Sprak RDD简单应用
来自：http://my.oschina.net/scipio/blog/284957#OSC_h5_11
目录[-]
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)
