有条件地将列和值添加到Spark Rows

smeeb 发表于 Dev

斯迈布

我正在处理一个Spark DataFrame（DF），并且需要在流中添加一个列，方法是mapPartitions：

// Don't worry about what 'widget' is or represents
val rdd = df.mapPartitions { rows => addColIfNecessary(rows, widget) }

然后：

def addColIfNecessary(rows : Iterator[Row], widget : Widget) : Iterator[Row] = {
    rows.foreach { row =>
        if(widget.determineWhetherRowNeedsNewCol(row)) {
            // TODO: Add a new "fizz" column (of StringType) to the row
            val newVal : String = widget.getValueOfNewCol(row)
            row.addColumn("fizz", StringType, newVal)
        }
    }

    rows
}

这显然只是伪代码，但传达了我想做的事情。关于如何实际实施的任何想法？

马斯格

DataFrame是面向列的结构，这意味着将列添加到某些行不是一个好主意。相反，您可以利用对DataFrames中可为空值的支持，而不是添加额外的列，而是根据一些条件向行添加可选值。

一个例子：让我们看一下用户和页面的DF：

val users = Seq("Alice" , "Bob", "Charly", "Dean", "Eve", "Flor", "Greta")
val pages = (1 to 9).map(i => s"page_$i")
val userPages = for {u <- users
                     p <- pages} yield (u,p) 

val userPagesDF = sparkContext.parallelize(userPages).toDF("user","page")

// a user defined function that takes the last digit from the page and uses it to calculate a "rank". It only ranks pages with a number higher than 7

val rankUDF = udf((p:String) => if (p.takeRight(1).toInt>7) "top" else null)

// New DF with the extra column "rank", which contains values for only some rows
val ranked = userPagesDF.withColumn("rank", topPage($"page"))

ranked.show

+-----+-------+----+
| user|   page|rank|
+-----+-------+----+
|Alice| page_1|null|
|Alice| page_2|null|
|Alice| page_3|null|
|Alice| page_4|null|
|Alice| page_5|null|
|Alice| page_6|null|
|Alice| page_7|null|
|Alice| page_8| top|
|Alice| page_9| top|
|  Bob| page_1|null|
|  Bob| page_2|null|
|  Bob| page_3|null|
|  Bob| page_4|null|
|  Bob| page_5|null|
|  Bob| page_6|null|
|  Bob| page_7|null|
|  Bob| page_8| top|
|  Bob| page_9| top|
+-----+-------+----+

ranked.printSchema

root
 |-- user: string (nullable = true)
 |-- page: string (nullable = true)
 |-- rank: string (nullable = true)