mirror of https://github.com/apache/kafka.git
KAFKA-4702: Parametrize streams benchmarks to run at scale
Author: Eno Thereska <eno.thereska@gmail.com> Author: Eno Thereska <eno@confluent.io> Author: Ubuntu <ubuntu@ip-172-31-22-146.us-west-2.compute.internal> Reviewers: Matthias J. Sax, Guozhang Wang Closes #2478 from enothereska/minor-benchmark-args
This commit is contained in:
Eno Thereska
Guozhang Wang
parent
c7c113af63
commit
13a82b48ca
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@ -51,18 +51,32 @@ import org.apache.kafka.test.TestUtils;
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import java.io.File;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Locale;
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import java.util.concurrent.CountDownLatch;
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import java.util.Properties;
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import java.util.Random;
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/**
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* Class that provides support for a series of benchmarks. It is usually driven by
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* tests/kafkatest/benchmarks/streams/streams_simple_benchmark_test.py.
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* If ran manually through the main() function below, you must do the following:
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* 1. Have ZK and a Kafka broker set up
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* 2. Run the loading step first: SimpleBenchmark localhost:9092 /tmp/statedir numRecords true "all"
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* 3. Run the stream processing step second: SimpleBenchmark localhost:9092 /tmp/statedir numRecords false "all"
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* Note that what changed is the 4th parameter, from "true" indicating that is a load phase, to "false" indicating
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* that this is a real run.
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*/
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public class SimpleBenchmark {
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private final String kafka;
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private final File stateDir;
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private final Boolean loadPhase;
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private final String testName;
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private static final String ALL_TESTS = "all";
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private static final String SOURCE_TOPIC = "simpleBenchmarkSourceTopic";
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private static final String SINK_TOPIC = "simpleBenchmarkSinkTopic";
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private static final String COUNT_TOPIC = "countTopic";
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private static final String JOIN_TOPIC_1_PREFIX = "joinSourceTopic1";
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private static final String JOIN_TOPIC_2_PREFIX = "joinSourceTopic2";
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private static final ValueJoiner VALUE_JOINER = new ValueJoiner<byte[], byte[], byte[]>() {
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@ -83,7 +97,7 @@ public class SimpleBenchmark {
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};
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private static int numRecords;
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private static Integer endKey;
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private static int processedRecords = 0;
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private static final int KEY_SIZE = 8;
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private static final int VALUE_SIZE = 100;
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private static final int RECORD_SIZE = KEY_SIZE + VALUE_SIZE;
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@ -91,17 +105,80 @@ public class SimpleBenchmark {
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private static final Serde<byte[]> BYTE_SERDE = Serdes.ByteArray();
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private static final Serde<Integer> INTEGER_SERDE = Serdes.Integer();
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public SimpleBenchmark(File stateDir, String kafka) {
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public SimpleBenchmark(final File stateDir, final String kafka, final Boolean loadPhase, final String testName) {
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super();
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this.stateDir = stateDir;
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this.kafka = kafka;
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this.loadPhase = loadPhase;
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this.testName = testName;
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}
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private void run() throws Exception {
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switch (testName) {
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case ALL_TESTS:
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// producer performance
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produce(SOURCE_TOPIC);
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// consumer performance
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consume(SOURCE_TOPIC);
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// simple stream performance source->process
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processStream(SOURCE_TOPIC);
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// simple stream performance source->sink
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processStreamWithSink(SOURCE_TOPIC);
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// simple stream performance source->store
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processStreamWithStateStore(SOURCE_TOPIC);
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// simple stream performance source->cache->store
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processStreamWithCachedStateStore(SOURCE_TOPIC);
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// simple aggregation
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count(COUNT_TOPIC);
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// simple streams performance KSTREAM-KTABLE join
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kStreamKTableJoin(JOIN_TOPIC_1_PREFIX + "KStreamKTable", JOIN_TOPIC_2_PREFIX + "KStreamKTable");
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// simple streams performance KSTREAM-KSTREAM join
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kStreamKStreamJoin(JOIN_TOPIC_1_PREFIX + "KStreamKStream", JOIN_TOPIC_2_PREFIX + "KStreamKStream");
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// simple streams performance KTABLE-KTABLE join
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kTableKTableJoin(JOIN_TOPIC_1_PREFIX + "KTableKTable", JOIN_TOPIC_2_PREFIX + "KTableKTable");
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break;
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case "produce":
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produce(SOURCE_TOPIC, VALUE_SIZE, "simple-benchmark-produce", numRecords, true, numRecords, true);
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break;
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case "consume":
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consume(SOURCE_TOPIC);
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break;
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case "count":
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count(COUNT_TOPIC);
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break;
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case "processstream":
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processStream(SOURCE_TOPIC);
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break;
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case "processstreamwithsink":
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processStreamWithSink(SOURCE_TOPIC);
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break;
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case "processstreamwithstatestore":
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processStreamWithStateStore(SOURCE_TOPIC);
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break;
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case "processstreamwithcachedstatestore":
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processStreamWithCachedStateStore(SOURCE_TOPIC);
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break;
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case "kstreamktablejoin":
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kStreamKTableJoin(JOIN_TOPIC_1_PREFIX + "KStreamKTable", JOIN_TOPIC_2_PREFIX + "KStreamKTable");
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break;
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case "kstreamkstreamjoin":
