You can manually specify schema, e.g. from (https://github.com/databricks/spark-csv):
import org.apache.spark.sql.SQLContext import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType};
val sqlContext = new SQLContext(sc) val customSchema = StructType(Array( StructField("year", IntegerType, true), StructField("make", StringType, true), StructField("model", StringType, true), StructField("comment", StringType, true), StructField("blank", StringType, true)))
val df = sqlContext.read .format("com.databricks.spark.csv") .option("header", "true") // Use first line of all files as header .schema(customSchema) .load("cars.csv")
val selectedData = df.select("year", "model") selectedData.write .format("com.databricks.spark.csv") .option("header", "true") .save("newcars.csv")
