如何在数组[DataFrame]中组合(联接)信息

Question

我有一个Array[DataFrame]，我想检查每个数据帧的每一行的值是否按列发生了任何变化。假设我有三个数据帧的第一行，比如：

 (0,1.0,0.4,0.1)
 (0,3.0,0.2,0.1)
 (0,5.0,0.4,0.1)

第一列是ID，我对这个ID的理想输出是：

 (0, 1, 1, 0)

这意味着第二列和第三列发生了变化，而第三列没有变化。我在这里附上一点数据来复制我的设置

val rdd = sc.parallelize(Array((0,1.0,0.4,0.1),
                               (1,0.9,0.3,0.3),
                               (2,0.2,0.9,0.2),
                               (3,0.9,0.2,0.2),
                               (4,0.3,0.5,0.5)))
val rdd2 = sc.parallelize(Array((0,3.0,0.2,0.1),
                                (1,0.9,0.3,0.3),
                                (2,0.2,0.5,0.2),
                                (3,0.8,0.1,0.1),
                                (4,0.3,0.5,0.5)))
val rdd3 = sc.parallelize(Array((0,5.0,0.4,0.1),
                                (1,0.5,0.3,0.3),
                                (2,0.3,0.3,0.5),
                                (3,0.3,0.3,0.1),
                                (4,0.3,0.5,0.5)))
val df = rdd.toDF("id", "prop1", "prop2", "prop3")
val df2 = rdd2.toDF("id", "prop1", "prop2", "prop3")
val df3 = rdd3.toDF("id", "prop1", "prop2", "prop3")
val result:Array[DataFrame] = new Array[DataFrame](3)
result.update(0, df)
result.update(1,df2)
result.update(2,df3)

如何映射数组并获得输出？

Answer 1

您可以将countDistinct与groupBy:一起使用

import org.apache.spark.sql.functions.{countDistinct}
val exprs = Seq("prop1", "prop2", "prop3")
  .map(c => (countDistinct(c) > 1).cast("integer").alias(c))
val combined = result.reduce(_ unionAll _)
val aggregatedViaGroupBy = combined
  .groupBy($"id")
  .agg(exprs.head, exprs.tail: _*)
aggregatedViaGroupBy.show
// +---+-----+-----+-----+
// | id|prop1|prop2|prop3|
// +---+-----+-----+-----+
// |  0|    1|    1|    0|
// |  1|    1|    0|    0|
// |  2|    1|    1|    1|
// |  3|    1|    1|    1|
// |  4|    0|    0|    0|
// +---+-----+-----+-----+

Answer 2

首先，我们需要将所有DataFrames连接在一起。

val combined = result.reduceLeft((a,b) => a.join(b,"id"))

为了比较同一标签的所有列（例如"prod1"），我发现在RDD级别上操作（至少对我来说）更容易。我们首先将数据转换为(id, Seq[Double])。

val finalResults = combined.rdd.map{
  x => 
    (x.getInt(0), x.toSeq.tail.map(_.asInstanceOf[Double]))
}.map{ 
  case(i,d) => 
     def checkAllEqual(l: Seq[Double]) = if(l.toSet.size == 1) 0 else 1
     val g = d.grouped(3).toList 
     val g1 = checkAllEqual(g.map(x => x(0)))
     val g2 = checkAllEqual(g.map(x => x(1)))
     val g3 = checkAllEqual(g.map(x => x(2)))
     (i, g1,g2,g3)
}.toDF("id", "prod1", "prod2", "prod3")
finalResults.show()

这将打印：

+---+-----+-----+-----+
| id|prod1|prod2|prod3|
+---+-----+-----+-----+
|  0|    1|    1|    0|
|  1|    1|    0|    0|
|  2|    1|    1|    1|
|  3|    1|    1|    1|
|  4|    0|    0|    0|
+---+-----+-----+-----+