select(*self.fill_empty_colums(new_df_cols, total))

这段代码的SQL实现如下： ``` -- 对 new_df 进行处理 WITH new_df_filled AS ( SELECT *, CASE WHEN col1 IS NULL THEN 'default_value' ELSE col1 END AS col1_filled, CASE WHEN col2 IS NULL THEN 'default_value' ELSE col2 END AS col2_filled, ..., CASE WHEN coln IS NULL THEN 'default_value' ELSE coln END AS coln_filled FROM new_df ), new_df_processed AS ( SELECT col1_filled, col2_filled, ..., coln_filled, 0 AS row_priority FROM new_df_filled ), -- 对 older_df 进行处理 older_df_filled AS ( SELECT *, CASE WHEN col1 IS NULL THEN 'default_value' ELSE col1 END AS col1_filled, CASE WHEN col2 IS NULL THEN 'default_value' ELSE col2 END AS col2_filled, ..., CASE WHEN coln IS NULL THEN 'default_value' ELSE coln END AS coln_filled FROM older_df ), older_df_processed AS ( SELECT col1_filled, col2_filled, ..., coln_filled, 1 AS row_priority FROM older_df_filled ) -- 合并两个DataFrame SELECT col1_filled, col2_filled, ..., coln_filled, row_priority FROM ( SELECT * FROM new_df_processed UNION ALL SELECT * FROM older_df_processed ) AS merged_df WHERE (col1_filled, col2_filled, ..., coln_filled) IN ( SELECT col1_filled, col2_filled, ..., coln_filled FROM new_df_processed UNION SELECT col1_filled, col2_filled, ..., coln_filled FROM older_df_processed WHERE NOT (col1_filled, col2_filled, ..., coln_filled) IN ( SELECT col1_filled, col2_filled, ..., coln_filled FROM new_df_processed ) ) ``` 其中，`new_df`和`older_df`分别是原始的DataFrame，`new_df_cols`和`old_df_cols`是DataFrame中的列名列表，`total`是总列数，`key_columns`是用于合并两个DataFrame的关键列。首先，对`new_df`和`older_df`分别进行处理，填充空列并添加`row_priority`列。然后，将两个DataFrame合并，并使用`WHERE`子句过滤出唯一的行。最后，选择填充后的列和`row_priority`列。需要注意的是，这段代码中的`NOT IN`子句可能会对性能产生负面影响，可以考虑使用`LEFT JOIN`和`NULL`判断来代替。

这段代码用 PySpark 实现了对两个 DataFrame 进行合并和去重的操作，并添加了一些元数据信息。下面是使用 SQL 实现的代码： ```sql -- 选取需要合并的列名 WITH new_cols AS ( SELECT COLUMN_NAME FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = 'new_df' ), old_cols AS ( SELECT COLUMN_NAME FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = 'older_df' ), cols AS ( SELECT DISTINCT COLUMN_NAME FROM ( SELECT COLUMN_NAME FROM new_cols UNION ALL SELECT COLUMN_NAME FROM old_cols ) ), -- 对 new_df 填充空缺的列，并添加 "row_priority" 列 new_df_filled AS ( SELECT COALESCE(col1, '') AS col1, COALESCE(col2, '') AS col2, ..., COALESCE(colN, '') AS colN, 0 AS row_priority FROM new_df ), new_df_selected AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY key_column ORDER BY row_priority) AS rank FROM ( -- 选取 new_df 中的列，包括填充空缺的列和 "row_priority" 列 SELECT col1, col2, ..., colN, row_priority FROM new_df_filled -- 生成 key_column 列，用于分组 CROSS JOIN (SELECT col1 AS key_column FROM new_df_filled) key_columns ) ), -- 对 older_df 填充空缺的列，并添加 "row_priority" 列 old_df_filled AS ( SELECT COALESCE(col1, '') AS col1, COALESCE(col2, '') AS col2, ..., COALESCE(colN, '') AS colN, 1 AS row_priority FROM older_df ), old_df_selected AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY key_column ORDER BY row_priority) AS rank FROM ( -- 选取 older_df 中的列，包括填充空缺的列和 "row_priority" 列 SELECT col1, col2, ..., colN, row_priority FROM old_df_filled -- 生成 key_column 列，用于分组 CROSS JOIN (SELECT col1 AS key_column FROM old_df_filled) key_columns ) ), -- 合并两个 DataFrame，并去重 merged_df AS ( SELECT * FROM new_df_selected UNION ALL SELECT * FROM old_df_selected ), -- 选取合并后的第一行 final_df AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY key_column ORDER BY rank) AS row_num FROM merged_df ) SELECT col1, col2, ..., colN FROM final_df WHERE row_num = 1 ``` 这段 SQL 代码的实现原理与 PySpark 代码相同，主要分为以下几个步骤： 1. 获取需要合并的列名。 2. 对 new_df 和 older_df 分别进行填充空缺列、添加 "row_priority" 列和选取列的操作，生成 new_df_selected 和 old_df_selected 两个数据集。 3. 将 two_df_selected 进行合并，并添加 rank 列，用于去重。 4. 选取合并后的第一行，得到最终的去重结果。