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kStreamKStreamJoin(JOIN_TOPIC_1_PREFIX + "KStreamKStream", JOIN_TOPIC_2_PREFIX + "KStreamKStream");
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break;
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case "ktablektablejoin":
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kTableKTableJoin(JOIN_TOPIC_1_PREFIX + "KTableKTable", JOIN_TOPIC_2_PREFIX + "KTableKTable");
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break;
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default:
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throw new Exception("Unknown test name " + testName);
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}
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}
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public static void main(String[] args) throws Exception {
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String kafka = args.length > 0 ? args[0] : "localhost:9092";
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String stateDirStr = args.length > 1 ? args[1] : TestUtils.tempDirectory().getAbsolutePath();
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numRecords = args.length > 2 ? Integer.parseInt(args[2]) : 10000000;
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endKey = numRecords - 1;
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boolean loadPhase = args.length > 3 ? Boolean.parseBoolean(args[3]) : false;
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String testName = args.length > 4 ? args[4].toLowerCase(Locale.ROOT) : ALL_TESTS;
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final File stateDir = new File(stateDirStr);
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stateDir.mkdir();
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@ -113,30 +190,14 @@ public class SimpleBenchmark {
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System.out.println("kafka=" + kafka);
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System.out.println("stateDir=" + stateDir);
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System.out.println("numRecords=" + numRecords);
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System.out.println("loadPhase=" + loadPhase);
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System.out.println("testName=" + testName);
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SimpleBenchmark benchmark = new SimpleBenchmark(stateDir, kafka);
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// producer performance
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benchmark.produce(SOURCE_TOPIC, VALUE_SIZE, "simple-benchmark-produce", numRecords, true, numRecords, true);
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// consumer performance
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benchmark.consume(SOURCE_TOPIC);
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// simple stream performance source->process
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benchmark.processStream(SOURCE_TOPIC);
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// simple stream performance source->sink
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benchmark.processStreamWithSink(SOURCE_TOPIC);
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// simple stream performance source->store
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benchmark.processStreamWithStateStore(SOURCE_TOPIC);
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// simple stream performance source->cache->store
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benchmark.processStreamWithCachedStateStore(SOURCE_TOPIC);
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// simple streams performance KSTREAM-KTABLE join
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benchmark.kStreamKTableJoin(JOIN_TOPIC_1_PREFIX + "KStreamKTable", JOIN_TOPIC_2_PREFIX + "KStreamKTable");
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// simple streams performance KSTREAM-KSTREAM join
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benchmark.kStreamKStreamJoin(JOIN_TOPIC_1_PREFIX + "KStreamKStream", JOIN_TOPIC_2_PREFIX + "KStreamKStream");
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// simple streams performance KTABLE-KTABLE join
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benchmark.kTableKTableJoin(JOIN_TOPIC_1_PREFIX + "KTableKTable", JOIN_TOPIC_2_PREFIX + "KTableKTable");
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SimpleBenchmark benchmark = new SimpleBenchmark(stateDir, kafka, loadPhase, testName);
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benchmark.run();
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}
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private Properties setJoinProperties(final String applicationId) {
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private Properties setStreamProperties(final String applicationId) {
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Properties props = new Properties();
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props.put(StreamsConfig.APPLICATION_ID_CONFIG, applicationId);
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props.put(StreamsConfig.STATE_DIR_CONFIG, stateDir.toString());
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@ -148,25 +209,85 @@ public class SimpleBenchmark {
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return props;
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}
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private Properties setProduceConsumeProperties(final String clientId) {
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Properties props = new Properties();
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props.put(ProducerConfig.CLIENT_ID_CONFIG, clientId);
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props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, kafka);
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props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, IntegerSerializer.class);
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props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, ByteArraySerializer.class);
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props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, IntegerDeserializer.class);
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props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, ByteArrayDeserializer.class);
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props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
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return props;
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}
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private boolean maybeSetupPhase(final String topic, final String clientId,
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final boolean skipIfAllTests) throws Exception {
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processedRecords = 0;
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// initialize topics
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if (loadPhase) {
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if (skipIfAllTests) {
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// if we run all tests, the produce test will have already loaded the data
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if (testName.equals(ALL_TESTS)) {
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// Skipping loading phase since previously loaded
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return true;
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}
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}
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System.out.println("Initializing topic " + topic);
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// WARNING: The keys must be sequential, i.e., unique, otherwise the logic for when this test
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// stops will not work (in createCountStreams)
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produce(topic, VALUE_SIZE, clientId, numRecords, true, numRecords, false);
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return true;
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}
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return false;
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}
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private KafkaStreams createCountStreams(Properties streamConfig, String topic, final CountDownLatch latch) {
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final KStreamBuilder builder = new KStreamBuilder();
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final KStream<Integer, byte[]> input = builder.stream(topic);
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input.groupByKey()
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.count("tmpStoreName").foreach(new CountDownAction(latch));
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return new KafkaStreams(builder, streamConfig);
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}
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/**
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* Measure the performance of a simple aggregate like count.
