将 Uber Jar 提交到 Google Dataproc 时如何解决 Guava 依赖问题答案

【问题标题】：How to resolve Guava dependency issue while submitting Uber Jar to Google Dataproc将 Uber Jar 提交到 Google Dataproc 时如何解决 Guava 依赖问题
【发布时间】：2017-10-10 04:46:33
【问题描述】：

我正在使用 maven shade 插件构建 Uber jar 以将其作为作业提交给 google dataproc 集群。 Google 已在其集群上安装了 Apache Spark 2.0.2 Apache Hadoop 2.7.3。

Apache spark 2.0.2 使用 com.google.guava 的 14.0.1 而 apache hadoop 2.7.3 使用 11.0.2，这两者都应该已经在类路径中了。

<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 shade 插件中包含 guava 16.0.1 jar 时，我得到了这个 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

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

那么这里有什么问题呢？ dataproc 上的类加载器是从 hadoop 中选择 guava 11.0.2 吗？因为 guava 11.0.2 没有类 com/google/common/reflect/TypeParameter 。请所有关注此标签的 google dataproc 开发人员提供帮助。

【问题讨论】：

标签： hadoop apache-spark spark-cassandra-connector google-cloud-dataproc

【解决方案1】：

已编辑：有关 Maven 和 SBT 的完整示例，请参阅 https://cloud.google.com/blog/products/data-analytics/managing-java-dependencies-apache-spark-applications-cloud-dataproc。

原答案 当我制作 uber jar 以在 Hadoop/Spark/Dataproc 上运行时，我经常使用适合我需要的任何版本的 guava，然后使用允许不同版本共存而不会出现问题的阴影重定位：

<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>

【讨论】：

谢谢你，它成功了 :) packageof 类的重命名是如何工作的？ spark现在如何知道从类路径中选择这些类而不是 11.0.2 的 Hadoop？
使用重定位时，shade 会重写你的类以使用一个名为“repackaged.com.google.common”的新包，并将你的番石榴版本放在该包下。 hadoop 中的 guava 版本仍将使用 com.google.common 包，并且不会与您的 uber jar 冲突，因为它将不再包含该包中的类。
cloud.google.com/blog/products/data-analytics/…