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Spark Transformations

To implement custom transformation logic, specify transformers attribute of an Action. It allows you to chain several transformations in a linear process, where the output SubFeeds of one transformation are used as input for the next.

Depending on your Action type the transformations have different format (described later). The two types are:

  • 1-to-1 transformations (*DfTransformer): One input DataFrame is transformed into one output DataFrame. This is the case for CopyAction, DeduplicateAction and HistorizeAction.
  • many-to-many transformations (*DfsTransformer): Many input DataFrames can be transformed into many output DataFrames. This is the case for CustomDataFrameAction.

The configuration allows you to use predefined standard transformations or to define custom transformation in various languages.

Deprecation Warning

there has been a refactoring of transformations in Version 2.0.5. The attribute transformer is deprecated and will be removed in future versions. Use transformers instead.

Predefined Transformations

Predefined transformations implement generic logic to be reused in different actions. Depending on the transformer there are a couple of properties you can specify, see Configuration Schema Viewer. The following Transformers exist:

  • AdditionalColumnsTransformer (1-to-1): Add additional columns to the DataFrame by extracting information from the context

  • BlacklistTransformer (1-to-1): Apply a column blacklist to a DataFrame

  • ColNamesLowercaseTransformer (1-to-1): change column name to lower case column names in output

  • DataValidationTransformer (1-to-1): validates DataFrame with user defined set of rules and creates column with potential error messages

  • FilterTransformer (1-to-1): Filter DataFrame with expression

  • StandardizeDatatypesTransformer (1-to-1): Standardize data types of a DataFrame

  • WhitelistTransformer (1-to-1): Apply a column whitelist to a DataFrame

  • SparkRepartitionTransformer (1-to-1): Repartions a DataFrame

  • DfTransformerWrapperDfsTransformer (many-to-many): use 1-to-1 transformer as many-to-many transformer by specifying the SubFeeds it should be applied to

Custom Transformations

Custom transformers provide an easy way to define your own data transformation logic in SQL, Scala/Java, and Python. The transformation can be defined within the configuration file or in a separate code file.

Additionally, static options and runtimeOptions can be defined within the custom transformers. runtimeOptions are extracted at runtime from the context. Specifying options allows to reuse a transformation in different settings. For an example see SQL example below.


In general, Scala/Java transformations can be provided within the configuration file or in seperate source files. You can use Spark Dataset API in Java/Scala to define custom transformations. If you have a Java project, create a class that extends CustomDfTransformer or CustomDfsTransformer and implement transform method. Then use type = ScalaClassSparkDfTransformer or type = ScalaClassSparkDfsTransformer and configure className attribute.

If you work without Java project, it's still possible to define your transformation in Java/Scala and compile it at runtime. For a 1-to-1 transformation use type = ScalaCodeSparkDfTransformer and configure code or file as a function that takes session: SparkSession, options: Map[String,String], df: DataFrame, dataObjectName: String as parameters and returns a DataFrame. For many-to-many transformations use type = ScalaCodeSparkDfsTransformer and configure code or file as a function that takes session: SparkSession, options: Map[String,String], dfs: Map[String,DataFrame] with DataFrames per input DataObject as parameter, and returns a Map[String,DataFrame] with the DataFrame per output DataObject.

Examples within the configuration file: Example 1-to-1: select 2 specific columns (col1 and col2) from stg-tbl1 into int-tbl:

  myactionName {
metadata.feed = myfeedName
type = CopyAction
inputId = stg-tbl1
outputId = int-tbl1
transformer = {
scalaCode = """
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions.explode
(session: SparkSession, options: Map[String,String], df: DataFrame, dataObjectId: String) => {
import session.implicits._"col1","col2")

Example many-to-many: joining stg-tbl1 and stg-tbl2 using two indexes, note the mapping of DataFrames:

  myactionName {
metadata.feed = myfeedName
type = CustomDataFrameAction
inputIds = [stg-tbl1, stg-tbl2]
outputIds = [int-tab12]
transformers = [{
type = ScalaCodeSparkDfsTransformer
code = """
import org.apache.spark.sql.{DataFrame, SparkSession}
// define the function, with this fixed argument types)
(session: SparkSession, options: Map[String,String], dfs: Map[String,DataFrame]) => {
import session.implicits._
val df_in1 = dfs("stg-tbl1") // here we map the input DataFrames
val df_in2 = dfs("stg-tbl2")
val df_res = df_in1.join(df_in2, $"tbl1_id" === $"tbl2_id", "left").drop("tbl2_id")
Map("int-tbl12" -> df_res) // map output DataFrame

See more examples at sdl-examples.


You can use Spark SQL to define custom transformations. Input DataObjects are available as tables to select from. Use tokens %{<key>} to replace with runtimeOptions in SQL code. For a 1-to-1 transformation use type = SQLDfTransformer and configure code as your SQL transformation statement. For many-to-many transformations use type = SQLDfsTransformer and configure code as a Map of "<outputDataObjectId>, <SQL transformation statement>".

Example - using options in sql code for 1-to-1 transformation:

transformers = [{
type = SQLDfTransformer
name = "test run"
description = "description of test run..."
sqlCode = "select id, cnt, '%{test}' as test, %{run_id} as last_run_id from dataObject1"
options = {
test = "test run"
runtimeOptions = {
last_run_id = "runId - 1" // runtime options are evaluated as spark SQL expressions against DefaultExpressionData

Example - defining a many-to-many transformation:

transformers = [{
type = SQLDfsTransformer
code = {
dataObjectOut1 = "select id,cnt from dataObjectIn1 where group = 'test1'",
dataObjectOut2 = "select id,cnt from dataObjectIn1 where group = 'test2'"

See sdl-examples for details.


It's also possible to use Python to define a custom Spark transformation. For a 1-to-1 transformation use type = PythonCodeDfTransformer and configure code or file as a python function. PySpark session is initialize and available under variables sc, session, sqlContext. Other variables available are

  • inputDf: Input DataFrame
  • options: Transformation options as Map[String,String]
  • dataObjectId: Id of input DataObject as String

Output DataFrame must be set with setOutputDf(df).

For now using Python for many-to-many transformations is not possible, although it would be not so hard to implement.

Example - apply some python calculation as udf:

transformers = [{
type = PythonCodeDfTransformer
code = """
|from pyspark.sql.functions import *
|udf_multiply = udf(lambda x, y: x * y, "int")
|dfResult ="name"), col("cnt"))\
| .withColumn("test", udf_multiply(col("cnt").cast("int"), lit(2)))


  • Spark 2.4.x:
    • Python version >= 3.4 an <= 3.7
    • PySpark package matching your spark version
  • Spark 3.x:
    • Python version >= 3.4
    • PySpark package matching your spark version

See Readme of sdl-examples for a working example and instructions to setup python environment for IntelliJ

How it works: under the hood a PySpark DataFrame is a proxy for a Java Spark DataFrame. PySpark uses Py4j to access Java objects in the JVM.