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* Counts the occurrence of numbers (note that normally people count words, this
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* example counts numbers)
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* @param countTopic Topic where numbers are stored
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* @throws Exception
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*/
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public void count(String countTopic) throws Exception {
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if (maybeSetupPhase(countTopic, "simple-benchmark-produce-count", false)) {
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return;
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}
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CountDownLatch latch = new CountDownLatch(1);
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Properties props = setStreamProperties("simple-benchmark-count");
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final KafkaStreams streams = createCountStreams(props, countTopic, latch);
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runGenericBenchmark(streams, "Streams Count Performance [records/latency/rec-sec/MB-sec counted]: ", latch);
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}
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/**
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* Measure the performance of a KStream-KTable left join. The setup is such that each
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* KStream record joins to exactly one element in the KTable
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*/
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public void kStreamKTableJoin(String kStreamTopic, String kTableTopic) throws Exception {
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CountDownLatch latch = new CountDownLatch(numRecords);
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if (maybeSetupPhase(kStreamTopic, "simple-benchmark-produce-kstream", false)) {
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maybeSetupPhase(kTableTopic, "simple-benchmark-produce-ktable", false);
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return;
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}
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// initialize topics
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System.out.println("Initializing kStreamTopic " + kStreamTopic);
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produce(kStreamTopic, VALUE_SIZE, "simple-benchmark-produce-kstream", numRecords, false, numRecords, false);
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System.out.println("Initializing kTableTopic " + kTableTopic);
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produce(kTableTopic, VALUE_SIZE, "simple-benchmark-produce-ktable", numRecords, true, numRecords, false);
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CountDownLatch latch = new CountDownLatch(1);
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// setup join
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Properties props = setJoinProperties("simple-benchmark-kstream-ktable-join");
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Properties props = setStreamProperties("simple-benchmark-kstream-ktable-join");
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final KafkaStreams streams = createKafkaStreamsKStreamKTableJoin(props, kStreamTopic, kTableTopic, latch);
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// run benchmark
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runJoinBenchmark(streams, "Streams KStreamKTable LeftJoin Performance [MB/s joined]: ", latch);
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runGenericBenchmark(streams, "Streams KStreamKTable LeftJoin Performance [records/latency/rec-sec/MB-sec joined]: ", latch);
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}
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/**
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@ -174,20 +295,19 @@ public class SimpleBenchmark {
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* KStream record joins to exactly one element in the other KStream
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*/
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public void kStreamKStreamJoin(String kStreamTopic1, String kStreamTopic2) throws Exception {
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CountDownLatch latch = new CountDownLatch(numRecords);
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if (maybeSetupPhase(kStreamTopic1, "simple-benchmark-produce-kstream-topic1", false)) {
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maybeSetupPhase(kStreamTopic2, "simple-benchmark-produce-kstream-topic2", false);
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return;
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}
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// initialize topics
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System.out.println("Initializing kStreamTopic " + kStreamTopic1);
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produce(kStreamTopic1, VALUE_SIZE, "simple-benchmark-produce-kstream-topic1", numRecords, true, numRecords, false);
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System.out.println("Initializing kStreamTopic " + kStreamTopic2);
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produce(kStreamTopic2, VALUE_SIZE, "simple-benchmark-produce-kstream-topic2", numRecords, true, numRecords, false);
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CountDownLatch latch = new CountDownLatch(1);
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// setup join
