blob: e095920e904d3e47561b89ae7cefbb5b0e6f9195 [file] [log] [blame]
#!/usr/bin/env python
#
# 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 time
TS = "ts"
TIME_SEC = 1000000000
MILLISECOND = 1000
class Statistic:
def message(self, msg):
return
def report(self):
return ""
def header(self):
return ""
class Throughput(Statistic):
def __init__(self):
self.messages = 0
self.started = False
def message(self, m):
self.messages += 1
if not self.started:
self.start = time.time()
self.started = True
def header(self):
return "tp(m/s)"
def report(self):
if self.started:
elapsed = time.time() - self.start
return str(int(self.messages/elapsed))
else:
return "0"
class ThroughputAndLatency(Throughput):
def __init__(self):
Throughput.__init__(self)
self.total = 0.0
self.min = float('inf')
self.max = -float('inf')
self.samples = 0
def message(self, m):
Throughput.message(self, m)
if TS in m.properties:
self.samples+=1
latency = MILLISECOND * (time.time() - float(m.properties[TS])/TIME_SEC)
if latency > 0:
self.total += latency
if latency < self.min:
self.min = latency
if latency > self.max:
self.max = latency
def header(self):
# Throughput.header(self)
return "%s\tl-min\tl-max\tl-avg" % Throughput.header(self)
def report(self):
output = Throughput.report(self)
if (self.samples > 0):
output += "\t%.2f\t%.2f\t%.2f" %(self.min, self.max, self.total/self.samples)
return output
# Report batch and overall statistics
class ReporterBase:
def __init__(self, batch, wantHeader):
self.batchSize = batch
self.batchCount = 0
self.headerPrinted = not wantHeader
self.overall = None
self.batch = None
def create(self):
return
# Count message in the statistics
def message(self, m):
if self.overall == None:
self.overall = self.create()
self.overall.message(m)
if self.batchSize:
if self.batch == None:
self.batch = self.create()
self.batch.message(m)
self.batchCount+=1
if self.batchCount == self.batchSize:
self.header()
print self.batch.report()
self.create()
self.batchCount = 0
# Print overall report.
def report(self):
if self.overall == None:
self.overall = self.create()
self.header()
print self.overall.report()
def header(self):
if not self.headerPrinted:
if self.overall == None:
self.overall = self.create()
print self.overall.header()
self.headerPrinted = True
class Reporter(ReporterBase):
def __init__(self, batchSize, wantHeader, Stats):
ReporterBase.__init__(self, batchSize, wantHeader)
self.__stats = Stats
def create(self):
ClassName = self.__stats.__class__
return ClassName()