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#!/usr/bin/env python
# Adapted from https://github.com/mrafayaleem/kafka-jython
from __future__ import absolute_import, print_function
import argparse
import pprint
import sys
import threading
import traceback
from kafka import KafkaProducer
from test.fixtures import KafkaFixture, ZookeeperFixture
def start_brokers(n):
print('Starting {0} {1}-node cluster...'.format(KafkaFixture.kafka_version, n))
print('-> 1 Zookeeper')
zk = ZookeeperFixture.instance()
print('---> {0}:{1}'.format(zk.host, zk.port))
print()
partitions = min(n, 3)
replicas = min(n, 3)
print('-> {0} Brokers [{1} partitions / {2} replicas]'.format(n, partitions, replicas))
brokers = [
KafkaFixture.instance(i, zk.host, zk.port, zk_chroot='',
partitions=partitions, replicas=replicas)
for i in range(n)
]
for broker in brokers:
print('---> {0}:{1}'.format(broker.host, broker.port))
print()
return brokers
class ProducerPerformance(object):
@staticmethod
def run(args):
try:
props = {}
for prop in args.producer_config:
k, v = prop.split('=')
try:
v = int(v)
except ValueError:
pass
if v == 'None':
v = None
props[k] = v
if args.brokers:
brokers = start_brokers(args.brokers)
props['bootstrap_servers'] = ['{0}:{1}'.format(broker.host, broker.port)
for broker in brokers]
print("---> bootstrap_servers={0}".format(props['bootstrap_servers']))
print()
print('-> OK!')
print()
print('Initializing producer...')
record = bytes(bytearray(args.record_size))
props['metrics_sample_window_ms'] = args.stats_interval * 1000
producer = KafkaProducer(**props)
for k, v in props.items():
print('---> {0}={1}'.format(k, v))
print('---> send {0} byte records'.format(args.record_size))
print('---> report stats every {0} secs'.format(args.stats_interval))
print('---> raw metrics? {0}'.format(args.raw_metrics))
timer_stop = threading.Event()
timer = StatsReporter(args.stats_interval, producer,
event=timer_stop,
raw_metrics=args.raw_metrics)
timer.start()
print('-> OK!')
print()
for i in xrange(args.num_records):
producer.send(topic=args.topic, value=record)
producer.flush()
timer_stop.set()
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
sys.exit(1)
class StatsReporter(threading.Thread):
def __init__(self, interval, producer, event=None, raw_metrics=False):
super(StatsReporter, self).__init__()
self.interval = interval
self.producer = producer
self.event = event
self.raw_metrics = raw_metrics
def print_stats(self):
metrics = self.producer.metrics()
if self.raw_metrics:
pprint.pprint(metrics)
else:
print('{record-send-rate} records/sec ({byte-rate} B/sec),'
' {request-latency-avg} latency,'
' {record-size-avg} record size,'
' {batch-size-avg} batch size,'
' {records-per-request-avg} records/req'
.format(**metrics['producer-metrics']))
def print_final(self):
self.print_stats()
def run(self):
while self.event and not self.event.wait(self.interval):
self.print_stats()
else:
self.print_final()
def get_args_parser():
parser = argparse.ArgumentParser(
description='This tool is used to verify the producer performance.')
parser.add_argument(
'--topic', type=str,
help='Topic name for test',
default='kafka-python-benchmark-test')
parser.add_argument(
'--num-records', type=long,
help='number of messages to produce',
default=1000000)
parser.add_argument(
'--record-size', type=int,
help='message size in bytes',
default=100)
parser.add_argument(
'--producer-config', type=str, nargs='+', default=(),
help='kafka producer related configuaration properties like '
'bootstrap_servers,client_id etc..')
parser.add_argument(
'--brokers', type=int,
help='Number of kafka brokers to start',
default=0)
parser.add_argument(
'--stats-interval', type=int,
help='Interval in seconds for stats reporting to console',
default=5)
parser.add_argument(
'--raw-metrics', action='store_true',
help='Enable this flag to print full metrics dict on each interval')
return parser
if __name__ == '__main__':
args = get_args_parser().parse_args()
ProducerPerformance.run(args)
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