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Properties props = setJoinProperties("simple-benchmark-kstream-kstream-join");
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Properties props = setStreamProperties("simple-benchmark-kstream-kstream-join");
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final KafkaStreams streams = createKafkaStreamsKStreamKStreamJoin(props, kStreamTopic1, kStreamTopic2, latch);
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// run benchmark
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runJoinBenchmark(streams, "Streams KStreamKStream LeftJoin Performance [MB/s joined]: ", latch);
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runGenericBenchmark(streams, "Streams KStreamKStream LeftJoin Performance [records/latency/rec-sec/MB-sec joined]: ", latch);
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}
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/**
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@ -195,23 +315,29 @@ public class SimpleBenchmark {
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* KTable record joins to exactly one element in the other KTable
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*/
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public void kTableKTableJoin(String kTableTopic1, String kTableTopic2) throws Exception {
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CountDownLatch latch = new CountDownLatch(numRecords);
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// initialize topics
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System.out.println("Initializing kTableTopic " + kTableTopic1);
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produce(kTableTopic1, VALUE_SIZE, "simple-benchmark-produce-ktable-topic1", numRecords, true, numRecords, false);
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System.out.println("Initializing kTableTopic " + kTableTopic2);
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produce(kTableTopic2, VALUE_SIZE, "simple-benchmark-produce-ktable-topic2", numRecords, true, numRecords, false);
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if (maybeSetupPhase(kTableTopic1, "simple-benchmark-produce-ktable-topic1", false)) {
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maybeSetupPhase(kTableTopic2, "simple-benchmark-produce-ktable-topic2", false);
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return;
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}
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CountDownLatch latch = new CountDownLatch(1);
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// setup join
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Properties props = setJoinProperties("simple-benchmark-ktable-ktable-join");
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Properties props = setStreamProperties("simple-benchmark-ktable-ktable-join");
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final KafkaStreams streams = createKafkaStreamsKTableKTableJoin(props, kTableTopic1, kTableTopic2, latch);
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// run benchmark
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runJoinBenchmark(streams, "Streams KTableKTable LeftJoin Performance [MB/s joined]: ", latch);
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runGenericBenchmark(streams, "Streams KTableKTable LeftJoin Performance [records/latency/rec-sec/MB-sec joined]: ", latch);
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}
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private void runJoinBenchmark(final KafkaStreams streams, final String nameOfBenchmark, final CountDownLatch latch) {
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private void printResults(final String nameOfBenchmark, final long latency) {
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System.out.println(nameOfBenchmark +
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processedRecords + "/" +
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latency + "/" +
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recordsPerSec(latency, processedRecords) + "/" +
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megaBytePerSec(latency, processedRecords, RECORD_SIZE));
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}
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private void runGenericBenchmark(final KafkaStreams streams, final String nameOfBenchmark, final CountDownLatch latch) {
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streams.start();
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long startTime = System.currentTimeMillis();
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@ -224,20 +350,12 @@ public class SimpleBenchmark {
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}
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}
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long endTime = System.currentTimeMillis();
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System.out.println(nameOfBenchmark + megaBytePerSec(endTime - startTime, numRecords, KEY_SIZE + VALUE_SIZE));
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printResults(nameOfBenchmark, endTime - startTime);
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streams.close();
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}
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public void processStream(String topic) {
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CountDownLatch latch = new CountDownLatch(1);
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final KafkaStreams streams = createKafkaStreams(topic, stateDir, kafka, latch);
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private long startStreamsThread(final KafkaStreams streams, final CountDownLatch latch) throws Exception {
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Thread thread = new Thread() {
