- 1、准备文件
- 2、加载文件
- 3、显示一行
- 4、函数运用
- （1）map
- （2）collecct
- （3）filter
- （4）flatMap
- （5）union
- （6） join
- （7）lookup
- （8）groupByKey
- （9）sortByKey
1、准备文件
wget http: //statweb.stanford.edu/~tibs/ElemStatLearn/datasets/spam.data
2、加载文件
scala> val inFile = sc.textFile( "/home/scipio/spam.data" )
输出
14 / 06 / 28 12 : 15 : 34 INFO MemoryStore: ensureFreeSpace( 32880 ) called with curMem= 65736 , maxMem= 311387750
14 / 06 / 28 12 : 15 : 34 INFO MemoryStore: Block broadcast_2 stored as values to memory (estimated size 32.1 KB, free 296.9 MB)
inFile: org.apache.spark.rdd.RDD[String] = MappedRDD[ 7 ] at textFile at <console>: 12
3、显示一行
scala> inFile.first()
输出
14 / 06 / 28 12 : 15 : 39 INFO FileInputFormat: Total input paths to process : 1
14 / 06 / 28 12 : 15 : 39 INFO SparkContext: Starting job: first at <console>: 15
14 / 06 / 28 12 : 15 : 39 INFO DAGScheduler: Got job 0 (first at <console>: 15 ) with 1 output partitions (allowLocal= true )
14 / 06 / 28 12 : 15 : 39 INFO DAGScheduler: Final stage: Stage 0 (first at <console>: 15 )
14 / 06 / 28 12 : 15 : 39 INFO DAGScheduler: Parents of final stage: List()
14 / 06 / 28 12 : 15 : 39 INFO DAGScheduler: Missing parents: List()
14 / 06 / 28 12 : 15 : 39 INFO DAGScheduler: Computing the requested partition locally
14 / 06 / 28 12 : 15 : 39 INFO HadoopRDD: Input split: file:/home/scipio/spam.data: 0 + 349170
14 / 06 / 28 12 : 15 : 39 INFO SparkContext: Job finished: first at <console>: 15 , took 0.532360118 s
res2: String = 0 0.64 0.64 0 0.32 0 0 0 0 0 0 0.64 0 0 0 0.32 0 1.29 1.93 0 0.96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.778 0 0 3.756 61 278 1
4、函数运用
（1）map
scala> val nums = inFile.map(x=>x.split( ' ' ).map(_.toDouble))
nums: org.apache.spark.rdd.RDD[Array[Double]] = MappedRDD[ 8 ] at map at <console>: 14
scala> nums.first()
14 / 06 / 28 12 : 19 : 07 INFO SparkContext: Starting job: first at <console>: 17
14 / 06 / 28 12 : 19 : 07 INFO DAGScheduler: Got job 1 (first at <console>: 17 ) with 1 output partitions (allowLocal= true )
14 / 06 / 28 12 : 19 : 07 INFO DAGScheduler: Final stage: Stage 1 (first at <console>: 17 )
14 / 06 / 28 12 : 19 : 07 INFO DAGScheduler: Parents of final stage: List()
14 / 06 / 28 12 : 19 : 07 INFO DAGScheduler: Missing parents: List()
14 / 06 / 28 12 : 19 : 07 INFO DAGScheduler: Computing the requested partition locally
14 / 06 / 28 12 : 19 : 07 INFO HadoopRDD: Input split: file:/home/scipio/spam.data: 0 + 349170
14 / 06 / 28 12 : 19 : 07 INFO SparkContext: Job finished: first at <console>: 17 , took 0.011412903 s
res3: Array[Double] = Array( 0.0 , 0.64 , 0.64 , 0.0 , 0.32 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.64 , 0.0 , 0.0 , 0.0 , 0.32 , 0.0 , 1.29 , 1.93 , 0.0 , 0.96 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.778 , 0.0 , 0.0 , 3.756 , 61.0 , 278.0 , 1.0 )
