.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing) 错误 Spark Scala

Question

你好，我正在尝试将每个 window 的最后一个值扩展到 count 列的 window 的其余部分，以便创建一个标志来识别寄存器是否是 window 的最后一个值。我以这种方式尝试过但没有用。

样本 DF：

val df_197 = Seq [(Int, Int, Int, Int)]((1,1,7,10),(1,10,4,300),(1,3,14,50),(1,20,24,70),(1,30,12,90),(2,10,4,900),(2,25,30,40),(2,15,21,60),(2,5,10,80)).toDF("policyId","FECMVTO","aux","IND_DEF").orderBy(asc("policyId"), asc("FECMVTO"))
df_197.show

+--------+-------+---+-------+
|policyId|FECMVTO|aux|IND_DEF|
+--------+-------+---+-------+
|       1|      1|  7|     10|
|       1|      3| 14|     50|
|       1|     10|  4|    300|
|       1|     20| 24|     70|
|       1|     30| 12|     90|
|       2|      5| 10|     80|
|       2|     10|  4|    900|
|       2|     15| 21|     60|
|       2|     25| 30|     40|
+--------+-------+---+-------+

val juntar_riesgo = 1
val var_entidad_2 = $"aux"

//Particionar por uno o dos campos en funcion del valor de la variable juntar_riesgo
//Se creará window_number_2 basado en este particionamiento
val winSpec = if(juntar_riesgo == 1) {
  Window.partitionBy($"policyId").orderBy($"FECMVTO")  
} else {
  Window.partitionBy(var_entidad_2,$"policyId").orderBy("FECMVTO")
}

val df_308 = df_307.withColumn("window_number", row_number().over(winSpec))
                   .withColumn("count", last("window_number",true) over (winSpec))
                   .withColumn("FLG_LAST_WDW", when(col("window_number") === col("count"),1).otherwise(lit(0))).show

结果（第一个分区的所有元素的列数需要为 5，第二个分区的所有元素需要为 4）：

+--------+-------+---+-------+-------------+-----+------------+
|policyId|FECMVTO|aux|IND_DEF|window_number|count|FLG_LAST_WDW|
+--------+-------+---+-------+-------------+-----+------------+
|       1|      1|  7|     10|            1|    1|           1|
|       1|      3| 14|     50|            2|    2|           1|
|       1|     10|  4|    300|            3|    3|           1|
|       1|     20| 24|     70|            4|    4|           1|
|       1|     30| 12|     90|            5|    5|           1|
|       2|      5| 10|     80|            1|    1|           1|
|       2|     10|  4|    900|            2|    2|           1|
|       2|     15| 21|     60|            3|    3|           1|
|       2|     25| 30|     40|            4|    4|           1|
+--------+-------+---+-------+-------------+-----+------------+

然后我读到当你在windowPartition子句后使用orderBy时，你必须指定子句.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)来实现我需要的。但是，当我尝试时，我遇到了这个错误：

val juntar_riesgo = 1
val var_entidad_2 = $"aux"

//Particionar por uno o dos campos en funcion del valor de la variable juntar_riesgo
//Se creará window_number_2 basado en este particionamiento
val winSpec = if(juntar_riesgo == 1) {
  Window.partitionBy($"policyId").orderBy($"FECMVTO")  
        .rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)
} else {
  Window.partitionBy(var_entidad_2,$"policyId").orderBy("FECMVTO")
        .rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)
}

val df_198 = df_197.withColumn("window_number", row_number().over(winSpec))
                   .withColumn("count", last("window_number",true) over (winSpec))
                   .withColumn("FLG_LAST_WDW", when(col("window_number") === col("count"),1).otherwise(lit(0))).show

ERROR: org.apache.spark.sql.AnalysisException: Window Frame specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$()) must match the required frame specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$());

感谢您的帮助！

Answer 1

你不应该在这里使用 last 但 max 没有指定顺序：

val df_198 = df_197
  .withColumn("window_number", row_number().over(Window.partitionBy($"policyId").orderBy($"FECMVTO")))
  .withColumn("count", max("window_number") over (Window.partitionBy($"policyId")))
  .withColumn("FLG_LAST_WDW", when(col("window_number") === col("count"),1).otherwise(lit(0))).show


+--------+-------+---+-------+-------------+-----+------------+
|policyId|FECMVTO|aux|IND_DEF|window_number|count|FLG_LAST_WDW|
+--------+-------+---+-------+-------------+-----+------------+
|       1|      1|  7|     10|            1|    5|           0|
|       1|      3| 14|     50|            2|    5|           0|
|       1|     10|  4|    300|            3|    5|           0|
|       1|     20| 24|     70|            4|    5|           0|
|       1|     30| 12|     90|            5|    5|           1|
|       2|      5| 10|     80|            1|    4|           0|
|       2|     10|  4|    900|            2|    4|           0|
|       2|     15| 21|     60|            3|    4|           0|
|       2|     25| 30|     40|            4|    4|           1|
+--------+-------+---+-------+-------------+-----+------------+

请注意，您可以通过按降序计算 row_number 然后取 row_number===1:

来写得更短

val df_198 = df_197
  .withColumn("FLG_LAT_WDW", when(row_number().over(Window.partitionBy($"policyId").orderBy($"FECMVTO".desc))===1,1).otherwise(0))
  .show

.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing) 错误 Spark Scala

.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing) Error Spark Scala

scala

apache-spark

window

partition-by