spark的sample算子

sample(withReplacement, fraction, seed) 
以指定的随机种子随机抽样出数量为 fraction 的数据，withReplacement 表示是抽出的数据是否放回，true 为有放回的抽样，false 为无放回的抽样，seed 用于指定随机数生成器种子。
例如：从 RDD 中随机且有放回的抽出 50% 的数据，随机种子值为 3（即可能以1 2 3的其中一个起始值）。主要用于观察大数据集的分布情况。

源码：

**
 * Return a sampled subset of this RDD.
 *
 * @param withReplacement can elements be sampled multiple times (replaced when sampled out)
 * @param fraction expected size of the sample as a fraction of this RDD's size
 *  without replacement: probability that each element is chosen; fraction must be [0, 1]
 *  with replacement: expected number of times each element is chosen; fraction must be >= 0
 * @param seed seed for the random number generator
 */
def sample(
    withReplacement: Boolean,
    fraction: Double,
    seed: Long = Utils.random.nextLong): RDD[T] = withScope {
  require(fraction >= 0.0, "Negative fraction value: " + fraction)
  if (withReplacement) {
    new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed)
  } else {
    new PartitionwiseSampledRDD[T, T](this, new BernoulliSampler[T](fraction), true, seed)
  }
}

示例代码： 

scala> val rdd = sc.parallelize(1 to 10)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[8] at parallelize at <console>:24

scala> rdd.collect()
res11: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

scala> var sample1 = rdd.sample(true, 0.4, 2).collect
sample1: Array[Int] = Array(1, 2, 2, 7, 7, 8, 9)    为什么抽样出7个数据呢？

scala> var sample2 = rdd.sample(false, 0.2, 3).collect
sample2: Array[Int] = Array(1, 9)