（2）collecct
scala> val rdd = sc.parallelize(List( 1 , 2 , 3 , 4 , 5 ))
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[ 9 ] at parallelize at <console>: 12
scala> val mapRdd = rdd.map( 2 *_)
mapRdd: org.apache.spark.rdd.RDD[Int] = MappedRDD[ 10 ] at map at <console>: 14
scala> mapRdd.collect
14 / 06 / 28 12 : 24 : 45 INFO SparkContext: Job finished: collect at <console>: 17 , took 1.789249751 s
res4: Array[Int] = Array( 2 , 4 , 6 , 8 , 10 )
（3）filter
scala> val filterRdd = sc.parallelize(List( 1 , 2 , 3 , 4 , 5 )).map(_* 2 ).filter(_> 5 )
filterRdd: org.apache.spark.rdd.RDD[Int] = FilteredRDD[ 13 ] at filter at <console>: 12
scala> filterRdd.collect
14 / 06 / 28 12 : 27 : 45 INFO SparkContext: Job finished: collect at <console>: 15 , took 0.056086178 s
res5: Array[Int] = Array( 6 , 8 , 10 )
（4）flatMap
scala> val rdd = sc.textFile( "/home/scipio/README.md" )
14 / 06 / 28 12 : 31 : 55 INFO MemoryStore: ensureFreeSpace( 32880 ) called with curMem= 98616 , maxMem= 311387750
14 / 06 / 28 12 : 31 : 55 INFO MemoryStore: Block broadcast_3 stored as values to memory (estimated size 32.1 KB, free 296.8 MB)
rdd: org.apache.spark.rdd.RDD[String] = MappedRDD[ 15 ] at textFile at <console>: 12
scala> rdd.count
14 / 06 / 28 12 : 32 : 50 INFO SparkContext: Job finished: count at <console>: 15 , took 0.341167662 s
res6: Long = 127
scala> rdd.cache
res7: rdd.type = MappedRDD[ 15 ] at textFile at <console>: 12
scala> rdd.count
14 / 06 / 28 12 : 33 : 00 INFO SparkContext: Job finished: count at <console>: 15 , took 0.32015745 s
res8: Long = 127
scala> val wordCount = rdd.flatMap(_.split( ' ' )).map(x=>(x, 1 )).reduceByKey(_+_)
wordCount: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[ 20 ] at reduceByKey at <console>: 14
scala> wordCount.collect
res9: Array[(String, Int)] = Array((means, 1 ), (under, 2 ), ( this , 4 ), (Because, 1 ), (Python, 2 ), (agree, 1 ), (cluster., 1 ), (its, 1 ), (YARN,, 3 ), (have, 2 ), (pre-built, 1 ), (MRv1,, 1 ), (locally., 1 ), (locally, 2 ), (changed, 1 ), (several, 1 ), (only, 1 ), (sc.parallelize( 1 , 1 ), (This, 2 ), (basic, 1 ), (first, 1 ), (requests, 1 ), (documentation, 1 ), (Configuration, 1 ), (MapReduce, 2 ), (without, 1 ), (setting, 1 ), ( "yarn-client" , 1 ), ([params]`., 1 ), (any, 2 ), (application, 1 ), (prefer, 1 ), (SparkPi, 2 ), (<http: //spark.apache.org/>,1), (version,3), (file,1), (documentation,,1), (test,1), (MASTER,1), (entry,1), (example,3), (are,2), (systems.,1), (params,1), (scala>,1), (<artifactId>hadoop-client</artifactId>,1), (refer,1), (configure,1), (Interactive,2), (artifact,1), (can,7), (file's,1), (build,3), (when,2), (2.0.X,,1), (Apac...
