Question

我有一个以下格式的数据框：

+----------+-------+----------+---------+-----------------------+---------+---------+
|rownum    |viewid |skillid   |parentId |post_timestamp         |is_skill |column A |
+----------+-------+----------+---------+-----------------------+---------+---------+
|1         |251    |b         |xyz12    |2019-01-31 09:24:02.868|true     |abcde    |
|2         |251    |b         |abc34    |2019-01-31 10:24:02.868|false    |453aw    |
|3         |251    |b         |abc34    |2019-01-31 11:24:02.868|false    |abcde    |
|4         |94     |a         |ghi23    |2019-01-31 02:28:05.107|false    |bbbbb    |
|5         |94     |a         |yui67    |2019-01-31 09:06:57.976|true     |nnnn     |
|6         |94     |a         |qwe12    |2019-01-31 09:24:02.868|false    |2n21q    |
|7         |94     |a         |qwe12    |2019-01-31 10:06:57.976|false    |nnnnq    |
|8         |94     |a         |rty87    |2019-01-31 15:07:57.976|true     |1234     |
|9         |94     |a         |bnm22    |2019-01-31 16:28:05.107|true     |1234     |
|10        |94     |a         |bnm22    |2019-01-31 17:28:05.107|true     |6789     |
|11        |94     |b         |tyu12    |2019-01-31 09:24:02.868|true     |6789     |
+----------+-------+----------+---------+-----------------------+---------+---------+

对于一组viewid和skillid，如果当前行的parentId不等于上一行的parentId 然后在该组中找到具有SkillId值为true的最新行，然后检查当前行的columnA值不等于该行的columnA值。

Column matchedParentId = df.col("parentId").$eq$eq$eq(functions.lag("parentId",1);```

Now how can I go back to the dataframe until skillId is true? I guess going back would be doable as the dataframe is ordered by timestamp.

Answer 1

我使用Scala，但是我想到的解决方案是

-使用窗口函数在is_skill = true之前找到最后一行的行号，其中parent_Id 不等于先前的parent_Id -自动加入数据框以匹配行

所需的输出如下吗？

+------+------+-------+--------+--------------------+--------+--------+---------------+--------+
|rownum|viewid|skillid|parentId|      post_timestamp|is_skill|column A|matchedParentId|isAEqual|
+------+------+-------+--------+--------------------+--------+--------+---------------+--------+
|     1|   251|      b|   xyz12|20190131 09:24:02...|    true|   abcde|           null|    true|
|     2|   251|      b|   abc34|20190131 10:24:02...|   false|   453aw|          false|   false|
|     3|   251|      b|   abc34|20190131 11:24:02...|   false|   abcde|           true|    true|
|     5|    94|      a|   yui67|20190131 09:06:57...|    true|    nnnn|          false|    true|
|     6|    94|      a|   qwe12|20190131 09:24:02...|   false|   2n21q|          false|   false|
|     7|    94|      a|   qwe12|20190131 10:06:57...|   false|   nnnnq|           true|   false|
|     8|    94|      a|   rty87|20190131 15:07:57...|    true|    1234|          false|    true|
|     9|    94|      a|   bnm22|20190131 16:28:05...|    true|    1234|          false|    true|
|    10|    94|      a|   bnm22|20190131 17:28:05...|    true|    6789|           true|    true|
|    11|    94|      b|   tyu12|20190131 09:24:02...|    true|    6789|           null|    true|
+------+------+-------+--------+--------------------+--------+--------+---------------+--------+

代码如下：

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions
import spark.implicits._

val df = Seq((1,251 ,"b" ,"xyz12" ,"20190131 09:24:02.868",true  ,"abcde"),
             (2 ,251 ,"b" ,"abc34" ,"20190131 10:24:02.868", false ,"453aw"),
             (3 ,251 ,"b" ,"abc34" ,"20190131 11:24:02.868", false ,"abcde"),
             (4 ,94  ,"a" ,"ghi23" ,"20190131 02:28:05.107", false ,"bbbbb"),
             (5 ,94  ,"a" ,"yui67" ,"20190131 09:06:57.976", true  ,"nnnn"),
             (6 ,94  ,"a" ,"qwe12" ,"20190131 09:24:02.868", false ,"2n21q"),
             (7 ,94  ,"a" ,"qwe12" ,"20190131 10:06:57.976", false ,"nnnnq"),
             (8 ,94  ,"a" ,"rty87" ,"20190131 15:07:57.976", true  ,"1234"),
             (9 ,94  ,"a" ,"bnm22" ,"20190131 16:28:05.107", true  ,"1234"),
             (10  ,94  ,"a" ,"bnm22" ,"20190131 17:28:05.107",true  ,"6789"),
             (11  ,94  ,"b" ,"tyu12" ,"20190131 09:24:02.868",true  ,"6789")).
             toDF("rownum", "viewid", "skillid", "parentId" , "post_timestamp", "is_skill", "column A")

val w = Window.partitionBy("viewid", "skillid").orderBy("post_timestamp")

val df2 = df.withColumn("matchedParentId", lag($"parentId", 1).over(w).equalTo($"parentId")).
             withColumn("rank", rank.over(w)).withColumn("is_skill_int", when($"is_skill", 1).otherwise(0)).
             withColumn("test", max($"is_skill_int" * $"rank").over(w))

val df3 = df2.as("df_left").
             join(df2.as("df_right"), $"df_left.viewid".equalTo($"df_right.viewid").
                                  and($"df_left.skillid".equalTo($"df_right.skillid")).
                                  and($"df_left.rank".equalTo($"df_right.test"))).
             withColumn("isAEqual", $"df_left.column A".equalTo($"df_right.column A")).
             select("df_right.rownum", "df_right.viewid", "df_right.skillid", "df_right.parentId", "df_right.post_timestamp", "df_right.is_skill", "df_right.column A", "df_right.matchedParentId", "isAEqual").
             orderBy("rownum")

df3.show

Answer 2

这是我建议的方法

groupby（viewid，skillid），并将收集的记录作为列表
实施udf，该列表可接收并可以遍历和执行逻辑
根据udf返回值（可能是时间戳）选择记录

Spark：在数据帧中进行反向操作，直到满足条件

2 个答案: