在 spark 中访问 Hive 表 [英] Accessing Hive tables in spark

问题描述

我安装了 Hive 0.13 并创建了自定义数据库.我有使用 mvn -hive 选项构建的 spark 1.1.0 单节点集群.我想使用 hivecontext 在 spark 应用程序中访问此数据库中的表.但是 hivecontext 总是读取在 spark 目录中创建的本地元存储.我已将 hive-site.xml 复制到 spark/conf 目录中.
我需要做任何其他配置吗?

I have Hive 0.13 installation and have created custom databases. I have spark 1.1.0 single node cluster built using mvn -hive option. I want to access tables in this database in spark application using hivecontext. But hivecontext is always reading the local metastore created in spark directory. I have copied the hive-site.xml in spark/conf directory.
Do I need to do any other configuration?

推荐答案

第一步:使用最新版本设置 SPARK....

Step 1: Setup SPARK with latest version....

$ cd $SPARK_Home; ./sbt/sbt -Phive assembly
$ cd $SPARK_Home; ./sbt/sbt -Phivethriftserver assembly

通过执行这个你会下载一些jar文件，默认情况下它会被添加，不需要添加......

By executing this you will download some jar files and bydefault it will be added no need to add....

第 2 步:
将 hive-site.xml 从您的 Hive 集群复制到您的 $SPARK_HOME/conf/dir 并编辑 XML 文件并将这些属性添加到下面列出的该文件中:

Step 2:
Copy hive-site.xml from your Hive cluster to your $SPARK_HOME/conf/dir and edit the XML file and add these properties to that file which is listed below:

<property>
    <name>javax.jdo.option.ConnectionURL</name>
    <value>jdbc:mysql://MYSQL_HOST:3306/hive_{version}</value>
    <description>JDBC connect string for a JDBC metastore</description>
</property>
<property>
    <name>javax.jdo.option.ConnectionDriverName</name>
    <value>com.mysql.jdbc.Driver</value>
    <description>Driver class name for a JDBC metastore/description>
</property>
<property>
    <name>javax.jdo.option.ConnectionUserName</name>
    <value>XXXXXXXX</value>
    <description>Username to use against metastore database/description>
</property> 
<property>
    <name>javax.jdo.option.ConnectionPassword</name>
    <value>XXXXXXXX</value>
    <description>Password to use against metastore database/description>
</property>

第 3 步:下载 MYSQL JDBC 连接器并将其添加到 SPARK CLASSPATH.运行这个命令 bin/compute-classpath.sh
并为以下脚本添加以下行.

Step 3: Download MYSQL JDBC connector and add that to SPARK CLASSPATH. Run this command bin/compute-classpath.sh
and add the below line for the following script.

CLASSPATH="$CLASSPATH:$PATH_TO_mysql-connector-java-5.1.10.jar

<小时>

如何将数据从 HIVE 检索到 SPARK....

---

How to retrieve the data from HIVE to SPARK....

第一步:
通过以下命令启动所有守护进程....

Step 1:
Start all deamons by the following command....

start-all.sh

第 2 步:
通过以下命令启动 hive thrift server 2....

Step 2:
Start hive thrift server 2 by the following command....

hive --service hiveserver2 & 

第 3 步:
通过以下命令启动spark服务器....

Step 3:
Start spark server by the following command....

start-spark.sh 

最后通过检查以下命令来检查这些是否已启动....

And finally check whether these are started or not by checking with the following command....

RunJar 
ResourceManager 
Master 
NameNode 
SecondaryNameNode 
Worker 
Jps 
JobHistoryServer 
DataNode 
NodeManager

第 4 步:
通过以下命令启动master....

Step 4:
Start the master by the following command....

./sbin/start-master.sh 

要停止 master 使用以下命令.....

To stop the master use the below command.....

./sbin/stop-master.sh

第 5 步:
打开一个新终端....
通过以下路径开始直线......

Step 5:
Open a new terminal....
Start the beeline by the following path....

hadoop@localhost:/usr/local/hadoop/hive/bin$ beeline 

在它要求输入之后...传递下面列出的输入....

After it asks for input... Pass the input which is listed below....

!connect jdbc:hive2://localhost:10000 hadoop "" org.apache.hive.jdbc.HiveDriver 

然后通过以下命令设置SPARK....
注意:将这些配置设置在一个 conf 文件中，所以不需要总是运行......

After that set the SPARK by the following commands....
Note:set these configurations on a conf file so no need to run always....

set spark.master=spark://localhost:7077; 
set hive.execution.engines=spark; 
set spark.executor.memory=2g; // set the memory depends on your server
set spark.serializer=org.apache.spark.serializer.kryoSerializer; 
set spark.io.compression.codec=org.apache.spark.io.LZFCompressionCodec; 

在它要求输入之后....传递要检索数据的查询....并打开浏览器并通过以下命令检查 URL localhost:8080 您可以看到正在运行的作业和已完成URL 中的职位....

After it asks for input.... Pass the Query which you want to retrieve the data.... and open a browser and check in the URL by the following command localhost:8080 You can see the Running Jobs and Completed Jobs in the URL....

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