Question

如何打印特定分区的元素，比如说第5个，单独使用？

val distData = sc.parallelize(1 to 50, 10)

Answer 1

使用Spark / Scala：

val data = 1 to 50
val distData = sc.parallelize(data,10)
distData.mapPartitionsWithIndex( (index: Int, it: Iterator[Int]) =>it.toList.map(x => if (index ==5) {println(x)}).iterator).collect

产生

Answer 2

假设您仅为测试目的而执行此操作，然后使用glom（）。请参阅Spark文档：https://spark.apache.org/docs/1.6.0/api/python/pyspark.html#pyspark.RDD.glom

>>> rdd = sc.parallelize([1, 2, 3, 4], 2)
>>> rdd.glom().collect()
[[1, 2], [3, 4]]
>>> rdd.glom().collect()[1]
[3, 4]

编辑：Scala中的示例：

scala> val distData = sc.parallelize(1 to 50, 10)
scala> distData.glom().collect()(4)
res2: Array[Int] = Array(21, 22, 23, 24, 25)

Answer 3

您可以使用针对foreachPartition（）API的计数器来实现它。

这是一个打印每个分区内容的Java程序 JavaSparkContext context = new JavaSparkContext（conf）;

    JavaRDD<Integer> myArray = context.parallelize(Arrays.asList(1,2,3,4,5,6,7,8,9));
    JavaRDD<Integer> partitionedArray = myArray.repartition(2);

    System.out.println("partitioned array size is " + partitionedArray.count());
    partitionedArray.foreachPartition(new VoidFunction<Iterator<Integer>>() {

        public void call(Iterator<Integer> arg0) throws Exception {

            while(arg0.hasNext()) {
                System.out.println(arg0.next());
            }

        }
    });

如何在Spark中打印特定RDD分区的元素？

3 个答案: