ranked_df=new_df.unionByName(older_df).withColumn("rank", F.rank().over(merge_spec))这句话什么意思
时间: 2024-04-04 10:31:02 浏览: 48
这句话使用 Apache Spark 的 DataFrame API,将两个 DataFrame(new_df 和 older_df)按照列名进行合并,并添加一个新的列 "rank"。"rank" 列的值是根据指定的窗口规范(merge_spec)对合并后的 DataFrame 进行排名后得到的。具体来说,使用窗口函数 rank() 对合并后的 DataFrame 进行排名,排名的顺序由指定的窗口规范 merge_spec 决定。
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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:
```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。
self.update_audit_created_column(ranked_df,key_column).where(F.col("rank") == 1).drop("rank", "row_priority")转化为sql
这段代码转化为 SQL 的语句如下:
```sql
UPDATE ranked_df
SET audit_created = <current_timestamp>
WHERE key_column = <key_column_value>
AND rank = 1
AND row_priority = <row_priority_value>
ALTER TABLE ranked_df
DROP COLUMN rank, row_priority
```
需要注意的是,SQL 中的 `<current_timestamp>` 需要替换为当前时间的值,`<key_column_value>` 需要替换为 `key_column` 列的具体值,`<row_priority_value>` 需要替换为 `row_priority` 列的具体值。另外,`rank` 和 `row_priority` 列需要在 SQL 中先删除,可以使用 `ALTER TABLE` 语句实现。
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