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public void run() {
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streams.start();
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@ -257,96 +375,73 @@ public class SimpleBenchmark {
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long endTime = System.currentTimeMillis();
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System.out.println("Streams Performance [MB/sec read]: " + megaBytePerSec(endTime - startTime));
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streams.close();
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try {
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thread.join();
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} catch (Exception ex) {
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// ignore
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}
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return endTime - startTime;
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}
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public void processStream(final String topic) throws Exception {
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if (maybeSetupPhase(topic, "simple-benchmark-process-stream-load", true)) {
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return;
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}
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public void processStreamWithSink(String topic) {
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CountDownLatch latch = new CountDownLatch(1);
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final KafkaStreams streams = createKafkaStreamsWithSink(topic, stateDir, kafka, latch);
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final KafkaStreams streams = createKafkaStreams(topic, latch);
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long latency = startStreamsThread(streams, latch);
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Thread thread = new Thread() {
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public void run() {
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streams.start();
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}
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};
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thread.start();
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long startTime = System.currentTimeMillis();
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while (latch.getCount() > 0) {
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try {
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latch.await();
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} catch (InterruptedException ex) {
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Thread.interrupted();
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}
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printResults("Streams Performance [records/latency/rec-sec/MB-sec source]: ", latency);
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}
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long endTime = System.currentTimeMillis();
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System.out.println("Streams Performance [MB/sec read+write]: " + megaBytePerSec(endTime - startTime));
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streams.close();
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try {
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thread.join();
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} catch (Exception ex) {
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// ignore
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}
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public void processStreamWithSink(String topic) throws Exception {
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if (maybeSetupPhase(topic, "simple-benchmark-process-stream-with-sink-load", true)) {
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return;
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}
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private void internalProcessStreamWithStore(final KafkaStreams streams, final CountDownLatch latch,
|
||||
final String message) {
|
||||
Thread thread = new Thread() {
|
||||
public void run() {
|
||||
streams.start();
|
||||
}
|
||||
};
|
||||
thread.start();
|
||||
|
||||
long startTime = System.currentTimeMillis();
|
||||
|
||||
while (latch.getCount() > 0) {
|
||||
try {
|
||||
latch.await();
|
||||
} catch (InterruptedException ex) {
|
||||
Thread.interrupted();
|
||||
}
|
||||
}
|
||||
|
||||
long endTime = System.currentTimeMillis();
|
||||
|
||||
System.out.println(message + megaBytePerSec(endTime - startTime));
|
||||
|
||||
streams.close();
|
||||
try {
|
||||
thread.join();
|
||||
} catch (Exception ex) {
|
||||
// ignore
|
||||
}
|
||||
}
|
||||
public void processStreamWithStateStore(String topic) {
|
||||
CountDownLatch latch = new CountDownLatch(1);
|
||||
final KafkaStreams streams = createKafkaStreamsWithSink(topic, latch);
|
||||
long latency = startStreamsThread(streams, latch);
|
||||
|
||||
final KafkaStreams streams = createKafkaStreamsWithStateStore(topic, stateDir, kafka, latch, false);
|
||||
internalProcessStreamWithStore(streams, latch, "Streams Performance [MB/sec read+store]: ");
|
||||
printResults("Streams Performance [records/latency/rec-sec/MB-sec source+sink]: ", latency);
|
||||
|
||||
}
|
||||
|
||||
public void processStreamWithCachedStateStore(String topic) {
|
||||
public void processStreamWithStateStore(String topic) throws Exception {
|
||||
if (maybeSetupPhase(topic, "simple-benchmark-process-stream-with-state-store-load", true)) {
|
||||
return;
|
||||
}
|
||||
|
||||
CountDownLatch latch = new CountDownLatch(1);
|
||||
