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https://issues.apache.org/jira/browse/KAFKA-14020?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Artem Livshits reassigned KAFKA-14020:
--------------------------------------
Assignee: Artem Livshits
> Performance regression in Producer
> ----------------------------------
>
> Key: KAFKA-14020
> URL: https://issues.apache.org/jira/browse/KAFKA-14020
> Project: Kafka
> Issue Type: Bug
> Components: producer
> Affects Versions: 3.3.0
> Reporter: John Roesler
> Assignee: Artem Livshits
> Priority: Blocker
>
> [https://github.com/apache/kafka/commit/f7db6031b84a136ad0e257df722b20faa7c37b8a]
> introduced a 10% performance regression in the KafkaProducer under a default
> config.
>
> The context for this result is a benchmark that we run for Kafka Streams. The
> benchmark provisions 5 independent AWS clusters, including one broker node on
> an i3.large and one client node on an i3.large. During a benchmark run, we
> first run the Producer for 10 minutes to generate test data, and then we run
> Kafka Streams under a number of configurations to measure its performance.
> Our observation was a 10% regression in throughput under the simplest
> configuration, in which Streams simply consumes from a topic and does nothing
> else. That benchmark actually runs faster than the producer that generates
> the test data, so its thoughput is bounded by the data generator's
> throughput. After investigation, we realized that the regression was in the
> data generator, not the consumer or Streams.
> We have numerous benchmark runs leading up to the commit in question, and
> they all show a throughput in the neighborhood of 115,000 records per second.
> We also have 40 runs including and after that commit, and they all show a
> throughput in the neighborhood of 105,000 records per second. A test on
> [trunk with the commit reverted |https://github.com/apache/kafka/pull/12342]
> shows a return to around 115,000 records per second.
> Config:
> {code:java}
> final Properties properties = new Properties();
> properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, broker);
> properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
> StringSerializer.class);
> properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
> StringSerializer.class);
> {code}
> Here's the producer code in the data generator. Our tests were running with
> three produceThreads.
> {code:java}
> for (int t = 0; t < produceThreads; t++) {
> futures.add(executorService.submit(() -> {
> int threadTotal = 0;
> long lastPrint = start;
> final long printInterval = Duration.ofSeconds(10).toMillis();
> long now;
> try (final org.apache.kafka.clients.producer.Producer<String, String>
> producer = new KafkaProducer<>(producerConfig(broker))) {
> while (limit > (now = System.currentTimeMillis()) - start) {
> for (int i = 0; i < 1000; i++) {
> final String key = keys.next();
> final String data = dataGen.generate();
> producer.send(new ProducerRecord<>(topic, key,
> valueBuilder.apply(key, data)));
> threadTotal++;
> }
> if ((now - lastPrint) > printInterval) {
> System.out.println(Thread.currentThread().getName() + "
> produced " + numberFormat.format(threadTotal) + " to " + topic + " in " +
> Duration.ofMillis(now - start));
> lastPrint = now;
> }
> }
> }
> total.addAndGet(threadTotal);
> System.out.println(Thread.currentThread().getName() + " finished (" +
> numberFormat.format(threadTotal) + ") in " + Duration.ofMillis(now - start));
> }));
> }{code}
> As you can see, this is a very basic usage.
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