spark 2.0.0集群安装与hive on spark配置

1. 环境准备:

JDK1.8

hive 2.3.4

hadoop 2.7.3

hbase 1.3.3

scala 2.11.12

mysql5.7

2. 下载spark2.0.0

cd /home/worksapce/software
wget https://archive.apache.org/dist/spark/spark-2.0.0/spark-2.0.0-bin-hadoop2.7.tgz
tar -xzvf spark-2.0.0-bin-hadoop2.7.tgz
mv spark-2.0.0-bin-hadoop2.7 spark-2.0.0

3. 配置系统环境变量

vim /etc/profile

末尾添加

#spark
export SPARK_HOME=/home/workspace/software/spark-2.0.0
export PATH=:$PATH:$SPARK_HOME/bin

4. 配置spark-env.sh

cd /home/workspace/software/spark-2.0.0/conf
cp spark-env.sh.template spark-env.sh
vim spark-env.sh

末尾添加:

export JAVA_HOME=/usr/java/jdk1.8.0_172-amd64
export SCALA_HOME=/home/workspace/software/scala-2.11.12
export HADOOP_HOME=/home/workspace/hadoop-2.7.3
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export YARN_CONF_DIR=$HADOOP_HOME/etc/hadoop

export SPARK_HOME=/home/workspace/software/spark-2.0.0
export SPARK_DIST_CLASSPATH=$(/home/workspace/hadoop-2.7.3/bin/hadoop classpath)
export SPARK_LIBRARY_PATH=$SPARK_HOME/lib
export SPARK_LAUNCH_WITH_SCALA=0

export SPARK_WORKER_DIR=$SPARK_HOME/work
export SPARK_LOG_DIR=$SPARK_HOME/logs
export SPARK_PID_DIR=$SPARK_HOME/run　

export SPARK_MASTER_IP=192.168.1.101
export SPARK_MASTER_HOST=192.168.1.101
export SPARK_MASTER_WEBUI_PORT=18080
export SPARK_MASTER_PORT=7077

export SPARK_LOCAL_IP=192.168.1.101

export SPARK_WORKER_CORES=4
export SPARK_WORKER_PORT=7078

export SPARK_WORKER_MEMORY=4g
export SPARK_DRIVER_MEMORY=4g
export SPARK_EXECUTOR_MEMORY=4g

5. 配置spark-defaults.conf

cd /home/workspace/software/spark-2.0.0/conf
cp spark-defaults.conf.template spark-defaults.conf
vim spark-defaults.conf

末尾添加

spark.master                     spark://192.168.1.101:7077
spark.eventLog.enabled           true
spark.eventLog.dir               hdfs://192.168.1.101:9000/spark-log
spark.serializer                 org.apache.spark.serializer.KryoSerializer
spark.executor.memory            4g
spark.driver.memory              4g
spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"

6. 配置slaves

cd /home/workspace/software/spark-2.0.0/conf
cp slaves.template slaves
vim slaves

末尾添加

192.168.1.101
192.168.1.102
192.168.1.103

7. 创建相关目录（在spark-env.sh中定义)

hdfs dfs  -mkdir  -p   /spark-log
hdfs dfs  -chmod  777  /spark-log
mkdir -p  $SPARK_HOME/work  $SPARK_HOME/logs  $SPARK_HOME/run
mkdir -p  $HIVE_HOME/logs

