return self.get_linear_interpolation(0.50)

	def get_percentile_info(self):
	def _format(f):
	if f == float('-inf'):
	return '<%s' % self._bucket_type.range_from()
	elif f == float('inf'):
	return '>=%s' % self._bucket_type.range_to()
	else:
	return str(int(round(f))) # pylint: disable=round-builtin

	with self._lock:
	return (
	'Total count: %s, '
	'P99: %s, P90: %s, P50: %s' % (
	self.total_count(),
	_format(self._get_linear_interpolation(0.99)),
	_format(self._get_linear_interpolation(0.90)),
	_format(self._get_linear_interpolation(0.50))))

	def get_linear_interpolation(self, percentile):
	"""Calculate percentile estimation based on linear interpolation.

	It first finds the bucket which includes the target percentile and
	projects the estimated point in the bucket by assuming all the elements
	in the bucket are uniformly distributed.

	Args:
	percentile: The target percentile of the value returning from this
	method. Should be a floating point number greater than 0 and less
	than 1.
	"""
	with self._lock:
	return self._get_linear_interpolation(percentile)

	def _get_linear_interpolation(self, percentile):
	total_num_records = self.total_count()
	if total_num_records == 0:
	raise RuntimeError('histogram has no record.')

	index = 0
	record_sum = self._num_bot_records
	if record_sum / total_num_records >= percentile:
	return float('-inf')
	while index < self._bucket_type.num_buckets():
	record_sum += self._buckets.get(index, 0)
	if record_sum / total_num_records >= percentile:
	break
	index += 1
	if index == self._bucket_type.num_buckets():
	return float('inf')

	frac_percentile = percentile - (
	record_sum - self._buckets[index]) / total_num_records
	bucket_percentile = self._buckets[index] / total_num_records
	frac_bucket_size = frac_percentile * self._bucket_type.bucket_size(
	index) / bucket_percentile
	return (
	self._bucket_type.range_from() +
	self._bucket_type.accumulated_bucket_size(index) + frac_bucket_size)

	def __eq__(self, other):
	if not isinstance(other, Histogram):
	return False
	return (
	self._bucket_type == other._bucket_type and
	self._num_records == other._num_records and
	self._num_top_records == other._num_top_records and
	self._num_bot_records == other._num_bot_records and
	self._buckets == other._buckets)

	def __hash__(self):
	return hash((
	self._bucket_type,
	self._num_records,
	self._num_top_records,
	self._num_bot_records,
	frozenset(self._buckets.items())))


	class BucketType(object):
	def range_from(self):
	"""Lower bound of a starting bucket."""
	raise NotImplementedError

	def range_to(self):
	"""Upper bound of an ending bucket."""
	raise NotImplementedError

	def num_buckets(self):
	"""The number of buckets."""
	raise NotImplementedError

	def bucket_index(self, value):
	"""Get the bucket array index for the given value."""
	raise NotImplementedError

	def bucket_size(self, index):
	"""Get the bucket size for the given bucket array index."""
	raise NotImplementedError

	def accumulated_bucket_size(self, end_index):
	"""Get the accumulated bucket size from bucket index 0 until endIndex.

	Generally, this can be calculated as
	`sigma(0 <= i < endIndex) getBucketSize(i)`. However, a child class could
	provide better optimized calculation.
	"""
	raise NotImplementedError


	class LinearBucket(BucketType):
	def __init__(self, start, width, num_buckets):
	"""Create a histogram with linear buckets.

	Args:
	start: Lower bound of a starting bucket.
	width: Bucket width. Smaller width implies a better resolution for
	percentile estimation.
	num_buckets: The number of buckets. Upper bound of an ending bucket is
	defined by start + width * numBuckets.
	"""
	self._start = start
	self._width = width
	self._num_buckets = num_buckets

	def range_from(self):
	return self._start

	def range_to(self):
	return self._start + self._width * self._num_buckets

	def num_buckets(self):
	return self._num_buckets

	def bucket_index(self, value):
	return math.floor((value - self._start) / self._width)

	def bucket_size(self, index):
	return self._width

	def accumulated_bucket_size(self, end_index):
	return self._width * end_index

	def __eq__(self, other):
	if not isinstance(other, LinearBucket):
	return False
	return (
	self._start == other._start and self._width == other._width and
	self._num_buckets == other._num_buckets)

	def __hash__(self):
	return hash((self._start, self._width, self._num_buckets))