【原創 Hadoop&Spark 動手實踐 5】Spark 基礎入門，集羣搭建以及Spark Shell

Spark 基礎入門，集羣搭建以及Spark Shell

主要藉助Spark基礎的PPT，再加上實際的動手操做來增強概念的理解和實踐。

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Spark 安裝部署

 

 

 

 

 

 

 

理論已經瞭解的差很少了，接下來是實際動手實驗：

練習1 利用Spark Shell（本機模式） 完成WordCount

 

spark-shell 進行Spark-shell本機模式

 

第一步：經過文件方式導入數據

scala> val rdd1 = sc.textFile("file:///tmp/wordcount.txt")
rdd1: org.apache.spark.rdd.RDD[String] = file:///tmp/wordcount.txt MapPartitionsRDD[3] at textFile at <console>:24

scala> rdd1.count
res1: Long = 3

 

第二步：利用flatmap(_.split(" ")) 進行分詞操做

scala> val rdd2 = rdd1.flatMap(_.split(" "))
rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[4] at flatMap at <console>:26

scala> rdd2.count
res2: Long = 8

scala> rdd2.take
take takeAsync takeOrdered takeSample

scala> rdd2.take(8)
res3: Array[String] = Array(hello, world, spark, world, hello, spark, hadoop, great)

 

第三步：利用map 轉化爲KV的形式

scala> val kvrdd1 = rdd2.map(x => (x,1))
kvrdd1: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[5] at map at <console>:28

scala> kvrdd1.count
res4: Long = 8

scala> kvrdd1.take(8)
res5: Array[(String, Int)] = Array((hello,1), (world,1), (spark,1), (world,1), (hello,1), (spark,1), (hadoop,1), (great,1))

 

第四步：把KV的map進行ReduceByKey操做

scala> val resultRdd1 = kvrdd1.reduceByKey(_+_)
resultRdd1: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[6] at reduceByKey at <console>:30

scala> resultRdd1.count
res6: Long = 5

scala> resultRdd1.take(5)
res7: Array[(String, Int)] = Array((hello,2), (world,2), (spark,2), (hadoop,1), (great,1))

 

第五步：將結果保持到文件之中

scala> resultRdd1.saveAsTextFile("file:///tmp/output1")

 

練習2 利用Spark Shell（Yarn Client模式） 完成WordCount

 

spark-shell --master yarn-client 啓動Spark-shell Yarn Client模式

 

第一步：經過文件方式導入數據

scala> val rdd1 = sc.textFile("hdfs:///input/wordcount.txt")
rdd1: org.apache.spark.rdd.RDD[String] = hdfs:///input/wordcount.txt MapPartitionsRDD[1] at textFile at <console>:24

scala> rdd1.count
res0: Long = 260

scala> rdd1.take(100)
res1: Array[String] = Array(HDFS Users Guide, "", HDFS Users Guide, Purpose, Overview, Prerequisites, Web Interface, Shell Commands, DFSAdmin Command, Secondary NameNode, Checkpoint Node, Backup Node, Import Checkpoint, Balancer, Rack Awareness, Safemode, fsck, fetchdt, Recovery Mode, Upgrade and Rollback, DataNode Hot Swap Drive, File Permissions and Security, Scalability, Related Documentation, Purpose, "", This document is a starting point for users working with Hadoop Distributed File System (HDFS) either as a part of a Hadoop cluster or as a stand-alone general purpose distributed file system. While HDFS is designed to 「just work」 in many environments, a working knowledge of HDFS helps greatly with configuration improvements and diagnostics on a specific cluster., "", Overview, "",...