8.修改hive-site.xml

vim $HIVE_HOME/conf/hive-site.xml

把文件内容修改为

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>hive.metastore.schema.verification</name>
        <value>false</value>
    </property>
    <property>
        <name>hive.metastore.warehouse.dir</name>
        <value>/hive/warehouse</value>
        <description>location of default database for the warehouse</description>
    </property>
    <property>
        <name>hive.exec.scratchdir</name>
        <value>/hive/tmp</value>
        <description>Scratch space for Hive jobs</description>
    </property>
    <property>
        <name>hive.querylog.location</name>
        <value>/hive/log</value>
    </property>
    <property>
        <name>hive.metastore.uris</name>
        <value>thrift://192.168.1.103:9083</value>
    </property>
    <!--hive server2 settings-->
    <property>
        <name>hive.server2.thrift.bind.host</name>
        <value>192.168.1.103</value>
    </property>
    <property>
        <name>hive.server2.thrift.port</name>
        <value>10000</value>
    </property>
    <property>
        <name>hive.server2.webui.host</name>
        <value>192.168.1.103</value>
    </property>
    <property>
        <name>hive.server2.webui.host.port</name>
        <value>10002</value>
    </property>
    <property>
        <name>hive.server2.long.polling.timeout</name>
        <value>5000</value>
    </property>
    <property>
        <name>hive.server2.enable.doAs</name>
        <value>true</value>
    </property>
    <!--metadata database connection string settings-->
    <property>
        <name>javax.jdo.option.ConnectionURL</name>
        <value>jdbc:mysql://192.168.1.103:3308/hive?createDatabaseIfNotExist=true</value>
    </property>
    <property>
        <name>javax.jdo.option.ConnectionDriverName</name>
        <value>com.mysql.jdbc.Driver</value>
    </property>
    <property>
        <name>javax.jdo.option.ConnectionUserName</name>
        <value>hive</value>
    </property>
    <property>
        <name>javax.jdo.option.ConnectionPassword</name>
        <value>hive</value>
    </property>
    <property>
        <name>datanucleus.autoCreateSchema </name>
        <value>false</value>
        <description>creates necessary schema on a startup if one doesn't exist. set this to false, after creating it once</description>
    </property>
    <property>
        <name>datanucleus.fixedDatastore</name>
        <value>true</value>
    </property>
       <!-- hive on mr-->
    <!-- 
    <property>
        <name>mapred.job.tracker</name>
        <value>http://192.168.1.101:9001</value>
    </property>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property> 
    -->
    <!--hive on spark or spark on yarn -->
    <property>
        <name>hive.execution.engine</name>
        <value>spark</value>
    </property>
    <property>
        <name>spark.home</name>
        <value>/home/workspace/software/spark-2.0.0</value>
    </property>
    <property>
        <name>spark.master</name>
        <value>spark://192.168.1.101:7077</value>
        <!-- 或者yarn-cluster/yarn-client -->
    </property>
    <property>
        <name>spark.submit.deployMode</name>
        <value>client</value>
    </property>
    <property>
        <name>spark.eventLog.enabled</name>
        <value>true</value>
    </property>
    <property>
        <name>spark.eventLog.dir</name>
        <value>hdfs://192.168.1.101:9000/spark-log</value>
    </property>
    <property>
        <name>spark.serializer</name>
        <value>org.apache.spark.serializer.KryoSerializer</value>
    </property>
    <property>
        <name>spark.executor.memeory</name>
        <value>4g</value>
    </property>
    <property>
        <name>spark.driver.memeory</name>
        <value>4g</value>
    </property>
    <property>
        <name>spark.executor.extraJavaOptions</name>
        <value>-XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"</value>
    </property>
    <!--concurrency support-->
    <property>
        <name>hive.support.concurrency</name>
        <value>true</value>
        <description>Whether hive supports concurrency or not. A zookeeper instance must be up and running for the default hive lock manager to support read-write locks.</description>
    </property>
    <property>
        <name>hive.exec.dynamic.partition.mode</name>
        <value>nonstrict</value>
    </property>
        <!--transaction support-->
    <property>
        <name>hive.txn.manager</name>
        <value>org.apache.hadoop.hive.ql.lockmgr.DbTxnManager</value>
    </property>
    <property>
        <name>hive.compactor.initiator.on</name>
        <value>true</value>
    </property>
    <property>
        <name>hive.compactor.worker.threads</name>
        <value>1</value>
    </property>
    <property>
        <name>hive.stats.autogather</name>
        <value>true</value>
        <description>A flag to gather statistics automatically during the INSERT OVERWRITE command.</description>
    </property>
    <!--hive web interface settings, I think this is useless,so comment it-->
    <!-- 
    <property>
        <name>hive.hwi.listen.host</name>
        <value>192.168.1.131</value>
    </property>
    <property>
        <name>hive.hwi.listen.port</name>
        <value>9999</value>
    </property>
    <property>
        <name>hive.hwi.war.file</name>
        <value>lib/hive-hwi-2.1.1.war</value>
    </property>  
    -->
</configuration>

9. 拷贝hive-site.xml到spark/conf下

cp $HIVE_HOME/conf/hive-site.xml $SPARK_HOME/conf

10 分发到192.168.1.102，192.168.1.103

 cd /home/workspace/software/
 scp -r spark-2.0.0  192.168.1.102:/home/workspace/software
 scp -r spark-2.0.0  192.168.1.103:/home/workspace/software

