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Ich benutze Spark Streaming mit Kafka Thema. Thema wird mit 5 Partitionen erstellt. Alle meine Nachrichten werden unter Verwendung von Tabellenname als Schlüssel im kafka-Thema veröffentlicht. Gegeben, ich nehme an, alle Nachrichten für diese Tabelle sollten zu der gleichen Partition gehen. Aber ich merke in den Spark-Log-Nachrichten für die gleiche Tabelle geht manchmal zu Executor-Knoten-1 und irgendwann geht zum Executor-Knoten-2.Kafka Topic Partition und Spark Executor Mapping
Ich verwende Code in Garn-Cluster-Modus folgenden Befehl:
spark-submit --name DataProcessor --master yarn-cluster --files /opt/ETL_JAR/executor-log4j-spark.xml,/opt/ETL_JAR/driver-log4j-spark.xml,/opt/ETL_JAR/application.properties --conf "spark.driver.extraJavaOptions=-Dlog4j.configuration=driver-log4j-spark.xml" --conf "spark.executor.extraJavaOptions=-Dlog4j.configuration=executor-log4j-spark.xml" --class com.test.DataProcessor /opt/ETL_JAR/etl-all-1.0.jar
und diese Vorlage erstellt 1-Fahrer sagen auf node-1 und 2 Testamentsvollstrecker auf node-1 und Knoten-2 lassen.
Ich möchte nicht Node-1 und Node-2-Executoren die gleiche Partition lesen. aber das passiert
Auch versucht folgende Konfiguration, um Verbrauchergruppe, aber keinen Unterschied zu spezifizieren.
kafkaParams.put("group.id", "app1");
Dies ist, wie wir den Strom mit createDirectStream Methode * Nicht durch zookeeper erstellen.
HashMap<String, String> kafkaParams = new HashMap<String, String>();
kafkaParams.put("metadata.broker.list", brokers);
kafkaParams.put("auto.offset.reset", "largest");
kafkaParams.put("group.id", "app1");
JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(
jssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams,
topicsSet
);
kompletter Code:
import java.io.Serializable;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import org.apache.commons.lang3.StringUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.api.java.JavaStreamingContextFactory;
import org.apache.spark.streaming.kafka.KafkaUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import kafka.serializer.StringDecoder;
import scala.Tuple2;
public class DataProcessor2 implements Serializable {
private static final long serialVersionUID = 3071125481526170241L;
private static Logger log = LoggerFactory.getLogger("DataProcessor");
public static void main(String[] args) {
final String sparkCheckPointDir = ApplicationProperties.getProperty(Consts.SPARK_CHECKPOINTING_DIR);
DataProcessorContextFactory3 factory = new DataProcessorContextFactory3();
JavaStreamingContext jssc = JavaStreamingContext.getOrCreate(sparkCheckPointDir, factory);
// Start the process
jssc.start();
jssc.awaitTermination();
}
}
class DataProcessorContextFactory3 implements JavaStreamingContextFactory, Serializable {
private static final long serialVersionUID = 6070911284191531450L;
private static Logger logger = LoggerFactory.getLogger(DataProcessorContextFactory.class);
DataProcessorContextFactory3() {
}
@Override
public JavaStreamingContext create() {
logger.debug("creating new context..!");
final String brokers = ApplicationProperties.getProperty(Consts.KAFKA_BROKERS_NAME);
final String topic = ApplicationProperties.getProperty(Consts.KAFKA_TOPIC_NAME);
final String app = "app1";
final String offset = ApplicationProperties.getProperty(Consts.KAFKA_CONSUMER_OFFSET, "largest");
logger.debug("Data processing configuration. brokers={}, topic={}, app={}, offset={}", brokers, topic, app,
offset);
if (StringUtils.isBlank(brokers) || StringUtils.isBlank(topic) || StringUtils.isBlank(app)) {
System.err.println("Usage: DataProcessor <brokers> <topic>\n" + Consts.KAFKA_BROKERS_NAME
+ " is a list of one or more Kafka brokers separated by comma\n" + Consts.KAFKA_TOPIC_NAME
+ " is a kafka topic to consume from \n\n\n");
System.exit(1);
}
final String majorVersion = "1.0";
final String minorVersion = "3";
final String version = majorVersion + "." + minorVersion;
final String applicationName = "DataProcessor-" + topic + "-" + version;
// for dev environment
SparkConf sparkConf = new SparkConf().setMaster("local[*]").setAppName(applicationName);
// for cluster environment
//SparkConf sparkConf = new SparkConf().setAppName(applicationName);
final long sparkBatchDuration = Long
.valueOf(ApplicationProperties.getProperty(Consts.SPARK_BATCH_DURATION, "10"));
final String sparkCheckPointDir = ApplicationProperties.getProperty(Consts.SPARK_CHECKPOINTING_DIR);
JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(sparkBatchDuration));
logger.debug("setting checkpoint directory={}", sparkCheckPointDir);
jssc.checkpoint(sparkCheckPointDir);
HashSet<String> topicsSet = new HashSet<String>(Arrays.asList(topic.split(",")));
HashMap<String, String> kafkaParams = new HashMap<String, String>();
kafkaParams.put("metadata.broker.list", brokers);
kafkaParams.put("auto.offset.reset", offset);
kafkaParams.put("group.id", "app1");
// @formatter:off
JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(
jssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams,
topicsSet
);
// @formatter:on
processRDD(messages, app);
return jssc;
}
private void processRDD(JavaPairInputDStream<String, String> messages, final String app) {
JavaDStream<MsgStruct> rdd = messages.map(new MessageProcessFunction());
rdd.foreachRDD(new Function<JavaRDD<MsgStruct>, Void>() {
private static final long serialVersionUID = 250647626267731218L;
@Override
public Void call(JavaRDD<MsgStruct> currentRdd) throws Exception {
if (!currentRdd.isEmpty()) {
logger.debug("Receive RDD. Create JobDispatcherFunction at HOST={}", FunctionUtil.getHostName());
currentRdd.foreachPartition(new VoidFunction<Iterator<MsgStruct>>() {
@Override
public void call(Iterator<MsgStruct> arg0) throws Exception {
while(arg0.hasNext()){
System.out.println(arg0.next().toString());
}
}
});
} else {
logger.debug("Current RDD is empty.");
}
return null;
}
});
}
public static class MessageProcessFunction implements Function<Tuple2<String, String>, MsgStruct> {
@Override
public MsgStruct call(Tuple2<String, String> data) throws Exception {
String message = data._2();
System.out.println("message:"+message);
return MsgStruct.parse(message);
}
}
public static class MsgStruct implements Serializable{
private String message;
public static MsgStruct parse(String msg){
MsgStruct m = new MsgStruct();
m.message = msg;
return m;
}
public String toString(){
return "content inside="+message;
}
}
}