#
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# 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.
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# limitations under the License.
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from __future__ import absolute_import
from __future__ import division
import logging
import math
import threading
_LOGGER = logging.getLogger(__name__)
[docs]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 = {}
self._num_records = 0
self._num_top_records = 0
self._num_bot_records = 0
[docs] def clear(self):
with self._lock:
self._buckets = {}
self._num_records = 0
self._num_top_records = 0
self._num_bot_records = 0
[docs] 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
[docs] def total_count(self):
return self._num_records + self._num_top_records + self._num_bot_records
[docs] def p99(self):
return self.get_linear_interpolation(0.99)
[docs] def p90(self):
return self.get_linear_interpolation(0.90)
[docs] def p50(self):
return self.get_linear_interpolation(0.50)
[docs] def get_percentile_info(self, elem_type, unit):
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(round(f)) # pylint: disable=round-builtin
with self._lock:
return (
'Total number of %s: %s, '
'P99: %s%s, P90: %s%s, P50: %s%s' % (
elem_type,
self.total_count(),
_format(self._get_linear_interpolation(0.99)),
unit,
_format(self._get_linear_interpolation(0.90)),
unit,
_format(self._get_linear_interpolation(0.50)),
unit))
[docs] 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)
[docs]class BucketType(object):
[docs] def range_from(self):
"""Lower bound of a starting bucket."""
raise NotImplementedError
[docs] def range_to(self):
"""Upper bound of an ending bucket."""
raise NotImplementedError
[docs] def num_buckets(self):
"""The number of buckets."""
raise NotImplementedError
[docs] def bucket_index(self, value):
"""Get the bucket array index for the given value."""
raise NotImplementedError
[docs] def bucket_size(self, index):
"""Get the bucket size for the given bucket array index."""
raise NotImplementedError
[docs] 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
[docs]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
[docs] def range_from(self):
return self._start
[docs] def range_to(self):
return self._start + self._width * self._num_buckets
[docs] def num_buckets(self):
return self._num_buckets
[docs] def bucket_index(self, value):
return math.floor((value - self._start) / self._width)
[docs] def bucket_size(self, index):
return self._width
[docs] def accumulated_bucket_size(self, end_index):
return self._width * end_index