一、搭建1、解压

tar -zxvf spark-3.0.0-bin-hadoop3.2.tgz -C /opt/modulecd /opt/module mv spark-3.0.0-bin-hadoop3.2 spark-local

2、local环境

bin/spark-shell

1、本地提交作业

bin/spark-submit \--class org.apache.spark.examples.SparkPi \--master local[2] \./examples/jars/spark-examples_2.12-3.0.0.jar \10

3、Standalone环境1、 修改slaves.template文件名为slaves

bigdata

2、修改spark-env.sh.template文件名为spark-env.sh

export JAVA_HOME=/opt/module/jdk1.8.0_212SPARK_MASTER_HOST=bigdataSPARK_MASTER_PORT=7077

3、启动集群

sbin/start-all.sh

4、查看UI界面

5、提交作业

bin/spark-submit \--class org.apache.spark.examples.SparkPi \--master spark://bigdata:7077 \./examples/jars/spark-examples_2.12-3.0.0.jar \10

6、配置历史服务

1) 修改spark-defaults.conf.template文件名为spark-defaults.conf

mv spark-defaults.conf.template spark-defaults.conf

2) 修改spark-default.conf文件，配置日志存储路径

spark.eventLog.enabled     truespark.eventLog.dir        hdfs://linux1:8020/directory

注意：需要启动hadoop集群，HDFS上的directory目录需要提前存在。

sbin/start-dfs.shhadoop fs -mkdir /directory

3) 修改spark-env.sh文件, 添加日志配置

export SPARK_HISTORY_OPTS="-Dspark.history.ui.port=18080 -Dspark.history.fs.logDirectory=hdfs://linux1:8020/directory -Dspark.history.retainedApplications=30"

4）启动集群和历史服务

sbin/start-all.shsbin/start-history-server.sh

5）提交任务

bin/spark-submit \--class org.apache.spark.examples.SparkPi \--master spark://bigdata:7077 \./examples/jars/spark-examples_2.12-3.0.0.jar \10

6) 查看历史服务：

7) 配置高可用

条件：停止spark和启动zk

vim park-env.sh注释如下内容：#SPARK_MASTER_HOST=bigdata#SPARK_MASTER_PORT=7077添加如下内容:#Master监控页面默认访问端口为8080，但是可能会和Zookeeper冲突，所以改成8989，也可以自定义，访问UI监控页面时请注意SPARK_MASTER_WEBUI_PORT=8989export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=bigdata -Dspark.deploy.zookeeper.dir=/spark"

再启动集群

sbin/start-all.sh sbin/start-master.sh 

提交job

bin/spark-submit \--class org.apache.spark.examples.SparkPi \--master spark://bigdata:7077 \./examples/jars/spark-examples_2.12-3.0.0.jar \10

4、yarn模式1、修改yarn-site.xml

<!--是否启动一个线程检查每个任务正使用的物理内存量，如果任务超出分配值，则直接将其杀掉，默认是true --><property>     <name>yarn.nodemanager.pmem-check-enabled</name>     <value>false</value></property><!--是否启动一个线程检查每个任务正使用的虚拟内存量，如果任务超出分配值，则直接将其杀掉，默认是true --><property>     <name>yarn.nodemanager.vmem-check-enabled</name>     <value>false</value></property>

2、修改spark-env.sh

cd /opt/module/spark-local/confmv spark-env.sh.template spark-env.shexport JAVA_HOME=/opt/module/jdk1.8.0_212YARN_CONF_DIR=/opt/module/hadoop-3.1.3/etc/hadoop

3、启动hdfs和yarn集群4、提交job

bin/spark-submit \--class org.apache.spark.examples.SparkPi \--master yarn \--deploy-mode cluster \./examples/jars/spark-examples_2.12-3.0.0.jar \10

5、配置历史服务器

cp spark-defaults.conf.template spark-defaults.confspark.eventLog.enabled          truespark.eventLog.dir               hdfs://bigdata:9820/spark-directory

注意：需要启动hadoop集群，HDFS上的目录需要提前存在。

 hadoop fs -mkdir /spark-directory

6、 修改spark-env.sh文件, 添加日志配置

export SPARK_HISTORY_OPTS="-Dspark.history.ui.port=18080 -Dspark.history.fs.logDirectory=hdfs://bigdata:9820/spark-directory-Dspark.history.retainedApplications=30"

7、修改spark-defaults.conf

spark.yarn.historyServer.address=bigdata:18080spark.history.ui.port=18080

8、重新提交job

bin/spark-submit \--class org.apache.spark.examples.SparkPi \--master yarn \--deploy-mode client \./examples/jars/spark-examples_2.12-3.0.0.jar \10

9、页面查看

bigdata:8088

10、配置高可用

vim /opt/module/spark-local/conf/spark-env.sh

SPARK_MASTER_WEBUI_PORT=8989export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=bigdata -Dspark.deploy.zookeeper.dir=/spark"

cp slaves.template slavesvim slavesbigdata

注意：启动spark前，先启动zk，hdfs和yarn

5、启动脚本

vim  /home/bigdata/bin/mysparkservices.sh

#!/bin/bashif [ $# -lt 1 ]then   echo "Input Args Error....."  exitfifor i in bigdatadocase $1 instart)  echo "==================START $i Spark集群==================="  ssh $i /opt/module/spark-local/sbin/start-all.sh  echo "==================START $i Spark历史服务器==================="  ssh $i /opt/module/spark-local/sbin/start-history-server.sh   echo "==================START $i Spark的thriftserver的hive2==================="  ssh $i /opt/module/spark-local/sbin/start-thriftserver.sh;;stop)  echo "==================STOP $i Spark集群==================="  ssh $i /opt/module/spark-local/sbin/stop-all.sh  echo "==================STOP $i Spark历史服务器==================="  ssh $i /opt/module/spark-local/sbin/stop-history-server.sh  echo "==================STOP $i Spark的thriftserver的hive2==================="  ssh $i /opt/module/spark-local/sbin/stop-thriftserver.sh;;*) echo "Input Args Error....." exit;;  esacdone

#授权chmod +x mysparkservices.sh#启动sh mysparkservices.sh start#关闭sh mysparkservices.sh stop

6、hive on saprk1、环境准备启动hive metastorehive --service metastore 2>&1 >> /opt/module/hive/logs/metastore.log &启动spark thriftserver注意：相当于启动hive2sh /opt/module/spark-local/sbin/start-thriftserver.sh2、搭建1、复制hive-site.xml到Spark的conf目录

cp /opt/module/hive/conf/hive-site.xml  /opt/module/spark-local/conf编辑Spark的conf目录下的hive-site.xml配置文件，开启动态分区vim /opt/module/spark-local/conf/hive-site.xml  增加以下属性<property>    <name>hive.exec.dynamic.partition.mode</name>    <value>nonstrict</value></property>

2、在/opt/module/spark/jars目录下增加mysql驱动和lzo依赖

cp /opt/module/hive/lib/mysql-connector-java-5.1.37.jar cp /opt/module/hadoop-3.1.3/share/hadoop/common/hadoop-lzo-0.4.20.jar /opt/module/spark/jars/

3、配置spark-default.conf

#指定Spark master为yarnspark.master=yarn#是否记录Spark任务日志spark.eventLog.enabled=true#Spark任务日志的存储路径spark.eventLog.dir=hdfs://bigdata:9820/spark_historylog#Spark历史服务器地址spark.yarn.historyServer.address=bigdata:18080#Spark历史服务器读取历史任务日志的路径spark.history.fs.logDirectory=hdfs://bigdata:9820/spark_historylog#开启Spark-sql自适应优化spark.sql.adaptive.enabled=true#开启Spark-sql中Reduce阶段分区数自适应spark.sql.adaptive.coalescePartitions.enabled=true#使用Hive提供的Parquet文件的序列化和反序列化工具，以兼容Hivespark.sql.hive.convertMetastoreParquet=false#使用老版的Parquet文件格式，以兼容Hivespark.sql.parquet.writeLegacyFormat=true#解决SPARK-21725问题spark.hadoop.fs.hdfs.impl.disable.cache=true#降低Spark-sql中类型检查级别，兼容Hivespark.sql.storeAssignmentPolicy=LEGACY

4、配置spark-env.sh

YARN_CONF_DIR=/opt/module/hadoop-3.1.3/etc/hadoop

5、增加ApplicationMaster资源比例

vim /opt/module/hadoop-3.1.3/etc/hadoop/capacity-scheduler.xml<property>    <name>yarn.scheduler.capacity.maximum-am-resource-percent</name>    <value>0.8</value></property

6、启动集群

sh /home/bigdata/bin/mysparkservices.sh start