 

第二步：利用flatmap(_.split(" ")) 進行分詞操做

scala> val rdd2 = rdd1.flatMap(_.split(" "))
rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at flatMap at <console>:26

scala> rdd2.count
res2: Long = 3687

scala> rdd2.take(100)
res3: Array[String] = Array(HDFS, Users, Guide, "", HDFS, Users, Guide, Purpose, Overview, Prerequisites, Web, Interface, Shell, Commands, DFSAdmin, Command, Secondary, NameNode, Checkpoint, Node, Backup, Node, Import, Checkpoint, Balancer, Rack, Awareness, Safemode, fsck, fetchdt, Recovery, Mode, Upgrade, and, Rollback, DataNode, Hot, Swap, Drive, File, Permissions, and, Security, Scalability, Related, Documentation, Purpose, "", This, document, is, a, starting, point, for, users, working, with, Hadoop, Distributed, File, System, (HDFS), either, as, a, part, of, a, Hadoop, cluster, or, as, a, stand-alone, general, purpose, distributed, file, system., While, HDFS, is, designed, to, 「just, work」, in, many, environments,, a, working, knowledge, of, HDFS, helps, greatly, with, configuratio...

 

 

第三步：利用map 轉化爲KV的形式

scala> val kvrdd1 = rdd2.map(x => (x,1))
kvrdd1: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[3] at map at <console>:28

scala> kvrdd1.count
res4: Long = 3687

scala> kvrdd1.take(100)
res5: Array[(String, Int)] = Array((HDFS,1), (Users,1), (Guide,1), ("",1), (HDFS,1), (Users,1), (Guide,1), (Purpose,1), (Overview,1), (Prerequisites,1), (Web,1), (Interface,1), (Shell,1), (Commands,1), (DFSAdmin,1), (Command,1), (Secondary,1), (NameNode,1), (Checkpoint,1), (Node,1), (Backup,1), (Node,1), (Import,1), (Checkpoint,1), (Balancer,1), (Rack,1), (Awareness,1), (Safemode,1), (fsck,1), (fetchdt,1), (Recovery,1), (Mode,1), (Upgrade,1), (and,1), (Rollback,1), (DataNode,1), (Hot,1), (Swap,1), (Drive,1), (File,1), (Permissions,1), (and,1), (Security,1), (Scalability,1), (Related,1), (Documentation,1), (Purpose,1), ("",1), (This,1), (document,1), (is,1), (a,1), (starting,1), (point,1), (for,1), (users,1), (working,1), (with,1), (Hadoop,1), (Distributed,1), (File,1), (System,1), ((HDF...

 

 

第四步：把KV的map進行ReduceByKey操做

scala> var resultRdd1 = kvrdd1.reduce
reduce reduceByKey reduceByKeyLocally

scala> var resultRdd1 = kvrdd1.reduceByKey
reduceByKey reduceByKeyLocally

scala> var resultRdd1 = kvrdd1.reduceByKey(_+_)
resultRdd1: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:30

scala> resultRdd1.count
res6: Long = 1084

scala> resultRdd1.take(100)
res7: Array[(String, Int)] = Array((Because,1), (-reconfig,2), (guide,4), (under-replicated,1), (blocks,5), (maintained,1), (responsibility,1), (filled,1), (order,5), ([key-value,1), (prematurely,1), (cluster:,1), (type,1), (behind,1), (However,,1), (competing,1), (been,2), (begins,1), (up-to-date,3), (Permissions,3), (browse,1), (List:,1), (improved,1), (Balancer,2), (fine.,1), (over,1), (dfs.hosts,,2), (any,7), (connect,1), (select,2), (version,7), (disks.,1), (file,33), (documentation,,1), (file.,7), (performs,2), (million,2), (RAM,1), (are,27), ((data,1), (supported.,1), (consists,1), (existed,1), (brief,2), (overwrites,1), (safely,1), (Guide:,1), (Safemode,6), (Only,1), (Currently,1), (first-time,1), (dfs.namenode.name.dir,1), (thus,2), (salient,1), (query,1), (page).,1), (status,5...

 

 

第五步：將結果保持到HDFS文件之中

scala> resultRdd1.saveAsTextFile("hdfs:///output/wordcount1")

 

localhost:tmp jonsonli$ hadoop fs -ls /output/wordcount1
17/05/13 17:49:28 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 3 items
-rw-r--r-- 1 jonsonli supergroup 0 2017-05-13 17:47 /output/wordcount1/_SUCCESS
-rw-r--r-- 1 jonsonli supergroup 6562 2017-05-13 17:47 /output/wordcount1/part-00000
-rw-r--r-- 1 jonsonli supergroup 6946 2017-05-13 17:47 /output/wordcount1/part-00001