How to generate datasets dynamically based on schema?

Question

I have multiple schema like below with different column names and data types. I want to generate test/simulated data using DataFrame with Scala for each schema and save it to parquet file.

Below is the example schema (from a sample json) to generate data dynamically with dummy values in it.

val schema1 = StructType(
  List(
    StructField("a", DoubleType, true),
    StructField("aa", StringType, true)
    StructField("p", LongType, true),
    StructField("pp", StringType, true)
  )
)

I need rdd/dataframe like this with 1000 rows each based on number of columns in the above schema.

val data = Seq(
  Row(1d, "happy", 1L, "Iam"),
  Row(2d, "sad", 2L, "Iam"),
  Row(3d, "glad", 3L, "Iam")
)

Basically.. like this 200 datasets are there for which I need to generate data dynamically, writing separate programs for each scheme is merely impossible for me.

Pls. help me with your ideas or impl. as I am new to spark.

Is it possible to generate dynamic data based on schema of different types?

Answer 1

Using @JacekLaskowski's advice, you could generate dynamic data using generators with ScalaCheck (Gen) based on field/types you are expecting.

It could look like this:

import org.apache.spark.sql.types._
import org.apache.spark.sql.{Row, SaveMode}
import org.scalacheck._

import scala.collection.JavaConverters._

val dynamicValues: Map[(String, DataType), Gen[Any]] = Map(
  ("a", DoubleType) -> Gen.choose(0.0, 100.0),
  ("aa", StringType) -> Gen.oneOf("happy", "sad", "glad"),
  ("p", LongType) -> Gen.choose(0L, 10L),
  ("pp", StringType) -> Gen.oneOf("Iam", "You're")
)

val schemas = Map(
  "schema1" -> StructType(
    List(
      StructField("a", DoubleType, true),
      StructField("aa", StringType, true),
      StructField("p", LongType, true),
      StructField("pp", StringType, true)
    )),
  "schema2" -> StructType(
    List(
      StructField("a", DoubleType, true),
      StructField("pp", StringType, true),
      StructField("p", LongType, true)
    )
  )
)

val numRecords = 1000

schemas.foreach {
  case (name, schema) =>
    // create a data frame
    spark.createDataFrame(
      // of #numRecords records
      (0 until numRecords).map { _ =>
        // each of them a row
        Row.fromSeq(schema.fields.map(field => {
          // with fields based on the schema's fieldname & type else null
          dynamicValues.get((field.name, field.dataType)).flatMap(_.sample).orNull
        }))
      }.asJava, schema)
      // store to parquet
      .write.mode(SaveMode.Overwrite).parquet(name)
}