Question

我有这种奇怪的行为，我的用例是使用

将Spark数据帧写入hive分区表

sqlContext.sql("INSERT OVERWRITE TABLE <table> PARTITION (<partition column) SELECT * FROM <temp table from dataframe>")

奇怪的是，当使用来自主机A的pyspark shell时，这是有效的，但使用相同的hive表连接到同一群集的相同的确切代码在jupyter笔记本中不起作用，它返回：

java.lang.NoSuchMethodException: org.apache.hadoop.hive.ql.metadata.Hive.loadDynamicPartitions

异常所以在我看来，启动pyspark shell的主机与启动jupyter的主机之间的jar不匹配，我的问题是，我如何确定在pyspark shell中使用哪个版本的相应jar？，并在jupyter笔记本中的代码（我无法访问jupyter服务器）？如果pyspark shell和jupyter连接到同一个集群，为什么可以使用2个不同的版本呢？

更新：经过一番研究后我发现jupyter正在使用“Livy”而Livy主机使用hive-exec-2.0.1.jar，我们使用pyspark shell的主机使用hive-exec-1.2 .1000.2.5.3.58-3.jar，所以我从maven资源库下载了两个jar并反编译它们，我发现altough loadDynamicPartitions方法同时存在，方法签名（参数）不同，在livy版本中boolean holdDDLTime参数缺失。 / p>

Answer 1

我有类似的问题尝试从cloudera获取maven依赖项

 <dependencies>
    <!-- Scala and Spark dependencies -->

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.10</artifactId>
        <version>1.6.0-cdh5.9.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.10</artifactId>
        <version>1.6.0-cdh5.9.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-hive_2.10</artifactId>
        <version>1.6.0-cdh5.9.2</version>
    </dependency>
     <!-- https://mvnrepository.com/artifact/org.apache.hive/hive-exec -->
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-exec</artifactId>
        <version>1.1.0-cdh5.9.2</version>
    </dependency>
    <dependency>
        <groupId>org.scalatest</groupId>
        <artifactId>scalatest_2.10</artifactId>
        <version>3.0.0-SNAP4</version>
    </dependency>
    <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>4.11</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-mllib_2.10</artifactId>
        <version>1.4.1</version>
    </dependency>
    <dependency>
        <groupId>commons-dbcp</groupId>
        <artifactId>commons-dbcp</artifactId>
        <version>1.2.2</version>
    </dependency>
    <dependency>
        <groupId>com.databricks</groupId>
        <artifactId>spark-csv_2.10</artifactId>
        <version>1.4.0</version>
    </dependency>
    <dependency>
        <groupId>com.databricks</groupId>
        <artifactId>spark-xml_2.10</artifactId>
        <version>0.2.0</version>
    </dependency>
    <dependency>
        <groupId>com.amazonaws</groupId>
        <artifactId>aws-java-sdk</artifactId>
        <version>1.0.12</version>
    </dependency>
    <dependency>
        <groupId>com.amazonaws</groupId>
        <artifactId>aws-java-sdk-s3</artifactId>
        <version>1.11.172</version>
    </dependency>
    <dependency>
        <groupId>com.github.scopt</groupId>
        <artifactId>scopt_2.10</artifactId>
        <version>3.2.0</version>
    </dependency>
    <dependency>
        <groupId>javax.mail</groupId>
        <artifactId>mail</artifactId>
        <version>1.4</version>
    </dependency>
</dependencies>
<repositories>
    <repository>
        <id>maven-hadoop</id>
        <name>Hadoop Releases</name>
        <url>https://repository.cloudera.com/content/repositories/releases/</url>
    </repository>
    <repository>
        <id>cloudera-repos</id>
        <name>Cloudera Repos</name>
        <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
    </repository>
</repositories>

从spark（2.11）数据帧编写hive分区表时的org.apache.hadoop.hive.ql.metadata.Hive.loadDynamicPartitions异常

1 个答案: