中山网站建设外包内容营销
概述
环境是spark 3.2.4 hadoop版本 3.2.4，所以官网下载的包为 spark-3.2.4-bin-hadoop3.2.tgz
在具体安装部署之前，需要先下载Spark的安装包，进到 spark的官网，点击download按钮
使用Spark的时候一般都是需要和Hadoop交互的，所以需要下载带有Hadoop依赖的安装包
这个时候就需要选择Hadoop版本对应的Spark安装包
机器配置
注意：需要确保这几台机器上的基础环境是OK的，防火墙、免密码登录、还有JDK
因为这几台机器之前已经使用过了，基础环境都是配置过的，所以说在这就直接使用了
linux机器配置请参考此链接
| 机器ip | 机器名 |
|---|---|
| 10.32.xx.142 | hadoop01 |
| 10.32.xx.143 | hadoop02 |
| 10.32.xx.144 | hadoop03 |
| 10.32.xx.145 | hadoop04 |
standalone 配置
主节点
先在
hadoop01上进行配置
解压
# 解压
[root@hadoop01 soft]# tar -zxvf spark-3.2.4-bin-hadoop3.2.tgz[root@hadoop01 soft]# cd spark-3.2.4-bin-hadoop3.2
[root@hadoop01 spark-3.2.4-bin-hadoop3.2]# cd conf/
[root@hadoop01 conf]# ls
fairscheduler.xml.template log4j.properties.template metrics.properties.template spark-defaults.conf.template spark-env.sh.template workers.template
配置spark-env.sh及workers
spark 2.x版本 从节点叫
slaves.templatespark 3.x 这后叫workers.template，这个需要注意
# 重命名spark-env.sh.template
[root@hadoop01 conf]# mv spark-env.sh.template spark-env.sh
[root@hadoop01 conf]# vi spark-env.sh
# 在最后加入下面两句
export JAVA_HOME=/data/soft/jdk1.8
export SPARK_MASTER_HOST=hadoop01# 重命名workers.template
[root@hadoop01 conf]# mv workers.template workers
# 修改workers
[root@hadoop01 conf]# vi workers hadoop02
hadoop03
hadoop04
分发
将修改好配置的spark安装包 分发到其它worker节点，即上文所说的
hadoop02hadoop03hadoop04三台机器
# 将修改好配置的spark安装包 分发到其它worker节点
[root@hadoop01 soft]# scp -rq spark-3.2.4-bin-hadoop3.2 hadoop02:/data/soft/
[root@hadoop01 soft]# scp -rq spark-3.2.4-bin-hadoop3.2 hadoop03:/data/soft/
[root@hadoop01 soft]# scp -rq spark-3.2.4-bin-hadoop3.2 hadoop04:/data/soft/
启动集群
启动Spark集群
[root@hadoop01 spark-3.2.4-bin-hadoop3.2]# ls
bin conf data examples jars kubernetes LICENSE licenses NOTICE python R README.md RELEASE sbin yarn
[root@hadoop01 spark-3.2.4-bin-hadoop3.2]# sbin/start-all.sh
starting org.apache.spark.deploy.master.Master, logging to /data/soft/spark-3.2.4-bin-hadoop3.2/logs/spark-root-org.apache.spark.deploy.master.Master-1-hadoop01.out
hadoop04: starting org.apache.spark.deploy.worker.Worker, logging to /data/soft/spark-3.2.4-bin-hadoop3.2/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-hadoop04.out
hadoop03: starting org.apache.spark.deploy.worker.Worker, logging to /data/soft/spark-3.2.4-bin-hadoop3.2/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-hadoop03.out
hadoop02: starting org.apache.spark.deploy.worker.Worker, logging to /data/soft/spark-3.2.4-bin-hadoop3.2/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-hadoop02.out
验证
[root@hadoop01 spark-3.2.4-bin-hadoop3.2]# jps
10520 Master
12254 Jps[root@hadoop02 soft]# jps
4224 Worker
7132 Jps
还可以访问主节点的8080端口来查看集群信息
执行任务
验证结束后，跑个
spark任务来测试一下吧
[root@hadoop01 spark-3.2.4-bin-hadoop3.2]# bin/spark-submit --class org.apache.spark.examples.SparkPi --master spark://hadoop01:7077 examples/jars/spark-examples_2.12-3.2.4.jar 2
结束
这就是
Spark的独立集群