Unit testing with Spark dataframes

Question

I'm trying to test a part of my program which performs transformations on dataframes I want to test several different variations of these dataframe which rules out the option of reading a specific DF from a file

And so my questions are:

1. Is there any good tutorial on how to perform unit testing with Spark and dataframes, especially regarding the dataframes creation?
2. How can I create these different several lines dataframes without a lot of boilerplate and without reading these from a file?
3. Are there any utility classes for checking for specific values inside a dataframe?

I obviously googled that before but could not find anything which was very useful. Among the more useful links I found were:

• Running a basic unit test with a dataframe
• Custom made assertions with DF

It would be great if examples/tutorials are in Scala but I'll take whatever language you've got

Thanks in advance

Answer 1

This link shows how we can programmatically create a data frame with schema. You can keep the data in separate traits and mix it in with your tests. For instance,

// This example assumes CSV data. But same approach should work for other formats as well.

trait TestData {
  val data1 = List(
    "this,is,valid,data",
    "this,is,in-valid,data",
  )
  val data2 = ...  
}

Then with ScalaTest, we can do something like this.

class MyDFTest extends FlatSpec with Matchers {

  "method" should "perform this" in new TestData {
     // You can access your test data here. Use it to create the DataFrame.
     // Your test here.
  }
}

To create the DataFrame, you can have few util methods like below.

  def schema(types: Array[String], cols: Array[String]) = {
    val datatypes = types.map {
      case "String" => StringType
      case "Long" => LongType
      case "Double" => DoubleType
      // Add more types here based on your data.
      case _ => StringType
    }
    StructType(cols.indices.map(x => StructField(cols(x), datatypes(x))).toArray)
  }

  def df(data: List[String], types: Array[String], cols: Array[String]) = {
    val rdd = sc.parallelize(data)
    val parser = new CSVParser(',')
    val split = rdd.map(line => parser.parseLine(line))
    val rdd = split.map(arr => Row(arr(0), arr(1), arr(2), arr(3)))
    sqlContext.createDataFrame(rdd, schema(types, cols))
  }

I am not aware of any utility classes for checking specific values in a DataFrame. But I think it should be simple to write one using the DataFrame APIs.

Answer 2

You could use SharedSQLContext and SharedSparkSession that Spark uses for its own unit tests. Check my answer for examples.

Answer 3

For those looking to achieve something similar in Java, you can use start by using this project to initialize a SparkContext within your unit tests: https://github.com/holdenk/spark-testing-base

I personally had to mimick the file structure of some AVRO files. So I used Avro-tools (https://avro.apache.org/docs/1.8.2/gettingstartedjava.html#download_install) to extract the schema from my binary records using the following command:

java -jar $AVRO_HOME/avro tojson largeAvroFile.avro | head -3

Then, using this small helper method, you can convert the output JSON into a DataFrame to use in your unit tests.

private DataFrame getDataFrameFromList() {
    SQLContext sqlContext = new SQLContext(jsc());
    ImmutableList<String> elements = ImmutableList.of(
        {"header":{"appId":"myAppId1","clientIp":"10.22.63.3","createdDate":"2017-05-10T02:09:59.984Z"}}
        {"header":{"appId":"myAppId1","clientIp":"11.22.63.3","createdDate":"2017-05-11T02:09:59.984Z"}}
        {"header":{"appId":"myAppId1","clientIp":"12.22.63.3","createdDate":"2017-05-11T02:09:59.984Z"}}
    );
    JavaRDD<String> parallelize = jsc().parallelize(elements);
    return sqlContext.read().json(parallelize);
}