大多数情况下，数据透视无法正常工作，即增加源表记录。
source_df
+---------------+-------------------+--------------------+-------------------+-------------------+--------------+-----------------------+----------------------+-----------+--------------+-------------------+----------------+---------------+---------------+
|model_family_id|classification_type|classification_value|benchmark_type_code| data_date|data_item_code|data_item_value_numeric|data_item_value_string|fiscal_year|fiscal_quarter| create_date|last_update_date|create_user_txt|update_user_txt|
+---------------+-------------------+--------------------+-------------------+-------------------+--------------+-----------------------+----------------------+-----------+--------------+-------------------+----------------+---------------+---------------+
| 1| COUNTRY| HKG| MEAN|2017-12-31 00:00:00| CREDITSCORE| 13| bb-| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| OBS_CNT|2017-12-31 00:00:00| CREDITSCORE| 649| aa| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| OBS_CNT_CA|2017-12-31 00:00:00| CREDITSCORE| 649| null| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| PERCENTILE_0|2017-12-31 00:00:00| CREDITSCORE| 3| aa| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| PERCENTILE_10|2017-12-31 00:00:00| CREDITSCORE| 8| bbb+| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| PERCENTILE_100|2017-12-31 00:00:00| CREDITSCORE| 23| d| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| PERCENTILE_25|2017-12-31 00:00:00| CREDITSCORE| 11| bb+| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| PERCENTILE_50|2017-12-31 00:00:00| CREDITSCORE| 14| b+| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| PERCENTILE_75|2017-12-31 00:00:00| CREDITSCORE| 15| b| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
| 1| COUNTRY| HKG| PERCENTILE_90|2017-12-31 00:00:00| CREDITSCORE| 17| ccc+| 2017| 4|2018-03-31 14:04:18| null| LOAD| null|
+---------------+-------------------+--------------------+-------------------+-------------------+--------------+-----------------------+----------------------+-----------+--------------+-------------------+----------------+---------------+---------------+
我尝试了以下代码
val pivot_df = source_df.groupBy("model_family_id","classification_type","classification_value" ,"data_item_code","data_date","fiscal_year","fiscal_quarter" , "create_user_txt", "create_date")
.pivot("benchmark_type_code" ,
Seq("mean","obs_cnt","obs_cnt_ca","percentile_0","percentile_10","percentile_25","percentile_50","percentile_75","percentile_90","percentile_100")
)
.agg( first(
when( col("data_item_code") === "CREDITSCORE" , col("data_item_value_string"))
.otherwise(col("data_item_value_numeric"))
)
)
我得到的结果不足，不确定我的代码有什么问题。
+---------------+-------------------+--------------------+--------------+-------------------+-----------+--------------+---------------+-------------------+----+-------+----------+------------+-------------+-------------+-------------+-------------+-------------+--------------+
|model_family_id|classification_type|classification_value|data_item_code| data_date|fiscal_year|fiscal_quarter|create_user_txt| create_date|mean|obs_cnt|obs_cnt_ca|percentile_0|percentile_10|percentile_25|percentile_50|percentile_75|percentile_90|percentile_100|
+---------------+-------------------+--------------------+--------------+-------------------+-----------+--------------+---------------+-------------------+----+-------+----------+------------+-------------+-------------+-------------+-------------+-------------+--------------+
| 1| COUNTRY| HKG| CREDITSCORE|2017-12-31 00:00:00| 2017| 4| LOAD|2018-03-31 14:04:18|null| null| null| null| null| null| null| null| null| null|
+---------------+-------------------+--------------------+--------------+-------------------+-----------+--------------+---------------+-------------------+----+-------+----------+------------+-------------+-------------+-------------+-------------+-------------+--------------+
我尝试过在数据透视函数中没有列的Seq。但仍然没有按预期进行，请提供任何帮助？？
2）在when子句中，如果枢轴列为$“ benchmark_type_code” ==='OBS_CNT'| 'OBS_CNT'，则应采用$ data_item_value_numeric。如何实现这一目标？
答案 0 :(得分：0)
我不确定您的Spark版本是否为2.X。我的软件版本如下： spark ==> 2.2.1 斯卡拉==> 2.11 根据以上所述，我得到了正确的答案：
+---------------+-------------------+--------------------+--------------+-------------------+-----------+--------------+---------------+-------------------+----+-------+----------+------------+-------------+--------------+-------------+-------------+-------------+-------------+
|model_family_id|classification_type|classification_value|data_item_code| data_date|fiscal_year|fiscal_quarter|create_user_txt| create_date|MEAN|OBS_CNT|OBS_CNT_CA|PERCENTILE_0|PERCENTILE_10|PERCENTILE_100|PERCENTILE_25|PERCENTILE_50|PERCENTILE_75|PERCENTILE_90|
+---------------+-------------------+--------------------+--------------+-------------------+-----------+--------------+---------------+-------------------+----+-------+----------+------------+-------------+--------------+-------------+-------------+-------------+-------------+
| 1| COUNTRY| HKG| CREDITSCORE|2017-12-31 00:00:00| 2017| 4| LOAD|2018-03-31 14:04:18| bb-| aa| | aa| bbb+| d| bb+| b+| b| ccc+|
+---------------+-------------------+--------------------+--------------+-------------------+-----------+--------------+---------------+-------------------+----+-------+----------+------------+-------------+--------------+-------------+-------------+-------------+-------------+
这是我的代码，您可以尝试
import spark.implicits._
source_df
.groupBy($"model_family_id",$"classification_type",$"classification_value",$"data_item_code",$"data_date",$"fiscal_year",$"fiscal_quarter",$"create_user_txt",$"create_date")
.pivot("benchmark_type_code")
.agg(
first(
when($"data_item_code"==="CREDITSCORE", $"data_item_value_string")
.otherwise($"data_item_value_numeric")
)
).show()
答案 1 :(得分：0)
我们可以在以下条件下的when条件下使用when条件。
.agg( first(
when( col("data").isin("x","a","y","z") ,
when( col("code").isin("aa","bb") , col("numeric")).otherwise(col("string"))
)
.otherwise(col("numeric"))
)