final KafkaStreams streams = createKafkaStreamsWithStateStore(topic, latch, false);
|
||||
long latency = startStreamsThread(streams, latch);
|
||||
printResults("Streams Performance [records/latency/rec-sec/MB-sec source+store]: ", latency);
|
||||
|
||||
final KafkaStreams streams = createKafkaStreamsWithStateStore(topic, stateDir, kafka, latch, true);
|
||||
|
||||
internalProcessStreamWithStore(streams, latch, "Streams Performance [MB/sec read+cache+store]: ");
|
||||
}
|
||||
|
||||
public void processStreamWithCachedStateStore(String topic) throws Exception {
|
||||
if (maybeSetupPhase(topic, "simple-benchmark-process-stream-with-cached-state-store-load", true)) {
|
||||
return;
|
||||
}
|
||||
|
||||
CountDownLatch latch = new CountDownLatch(1);
|
||||
final KafkaStreams streams = createKafkaStreamsWithStateStore(topic, latch, true);
|
||||
long latency = startStreamsThread(streams, latch);
|
||||
printResults("Streams Performance [records/latency/rec-sec/MB-sec source+cache+store]: ", latency);
|
||||
}
|
||||
|
||||
public void produce(String topic) throws Exception {
|
||||
// loading phase does not make sense for producer
|
||||
if (loadPhase) {
|
||||
return;
|
||||
}
|
||||
produce(topic, VALUE_SIZE, "simple-benchmark-produce", numRecords, true, numRecords, true);
|
||||
|
||||
}
|
||||
/**
|
||||
* Produce values to a topic
|
||||
* @param topic Topic to produce to
|
||||
|
|
@ -358,17 +453,18 @@ public class SimpleBenchmark {
|
|||
* @param printStats if True, print stats on how long producing took. If False, don't print stats. False can be used
|
||||
* when this produce step is part of another benchmark that produces its own stats
|
||||
*/
|
||||
public void produce(String topic, int valueSizeBytes, String clientId, int numRecords, boolean sequential,
|
||||
private void produce(String topic, int valueSizeBytes, String clientId, int numRecords, boolean sequential,
|
||||
int upperRange, boolean printStats) throws Exception {
|
||||
|
||||
|
||||
if (sequential) {
|
||||
if (upperRange < numRecords) throw new Exception("UpperRange must be >= numRecords");
|
||||
}
|
||||
Properties props = new Properties();
|
||||
props.put(ProducerConfig.CLIENT_ID_CONFIG, clientId);
|
||||
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, kafka);
|
||||
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, IntegerSerializer.class);
|
||||
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, ByteArraySerializer.class);
|
||||
if (!sequential) {
|
||||
System.out.println("WARNING: You are using non-sequential keys. If your tests' exit logic expects to see a final key, random keys may not work.");
|
||||
}
|
||||
Properties props = setProduceConsumeProperties(clientId);
|
||||
|
||||
int key = 0;
|
||||
Random rand = new Random();
|
||||
KafkaProducer<Integer, byte[]> producer = new KafkaProducer<>(props);
|
||||
|
|
@ -387,17 +483,22 @@ public class SimpleBenchmark {
|
|||
|
||||
long endTime = System.currentTimeMillis();
|
||||
|
||||
if (printStats)
|
||||
System.out.println("Producer Performance [MB/sec write]: " + megaBytePerSec(endTime - startTime, numRecords, KEY_SIZE + valueSizeBytes));
|
||||
if (printStats) {
|
||||
System.out.println("Producer Performance [records/latency/rec-sec/MB-sec write]: " +
|
||||
numRecords + "/" +
|
||||
(endTime - startTime) + "/" +
|
||||
recordsPerSec(endTime - startTime, numRecords) + "/" +
|
||||
megaBytePerSec(endTime - startTime, numRecords, KEY_SIZE + valueSizeBytes));
|
||||
}
|
||||
}
|
||||
|
||||
public void consume(String topic) {
|
||||
Properties props = new Properties();
|
||||
props.put(ConsumerConfig.CLIENT_ID_CONFIG, "simple-benchmark-consumer");
|
||||
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, kafka);
|
||||
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, IntegerDeserializer.class);
|
||||
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, ByteArrayDeserializer.class);
|
||||
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
|
||||
public void consume(String topic) throws Exception {
|
||||
int consumedRecords = 0;
|
||||
if (maybeSetupPhase(topic, "simple-benchmark-consumer-load", true)) {
|
||||
return;
|
||||
}
|
||||
|
||||
Properties props = setProduceConsumeProperties("simple-benchmark-consumer");
|
||||
|
||||
KafkaConsumer<Integer, byte[]> consumer = new KafkaConsumer<>(props);
|
||||
|
||||
|
|
@ -412,31 +513,34 @@ public class SimpleBenchmark {
|
|||
while (true) {
|
||||
ConsumerRecords<Integer, byte[]> records = consumer.poll(500);
|
||||
if (records.isEmpty()) {
|
||||
if (endKey.equals(key))
|
||||
if (consumedRecords == numRecords)
|
||||
break;
|
||||
} else {
|
||||
for (ConsumerRecord<Integer, byte[]> record : records) {
|
||||
consumedRecords++;
|
||||
Integer recKey = record.key();
|
||||
|
||||
if (key == null || key < recKey)
|
||||
key = recKey;
|
||||
if (consumedRecords == numRecords)
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (consumedRecords == numRecords)
|
||||
break;
|
||||
}
|
||||
|
||||
long endTime = System.currentTimeMillis();
|
||||
|
||||
consumer.close();
|
||||
System.out.println("Consumer Performance [MB/sec read]: " + megaBytePerSec(endTime - startTime));
|
||||
System.out.println("Consumer Performance [records/latency/rec-sec/MB-sec read]: " +
|
||||
consumedRecords + "/" +
|
||||
(endTime - startTime) + "/" +
|
||||
recordsPerSec(endTime - startTime, consumedRecords) + "/" +
|
||||
megaBytePerSec(endTime - startTime, consumedRecords, RECORD_SIZE));
|
||||
}
|
||||
|
||||
private KafkaStreams createKafkaStreams(String topic, File stateDir, String kafka, final CountDownLatch latch) {
|
||||
Properties props = new Properties();
|
||||
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "simple-benchmark-streams");
|
||||
props.put(StreamsConfig.STATE_DIR_CONFIG, stateDir.toString());
|
||||
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, kafka);
