spark rdd median 中位数求解

最新推荐文章于 2023-03-16 13:22:09 发布

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最新推荐文章于 2023-03-16 13:22:09 发布

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文章标签：大数据 scala

本文详细介绍了Apache Spark中RDD的lookup方法使用技巧，包括如何高效查找特定键对应的值，通过示例展示了对未排序和已排序的数据进行lookup操作的速度差异，并提供了一种利用lookup方法计算中位数的方法。

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lookup(key)

Return the list of values in the RDD for key key. This operation is done efficiently if the RDD has a known partitioner by only searching the partition that the key maps to.

>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
>>> rdd.lookup(42)  # slow
[42]
>>> sorted = rdd.sortByKey()
>>> sorted.lookup(42)  # fast
[42]
>>> sorted.lookup(1024)
[]
>>> rdd2 = sc.parallelize([(('a', 'b'), 'c')]).groupByKey()
>>> list(rdd2.lookup(('a', 'b'))[0])
['c']

You need to sort RDD and take element in the middle or average of two elements. Here is example with RDD[Int]:

  import org.apache.spark.SparkContext._

  val rdd: RDD[Int] = ???

  val sorted = rdd.sortBy(identity).zipWithIndex().map {
    case (v, idx) => (idx, v)
  }

  val count = sorted.count()

  val median: Double = if (count % 2 == 0) {
    val l = count / 2 - 1
    val r = l + 1
    (sorted.lookup(l).head + sorted.lookup(r).head).toDouble / 2
  } else sorted.lookup(count / 2).head.toDouble


实验：

all_data = sc.parallelize([25,1,2,3,4,5,6,7,8,100])
all_data.sortBy(lambda x:x).zipWithIndex().map(lambda x: (x[1],x[0])).collect
[(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 25), (9, 100)]