I want to create on DataFrame
with a specified schema in Scala. I have tried to use JSON read (I mean reading empty file) but I don't think that's the best practice.
Lets assume you want a data frame with the following schema:
root
|-- k: string (nullable = true)
|-- v: integer (nullable = false)
You simply define schema for a data frame and use empty RDD[Row]
:
import org.apache.spark.sql.types.{
StructType, StructField, StringType, IntegerType}
import org.apache.spark.sql.Row
val schema = StructType(
StructField("k", StringType, true) ::
StructField("v", IntegerType, false) :: Nil)
// Spark < 2.0
// sqlContext.createDataFrame(sc.emptyRDD[Row], schema)
spark.createDataFrame(sc.emptyRDD[Row], schema)
PySpark equivalent is almost identical:
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
schema = StructType([
StructField("k", StringType(), True), StructField("v", IntegerType(), False)
])
# or df = sc.parallelize([]).toDF(schema)
# Spark < 2.0
# sqlContext.createDataFrame([], schema)
df = spark.createDataFrame([], schema)
Using implicit encoders (Scala only) with Product
types like Tuple
:
import spark.implicits._
Seq.empty[(String, Int)].toDF("k", "v")
or case class:
case class KV(k: String, v: Int)
Seq.empty[KV].toDF
or
spark.emptyDataset[KV].toDF