Spark之命令

1.spark运行模式有4种：

a.local 多有用测试,

b. standalone：spark 集群模式，使用spark自己的调度方式。

c. Yarn: 对MapreduceV1升级的经典版本，支持spark。

d.Mesos:类似Yarn的资源调度框架，提供了有效的、跨分布式应用或框架的资源隔离和共享，可以运行hadoop、spark等框架

2.spark local 模式(shell )

Spark local模式(shell运行)
    windows：
       执行spark-shell.cmd

    Linux：
       执行spark-shell

     参数指定：

    ? MASTER=local[4]  ADD_JARS=code.jar ./spark-shell

    ? MASTER=spark://host:port

    ? 指定executor内存：export SPARK_MEM=25g

3. spark standalone 模式

Spark standalone加载数据(shell运行spark-shell)

     读取本地文件：
     var file = sc.textFile("/root/test.txt").collect

     加载远程hdfs文件：
     var files = sc.textFile("hdfs://192.168.2.2:8020/user/superman").collect
     (读取hdfs数据时使用的还是inputFormat)

       standalone WordCount
              sc.textFile("/root/test.txt").flatMap(_.split("\\t")).map(x=>(x,1))
.reduceByKey(_+_).collect

.csharpcode, .csharpcode pre
{
font-size: small;
color: black;
font-family: consolas, "Courier New", courier, monospace;
background-color: #ffffff;
/*white-space: pre;*/
}
.csharpcode pre { margin: 0em; }
.csharpcode .rem { color: #008000; }
.csharpcode .kwrd { color: #0000ff; }
.csharpcode .str { color: #006080; }
.csharpcode .op { color: #0000c0; }
.csharpcode .preproc { color: #cc6633; }
.csharpcode .asp { background-color: #ffff00; }
.csharpcode .html { color: #800000; }
.csharpcode .attr { color: #ff0000; }
.csharpcode .alt
{
background-color: #f4f4f4;
width: 100%;
margin: 0em;
}
.csharpcode .lnum { color: #606060; }

Spark standalone保存结果集数据

     保存数据到本地：
     result.saveAsTextFile("/root/tmp")   (tmp文件夹必须不存在)

     保存数据到远程hdfs文件：
     result.saveAsTextFile("hdfs://crxy165:8020/user/superman/tmp")
     (tmp文件夹必须不存在)

      设置输出结果集文件数量：
       result.repartition(1).saveAsTextFile

      任务提交
       spark-submit   (推荐)
       其它也可?，如sbt run, java -jar  等等

.csharpcode, .csharpcode pre
{
font-size: small;
color: black;
font-family: consolas, "Courier New", courier, monospace;
background-color: #ffffff;
/*white-space: pre;*/
}
.csharpcode pre { margin: 0em; }
.csharpcode .rem { color: #008000; }
.csharpcode .kwrd { color: #0000ff; }
.csharpcode .str { color: #006080; }
.csharpcode .op { color: #0000c0; }
.csharpcode .preproc { color: #cc6633; }
.csharpcode .asp { background-color: #ffff00; }
.csharpcode .html { color: #800000; }
.csharpcode .attr { color: #ff0000; }
.csharpcode .alt
{
background-color: #f4f4f4;
width: 100%;
margin: 0em;
}
.csharpcode .lnum { color: #606060; }

.csharpcode, .csharpcode pre
{
font-size: small;
color: black;
font-family: consolas, "Courier New", courier, monospace;
background-color: #ffffff;
/*white-space: pre;*/
}
.csharpcode pre { margin: 0em; }
.csharpcode .rem { color: #008000; }
.csharpcode .kwrd { color: #0000ff; }
.csharpcode .str { color: #006080; }
.csharpcode .op { color: #0000c0; }
.csharpcode .preproc { color: #cc6633; }
.csharpcode .asp { background-color: #ffff00; }
.csharpcode .html { color: #800000; }
.csharpcode .attr { color: #ff0000; }
.csharpcode .alt
{
background-color: #f4f4f4;
width: 100%;
margin: 0em;
}
.csharpcode .lnum { color: #606060; }

4.RDD，可恢复分布式数据集，弹性分布式数据集