How to declare an empty dataset in Spark?

All you need is to import implicit encoders from SparkSession instance before you create empty Dataset: import spark.implicits._ See full example here

EmptyDataFrame

package com.examples.sparksql

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext

object EmptyDataFrame {

  def main(args: Array[String]){

    //Create Spark Conf
    val sparkConf = new SparkConf().setAppName("Empty-Data-Frame").setMaster("local")

    //Create Spark Context - sc
    val sc = new SparkContext(sparkConf)

    //Create Sql Context
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)    

    //Import Sql Implicit conversions
    import sqlContext.implicits._
    import org.apache.spark.sql.Row
    import org.apache.spark.sql.types.{StructType,StructField,StringType}   

    //Create Schema RDD
    val schema_string = "name,id,dept"
    val schema_rdd = StructType(schema_string.split(",").map(fieldName => StructField(fieldName, StringType, true)) )

    //Create Empty DataFrame
    val empty_df = sqlContext.createDataFrame(sc.emptyRDD[Row], schema_rdd)

    //Some Operations on Empty Data Frame
    empty_df.show()
    println(empty_df.count())     

    //You can register a Table on Empty DataFrame, it's empty table though
    empty_df.registerTempTable("empty_table")

    //let's check it ;)
    val res = sqlContext.sql("select * from empty_table")
    res.show

  }

}