if (self.config.filter_deleted_flag) & ( is_deleted_column in df.columns): # if True removes records that marked deleted filter = (F.col(ranking_column) == 1) & (F.col(is_deleted_column) == "False") else: self.logger.warning(f"Records marked as deleted will be loaded to {self.config.table_full_name} table!") filter = F.col(ranking_column) == 1 return df.withColumn(ranking_column, F.row_number().over(Window.partitionBy(key_columns).orderBy(order_by_columns))).filter( filter).drop(ranking_column)这里可以用sql的case when做转化吗

可以的，可以使用SQL的CASE WHEN语句来实现相同的逻辑，下面是使用CASE WHEN语句的示例代码：

SELECT *
FROM (
  SELECT *,
    ROW_NUMBER() OVER (PARTITION BY key_columns ORDER BY order_by_columns) AS ranking_column
  FROM your_table
  WHERE CASE 
          WHEN filter_deleted_flag = true AND is_deleted_column IN df.columns AND is_deleted_column = 'False' THEN 1
          WHEN filter_deleted_flag = false THEN 1
          ELSE 0
        END = 1
) t
WHERE t.ranking_column = 1

注意，这里的CASE WHEN语句是将原来的Python函数转化为SQL代码的示例，具体实现可能需要根据你的数据表结构和需求来进行修改。

相关问题

def deduplicate(self, df: DataFrame) -> DataFrame: key_columns = [F.col(column_name) for column_name in self.config.deduplication_key_columns] order_by_columns = [F.col(column_name).desc() for column_name in self.config.deduplication_order_columns] if self.config.deduplicate_order_by_type == "asc": order_by_columns = [F.col(column_name) for column_name in self.config.deduplication_order_columns] ranking_column = "duplicates_rank" is_deleted_column = "IsDeleted" if (self.config.filter_deleted_flag) & ( is_deleted_column in df.columns): # if True removes records that marked deleted filter = (F.col(ranking_column) == 1) & (F.col(is_deleted_column) == "False") else: self.logger.warning(f"Records marked as deleted will be loaded to {self.config.table_full_name} table!") filter = F.col(ranking_column) == 1 return df.withColumn(ranking_column, F.row_number().over(Window.partitionBy(key_columns).orderBy(order_by_columns))).filter( filter).drop(ranking_column)

这段代码是用来去重的，输入是一个DataFrame，输出也是一个DataFrame。该函数首先根据配置文件中的列名获取需要去重的列和排序列，然后按照排序列进行降序或升序排列。接着，该函数给每个分组内的记录打上重复排名，最后根据重复排名过滤出排名为1的记录并删除重复排名列。如果配置文件中开启了筛选标记为删除的记录的功能，并且DataFrame中有IsDeleted列，则会将标记为删除的记录过滤掉。如果没有开启该功能，则会将标记为删除的记录加载到目标表中。

key_columns = [F.col(column_name) for column_name in self.config.deduplication_key_columns] order_by_columns = [F.col(column_name).desc() for column_name in self.config.deduplication_order_columns] if self.config.deduplicate_order_by_type == "asc": order_by_columns = [F.col(column_name) for column_name in self.config.deduplication_order_columns] ranking_column = "duplicates_rank" is_deleted_column = "IsDeleted" if (self.config.filter_deleted_flag) & ( is_deleted_column in df.columns): # if True removes records that marked deleted filter = (F.col(ranking_column) == 1) & (F.col(is_deleted_column) == "False") else: self.logger.warning(f"Records marked as deleted will be loaded to {self.config.table_full_name} table!") filter = F.col(ranking_column) == 1这里能看出源数据是怎么样的df结构，给我一个例子，并且告诉我sql怎么筛选df的列出来

假设源数据`df`的列名和数据如下：

+----+-------+--------+-----------+
| id | name  | gender | is_deleted|
+----+-------+--------+-----------+
|  1 | Alice | female | False     |
|  2 | Bob   | male   | False     |
|  3 | Bob   | male   | True      |
|  4 | Alice | female | True      |
|  5 | Carol | female | False     |
+----+-------+--------+-----------+

则根据Python代码，可以使用以下SQL语句筛选出`key_columns`和`is_deleted_column`所对应的列：

SELECT id, name, gender, is_deleted
FROM source_table;

即将`source_table`替换为源数据表的表名，将`id, name, gender, is_deleted`替换为`self.config.deduplication_key_columns`和`is_deleted_column`所对应的列名，即可筛选出指定的列。