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

`select(*self.fill_empty_colums(new_df_cols, total))` 是 PySpark 中的一个函数，它的作用是选取 DataFrame 中的一些列，这些列的列表由 `self.fill_empty_colums(new_df_cols, total)` 函数生成。该函数的具体实现可能在其他部分定义，但是它的作用是填充一个列名列表，使得列表中的元素数量等于 `total` 变量的值。如果 `new_df_cols` 列表中的元素数量少于 `total`，则会使用特定的值填充列表，以保证列表长度为 `total`。在 SQL 中，这个操作可以使用 `SELECT column1, column2, ..., columnN` 的形式来实现，其中 `column1, column2, ..., columnN` 是一个包含所有列名的列表，这个列表由 `self.fill_empty_colums(new_df_cols, total)` 函数生成。

相关问题

new_df = new_df.select(*self.fill_empty_colums(new_df_cols, total)).withColumn("row_priority",F.lit(0)) older_df = older_df.select(*self.fill_empty_colums(old_df_cols, total)).withColumn("row_priority",F.lit(1)) key_column = [F.col(column_name) for column_name in key_columns]

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。

new_df_cols = new_df.columns old_df_cols = older_df.columns total = set(new_df_cols + old_df_cols) new_df = new_df.select(*self.fill_empty_colums(new_df_cols, total)).withColumn("row_priority",F.lit(0)) older_df = older_df.select(*self.fill_empty_colums(old_df_cols, total)).withColumn("row_priority",F.lit(1)) key_column = [F.col(column_name) for column_name in key_columns] merge_spec = Window.partitionBy(key_column).orderBy("row_priority") ranked_df=new_df.unionByName(older_df).withColumn("rank", F.rank().over(merge_spec)) return self.update_audit_created_column(ranked_df,key_column).where(F.col("rank") == 1).drop("rank", "row_priority")

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：

-- 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。