分组并在spark sql中获取第一个值

Question

我在spark sql中按行动进行分组.在某些行中包含具有不同ID的相同值.在这种情况下,我想选择第一行.

这是我的代码.

    val highvalueresult = highvalue.select($"tagShortID", $"Timestamp", $"ListenerShortID", $"rootOrgID", $"subOrgID",  $"RSSI_Weight_avg")
                          .groupBy("tagShortID", "Timestamp").agg(max($"RSSI_Weight_avg")
                          .alias("RSSI_Weight_avg"))

        val t2 = averageDF.join(highvalueresult, Seq("tagShortID", "Timestamp", "RSSI_Weight_avg"))

这是我的结果.

tag,timestamp,rssi,listner,rootorg,suborg
2,1496745906,0.7,3878,4,3
4,1496745907,0.6,362,4,3
4,1496745907,0.6,718,4,3
4,1496745907,0.6,1901,4,3

在上面的结果为时间戳1496745907三个listner相同的rssi值.在这种情况下,我想选择第一行.

Answer 1

您可以使用spark sql context具有的窗口函数支持假设您的数据帧是:

+---+----------+----+-------+-------+------+
|tag| timestamp|rssi|listner|rootorg|suborg|
+---+----------+----+-------+-------+------+
|  2|1496745906| 0.7|   3878|      4|     3|
|  4|1496745907| 0.6|    362|      4|     3|
|  4|1496745907| 0.6|    718|      4|     3|
|  4|1496745907| 0.6|   1901|      4|     3|
+---+----------+----+-------+-------+------+

将窗口函数定义为(您可以按列/按列排序):

val window = Window.partitionBy("timestamp", "rssi").orderBy("timestamp")

应用窗口功能:

res1.withColumn("rank", row_number().over(window))
+---+----------+----+-------+-------+------+----+
|tag| timestamp|rssi|listner|rootorg|suborg|rank|
+---+----------+----+-------+-------+------+----+
|  4|1496745907| 0.6|    362|      4|     3|   1|
|  4|1496745907| 0.6|    718|      4|     3|   2|
|  4|1496745907| 0.6|   1901|      4|     3|   3|
|  2|1496745906| 0.7|   3878|      4|     3|   1|
+---+----------+----+-------+-------+------+----+

从每个窗口中选择第一行

    res5.where($"rank" === 1)
+---+----------+----+-------+-------+------+----+
|tag| timestamp|rssi|listner|rootorg|suborg|rank|
+---+----------+----+-------+-------+------+----+
|  4|1496745907| 0.6|    362|      4|     3|   1|
|  2|1496745906| 0.7|   3878|      4|     3|   1|
+---+----------+----+-------+-------+------+----+