Spark API编程动手实战-02-以集群模式进行Spark API实战textFile、cache、count

本文主要是介绍Spark API编程动手实战-02-以集群模式进行Spark API实战textFile、cache、count，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

操作HDFS：先要保证HDFS启动了：

启动spark集群：

以spark-shell运行在spark集群上：

查看下之前上传到HDFS上的”LICENSE.txt“文件：

用spark读取这个文件：

使用count统计该文件的行数：

我们可以看到count 耗时为0.239708s

对该RDD进行cache操作并执行count使得缓存生效：

执行count结果为：

此时耗时为0.21132s

再执行count操作：

此时耗时为0.029580s，这时因为我们自己基于cache后的数据进行操作的。

接着我们对上面的rdd进行wordcount操作：

通过saveAsTextFile把数据存到HDFS中：

我们通过web控制台查看下运行结果：

我们通过命令行看下part-00000和part-00001内容：

[spark@S1PA222 ~]$ hadoop fs -cat /data/resultLicenseWordCount/part-00000
15/01/22 13:51:32 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
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[spark@S1PA222 ~]$ hadoop fs -cat /data/resultLicenseWordCount/part-00001

15/01/22 13:52:29 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
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[spark@S1PA222 ~]$