如何从结构元素的嵌套数组中创建Spark DataFrame

Question

我已经在Spark中读取了一个JSON文件。该文件具有以下结构：

scala> tweetBlob.printSchema
root
 |-- related: struct (nullable = true)
 |    |-- next: struct (nullable = true)
 |    |    |-- href: string (nullable = true)
 |-- search: struct (nullable = true)
 |    |-- current: long (nullable = true)
 |    |-- results: long (nullable = true)
 |-- tweets: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- cde: struct (nullable = true)
...
...
 |    |    |-- cdeInternal: struct (nullable = true)
...
...
 |    |    |-- message: struct (nullable = true)
...
...

理想情况下，我想要的是一个列为"cde"、"cdeInternal"、"message"的DataFrame。。。如下所示

root
|-- cde: struct (nullable = true)
...
...
|-- cdeInternal: struct (nullable = true)
...
...
|-- message: struct (nullable = true)
...
...

我已经设法使用"爆炸"将"tweets"数组中的元素提取到一个名为"tweet"的列中

scala> val tweets = tweetBlob.select(explode($"tweets").as("tweets"))
tweets: org.apache.spark.sql.DataFrame = [tweets: struct<cde:struct<author:struct<gender:string,location:struct<city:string,country:string,state:string>,maritalStatus:struct<evidence:string,isMarried:string>,parenthood:struct<evidence:string,isParent:string>>,content:struct<sentiment:struct<evidence:array<struct<polarity:string,sentimentTerm:string>>,polarity:string>>>,cdeInternal:struct<compliance:struct<isActive:boolean,userProtected:boolean>,tracks:array<struct<id:string>>>,message:struct<actor:struct<displayName:string,favoritesCount:bigint,followersCount:bigint,friendsCount:bigint,id:string,image:string,languages:array<string>,link:string,links:array<struct<href:string,rel:string>>,listedCount:bigint,location:struct<displayName:string,objectType:string>,objectType:string,postedTime...
scala> tweets.printSchema
root
 |-- tweets: struct (nullable = true)
 |    |-- cde: struct (nullable = true)
...
...
 |    |-- cdeInternal: struct (nullable = true)
...
...
 |    |-- message: struct (nullable = true)
...
...

如何选择结构中的所有列并从中创建DataFrame？如果我的理解是正确的，那么Explode对结构无效。

感谢您的帮助。

Answer 1

处理此问题的一种可能方法是从模式中提取所需信息。让我们从一些伪数据开始：

import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.types._

case class Bar(x: Int, y: String)
case class Foo(bar: Bar)
val df = sc.parallelize(Seq(Foo(Bar(1, "first")), Foo(Bar(2, "second")))).toDF
df.printSchema
// root
//  |-- bar: struct (nullable = true)
//  |    |-- x: integer (nullable = false)
//  |    |-- y: string (nullable = true)

和一个辅助功能：

def children(colname: String, df: DataFrame) = {
  val parent = df.schema.fields.filter(_.name == colname).head
  val fields = parent.dataType match {
    case x: StructType => x.fields
    case _ => Array.empty[StructField]
  }
  fields.map(x => col(s"$colname.${x.name}"))
}

最后的结果：

df.select(children("bar", df): _*).printSchema
// root
// |-- x: integer (nullable = true)
// |-- y: string (nullable = true)

Answer 2

您可以使用

df
  .select(explode(col("path_to_collection")).as("collection"))
  .select(col("collection.*"))`:

示例：

scala> val json = """{"name":"Michael", "schools":[{"sname":"stanford", "year":2010}, {"sname":"berkeley", "year":2012}]}"""
scala> val inline = sqlContext.read.json(sc.parallelize(json :: Nil)).select(explode(col("schools")).as("collection")).select(col("collection.*"))
scala> inline.printSchema
root
 |-- sname: string (nullable = true)
 |-- year: long (nullable = true)
scala> inline.show
+--------+----+
|   sname|year|
+--------+----+
|stanford|2010|
|berkeley|2012|
+--------+----+

或者，您也可以使用SQL函数inline:

scala> val json = """{"name":"Michael", "schools":[{"sname":"stanford", "year":2010}, {"sname":"berkeley", "year":2012}]}"""
scala> sqlContext.read.json(sc.parallelize(json :: Nil)).registerTempTable("tmp")
scala> val inline = sqlContext.sql("SELECT inline(schools) FROM tmp")
scala> inline.printSchema
root
 |-- sname: string (nullable = true)
 |-- year: long (nullable = true)
scala> inline.show
+--------+----+
|   sname|year|
+--------+----+
|stanford|2010|
|berkeley|2012|
+--------+----+

Answer 3

scala> import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.DataFrame
scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._
scala> case class Bar(x: Int, y: String)
defined class Bar
scala> case class Foo(bar: Bar)
defined class Foo
scala> val df = sc.parallelize(Seq(Foo(Bar(1, "first")), Foo(Bar(2, "second")))).toDF
df: org.apache.spark.sql.DataFrame = [bar: struct<x: int, y: string>]

scala> df.printSchema
root
 |-- bar: struct (nullable = true)
 |    |-- x: integer (nullable = false)
 |    |-- y: string (nullable = true)

scala> df.select("bar.*").printSchema
root
 |-- x: integer (nullable = true)
 |-- y: string (nullable = true)

scala>