我使用spark whith java,我想使用spark Job-Server。为此我在这个链接中跟随了所有内容: https://github.com/spark-jobserver/spark-jobserver
这是我项目中的scala类:
import _root_.spark.jobserver.SparkJob
import _root_.spark.jobserver.SparkJobValid
import _root_.spark.jobserver.SparkJobValidation
import com.typesafe.config._
import org.apache.spark._
import org.apache.spark.api.java.JavaSparkContext
import spark.jobserver.{SparkJob, SparkJobValid, SparkJobValidation}
object JavaWord extends SparkJob {
def main(args: Array[String]) {
val ctx = new SparkContext("local[4]", "JavaWordCount")
val config = ConfigFactory.parseString("")
val results = runJob(ctx, config)
}
override def validate(sc: SparkContext, config: Config): SparkJobValidation = {
SparkJobValid;
}
override def runJob(sc: SparkContext, config: Config): Any = {
val jsc = new JavaSparkContext(sc)
val j = new JavaCount()
return j.Mafonction(jsc: JavaSparkContext)
}
}
Java类"单词wount"
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import scala.Tuple2;
import java.io.Serializable;
import java.util.Arrays;
import java.util.regex.Pattern;
public final class JavaCount implements Serializable {
public static Object main(String[] args) throws Exception {
return null;
}
public Object Mafonction(JavaSparkContext sc){
String s= "a a a a b b c a";
JavaPairRDD<String, Integer> lines = sc.parallelize(Arrays.asList(s.split(" "))).mapToPair(new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String s) {
return new Tuple2<String, Integer>(s, 1);
}
}).reduceByKey(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer i1, Integer i2) {
return i1 + i2;
}
});
return lines.collect();
}
}
但是当我执行它时,我在spark job-server中遇到了这个错误curl: (52) Empty reply from server
:
> job-server[ERROR] Uncaught error from thread [JobServer-akka.actor.default-dispatcher-13] shutting down JVM since 'akka.jvm-exit-on-fatal-error' is enabled for ActorSystem[JobServer]
job-server[ERROR] java.lang.IncompatibleClassChangeError: Implementing class
job-server[ERROR] at java.lang.ClassLoader.defineClass1(Native Method)
job-server[ERROR] at java.lang.ClassLoader.defineClass(ClassLoader.java:800)
job-server[ERROR] at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
job-server[ERROR] at java.net.URLClassLoader.defineClass(URLClassLoader.java:449)
job-server[ERROR] at java.net.URLClassLoader.access$100(URLClassLoader.java:71)
job-server[ERROR] at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
job-server[ERROR] at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
job-server[ERROR] at java.security.AccessController.doPrivileged(Native Method)
job-server[ERROR] at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
job-server[ERROR] at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
job-server[ERROR] at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
job-server[ERROR] at spark.jobserver.JobManagerActor$$anonfun$spark$jobserver$JobManagerActor$$getJobFuture$4.apply(JobMan agerActor.scala:222)
job-server[ERROR] at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
job-server[ERROR] at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
job-server[ERROR] at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:42)
job-server[ERROR] at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
job-server[ERROR] at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
job-server[ERROR] at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
job-server[ERROR] at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
job-server[ERROR] at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
job-server ... finished with exit code 255
答案 0 :(得分:2)
我解决了这个问题。我刚删除了/ tmp / Spark-JobServer的所有大陆,我编译了JobServer,它工作正常^^非常感谢你的帮助