|
||||
props.put(StreamsConfig.NUM_STREAM_THREADS_CONFIG, 1);
|
||||
props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
|
||||
private KafkaStreams createKafkaStreams(String topic, final CountDownLatch latch) {
|
||||
Properties props = setStreamProperties("simple-benchmark-streams");
|
||||
|
||||
KStreamBuilder builder = new KStreamBuilder();
|
||||
|
||||
|
|
@ -453,7 +557,8 @@ public class SimpleBenchmark {
|
|||
|
||||
@Override
|
||||
public void process(Integer key, byte[] value) {
|
||||
if (endKey.equals(key)) {
|
||||
processedRecords++;
|
||||
if (processedRecords == numRecords) {
|
||||
latch.countDown();
|
||||
}
|
||||
}
|
||||
|
|
@ -472,13 +577,8 @@ public class SimpleBenchmark {
|
|||
return new KafkaStreams(builder, props);
|
||||
}
|
||||
|
||||
private KafkaStreams createKafkaStreamsWithSink(String topic, File stateDir, String kafka, final CountDownLatch latch) {
|
||||
Properties props = new Properties();
|
||||
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "simple-benchmark-streams-with-sink");
|
||||
props.put(StreamsConfig.STATE_DIR_CONFIG, stateDir.toString());
|
||||
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, kafka);
|
||||
props.put(StreamsConfig.NUM_STREAM_THREADS_CONFIG, 1);
|
||||
props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
|
||||
private KafkaStreams createKafkaStreamsWithSink(String topic, final CountDownLatch latch) {
|
||||
final Properties props = setStreamProperties("simple-benchmark-streams-with-sink");
|
||||
|
||||
KStreamBuilder builder = new KStreamBuilder();
|
||||
|
||||
|
|
@ -495,7 +595,8 @@ public class SimpleBenchmark {
|
|||
|
||||
@Override
|
||||
public void process(Integer key, byte[] value) {
|
||||
if (endKey.equals(key)) {
|
||||
processedRecords++;
|
||||
if (processedRecords == numRecords) {
|
||||
latch.countDown();
|
||||
}
|
||||
}
|
||||
|
|
@ -514,16 +615,19 @@ public class SimpleBenchmark {
|
|||
return new KafkaStreams(builder, props);
|
||||
}
|
||||
|
||||
private class CountDownAction<K, V> implements ForeachAction<K, V> {
|
||||
private class CountDownAction<V> implements ForeachAction<Integer, V> {
|
||||
private CountDownLatch latch;
|
||||
CountDownAction(final CountDownLatch latch) {
|
||||
this.latch = latch;
|
||||
}
|
||||
@Override
|
||||
public void apply(K key, V value) {
|
||||
public void apply(Integer key, V value) {
|
||||
processedRecords++;
|
||||
if (processedRecords == numRecords) {
|
||||
this.latch.countDown();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private KafkaStreams createKafkaStreamsKStreamKTableJoin(Properties streamConfig, String kStreamTopic,
|
||||
String kTableTopic, final CountDownLatch latch) {
|
||||
|
|
@ -562,15 +666,10 @@ public class SimpleBenchmark {
|
|||
return new KafkaStreams(builder, streamConfig);
|
||||
}
|
||||
|
||||
private KafkaStreams createKafkaStreamsWithStateStore(String topic, File stateDir, String kafka,
|
||||
private KafkaStreams createKafkaStreamsWithStateStore(String topic,
|
||||
final CountDownLatch latch,
|
||||
boolean enableCaching) {
|
||||
Properties props = new Properties();
|
||||
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "simple-benchmark-streams-with-store" + enableCaching);
|
||||
props.put(StreamsConfig.STATE_DIR_CONFIG, stateDir.toString());
|
||||
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, kafka);
|
||||
props.put(StreamsConfig.NUM_STREAM_THREADS_CONFIG, 1);
|
||||
props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
|
||||
Properties props = setStreamProperties("simple-benchmark-streams-with-store" + enableCaching);
|
||||
|
||||
KStreamBuilder builder = new KStreamBuilder();
|
||||
|
||||
|
|
@ -596,8 +695,8 @@ public class SimpleBenchmark {
|
|||
@Override
|
||||
public void process(Integer key, byte[] value) {
|
||||
store.put(key, value);
|
||||
|
||||
if (endKey.equals(key)) {
|
||||
processedRecords++;
|
||||
if (processedRecords == numRecords) {
|
||||
latch.countDown();
|
||||
}
|
||||
}
|
||||
|
|
@ -616,14 +715,15 @@ public class SimpleBenchmark {
|
|||
return new KafkaStreams(builder, props);
|
||||
}
|
||||
|
||||
private double megaBytePerSec(long time) {
|
||||
return (double) (RECORD_SIZE * numRecords / 1024 / 1024) / ((double) time / 1000);
|
||||
}
|
||||
|
||||
private double megaBytePerSec(long time, int numRecords, int recordSizeBytes) {
|
||||
return (double) (recordSizeBytes * numRecords / 1024 / 1024) / ((double) time / 1000);
|
||||
}
|
||||
|
||||
private double recordsPerSec(long time, int numRecords) {
|
||||
return (double) numRecords / ((double) time / 1000);
|
||||
}
|
||||
|
||||
private List<TopicPartition> getAllPartitions(KafkaConsumer<?, ?> consumer, String... topics) {
|
||||
ArrayList<TopicPartition> partitions = new ArrayList<>();
|
||||
|
||||
|
|
|
|||
|
|
@ -13,41 +13,86 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from ducktape.tests.test import Test
|
||||
from ducktape.mark.resource import cluster
|
||||
|
||||
from ducktape.mark import parametrize, matrix
|
||||
from kafkatest.tests.kafka_test import KafkaTest
|
||||
|
||||
from kafkatest.services.performance.streams_performance import StreamsSimpleBenchmarkService
|
||||
from kafkatest.services.zookeeper import ZookeeperService
|
||||
from kafkatest.services.kafka import KafkaService
|
||||
from kafkatest.version import DEV_BRANCH
|
||||
|
||||
|
||||
class StreamsSimpleBenchmarkTest(KafkaTest):
|
||||
class StreamsSimpleBenchmarkTest(Test):
|
||||
"""
|
||||
Simple benchmark of Kafka Streams.
|
||||
"""
|
||||
|
||||
def __init__(self, test_context):
|
||||
super(StreamsSimpleBenchmarkTest, self).__init__(test_context, num_zk=1, num_brokers=1,topics={
|
||||
'simpleBenchmarkSourceTopic' : { 'partitions': 1, 'replication-factor': 1 },
|
||||
'simpleBenchmarkSinkTopic' : { 'partitions': 1, 'replication-factor': 1 },
|
||||
