select rank() over(partition By key_column order By row_priority in (select row_priority from cte ))帮我改一下

根据您提供的 SQL 语句，您可能想要在 `select` 语句中使用 `rank()` 窗口函数，并将其与子查询和公共表表达式（CTE）结合使用。但是，您的 SQL 语法存在语法错误。我来帮您修正一下：

WITH cte AS (
  SELECT key_column, row_priority
  FROM your_table
  WHERE some_condition
)
SELECT key_column, row_priority, RANK() OVER (
  PARTITION BY key_column
  ORDER BY (SELECT row_priority FROM cte)
) AS ranking
FROM your_table
WHERE some_condition

上面的 SQL 语句中，首先使用 CTE `cte` 获取 `key_column` 和 `row_priority` 字段的值。然后，在主查询中，使用 `RANK()` 窗口函数对 `key_column` 进行分区，并按照子查询中的 `row_priority` 字段进行排序。最后，从 `your_table` 表中选择分区和排序后的结果，并添加名为 `ranking` 的新列。

请注意，我在假设您使用的是 PostgreSQL 数据库，因此可以使用窗口函数。如果您使用的是其他数据库，可能需要使用不同的语法或函数来实现相似的功能。

相关问题

with cte as{ select case when co1,co2,co3 有值 then co1,co2,co3 else null end as new_column, 0 as row_priority from new_df union all select case when co1,co2,co3 有值 then co1,co2,co3 else null end as order_column, 1 as row_priority from order_df } selct col,co2 from (select col,co2, rank() over(partition By key_column order By (select row_priority from cte )) as rank where rank=1) main这样写有没有漏写下面的语句和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")

这段代码的功能是将两个DataFrame对象（new_df和older_df）按照指定的key_column进行合并，并为每个合并后的行添加一个rank列，然后选择每个key_column分组中排名为1的行，并删除rank和row_priority列。其中，fill_empty_colums函数是用来填充空列的，update_audit_created_column函数是用来更新审计创建列的。

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。