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# Copyright 2016 gRPC authors.
#
# Licensed 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 math
import threading
from src.proto.grpc.testing import stats_pb2
class Histogram(object):
"""Histogram class used for recording performance testing data.
This class is thread safe.
"""
def __init__(self, resolution, max_possible):
self._lock = threading.Lock()
self._resolution = resolution
self._max_possible = max_possible
self._sum = 0
self._sum_of_squares = 0
self.multiplier = 1.0 + self._resolution
self._count = 0
self._min = self._max_possible
self._max = 0
self._buckets = [0] * (self._bucket_for(self._max_possible) + 1)
def reset(self):
with self._lock:
self._sum = 0
self._sum_of_squares = 0
self._count = 0
self._min = self._max_possible
self._max = 0
self._buckets = [0] * (self._bucket_for(self._max_possible) + 1)
def add(self, val):
with self._lock:
self._sum += val
self._sum_of_squares += val * val
self._count += 1
self._min = min(self._min, val)
self._max = max(self._max, val)
self._buckets[self._bucket_for(val)] += 1
def get_data(self):
with self._lock:
data = stats_pb2.HistogramData()
data.bucket.extend(self._buckets)
data.min_seen = self._min
data.max_seen = self._max
data.sum = self._sum
data.sum_of_squares = self._sum_of_squares
data.count = self._count
return data
def merge(self, another_data):
with self._lock:
for i in range(len(self._buckets)):
self._buckets[i] += another_data.bucket[i]
self._min = min(self._min, another_data.min_seen)
self._max = max(self._max, another_data.max_seen)
self._sum += another_data.sum
self._sum_of_squares += another_data.sum_of_squares
self._count += another_data.count
def _bucket_for(self, val):
val = min(val, self._max_possible)
return int(math.log(val, self.multiplier))
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