Adapt benchmark scripts from https://github.com/mrafayaleem/kafka-jythonbenchmarks

1 files changed, 158 insertions, 0 deletions

diff --git a/benchmarks/producer_performance.py b/benchmarks/producer_performance.py
new file mode 100755
index 0000000..e958735
--- /dev/null
+++ b/benchmarks/producer_performance.py
@@ -0,0 +1,158 @@
+#!/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)