spark中的List值列数 - 数据帧

Question

在cassandra中,我有一个列表列类型.我是新来的火花和斯卡拉,不知道从哪里开始.在火花中我想要计算每个值,是否可以这样做.以下是数据框

+--------------------+------------+
|                  id|        data|
+--------------------+------------+
|53e5c3b0-8c83-11e...|      [b, c]|
|508c1160-8c83-11e...|      [a, b]|
|4d16c0c0-8c83-11e...|   [a, b, c]|
|5774dde0-8c83-11e...|[a, b, c, d]|
+--------------------+------------+

我希望输出为

+--------------------+------------+
|   value            |      count |
+--------------------+------------+
|a                   |      3     |
|b                   |      4     |
|c                   |      3     |
|d                   |      1     |
+--------------------+------------+

火花版:1.4

Answer 1

干得好 :

scala> val rdd = sc.parallelize(
  Seq(
    ("53e5c3b0-8c83-11e", Array("b", "c")),
    ("53e5c3b0-8c83-11e1", Array("a", "b")),
    ("53e5c3b0-8c83-11e2", Array("a", "b", "c")),
    ("53e5c3b0-8c83-11e3", Array("a", "b", "c", "d"))))
// rdd: org.apache.spark.rdd.RDD[(String, Array[String])] = ParallelCollectionRDD[22] at parallelize at <console>:27

scala> rdd.flatMap(_._2).map((_, 1)).reduceByKey(_ + _)
// res11: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[21] at reduceByKey at <console>:30

scala> rdd.flatMap(_._2).map((_,1)).reduceByKey(_ + _).collect
// res16: Array[(String, Int)] = Array((a,3), (b,4), (c,3), (d,1))

使用DataFrame API,这实际上非常简单:

scala> val df = rdd.toDF("id", "data")
// res12: org.apache.spark.sql.DataFrame = ["id": string, "data": array<string>]

scala> df.select(explode($"data").as("value")).groupBy("value").count.show
// +-----+-----+
// |value|count|
// +-----+-----+
// |    d|    1|
// |    c|    3|
// |    b|    4|
// |    a|    3|
// +-----+-----+