如何在将Uber Jar提交给Google Dataproc时解决Guava依赖性问题 [英] How to resolve Guava dependency issue while submitting Uber Jar to Google Dataproc

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问题描述

我使用maven遮罩插件来构建Uber jar以将其作为工作提交给Google Dataproc群集。
Google已在其群集中安装了Apache Spark 2.0.2 Apache Hadoop 2.7.3。

I am using maven shade plugin to build Uber jar for submitting it as a job to google dataproc cluster. Google have installed Apache Spark 2.0.2 Apache Hadoop 2.7.3 on their cluster.

Apache spark 2.0.2使用com.google的14.0.1。番石榴和apache hadoop 2.7.3使用11.0.2，这两者都应该在classpath中。

Apache spark 2.0.2 uses 14.0.1 of com.google.guava and apache hadoop 2.7.3 uses 11.0.2, these both should be in the classpath already.

<plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-shade-plugin</artifactId>
            <version>3.0.0</version>
            <executions>
                <execution>
                    <phase>package</phase>
                    <goals>
                        <goal>shade</goal>
                    </goals>
                    <configuration>
                    <!--  
                        <artifactSet>
                            <includes>
                                <include>com.google.guava:guava:jar:19.0</include>
                            </includes>
                        </artifactSet>
                    -->
                        <artifactSet>
                            <excludes>
                                <exclude>com.google.guava:guava:*</exclude>                                 
                            </excludes>
                        </artifactSet>
                    </configuration>
                </execution>
            </executions>
        </plugin>

当我在阴影插件中包含番石榴16.0.1 jar时，我得到了这个Eexception： b
$ b

When I include guava 16.0.1 jar in shade plugin i get this Eexception : 

Exception in thread "main" java.io.IOException: Failed to open native connection to Cassandra at {10.148.0.3}:9042
at com.datastax.spark.connector.cql.CassandraConnector$.com$datastax$spark$connector$cql$CassandraConnector$$createSession(CassandraConnector.scala:163)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$3.apply(CassandraConnector.scala:149)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$3.apply(CassandraConnector.scala:149)
at com.datastax.spark.connector.cql.RefCountedCache.createNewValueAndKeys(RefCountedCache.scala:31)
at com.datastax.spark.connector.cql.RefCountedCache.acquire(RefCountedCache.scala:56)
at com.datastax.spark.connector.cql.CassandraConnector.openSession(CassandraConnector.scala:82)
at com.datastax.spark.connector.cql.CassandraConnector.withSessionDo(CassandraConnector.scala:110)
at com.datastax.spark.connector.cql.CassandraConnector.withClusterDo(CassandraConnector.scala:121)
at com.datastax.spark.connector.cql.Schema$.fromCassandra(Schema.scala:322)
at com.datastax.spark.connector.cql.Schema$.tableFromCassandra(Schema.scala:342)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.tableDef(CassandraTableRowReaderProvider.scala:50)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef$lzycompute(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.verify(CassandraTableRowReaderProvider.scala:137)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.verify(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.getPartitions(CassandraTableScanRDD.scala:232)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1913)
at org.apache.spark.rdd.RDD.count(RDD.scala:1134)
at com.test.scala.CreateVirtualTable$.main(CreateVirtualTable.scala:47)
at com.test.scala.CreateVirtualTable.main(CreateVirtualTable.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:736)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:185)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:210)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:124)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.NoSuchMethodError: com.google.common.util.concurrent.Futures.withFallback(Lcom/google/common/util/concurrent/ListenableFuture;Lcom/google/common/util/concurrent/FutureFallback;Ljava/util/concurrent/Executor;)Lcom/google/common/util/concurrent/ListenableFuture;
at com.datastax.driver.core.Connection.initAsync(Connection.java:177)
at com.datastax.driver.core.Connection$Factory.open(Connection.java:731)
at com.datastax.driver.core.ControlConnection.tryConnect(ControlConnection.java:251)
at com.datastax.driver.core.ControlConnection.reconnectInternal(ControlConnection.java:199)
at com.datastax.driver.core.ControlConnection.connect(ControlConnection.java:77)
at com.datastax.driver.core.Cluster$Manager.init(Cluster.java:1414)
at com.datastax.driver.core.Cluster.getMetadata(Cluster.java:393)
at com.datastax.spark.connector.cql.CassandraConnector$.com$datastax$spark$connector$cql$CassandraConnector$$createSession(CassandraConnector.scala:156)

