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我正在使用 Spark Shell v_1.6.1.5。

我有以下 Spark Scala 数据框：

val data = sqlContext.read.avro("/my/location/*.avro")
data.printSchema
root
 |-- id: long (nullable = true)
 |-- stuff: map (nullable = true)
 |    |-- key: string
 |    |-- value: struct (valueContainsNull = false)
 |    |    |-- created: long (nullable = false)
 |    |    |-- lastModified: long (nullable = false)
 |    |    |-- . . .

将它“分解”为以下平面格式（丢弃可能的空值）的确切语法是什么：[id, key, value]？

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1 回答 1

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这可以使用udf和来完成explode。

但是，我们不知道映射中值的类别，因为此信息是推断出来的，不能作为显式类别使用。为了克服这个问题，我们可以通过创建一个具有相同类签名的案例类来“隐藏”推断的类。Spark 然后将这些类视为相同，因为推断类和我们的影子类都转换为相同的StructType

这是一个示例（案例类value是我们不知道的推断类的替代品）。

scala> case class value(created: Long, lastModified: Long)
defined class value

scala> val myDF = Seq((1, Map("a" -> value(1L,2L), "b" -> value(3L,4L))), (2, Map("c" -> value(5L,6L), "d" -> value(6L,7L)))).toDF("id", "stuff")
myDF: org.apache.spark.sql.DataFrame = [id: int, stuff: map<string,struct<created:bigint,lastModified:bigint>>]

scala> myDF.show
+---+--------------------+
| id|               stuff|
+---+--------------------+
|  1|Map(a -> [1,2], b...|
|  2|Map(c -> [5,6], d...|
+---+--------------------+


scala> myDF.printSchema
root
 |-- id: integer (nullable = false)
 |-- stuff: map (nullable = true)
 |    |-- key: string
 |    |-- value: struct (valueContainsNull = true)
 |    |    |-- created: long (nullable = false)
 |    |    |-- lastModified: long (nullable = false)


scala> case class shadowOfValue(created: Long, lastModified: Long)
defined class shadowOfValue

scala> val explodeUDF = udf( (map: Map[String, shadowOfValue]) => map.toVector)
explodeUDF: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,ArrayType(StructType(StructField(_1,StringType,true), StructField(_2,StructType(StructField(created,LongType,false), StructField(lastModified,LongType,false)),true)),true),Some(List(MapType(StringType,StructType(StructField(created,LongType,false), StructField(lastModified,LongType,false)),true))))

scala> var newDF = myDF.withColumn("TMP", explode(explodeUDF($"stuff"))).drop("stuff")
newDF: org.apache.spark.sql.DataFrame = [id: int, TMP: struct<_1: string, _2: struct<created: bigint, lastModified: bigint>>]

scala> newDF = newDF.withColumn("key", $"TMP".apply("_1")).withColumn("value", $"TMP".apply("_2"))
newDF: org.apache.spark.sql.DataFrame = [id: int, TMP: struct<_1: string, _2: struct<created: bigint, lastModified: bigint>> ... 2 more fields]

scala> newDF = newDF.drop("TMP")
newDF: org.apache.spark.sql.DataFrame = [id: int, key: string ... 1 more field]

scala> newDF.show
+---+---+-----+
| id|key|value|
+---+---+-----+
|  1|  a|[1,2]|
|  1|  b|[3,4]|
|  2|  c|[5,6]|
|  2|  d|[6,7]|
+---+---+-----+


scala> newDF.printSchema
root
 |-- id: integer (nullable = false)
 |-- key: string (nullable = true)
 |-- value: struct (nullable = true)
 |    |-- created: long (nullable = false)
 |    |-- lastModified: long (nullable = false)
于 2016-11-15T20:42:36.493 回答