spark RDD算子(三) distinct,union,intersection,subtract,cartesian
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spark伪集合
尽管 RDD 本身不是严格意义上的集合,但它也支持许多数学上的集合操作,比如合并和相交操作, 下图展示了这四种操作
distinct
distinct用于去重, 我们生成的RDD可能有重复的元素,使用distinct方法可以去掉重复的元素, 不过此方法涉及到混洗,操作开销很大
scala版本
scala> var RDD1 = sc.parallelize(List("aa","aa","bb","cc","dd")) scala> RDD1.collect res3: Array[String] = Array(aa, aa, bb, cc, dd) scala> var distinctRDD = RDD1.distinct scala> distinctRDD.collect res5: Array[String] = Array(aa, dd, bb, cc)
java版本
JavaRDD<String> RDD1 = sc.parallelize(Arrays.asList("aa", "aa", "bb", "cc", "dd")); JavaRDD<String> distinctRDD = RDD1.distinct(); List<String> collect = distinctRDD.collect(); for (String str:collect) { System.out.print(str+", "); }---------输出----------aa, dd, bb, cc,
union
两个RDD进行合并
scala版本
scala> var RDD1 = sc.parallelize(List("aa","aa","bb","cc","dd")) scala> var RDD2 = sc.parallelize(List("aa","dd","ff")) scala> RDD1.collect res6: Array[String] = Array(aa, aa, bb, cc, dd) scala> RDD2.collect res7: Array[String] = Array(aa, dd, ff) scala> RDD1.union(RDD2).collect res8: Array[String] = Array(aa, aa, bb, cc, dd, aa, dd, ff)
java版本
JavaRDD<String> RDD1 = sc.parallelize(Arrays.asList("aa", "aa", "bb", "cc", "dd")); JavaRDD<String> RDD2 = sc.parallelize(Arrays.asList("aa","dd","ff")); JavaRDD<String> unionRDD = RDD1.union(RDD2); List<String> collect = unionRDD.collect(); for (String str:collect) { System.out.print(str+", "); }-----------输出---------aa, aa, bb, cc, dd, aa, dd, ff,
intersection
RDD1.intersection(RDD2) 返回两个RDD的交集,并且去重
intersection 需要混洗数据,比较浪费性能
scala版本
scala> var RDD1 = sc.parallelize(List("aa","aa","bb","cc","dd")) scala> var RDD2 = sc.parallelize(List("aa","dd","ff")) scala> RDD1.collect res6: Array[String] = Array(aa, aa, bb, cc, dd) scala> RDD2.collect res7: Array[String] = Array(aa, dd, ff) scala> var insertsectionRDD = RDD1.intersection(RDD2) scala> insertsectionRDD.collect res9: Array[String] = Array(aa, dd)
java版本
JavaRDD<String> RDD1 = sc.parallelize(Arrays.asList("aa", "aa", "bb", "cc", "dd")); JavaRDD<String> RDD2 = sc.parallelize(Arrays.asList("aa","dd","ff")); JavaRDD<String> intersectionRDD = RDD1.intersection(RDD2); List<String> collect = intersectionRDD.collect(); for (String str:collect) { System.out.print(str+" "); }-------------输出-----------aa dd
subtract
RDD1.subtract(RDD2),返回在RDD1中出现,但是不在RDD2中出现的元素,不去重
scala版本
JavaRDD<String> RDD1 = sc.parallelize(Arrays.asList("aa", "aa","bb", "cc", "dd")); JavaRDD<String> RDD2 = sc.parallelize(Arrays.asList("aa","dd","ff")); scala> var substractRDD =RDD1.subtract(RDD2) scala> substractRDD.collect res10: Array[String] = Array(bb, cc)
java版本
JavaRDD<String> RDD1 = sc.parallelize(Arrays.asList("aa", "aa", "bb","cc", "dd")); JavaRDD<String> RDD2 = sc.parallelize(Arrays.asList("aa","dd","ff")); JavaRDD<String> subtractRDD = RDD1.subtract(RDD2); List<String> collect = subtractRDD.collect(); for (String str:collect) { System.out.print(str+" "); }------------输出-----------------bb cc
cartesian
RDD1.cartesian(RDD2) 返回RDD1和RDD2的笛卡儿积,这个开销非常大
scala版本
scala> var RDD1 = sc.parallelize(List("1","2","3")) scala> var RDD2 = sc.parallelize(List("a","b","c")) scala> var cartesianRDD = RDD1.cartesian(RDD2) scala> cartesianRDD.collect res11: Array[(String, String)] = Array((1,a), (1,b), (1,c), (2,a), (2,b), (2,c), (3,a), (3,b), (3,c))
java版本
JavaRDD<String> RDD1 = sc.parallelize(Arrays.asList("1", "2", "3")); JavaRDD<String> RDD2 = sc.parallelize(Arrays.asList("a","b","c")); JavaPairRDD<String, String> cartesian = RDD1.cartesian(RDD2); List<Tuple2<String, String>> collect1 = cartesian.collect(); for (Tuple2<String, String> tp:collect1) { System.out.println("("+tp._1+" "+tp._2+")"); }------------输出-----------------(1 a)(1 b)(1 c)(2 a)(2 b)(2 c)(3 a)(3 b)(3 c)
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