[英]How to match Dataframe column names to Scala case class attributes?
來自spark-sql的此示例中的列名來自case class Person
。
case class Person(name: String, age: Int)
val people: RDD[Person] = ... // An RDD of case class objects, from the previous example.
// The RDD is implicitly converted to a SchemaRDD by createSchemaRDD, allowing it to be stored using Parquet.
people.saveAsParquetFile("people.parquet")
https://spark.apache.org/docs/1.1.0/sql-programming-guide.html
但是,在許多情況下,參數名稱可能會更改。 如果文件尚未更新以反映更改,則會導致找不到列。
如何指定適當的映射?
我想的是:
val schema = StructType(Seq(
StructField("name", StringType, nullable = false),
StructField("age", IntegerType, nullable = false)
))
val ps: Seq[Person] = ???
val personRDD = sc.parallelize(ps)
// Apply the schema to the RDD.
val personDF: DataFrame = sqlContext.createDataFrame(personRDD, schema)
基本上,您需要做的所有映射都可以通過DataFrame.select(...)
來實現。 (這里,我假設,不需要進行任何類型的轉換。)給定前向和后向映射作為映射,基本部分是
val mapping = from.map{ (x:(String, String)) => personsDF(x._1).as(x._2) }.toArray
// personsDF your original dataframe
val mappedDF = personsDF.select( mapping: _* )
其中mapping是帶有別名的Column
s數組。
object Example {
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkContext, SparkConf}
case class Person(name: String, age: Int)
object Mapping {
val from = Map("name" -> "a", "age" -> "b")
val to = Map("a" -> "name", "b" -> "age")
}
def main(args: Array[String]) : Unit = {
// init
val conf = new SparkConf()
.setAppName( "Example." )
.setMaster( "local[*]")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._
// create persons
val persons = Seq(Person("bob", 35), Person("alice", 27))
val personsRDD = sc.parallelize(persons, 4)
val personsDF = personsRDD.toDF
writeParquet( personsDF, "persons.parquet", sc, sqlContext)
val otherPersonDF = readParquet( "persons.parquet", sc, sqlContext )
}
def writeParquet(personsDF: DataFrame, path:String, sc: SparkContext, sqlContext: SQLContext) : Unit = {
import Mapping.from
val mapping = from.map{ (x:(String, String)) => personsDF(x._1).as(x._2) }.toArray
val mappedDF = personsDF.select( mapping: _* )
mappedDF.write.parquet("/output/path.parquet") // parquet with columns "a" and "b"
}
def readParquet(path: String, sc: SparkContext, sqlContext: SQLContext) : Unit = {
import Mapping.to
val df = sqlContext.read.parquet(path) // this df has columns a and b
val mapping = to.map{ (x:(String, String)) => df(x._1).as(x._2) }.toArray
df.select( mapping: _* )
}
}
如果需要將數據幀轉換回RDD [Person],那么
val rdd : RDD[Row] = personsDF.rdd
val personsRDD : Rdd[Person] = rdd.map { r: Row =>
Person( r.getAs("person"), r.getAs("age") )
}
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