spark的standlone模式安装和application 提交

spark的standlone模式安装

安装一个standlone模式的spark集群，这里是最基本的安装，并测试一下如何进行任务提交。
require：提前安装好jdk 1.7.0_80 ；scala 2.11.8
可以参考官网的说明：http://spark.apache.org/docs/latest/spark-standalone.html

1. 到spark的官网下载spark的安装包

http://spark.apache.org/downloads.html

spark-2.0.2-bin-hadoop2.7.tgz.tar

2. 解压缩

cd /home/hadoop/soft
tar -zxvf spark-2.0.2-bin-hadoop2.7.tgz.tar
ln -s /home/hadoop/soft/spark-2.0.2-bin-hadoop2.7 /usr/local/spark

3.配置环境变量

su - hadoop
vi ~/.bashrc

export SPARK_HOME="/usr/local/spark"
export PATH="$SPARK_HOME/bin:$SPARK_HOME/sbin:$PATH"

source ~/.bashrc
which spark-shell

4.修改spark的配置

进入spark配置目录进行配置：

cd /usr/local/spark/conf
cp log4j.properties.template log4j.properties  ##修改 log4j.rootCategory=WARN, console

cp spark-env.sh.template spark-env.sh

vi spark-env.sh ##设置spark的环境变量，进入spark-env.sh文件添加：

export SPARK_HOME=/usr/local/spark
export SCALA_HOME=/usr/local/scala

至此，Spark就已经安装好了

5. 运行spark：

Spark-Shell命令可以进入spark，可以使用Ctrl D组合键退出Shell：
Spark-Shell

hadoop@ubuntuServer01:~$ spark-shell 
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel).
16/12/08 16:44:41 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/12/08 16:44:44 WARN SparkContext: Use an existing SparkContext, some configuration may not take effect.
Spark context Web UI available at http://192.168.17.50:4040
Spark context available as 'sc' (master = local[*], app id = local-1481186684381).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _ / _ / _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_   version 2.0.2
      /_/
         
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_80)
Type in expressions to have them evaluated.
Type :help for more information.

scala> 

启动spark服务：
start-master.sh ##

hadoop@ubuntuServer01:~$ start-master.sh 
starting org.apache.spark.deploy.master.Master, logging to /usr/local/spark/logs/spark-hadoop-org.apache.spark.deploy.master.Master-1-ubuntuServer01.out
hadoop@ubuntuServer01:~$ jps
2630 Master
2683 Jps

这里我们启动了主结点，jps多了一个Master的spark进程。
如果主节点启动成功，master默认可以通过web访问:http://ubuntuServer01:8080,查看sparkMaster的UI。

图中所述的spark://ubuntuServer01:7077 就是从结点启动的参数。
spark的master节点HA可以通过zookeeper和Local File System两种方法实现，具体可以参考官方的文档 http://spark.apache.org/docs/latest/spark-standalone.html#high-availability。
启动spark的slave从节点
start-slave.sh spark://ubuntuServer01:7077

hadoop@ubuntuServer01:~$ start-slave.sh spark://ubuntuServer01:7077
starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-ubuntuServer01.out
hadoop@ubuntuServer01:~$ jps
2716 Worker
2765 Jps
2630 Master
hadoop@ubuntuServer01:~$ 

运行jps命令，发现多了一个spark的worker进程。UI页面上的workers列表中也多了一条记录。

6. 运行一个Application在spark集群上。

运行一个交互式的spark shell在spark集群中：通过如下命令行：
spark-shell --master spark://ubuntuServer01:7077

hadoop@ubuntuServer01:~$ spark-shell --master spark://ubuntuServer01:7077
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel).
16/12/08 17:51:01 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/12/08 17:51:05 WARN SparkContext: Use an existing SparkContext, some configuration may not take effect.
Spark context Web UI available at http://192.168.17.50:4040
Spark context available as 'sc' (master = spark://ubuntuServer01:7077, app id = app-20161208175104-0000).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _ / _ / _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_   version 2.0.2
      /_/
         
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_80)
Type in expressions to have them evaluated.
Type :help for more information.

scala> 

从运行日志中可以看到job的UI（Spark web UI）页面地址：http://192.168.17.50:4040
和application id "app-20161208175104-0000"，任务运行结束后，Spark web UI页面也会随之关闭。

使用spark-submit脚本执行一个spark任务：

spark-submit 
  --class org.apache.spark.examples.SparkPi 
  --master spark://ubuntuServer01:7077 
  --executor-memory 1G 
  --total-executor-cores 1 
  $SPARK_HOME/examples/jars/spark-examples_2.11-2.0.2.jar 
  10

使用spark-submit 提交 application可以参考spark的官方文档。
http://spark.apache.org/docs/latest/submitting-applications.html