'joinSourceTopic1KStreamKStream' : { 'partitions': 1, 'replication-factor': 1 },
|
||||
'joinSourceTopic2KStreamKStream' : { 'partitions': 1, 'replication-factor': 1 },
|
||||
'joinSourceTopic1KStreamKTable' : { 'partitions': 1, 'replication-factor': 1 },
|
||||
'joinSourceTopic2KStreamKTable' : { 'partitions': 1, 'replication-factor': 1 },
|
||||
'joinSourceTopic1KTableKTable' : { 'partitions': 1, 'replication-factor': 1 },
|
||||
'joinSourceTopic2KTableKTable' : { 'partitions': 1, 'replication-factor': 1 }
|
||||
})
|
||||
super(StreamsSimpleBenchmarkTest, self).__init__(test_context)
|
||||
self.num_records = 2000000L
|
||||
self.replication = 1
|
||||
|
||||
self.driver = StreamsSimpleBenchmarkService(test_context, self.kafka, 1000000L)
|
||||
|
||||
@cluster(num_nodes=3)
|
||||
def test_simple_benchmark(self):
|
||||
@cluster(num_nodes=9)
|
||||
@matrix(test=["all"], scale=[1])
|
||||
def test_simple_benchmark(self, test, scale):
|
||||
"""
|
||||
Run simple Kafka Streams benchmark
|
||||
"""
|
||||
self.driver = [None] * (scale + 1)
|
||||
node = [None] * (scale)
|
||||
data = [None] * (scale)
|
||||
|
||||
self.driver.start()
|
||||
self.driver.wait()
|
||||
self.driver.stop()
|
||||
node = self.driver.node
|
||||
node.account.ssh("grep Performance %s" % self.driver.STDOUT_FILE, allow_fail=False)
|
||||
#############
|
||||
# SETUP PHASE
|
||||
#############
|
||||
self.zk = ZookeeperService(self.test_context, num_nodes=1)
|
||||
self.zk.start()
|
||||
self.kafka = KafkaService(self.test_context, num_nodes=scale, zk=self.zk, version=DEV_BRANCH, topics={
|
||||
'simpleBenchmarkSourceTopic' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'countTopic' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'simpleBenchmarkSinkTopic' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'joinSourceTopic1KStreamKStream' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'joinSourceTopic2KStreamKStream' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'joinSourceTopic1KStreamKTable' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'joinSourceTopic2KStreamKTable' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'joinSourceTopic1KTableKTable' : { 'partitions': scale, 'replication-factor': self.replication },
|
||||
'joinSourceTopic2KTableKTable' : { 'partitions': scale, 'replication-factor': self.replication }
|
||||
})
|
||||
self.kafka.start()
|
||||
|
||||
return self.driver.collect_data(node)
|
||||
################
|
||||
# LOAD PHASE
|
||||
################
|
||||
self.load_driver = StreamsSimpleBenchmarkService(self.test_context, self.kafka,
|
||||
self.num_records * scale, "true", test)
|
||||
self.load_driver.start()
|
||||
self.load_driver.wait()
|
||||
self.load_driver.stop()
|
||||
|
||||
################
|
||||
# RUN PHASE
|
||||
################
|
||||
for num in range(0, scale):
|
||||
self.driver[num] = StreamsSimpleBenchmarkService(self.test_context, self.kafka,
|
||||
self.num_records/(scale), "false", test)
|
||||
self.driver[num].start()
|
||||
|
||||
#######################
|
||||
# STOP + COLLECT PHASE
|
||||
#######################
|
||||
for num in range(0, scale):
|
||||
self.driver[num].wait()
|
||||
self.driver[num].stop()
|
||||
node[num] = self.driver[num].node
|
||||
node[num].account.ssh("grep Performance %s" % self.driver[num].STDOUT_FILE, allow_fail=False)
|
||||
data[num] = self.driver[num].collect_data(node[num], "" )
|
||||
|
||||
|
||||
final = {}
|
||||
for num in range(0, scale):
|
||||
for key in data[num]:
|
||||
final[key + str(num)] = data[num][key]
|
||||
|
||||
return final
|
||||
|
|
|
|||
|
|
@ -22,17 +22,19 @@ from kafkatest.services.streams import StreamsTestBaseService
|
|||
class StreamsSimpleBenchmarkService(StreamsTestBaseService):
|
||||
"""Base class for simple Kafka Streams benchmark"""
|
||||
|
||||
def __init__(self, test_context, kafka, numrecs):
|
||||
def __init__(self, test_context, kafka, numrecs, load_phase, test_name):
|
||||
super(StreamsSimpleBenchmarkService, self).__init__(test_context,
|
||||
kafka,
|
||||
"org.apache.kafka.streams.perf.SimpleBenchmark",
|
||||
numrecs)
|
||||
numrecs,
|
||||
load_phase,
|
||||
test_name)
|
||||
|
||||
def collect_data(self, node):
|
||||
def collect_data(self, node, tag = None):
|
||||
# Collect the data and return it to the framework
|
||||
output = node.account.ssh_capture("grep Performance %s" % self.STDOUT_FILE)
|
||||
data = {}
|
||||
for line in output:
|
||||
parts = line.split(':')
|
||||
data[parts[0]] = float(parts[1])
|
||||
data[tag + parts[0]] = parts[1]
|
||||
return data
|
||||
|
|
|
|||
|
|
@ -45,11 +45,14 @@ class StreamsTestBaseService(KafkaPathResolverMixin, Service):
|
|||
"collect_default": True},
|
||||
}
|
||||
|
||||
def __init__(self, test_context, kafka, streams_class_name, user_test_args):
|
||||
def __init__(self, test_context, kafka, streams_class_name, user_test_args, user_test_args1, user_test_args2):
|
||||
super(StreamsTestBaseService, self).__init__(test_context, 1)
|
||||
self.kafka = kafka
|
||||
self.args = {'streams_class_name': streams_class_name,
|
||||
'user_test_args': user_test_args}
|
||||
'user_test_args': user_test_args,
|
||||
'user_test_args1': user_test_args1,
|
||||
'user_test_args2': user_test_args2}
|
||||
self.log_level = "DEBUG"
|
||||
|
||||
@property
|
||||
def node(self):
|
||||
|
|
@ -118,7 +121,7 @@ class StreamsTestBaseService(KafkaPathResolverMixin, Service):
|
|||
|
||||
cmd = "( export KAFKA_LOG4J_OPTS=\"-Dlog4j.configuration=file:%(log4j)s\"; " \
|
||||
"INCLUDE_TEST_JARS=true %(kafka_run_class)s %(streams_class_name)s " \
|
||||
" %(kafka)s %(state_dir)s %(user_test_args)s" \
|
||||
" %(kafka)s %(state_dir)s %(user_test_args)s %(user_test_args1)s %(user_test_args2)s" \
|
||||
" & echo $! >&3 ) 1>> %(stdout)s 2>> %(stderr)s 3> %(pidfile)s" % args
|
||||
|
||||
return cmd
|
||||
|
|
|
|||
Loading…
Reference in New Issue