EMR 配置 Hive on Spark
Hive3 on spark 集成
前置条件
hadoop yarn环境正常
oracle jdk 1.8版本
1、spark2 下载准备
https://archive.apache.org/dist/spark/spark-2.4.5/spark-2.4.5-bin-without-hadoop.tgz
解压到opt目录
hive3环境配置
export SPARK_HOME=/opt/spark-2.4.5-bin-without-hadoop
export SPARK_CONF_DIR=/opt/spark-2.4.5-bin-without-hadoop/conf
export SPARK_DIST_CLASSPATH=$HADOOP_HOME/etc/hadoop/*:$HADOOP_HOME/share/hadoop/common/lib/*:$HADOOP_HOME/share/hadoop/common/*:$HADOOP_HOME/share/hadoop/hdfs/*:$HADOOP_HOME/share/hadoop/hdfs/lib/*:$HADOOP_HOME/share/hadoop/hdfs/*:$HADOOP_HOME/share/hadoop/yarn/lib/*:$HADOOP_HOME/share/hadoop/yarn/*:$HADOOP_HOME/share/hadoop/mapreduce/lib/*:$HADOOP_HOME/share/hadoop/mapreduce/*:$HADOOP_HOME/share/hadoop/tools/lib/*
hive-site.xml
<!-- Spark2 依赖库位置，在YARN 上运行的任务需要从HDFS 中查找依赖jar 文件 -->
<property>
<name>spark.yarn.jars</name>
<value>${fs.defaultFS}/spark-jars/*</value>
</property>
<!-- Hive3 和Spark2 连接超时时间 -->
<property>
<name>hive.spark.client.connect.timeout</name>
<value>30000ms</value>
</property>
<property>
<name>spark.executor.cores</name>
<value>1</value>
</property>
<property>
<name>spark.executor.memory</name>
<value>1g</value>
</property>
<property>
<name>spark.driver.memory</name>
<value>1g</value>
</property>
<property>
<name>spark.yarn.driver.memoryOverhead</name>
<value>102</value>
</property>
<property>
<name>spark.shuffle.service.enabled</name>
<value>true</value>
</property>
<property>
<name>spark.eventLog.enabled</name>
<value>true</value>
</property
spark-defaults.conf
spark.master=yarn
spark.eventLog.dir=hdfs:///user/spark/applicationHistory
spark.eventLog.enabled=true
spark.executor.memory=1g
spark.driver.memory=1g
spark 依赖库配置
cd /opt/spark-2.4.5-bin-without-hadoop/jars
mv orc-core-1.5.5-nohive.jar orc-core-1.5.5-nohive.jar.bak
//上传jar包到hdfs
hdfs dfs -rm -r -f /spark-jars
hdfs dfs -mkdir /spark-jars
cd /opt/spark-2.4.5-bin-without-hadoop/jars
hdfs dfs -put * /spark-jars
hdfs dfs -ls /spark-jars
hdfs dfs -mkdir /user/spark
hdfs dfs -mkdir /user/spark/applicationHistory
//拷贝jar包到hive
cp scala-compiler-2.11.12.jar scala-library-2.11.12.jar scala-reflect-2.11.12.jar spark-core_2.11-2.4.5.jar spark-network-common_2.11-2.4.5.jar spark-unsafe_2.11-2.4.5.jar spark-yarn_2.11-2.4.5.jar /opt/dtstack/Hive/hive_pkg/lib/
连接beeline进行测试
cd $HIVE_HOME
./bin/beeline -u 'jdbc:hive2://emr1:10000/default'
set hive.execution.engine=spark;
insert into test1 values(1);
Ps:
问题1:
java.lang.IllegalArgumentException: Required executor memory (1024), overhead (384 MB), and PySpark memory (0 MB) is above the max threshold (1024 MB) of this cluster
解决问题1:
Hive-site:
<property>
<name>spark.yarn.executor.memoryOverhead</name>
<value>4096</value>
</property>