修改102，103上的SPARK_LOCAL_IP值

vim /home/workspace/software/spark-2.0.0/conf/spark-env.sh

将SPARK_LOCAL_IP分别改为192.168.1.102，192.168.1.103

11 将mysql jar包复制到$SPARK_HOME/lib目录下（每台机器都要做)

cp $HIVE_HOME/lib/mysql-connector-java-5.1.47.jar $SPARK_HOME/lib

注：本例中之前已经安装好hive，如果没有，请到mysql官网网站下载对应的jdbc jar包

12. 启动spark集群

在spark master节点上（本例为192.168.1.101)执行下面语句

$SPARK_HOME/sbin/start-all.sh

192.168.1.101

192.168.1.102:

192.168.1.103:

浏览器打开http:192.168.1.101:18080

13.测试使用

[druid@palo101 apache-maven-3.6.0]$ hive
/tmp/druid
Logging initialized using configuration in file:/home/workspace/software/apache-hive-2.3.4/conf/hive-log4j2.properties Async: true
hive> use kylin_flat_db;
OK
Time taken: 1.794 seconds
hive> desc kylin_sales;
OK
trans_id                bigint                                      
part_dt                 date                    Order Date          
lstg_format_name        string                  Order Transaction Type
leaf_categ_id           bigint                  Category ID         
lstg_site_id            int                     Site ID             
slr_segment_cd          smallint                                    
price                   decimal(19,4)           Order Price         
item_count              bigint                  Number of Purchased Goods
seller_id               bigint                  Seller ID           
buyer_id                bigint                  Buyer ID            
ops_user_id             string                  System User ID      
ops_region              string                  System User Region  
Time taken: 0.579 seconds, Fetched: 12 row(s)
hive> select trans_id, sum(price) as total, count(seller_id) as cnt from kylin_sales group by trans_id order by cnt desc limit 10;
Query ID = druid_20190209000716_9676460c-1a76-456d-9bd6-b6f557d5e02c
Total jobs = 1
Launching Job 1 out of 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Spark Job = 72720bf1-750d-4f6f-bf9c-5cffa0e4c73b

Query Hive on Spark job[0] stages: [0, 1, 2]

Status: Running (Hive on Spark job[0])
--------------------------------------------------------------------------------------
          STAGES   ATTEMPT        STATUS  TOTAL  COMPLETED  RUNNING  PENDING  FAILED  
--------------------------------------------------------------------------------------
Stage-0 ........         0      FINISHED      1          1        0        0       0  
Stage-1 ........         0      FINISHED      1          1        0        0       0  
Stage-2 ........         0      FINISHED      1          1        0        0       0  
--------------------------------------------------------------------------------------
STAGES: 03/03    [==========================>>] 100%  ELAPSED TIME: 10.12 s    
--------------------------------------------------------------------------------------
Status: Finished successfully in 10.12 seconds
OK
8621    33.4547 1
384     15.4188 1
7608    88.6492 1
9166    40.4308 1
9215    63.5407 1
4551    59.2537 1
7041    79.8884 1
522     18.3204 1
5618    78.6241 1
9831    5.8088  1
Time taken: 21.788 seconds, Fetched: 10 row(s)
hive>

13 FAQ:

13.1 如果在使用过程中遇到类似下面的错误

Exception in thread "main" java.lang.NoSuchFieldError: SPARK_RPC_SERVER_ADDRESS

通过查看hive的日志文件（在/tmp/{user}/hive.log),这是因为默认使用的spark安装包是继承了hive的包，名字为spark-xxx-bin-hadoopxx.xx.tgz都是继承了hive的包，在hive on spark模式下，会出现冲突，解决办法有两个：
1）手动编译spark不包含hive的包，具体请参见本人的博文Spark2.0.0源码编译,编译指令为：

./make-distribution.sh  --name "hadoop2.7.3-without-hive"   --tgz  -Dhadoop.version=2.7.3    -Dscala-2.11    -Phadoop-2.7  -Pyarn      -DskipTests clean package

用编译出来的包来安装。

2）删除预编译包中hive的jar包，具体操作为：

cd $SPARK_HOME/jars
rm -f hive-*
rm -rf spark-hive_*
#删除下面6个文件
#    hive-beeline-1.2.1.spark2.jar
#    hive-cli-1.2.1.spark2.jar
#    hive-exec-1.2.1.spark2.jar
#    hive-jdbc-1.2.1.spark2.jar
#    hive-metastore-1.2.1.spark2.jar
#    spark-hive_2.11-2.0.0.jar
#    spark-hive-thriftserver_2.11-2.0.0.jar

注意：每台机器都要做.