scala> wordCount.saveAsTextFile( "/home/scipio/wordCountResult.txt" )
（5）union
scala> val rdd = sc.parallelize(List(( 'a' , 1 ),( 'a' , 2 )))
rdd: org.apache.spark.rdd.RDD[(Char, Int)] = ParallelCollectionRDD[ 10 ] at parallelize at <console>: 12
scala> val rdd2 = sc.parallelize(List(( 'b' , 1 ),( 'b' , 2 )))
rdd2: org.apache.spark.rdd.RDD[(Char, Int)] = ParallelCollectionRDD[ 11 ] at parallelize at <console>: 12
scala> rdd union rdd2
res3: org.apache.spark.rdd.RDD[(Char, Int)] = UnionRDD[ 12 ] at union at <console>: 17
scala> res3.collect
res4: Array[(Char, Int)] = Array((a, 1 ), (a, 2 ), (b, 1 ), (b, 2 ))
（6） join
scala> val rdd1 = sc.parallelize(List(( 'a' , 1 ),( 'a' , 2 ),( 'b' , 3 ),( 'b' , 4 )))
rdd1: org.apache.spark.rdd.RDD[(Char, Int)] = ParallelCollectionRDD[ 10 ] at parallelize at <console>: 12
scala> val rdd2 = sc.parallelize(List(( 'a' , 5 ),( 'a' , 6 ),( 'b' , 7 ),( 'b' , 8 )))
rdd2: org.apache.spark.rdd.RDD[(Char, Int)] = ParallelCollectionRDD[ 11 ] at parallelize at <console>: 12
scala> rdd1 join rdd2
res1: org.apache.spark.rdd.RDD[(Char, (Int, Int))] = FlatMappedValuesRDD[ 14 ] at join at <console>: 17
res1.collect
res2: Array[(Char, (Int, Int))] = Array((b,( 3 , 7 )), (b,( 3 , 8 )), (b,( 4 , 7 )), (b,( 4 , 8 )), (a,( 1 , 5 )), (a,( 1 , 6 )), (a,( 2 , 5 )), (a,( 2 , 6 )))
（7）lookup
val rdd1 = sc.parallelize(List(( 'a' , 1 ),( 'a' , 2 ),( 'b' , 3 ),( 'b' , 4 )))
rdd1.lookup( 'a' )
res3: Seq[Int] = WrappedArray( 1 , 2 )
（8）groupByKey
val wc = sc.textFile( "/home/scipio/README.md" ).flatMap(_.split( ' ' )).map((_, 1 )).groupByKey
wc.collect
14 / 06 / 28 12 : 56 : 14 INFO SparkContext: Job finished: collect at <console>: 15 , took 2.933392093 s
res0: Array[(String, Iterable[Int])] = Array((means,ArrayBuffer( 1 )), (under,ArrayBuffer( 1 , 1 )), ( this ,ArrayBuffer( 1 , 1 , 1 , 1 )), (Because,ArrayBuffer( 1 )), (Python,ArrayBuffer( 1 , 1 )), (agree,ArrayBuffer( 1 )), (cluster.,ArrayBuffer( 1 )), (its,ArrayBuffer( 1 )), (YARN,,ArrayBuffer( 1 , 1 , 1 )), (have,ArrayBuffer( 1 , 1 )), (pre-built,ArrayBuffer( 1 )), (MRv1,,ArrayBuffer( 1 )), (locally.,ArrayBuffer( 1 )), (locally,ArrayBuffer( 1 , 1 )), (changed,ArrayBuffer( 1 )), (sc.parallelize( 1 ,ArrayBuffer( 1 )), (only,ArrayBuffer( 1 )), (several,ArrayBuffer( 1 )), (This,ArrayBuffer( 1 , 1 )), (basic,ArrayBuffer( 1 )), (first,ArrayBuffer( 1 )), (documentation,ArrayBuffer( 1 )), (Configuration,ArrayBuffer( 1 )), (MapReduce,ArrayBuffer( 1 , 1 )), (requests,ArrayBuffer( 1 )), (without,ArrayBuffer( 1 )), ( "yarn-client" ,ArrayBuffer( 1 )), ([params]`.,Ar...
（9）sortByKey
val rdd = sc.textFile( "/home/scipio/README.md" )
val wordcount = rdd.flatMap(_.split( ' ' )).map((_, 1 )).reduceByKey(_+_)
val wcsort = wordcount.map(x => (x._2,x._1)).sortByKey( false ).map(x => (x._2,x._1))
wcsort.saveAsTextFile( "/home/scipio/sort.txt" )
升序的话，sortByKey(true)
