计算每组的最大观察数

Question

我用的是 Spark 1.6.2.

我需要找到每组的最大计数。

val myData = Seq(("aa1", "GROUP_A", "10"),("aa1","GROUP_A", "12"),("aa2","GROUP_A", "12"),("aa3", "GROUP_B", "14"),("aa3","GROUP_B", "11"),("aa3","GROUP_B","12" ),("aa2", "GROUP_B", "12"))

val df = sc.parallelize(myData).toDF("id","type","activity")

让我们先计算每组的观察次数：

df.groupBy("type","id").count.show

+-------+---+-----+
|   type| id|count|
+-------+---+-----+
|GROUP_A|aa1|    2|
|GROUP_A|aa2|    1|
|GROUP_B|aa2|    1|
|GROUP_B|aa3|    3|
+-------+---+-----+

这是预期的结果：

+--------+----+-----+
|type    |  id|count|
+----+--------+-----+
| GROUP_A| aa1|    2|
| GROUP_B| aa3|    3|
+--------+----+-----+

我试过了，但没用：

df.groupBy("type","id").count.filter("count = 'max'").show

Answer 1

group by后可以使用max函数

val myData = Seq(("aa1", "GROUP_A", "10"),("aa1","GROUP_A", "12"),("aa2","GROUP_A", "12"),("aa3", "GROUP_B", "14"),("aa3","GROUP_B", "11"),("aa3","GROUP_B","12" ),("aa2", "GROUP_B", "12"))

val df = sc.parallelize(myData).toDF("id","type","activity")

//在groupby之后进行计数，然后对count字段进行别名，然后在cnt字段中找到最大值。

val newDF = df1.groupBy("type", "id").agg(count("*").alias("cnt"))

val df1 = newDF.groupBy("type").max("cnt").show

现在您可以加入这两个数据框来获取输出。

df1.join(newDF.as("newDF"), col("cnt") === col("max(cnt)")).select($"newDF.*").show

Answer 2

要获得 "row with maximum value of column X"（而不仅仅是那个最大值），您可以使用 "grouping" 这个小技巧将相关列一起放入包含排序列的 struct作为第一列 - 然后计算该结构的 max 。由于 struct 的排序是 "dominated" 按其第一列的排序 - 我们将得到所需的结果：

df.groupBy("id","type").count()                // get count per id and type
  .groupBy("type")                             // now group by type only
  .agg(max(struct("count", "id")) as "struct") // get maximum of (count, id) structs - since count is first, and id is unique - count will decide the ordering
  .select($"type", $"struct.id" as "id", $"struct.count" as "count") // "unwrap" structs
  .show()

// +-------+---+-----+
// |   type| id|count|
// +-------+---+-----+
// |GROUP_A|aa1|    2|
// |GROUP_B|aa3|    3|
// +-------+---+-----+

Answer 3

您可以使用 Window 函数找到 max 并通过结合上面@Tzach 的回答删除 duplicates

val windowSpec = Window.partitionBy(col("type"))
import org.apache.spark.sql.functions._
df.groupBy("type","id").count()
  .withColumn("count", max(struct("count", "id")).over(windowSpec))
  .dropDuplicates("type")
  .select($"type", $"count.id" as "id", $"count.count" as "count").show

谢谢

计算每组的最大观察数

Calculate maximum number of observations per group

scala

apache-spark

apache-spark-1.6