13.2 如果出现类似下面的错误

Exception in thread "main" java.lang.NoClassDefFoundError: scala/collection/Iterable
        at org.apache.hadoop.hive.ql.optimizer.spark.SetSparkReducerParallelism.getSparkMemoryAndCores(SetSparkReducerParallelism.java:236)
        at org.apache.hadoop.hive.ql.optimizer.spark.SetSparkReducerParallelism.process(SetSparkReducerParallelism.java:173)
        at org.apache.hadoop.hive.ql.lib.DefaultRuleDispatcher.dispatch(DefaultRuleDispatcher.java:90)
        at org.apache.hadoop.hive.ql.lib.DefaultGraphWalker.dispatchAndReturn(DefaultGraphWalker.java:105)
        at org.apache.hadoop.hive.ql.lib.DefaultGraphWalker.dispatch(DefaultGraphWalker.java:89)
        at org.apache.hadoop.hive.ql.lib.PreOrderWalker.walk(PreOrderWalker.java:56)
        at org.apache.hadoop.hive.ql.lib.PreOrderWalker.walk(PreOrderWalker.java:61)
        at org.apache.hadoop.hive.ql.lib.PreOrderWalker.walk(PreOrderWalker.java:61)
        at org.apache.hadoop.hive.ql.lib.PreOrderWalker.walk(PreOrderWalker.java:61)
        at org.apache.hadoop.hive.ql.lib.DefaultGraphWalker.startWalking(DefaultGraphWalker.java:120)
        at org.apache.hadoop.hive.ql.parse.spark.SparkCompiler.runSetReducerParallelism(SparkCompiler.java:288)
        at org.apache.hadoop.hive.ql.parse.spark.SparkCompiler.optimizeOperatorPlan(SparkCompiler.java:122)
        at org.apache.hadoop.hive.ql.parse.TaskCompiler.compile(TaskCompiler.java:140)
        at org.apache.hadoop.hive.ql.parse.SemanticAnalyzer.analyzeInternal(SemanticAnalyzer.java:11273)
        at org.apache.hadoop.hive.ql.parse.CalcitePlanner.analyzeInternal(CalcitePlanner.java:286)
        at org.apache.hadoop.hive.ql.parse.BaseSemanticAnalyzer.analyze(BaseSemanticAnalyzer.java:258)
        at org.apache.hadoop.hive.ql.Driver.compile(Driver.java:512)
        at org.apache.hadoop.hive.ql.Driver.compileInternal(Driver.java:1317)
        at org.apache.hadoop.hive.ql.Driver.runInternal(Driver.java:1457)
        at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1237)
        at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1227)
        at org.apache.hadoop.hive.cli.CliDriver.processLocalCmd(CliDriver.java:233)
        at org.apache.hadoop.hive.cli.CliDriver.processCmd(CliDriver.java:184)
        at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:403)
        at org.apache.hadoop.hive.cli.CliDriver.executeDriver(CliDriver.java:821)
        at org.apache.hadoop.hive.cli.CliDriver.run(CliDriver.java:759)
        at org.apache.hadoop.hive.cli.CliDriver.main(CliDriver.java:686)
        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.hadoop.util.RunJar.run(RunJar.java:221)
        at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
Caused by: java.lang.ClassNotFoundException: scala.collection.Iterable
        at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
        at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:349)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:357)

这是因为hive无法加载spark的jar包，解决办法为：

$HIVE_HOME/bin/hive

在执行hive之前添加下面的语句，把spark的jar包添加到hive的class path中

SPARK_HOME=/home/workspace/software/spark-2.0.0
for f in ${SPARK_HOME}/jars/*.jar; do
      CLASSPATH=${CLASSPATH}:$f;
done

本人添加的位置为：

或者直接把$SPARK_HOME/jars/spark*复制到$HIVE_HOME/lib下，

cp $SPARK_HOME/jars/spark*   $HIVE_HOME/lib

个人感觉修改hive启动脚本更好一些。

14 参考资料

https://www.jianshu.com/p/a7f75b868568

相关阅读:
Java 开发环境配置
 kettle脚本定时任务不执行
 python 列表之队列
 tensorflow训练过程中内存溢出
 关于 numpy.array和list之间的转换
 vscode 小笔记
 【python2/3坑】从gensim的Word2Vec.load()的中文vector模型输出时显示unicode码
 WingIDE用法笔记
 numpy.ndarray类型的数组元素输出时，保留小数点后4位
 tensorboard 用法
原文地址：https://www.cnblogs.com/lenmom/p/10356643.html