... 32 more
17/05/10 09:07:36 INFO

如果我排除Guava 16.0.1，那么它会抛出这个异常

And If i exclude Guava 16.0.1 then it throws this exception 

Exception in thread "main" java.lang.NoClassDefFoundError: com/google/common/reflect/TypeParameter
at com.datastax.driver.core.SanityChecks.checkGuava(SanityChecks.java:50)
at com.datastax.driver.core.SanityChecks.check(SanityChecks.java:36)
at com.datastax.driver.core.Cluster.<clinit>(Cluster.java:67)
at com.datastax.spark.connector.cql.DefaultConnectionFactory$.clusterBuilder(CassandraConnectionFactory.scala:35)
at com.datastax.spark.connector.cql.DefaultConnectionFactory$.createCluster(CassandraConnectionFactory.scala:92)
at com.datastax.spark.connector.cql.CassandraConnector$.com$datastax$spark$connector$cql$CassandraConnector$$createSession(CassandraConnector.scala:154)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$3.apply(CassandraConnector.scala:149)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$3.apply(CassandraConnector.scala:149)
at com.datastax.spark.connector.cql.RefCountedCache.createNewValueAndKeys(RefCountedCache.scala:31)
at com.datastax.spark.connector.cql.RefCountedCache.acquire(RefCountedCache.scala:56)
at com.datastax.spark.connector.cql.CassandraConnector.openSession(CassandraConnector.scala:82)
at com.datastax.spark.connector.cql.CassandraConnector.withSessionDo(CassandraConnector.scala:110)
at com.datastax.spark.connector.cql.CassandraConnector.withClusterDo(CassandraConnector.scala:121)
at com.datastax.spark.connector.cql.Schema$.fromCassandra(Schema.scala:322)
at com.datastax.spark.connector.cql.Schema$.tableFromCassandra(Schema.scala:342)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.tableDef(CassandraTableRowReaderProvider.scala:50)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef$lzycompute(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.verify(CassandraTableRowReaderProvider.scala:137)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.verify(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.getPartitions(CassandraTableScanRDD.scala:232)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1913)
at org.apache.spark.rdd.RDD.count(RDD.scala:1134)
at com.test.scala.CreateVirtualTable$.main(CreateVirtualTable.scala:47)
at com.test.scala.CreateVirtualTable.main(CreateVirtualTable.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:736)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:185)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:210)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:124)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: com.google.common.reflect.TypeParameter
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 38 more
17/05/11 08:24:00 INFO org.spark_project.jetty.server.ServerConnector: Stopped ServerConnector@edc6a5d{HTTP/1.1}{0.0.0.0:4040}
17/05/11 08:24:00 INFO com.datastax.spark.connector.util.SerialShutdownHooks: Successfully executed shutdown hook: Clearing session cache for C* connector

那么这里可能会出现什么问题？
是dataproc上的类加载器从hadoop中挑选guava 11.0.2？
为番石榴11.0.2没有class com / google / common / reflect / TypeParameter。
所有google dataproc开发人员都在看这个标签，请大家帮忙。

So what can be the problem here ? is classloader on dataproc picking guava 11.0.2 from hadoop ? as guava 11.0.2 does not have class com/google/common/reflect/TypeParameter . All the google dataproc developers watching this tag please help.

推荐答案

当我使用uber jars在Hadoop / Spark / Dataproc，我经常使用任何番石榴版本适合我的需求，然后使用阴影重定位，允许不同版本共存而不发生问题：

When I make uber jars to run on Hadoop / Spark / Dataproc, I often use whichever version of guava suits my needs and then use a shade relocation which allows the different versions to co-exist without issue:

<plugin>
  <groupId>org.apache.maven.plugins</groupId>
  <artifactId>maven-shade-plugin</artifactId>
  <version>2.3</version>
  <executions>
    <execution>
      <phase>package</phase>
      <goals>
        <goal>shade</goal>
      </goals>
      <configuration>
      <artifactSet>
          <includes>
            <include>com.google.guava:*</include>
          </includes>
      </artifactSet>
      <minimizeJar>false</minimizeJar>
      <relocations>
          <relocation>
            <pattern>com.google.common</pattern>
            <shadedPattern>repackaged.com.google.common</shadedPattern>
          </relocation>
      </relocations>
      <shadedArtifactAttached>true</shadedArtifactAttached>
      </configuration>
  </execution>
</executions>
</plugin>

这篇关于如何在将Uber Jar提交给Google Dataproc时解决Guava依赖性问题的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！

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