242 lines
6.8 KiB
Python
242 lines
6.8 KiB
Python
# Copyright 2015: Mirantis Inc.
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# All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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from __future__ import division
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import abc
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import math
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import six
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from rally.task.processing import utils
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@six.add_metaclass(abc.ABCMeta)
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class StreamingAlgorithm(object):
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"""Base class for streaming computations that scale."""
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@abc.abstractmethod
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def add(self, value):
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"""Process a single value from the input stream."""
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@abc.abstractmethod
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def merge(self, other):
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"""Merge results processed by another instance."""
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@abc.abstractmethod
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def result(self):
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"""Return the result based on the values processed so far."""
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def _cast_to_float(self, value):
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try:
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return float(value)
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except (TypeError, ValueError):
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raise TypeError("Non-numerical value: %r" % value)
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class MeanComputation(StreamingAlgorithm):
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"""Compute mean for a stream of numbers."""
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def __init__(self):
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self.total = 0.0
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self.count = 0
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def add(self, value):
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self.count += 1
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self.total += value
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def merge(self, other):
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self.count += other.count
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self.total += other.total
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def result(self):
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if self.count:
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return self.total / self.count
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return None
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class StdDevComputation(StreamingAlgorithm):
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"""Compute standard deviation for a stream of numbers."""
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def __init__(self):
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self.count = 0
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# NOTE(msdubov): To compute std, we need the auxiliary variables below.
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self.dev_sum = 0.0
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self.mean_computation = MeanComputation()
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self.mean = 0.0
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def add(self, value):
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# NOTE(msdubov): This streaming method for std computation appears
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# in "The Art of Computer Programming" by D. Knuth,
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# Vol 2, p. 232, 3rd edition.
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self.count += 1
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mean_prev = self.mean
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self.mean_computation.add(value)
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self.mean = self.mean_computation.result()
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self.dev_sum = self.dev_sum + (value - mean_prev) * (value - self.mean)
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def merge(self, other):
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if not other.mean_computation.count:
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return
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dev_sum1 = self.dev_sum
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count1 = self.count
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mean1 = self.mean
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dev_sum2 = other.dev_sum
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count2 = other.count
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mean2 = other.mean
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self.mean_computation.merge(other.mean_computation)
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self.mean = self.mean_computation.result()
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self.count += other.count
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self.dev_sum = (dev_sum1 + count1 * mean1 ** 2
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+ dev_sum2 + count2 * mean2 ** 2
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- self.count * self.mean ** 2)
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def result(self):
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# NOTE(amaretskiy): Need at least two values to be processed
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if self.count < 2:
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return None
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return math.sqrt(self.dev_sum / (self.count - 1))
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class MinComputation(StreamingAlgorithm):
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"""Compute minimal value from a stream of numbers."""
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def __init__(self):
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self._value = None
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def add(self, value):
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value = self._cast_to_float(value)
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if self._value is None or value < self._value:
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self._value = value
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def merge(self, other):
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if other._value is not None:
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self.add(other._value)
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def result(self):
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return self._value
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class MaxComputation(StreamingAlgorithm):
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"""Compute maximal value from a stream of numbers."""
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def __init__(self):
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self._value = None
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def add(self, value):
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value = self._cast_to_float(value)
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if self._value is None or value > self._value:
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self._value = value
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def merge(self, other):
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if other._value is not None:
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self.add(other._value)
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def result(self):
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return self._value
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class PercentileComputation(StreamingAlgorithm):
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"""Compute percentile value from a stream of numbers."""
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def __init__(self, percent, length):
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"""Init streaming computation.
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:param percent: numeric percent (from 0.00..1 to 0.999..)
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:param length: count of the measurements
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"""
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if not 0 < percent < 1:
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raise ValueError("Unexpected percent: %s" % percent)
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self._percent = percent
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self._graph_zipper = utils.GraphZipper(length, 10000)
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def add(self, value):
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self._graph_zipper.add_point(value)
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def merge(self, other):
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# TODO(ikhudoshyn): Implement me
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raise NotImplementedError()
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def result(self):
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results = list(
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map(lambda x: x[1], self._graph_zipper.get_zipped_graph()))
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if results:
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# NOTE(amaretskiy): Calculate percentile of a list of values
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results.sort()
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k = (len(results) - 1) * self._percent
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f = math.floor(k)
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c = math.ceil(k)
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if f == c:
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return results[int(k)]
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d0 = results[int(f)] * (c - k)
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d1 = results[int(c)] * (k - f)
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return (d0 + d1)
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return None
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class IncrementComputation(StreamingAlgorithm):
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"""Simple incremental counter."""
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def __init__(self):
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self._count = 0
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def add(self, *args):
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self._count += 1
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def merge(self, other):
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self._count += other._count
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def result(self):
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return self._count
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class DegradationComputation(StreamingAlgorithm):
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"""Calculates degradation from a stream of numbers
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Finds min and max values from a stream and then calculates
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ratio between them in percentage. Works only with positive numbers.
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"""
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def __init__(self):
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self.min_value = MinComputation()
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self.max_value = MaxComputation()
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def add(self, value):
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if value <= 0.0:
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raise ValueError("Unexpected value: %s" % value)
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self.min_value.add(value)
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self.max_value.add(value)
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def merge(self, other):
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min_result = other.min_value.result()
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if min_result is not None:
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self.min_value.add(min_result)
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max_result = other.max_value.result()
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if max_result is not None:
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self.max_value.add(max_result)
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def result(self):
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min_result = self.min_value.result()
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max_result = self.max_value.result()
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if min_result is None or max_result is None:
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return 0.0
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return (max_result / min_result - 1) * 100.0
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