Spark 数据框填充

Question

我想对数据框执行“向下填充”类型的操作以删除空值并确保最后一行是一种摘要行，包含基于 [= 的每列的最后已知值13=]，按 itemId 分组。当我使用 Azure Synapse Notebooks 时，语言可以是 Scala、Pyspark、SparkSQL 甚至是 c#。然而这里的问题是真正的解决方案有数百万行和数百列，所以我需要一个可以利用 Spark 的动态解决方案。我们可以提供一个大集群，如何确保我们充分利用它？

示例数据：

// Assign sample data to dataframe
val df = Seq(
    ( 1, "10/01/2021", 1, "abc", null ),
    ( 2, "11/01/2021", 1, null, "bbb" ),
    ( 3, "12/01/2021", 1, "ccc", null ),
    ( 4, "13/01/2021", 1, null, "ddd" ),

    ( 5, "10/01/2021", 2, "eee", "fff" ),
    ( 6, "11/01/2021", 2, null, null ),
    ( 7, "12/01/2021", 2, null, null )
    ).
    toDF("eventId", "timestamp", "itemId", "attrib1", "attrib2")

df.show

第 4 行和第 7 行作为摘要行的预期结果：

+-------+----------+------+-------+-------+
|eventId| timestamp|itemId|attrib1|attrib2|
+-------+----------+------+-------+-------+
|      1|10/01/2021|     1|    abc|   null|
|      2|11/01/2021|     1|    abc|    bbb|
|      3|12/01/2021|     1|    ccc|    bbb|
|      4|13/01/2021|     1|    ccc|    ddd|
|      5|10/01/2021|     2|    eee|    fff|
|      6|11/01/2021|     2|    eee|    fff|
|      7|12/01/2021|     2|    eee|    fff|
+-------+----------+------+-------+-------+

我已查看此选项，但无法根据我的用例调整它。

我有一种有效的 SparkSQL 解决方案，但对于大量的列来说它会非常冗长，希望有一些更容易维护的东西：

%%sql
WITH cte (
SELECT
    eventId,
    itemId,
    ROW_NUMBER() OVER( PARTITION BY itemId ORDER BY timestamp ) AS rn,
    attrib1,
    attrib2
FROM df
)
SELECT
    eventId,
    itemId,
    CASE rn WHEN 1 THEN attrib1 
        ELSE COALESCE( attrib1, LAST_VALUE(attrib1, true) OVER( PARTITION BY itemId ) ) 
    END AS attrib1_xlast,
    CASE rn WHEN 1 THEN attrib2 
        ELSE COALESCE( attrib2, LAST_VALUE(attrib2, true) OVER( PARTITION BY itemId ) ) 
    END AS attrib2_xlast
    
FROM cte
ORDER BY eventId

Answer 1

对于许多 columns 你可以创建一个 expression 如下

val window = Window.partitionBy($"itemId").orderBy($"timestamp")

// Instead of selecting columns you could create a list of columns 
val expr = df.columns
  .map(c => coalesce(col(c), last(col(c), true).over(window)).as(c))

df.select(expr: _*).show(false)

更新：

val mainColumns = df.columns.filterNot(_.startsWith("attrib"))
val aggColumns = df.columns.diff(mainColumns).map(c => coalesce(col(c), last(col(c), true).over(window)).as(c))

df.select(( mainColumns.map(col) ++ aggColumns): _*).show(false)

结果：

+-------+----------+------+-------+-------+
|eventId|timestamp |itemId|attrib1|attrib2|
+-------+----------+------+-------+-------+
|1      |10/01/2021|1     |abc    |null   |
|2      |11/01/2021|1     |abc    |bbb    |
|3      |12/01/2021|1     |ccc    |bbb    |
|4      |13/01/2021|1     |ccc    |ddd    |
|5      |10/01/2021|2     |eee    |fff    |
|6      |11/01/2021|2     |eee    |fff    |
|7      |12/01/2021|2     |eee    |fff    |
+-------+----------+------+-------+-------+

Spark 数据框填充

Spark Dataframe filldown

scala

apache-spark

pyspark

azure-synapse