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Spark – How to create an empty Dataset?

Naveen Nelamali
spark-create-empty-dataframe

In this article, I will explain how to create an empty Spark Dataset with or without schema (emptyDataset()) by using several Scala examples. Before we start, I have explained one of the many scenarios where we need to create an empty Dataset.

While working with files in Spark, sometimes we may not receive a file for processing, however, we still need to create an empty Dataset similar (same schema) to the Dataset we create when we receive a file. If we don’t create with the same schema, our operations/transformations on Dataset would fail as we refer to the columns that may not present.

Related: Spark create empty DataFrame

To handle situations similar to these, we always need to create a Dataset with the same schema, which means the same column names and datatypes regardless of the file exists or empty file processing.

First, let’s create a SparkSession and Spark StructType schemas and case class which we will be using throughout our examples.


val spark:SparkSession = SparkSession.builder()
   .master("local[1]")
   .appName("SparkByExamples.com")
   .getOrCreate()

import spark.implicits._

val schema = StructType(
    StructField("firstName", StringType, true) ::
      StructField("lastName", IntegerType, false) ::
      StructField("middleName", IntegerType, false) :: Nil)

val colSeq = Seq("firstName","lastName","middleName")
case class Name(firstName: String, lastName: String, middleName:String)

1. emptyDataset() – Create Empty Dataset with zero columns

SparkSession provides an emptyDataset() method, which returns the empty Dataset without schema (zero columns), but this is not what we wanted. Below next example shows how to create with schema. 


// EmptyDataset() - Create Empty Dataset with zero columns
case class Empty()
val ds0 = spark.emptyDataset[Empty]
ds0.printSchema()

// Outputs following
root

2. emptyDataset() – Create Empty Dataset with Schema

Below example create Spark empty Dataset with schema (column names and data types).


val ds1=spark.emptyDataset[Name]
ds1.printSchema()

// Outputs following
root
 |-- firstName: string (nullable = true)
 |-- lastName: string (nullable = true)
 |-- middleName: string (nullable = true)

3. createDataset() – Create Empty Dataset with schema

We can create an empty Spark Dataset with schema using createDataset() method from SparkSession. The second example below explains how to create an empty RDD first and convert RDD to Dataset.


// CreateDataset() - Create Empty Dataset with schema
val ds2=spark.createDataset(Seq.empty[Name])
ds2.printSchema()
val ds3=spark.createDataset(spark.sparkContext.emptyRDD[Name])
ds3.printSchema()

//These both Outputs following
root
 |-- firstName: string (nullable = true)
 |-- lastName: string (nullable = true)
 |-- middleName: string (nullable = true)

4. createDataset() – Create Empty Dataset with default column names


// CreateDataset() - Create Empty Dataset with default column names
val ds4=spark.createDataset(Seq.empty[(String,String,String)])
ds4.printSchema()
// Outputs following
root
 |-- _1: string (nullable = true)
 |-- _2: string (nullable = true)
 |-- _3: string (nullable = true)

5. Using implicit encoder

Let’s see another way, which uses implicit encoders.


// Using implicit encoder
val ds5 = Seq.empty[(String,String,String)].toDS()
ds5.printSchema()
// Outputs following
root
 |-- _1: string (nullable = true)
 |-- _2: string (nullable = true)
 |-- _3: string (nullable = true)

6. Using case class

We can also create empty Dataset with the schema we wanted from scala case class.


// Using case class
val ds6 = Seq.empty[Name].toDS()
ds6.printSchema() 

// Outputs following
root
 |-- firstName: string (nullable = true)
 |-- lastName: string (nullable = true)
 |-- middleName: string (nullable = true)

Happy Learning !!

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