mirror of https://github.com/apache/kafka.git
164 lines
7.0 KiB
Python
164 lines
7.0 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain 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,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ducktape.services.background_thread import BackgroundThreadService
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class PerformanceService(BackgroundThreadService):
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def __init__(self, context, num_nodes):
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super(PerformanceService, self).__init__(context, num_nodes)
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self.results = [None] * self.num_nodes
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self.stats = [[] for x in range(self.num_nodes)]
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class ProducerPerformanceService(PerformanceService):
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def __init__(self, context, num_nodes, kafka, topic, num_records, record_size, throughput, settings={}, intermediate_stats=False):
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super(ProducerPerformanceService, self).__init__(context, num_nodes)
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self.kafka = kafka
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self.args = {
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'topic': topic,
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'num_records': num_records,
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'record_size': record_size,
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'throughput': throughput
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}
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self.settings = settings
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self.intermediate_stats = intermediate_stats
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def _worker(self, idx, node):
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args = self.args.copy()
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args.update({'bootstrap_servers': self.kafka.bootstrap_servers()})
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cmd = "/opt/kafka/bin/kafka-run-class.sh org.apache.kafka.clients.tools.ProducerPerformance "\
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"%(topic)s %(num_records)d %(record_size)d %(throughput)d bootstrap.servers=%(bootstrap_servers)s" % args
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for key,value in self.settings.items():
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cmd += " %s=%s" % (str(key), str(value))
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self.logger.debug("Producer performance %d command: %s", idx, cmd)
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def parse_stats(line):
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parts = line.split(',')
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return {
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'records': int(parts[0].split()[0]),
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'records_per_sec': float(parts[1].split()[0]),
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'mbps': float(parts[1].split('(')[1].split()[0]),
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'latency_avg_ms': float(parts[2].split()[0]),
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'latency_max_ms': float(parts[3].split()[0]),
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'latency_50th_ms': float(parts[4].split()[0]),
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'latency_95th_ms': float(parts[5].split()[0]),
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'latency_99th_ms': float(parts[6].split()[0]),
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'latency_999th_ms': float(parts[7].split()[0]),
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}
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last = None
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for line in node.account.ssh_capture(cmd):
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self.logger.debug("Producer performance %d: %s", idx, line.strip())
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if self.intermediate_stats:
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try:
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self.stats[idx-1].append(parse_stats(line))
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except:
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# Sometimes there are extraneous log messages
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pass
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last = line
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try:
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self.results[idx-1] = parse_stats(last)
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except:
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self.logger.error("Bad last line: %s", last)
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class ConsumerPerformanceService(PerformanceService):
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def __init__(self, context, num_nodes, kafka, topic, num_records, throughput, threads=1, settings={}):
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super(ConsumerPerformanceService, self).__init__(context, num_nodes)
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self.kafka = kafka
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self.args = {
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'topic': topic,
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'num_records': num_records,
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'throughput': throughput,
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'threads': threads,
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}
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self.settings = settings
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def _worker(self, idx, node):
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args = self.args.copy()
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args.update({'zk_connect': self.kafka.zk.connect_setting()})
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cmd = "/opt/kafka/bin/kafka-consumer-perf-test.sh "\
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"--topic %(topic)s --messages %(num_records)d --zookeeper %(zk_connect)s" % args
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for key,value in self.settings.items():
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cmd += " %s=%s" % (str(key), str(value))
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self.logger.debug("Consumer performance %d command: %s", idx, cmd)
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last = None
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for line in node.account.ssh_capture(cmd):
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self.logger.debug("Consumer performance %d: %s", idx, line.strip())
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last = line
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# Parse and save the last line's information
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parts = last.split(',')
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self.results[idx-1] = {
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'total_mb': float(parts[2]),
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'mbps': float(parts[3]),
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'records_per_sec': float(parts[5]),
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}
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class EndToEndLatencyService(PerformanceService):
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def __init__(self, context, num_nodes, kafka, topic, num_records, consumer_fetch_max_wait=100, acks=1):
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super(EndToEndLatencyService, self).__init__(context, num_nodes)
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self.kafka = kafka
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self.args = {
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'topic': topic,
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'num_records': num_records,
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'consumer_fetch_max_wait': consumer_fetch_max_wait,
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'acks': acks
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}
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def _worker(self, idx, node):
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args = self.args.copy()
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args.update({
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'zk_connect': self.kafka.zk.connect_setting(),
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'bootstrap_servers': self.kafka.bootstrap_servers(),
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})
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cmd = "/opt/kafka/bin/kafka-run-class.sh kafka.tools.EndToEndLatency "\
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"%(bootstrap_servers)s %(topic)s %(num_records)d "\
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"%(acks)d 20" % args
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self.logger.debug("End-to-end latency %d command: %s", idx, cmd)
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results = {}
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for line in node.account.ssh_capture(cmd):
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self.logger.debug("End-to-end latency %d: %s", idx, line.strip())
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if line.startswith("Avg latency:"):
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results['latency_avg_ms'] = float(line.split()[2])
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if line.startswith("Percentiles"):
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results['latency_50th_ms'] = float(line.split()[3][:-1])
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results['latency_99th_ms'] = float(line.split()[6][:-1])
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results['latency_999th_ms'] = float(line.split()[9])
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self.results[idx-1] = results
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def parse_performance_output(summary):
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parts = summary.split(',')
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results = {
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'records': int(parts[0].split()[0]),
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'records_per_sec': float(parts[1].split()[0]),
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'mbps': float(parts[1].split('(')[1].split()[0]),
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'latency_avg_ms': float(parts[2].split()[0]),
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'latency_max_ms': float(parts[3].split()[0]),
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'latency_50th_ms': float(parts[4].split()[0]),
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'latency_95th_ms': float(parts[5].split()[0]),
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'latency_99th_ms': float(parts[6].split()[0]),
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'latency_999th_ms': float(parts[7].split()[0]),
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}
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# To provide compatibility with ConsumerPerformanceService
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results['total_mb'] = results['mbps'] * (results['records'] / results['records_per_sec'])
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results['rate_mbps'] = results['mbps']
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results['rate_mps'] = results['records_per_sec']
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return results
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