Spark之spark shell,MapPartition和Map的区别

2024年5月11日 220次阅读 来源: spark

前言：要学习spark程序开发，建议先学习spark-shell交互式学习，加深对spark程序开发的理解。spark-shell提供了一种学习API的简单方式，以及一个能够进行交互式分析数据的强大工具，可以使用scala编写（scala运行与Java虚拟机可以使用现有的Java库）或使用Python编写。

1.启动spark-shell

    spark-shell的本质是在后台调用了spark-submit脚本来启动应用程序的，在spark-shell中已经创建了一个名为sc的SparkContext对象，在4个CPU核运行spark-shell命令如下：

spark-shell --master local[4]

    如果指定Jar包路径，则命令如下：

spark-shell --master local[4] --jars xxx.jar,yyy,jar

    –master用来设置context将要连接并使用的资源主节点，master的值是standalone模式中spark的集群地址、yarn或mesos集群的URL，或是一个local地址

    –jars可以添加需要用到的jar包，通过逗号分隔来添加多个包。

2.加载text文件

    spark创建sc后，可以加载本地文件创建RDD，这里测试是加载spark自带的本地文件README.md，返回一个MapPartitionsRDD文件。

scala> val textFile = sc.textFile(“file:///opt/cloud/spark-2.1.1-bin-hadoop2.7/README.md”);
textFile: org.apache.spark.rdd.RDD[String] = file:///opt/cloud/spark-2.1.1-bin-hadoop2.7/README.md MapPartitionsRDD[9] at textFile at <console>:24

    加载HDFS文件和本地文件都是使用textFile，区别是添加前缀(hdfs://和file://)进行标识，从本地读取文件直接返回MapPartitionsRDD,而从HDFS读取的文件是先转成HadoopRDD，然后隐试转换成MapPartitionsRDD。想了解MapPartitions可以看这篇MapPartition和Map的区别。

3.简单RDD操作

    对于RDD可以执行Transformation返回新的RDD，也可以执行Action得到返回结果。ｆｉｒｓｔ命令返回文件第一行，ｃｏｕｎｔ命令返回文件所有行数。

scala> textFile.first();
res6: String = # Apache Spark

scala> textFile.count();
res7: Long = 104

　接下来进行transformation操作，使用ｆｉｌｔｅｒ命令从README.md文件中抽取出一个子集，返回一个新的FilteredRDD。

scala> val textFilter = textFile.filter(line=>line.contains("Spark"));
textFilter: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[16] at filter at <console>:26

　链接多个Transformation和Action，计算包括”Spark”字符串的行数。

scala> textFile.filter(line=>line.contains("Spark")).count();
res10: Long = 20

４.RDD应用的简单操作

　(1)计算文本中单词最多的一行的单词数

scala> textFile.map(line =>line.split(" ").size).reduce((a,b) => if (a > b) a else b);
res11: Int = 22

　先将每一行的单词使用空格进行拆分，并统计每一行的单词数，创建一个基于单词数的新RDD，然后对该RDD进行Reduce操作返回最大值。

　(2)统计单词

　词频统计WordCount是大数据处理最流行的入门程序之一，Spark可以很容易实现WordCount操作。

//这个过程返回的是一个(string,int)类型的键值对ShuffledRDD(y执行reduceByKey的时候需要进行Shuffle操作，返回的是一个Shuffle形式的RDD），最后用Collect聚合统计结果
scala> val wordCount = textFile.flatMap(line =>line.split(" ")).map(x => (x,1)).reduceByKey((a,b) => a+b);
wordCount: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[23] at reduceByKey at <console>:26
scala> wordCount.collect
[Stage 7:>                                                          (0 + 0)
[Stage 7:>                                                          (0 + 2)                                                                           
res12: Array[(String, Int)] = Array((package,1), (this,1), (Version"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version),1), (Because,1), (Python,2), (page](http://spark.apache.org/documentation.html).,1), (cluster.,1), (its,1), ([run,1), (general,3), (have,1), (pre-built,1), (YARN,,1), ([http://spark.apache.org/developer-tools.html](the,1), (changed,1), (locally,2), (sc.parallelize(1,1), (only,1), (locally.,1), (several,1), (This,2), (basic,1), (Configuration,1), (learning,,1), (documentation,3), (first,1), (graph,1), (Hive,2), (info,1), (["Specifying,1), ("yarn",1), ([params]`.,1), ([project,1), (prefer,1), (SparkPi,2), (<http://spark.apache.org/>,1), (engine,1), (version,1), (file,1), (documentation,,1), (MASTER,1), (example,3), (["Parallel,1), (ar...
//这里使用了占位符_，使表达式更为简洁，是Scala语音的特色，每个_代表一个参数。
scala> val wordCount2 = textFile.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_);
wordCount2: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[26] at reduceByKey at <console>:26
scala> wordCount2.collect
res14: Array[(String, Int)] = Array((package,1), (this,1), (Version"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version),1), (Because,1), (Python,2), (page](http://spark.apache.org/documentation.html).,1), (cluster.,1), (its,1), ([run,1), (general,3), (have,1), (pre-built,1), (YARN,,1), ([http://spark.apache.org/developer-tools.html](the,1), (changed,1), (locally,2), (sc.parallelize(1,1), (only,1), (locally.,1), (several,1), (This,2), (basic,1), (Configuration,1), (learning,,1), (documentation,3), (first,1), (graph,1), (Hive,2), (info,1), (["Specifying,1), ("yarn",1), ([params]`.,1), ([project,1), (prefer,1), (SparkPi,2), (<http://spark.apache.org/>,1), (engine,1), (version,1), (file,1), (documentation,,1), (MASTER,1), (example,3), (["Parallel,1), (ar...
//Spark默认不进行排序，如有需要排序输出，排序的时候将key和value互换，使用sortByKey方法指定升序（true）和降序（false）
scala> val wordCount3 = textFile.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).map(x=>(x._2,x._1)).sortByKey(false).map(x=>(x._2,x._1));
wordCount3: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[34] at map at <console>:26

scala> wordCount3.collect
res15: Array[(String, Int)] = Array(("",71), (the,24), (to,17), (Spark,16), (for,12), (##,9), (and,9), (a,8), (can,7), (run,7), (on,7), (is,6), (in,6), (using,5), (of,5), (build,4), (Please,4), (with,4), (also,4), (if,4), (including,4), (an,4), (You,4), (you,4), (general,3), (documentation,3), (example,3), (how,3), (one,3), (For,3), (use,3), (or,3), (see,3), (Hadoop,3), (Python,2), (locally,2), (This,2), (Hive,2), (SparkPi,2), (refer,2), (Interactive,2), (Scala,2), (detailed,2), (return,2), (Shell,2), (class,2), (Python,,2), (set,2), (building,2), (SQL,2), (guidance,2), (cluster,2), (shell:,2), (supports,2), (particular,2), (following,2), (which,2), (should,2), (To,2), (be,2), (do,2), (./bin/run-example,2), (It,2), (1000:,2), (tests,2), (examples,2), (at,2), (`examples`,2), (that,2), (H...

5.RDD缓存使用RDD的cache()方法

 

    原文作者：spark
    原文地址: https://www.cnblogs.com/schoolbag/p/9635615.html
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