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Creating a StreamSets Spark Transformer in Scala

The Spark Evaluator stage allows you to implement custom processing in an Apache Spark application. The Spark Application runs for the lifetime of the pipeline, processing batches of records as they arrive. You can configure the number of threads to run in the Spark Application, allowing you to take advantage of parallel processing. Your application can use the Spark libraries, manipulate data via RDDs, and call existing Scala and Java library code.

This tutorial explains how to create a simple Apache Spark application, using Scala, that will compute the type of a credit card from its number, and configure the Spark Evaluator to use it. A companion tutorial explains how to implement the same functionality in Java.

If you have not yet completed the NYC Taxi Transactions tutorial in the Data Collector documentation, I highly recommend that you do so. You must understand the basics of StreamSets Data Collector before moving on to advanced topics such as building a custom Spark Transformer.

Many thanks to Maurin Lenglart of Lore IO for providing the skeleton Scala transformer.

Prerequisites

  • Download and install StreamSets Data Collector. This tutorial uses Data Collector 3.4.2, but the instructions should apply to subsequent versions. Please file an issue if this is not the case.
  • Install a stage library that supports the Spark Evaluator. The Spark Evaluator is available in several CDH and MapR stage libraries; this tutorial uses CDH 5.15. To verify the Spark version that a stage library includes, see the CDH or MapR documentation. For more information about the stage libraries that include the Spark Evaluator, see Available Stage Libraries. Note that the Spark Evaluator does not currently work with CDH 6.0.
  • Oracle Java Development Kit (JDK) 1.8 or later is needed to compile Java code and build JAR files.
  • Scala version 2.10 or later and sbt version 0.13 or later.

The stage libraries that include the Spark Evaluator also include all necessary Spark dependencies. You do not need to download or install a Spark distribution.

Implementing a Skeleton Transformer

The main class of the Spark Application must extend the com.streamsets.pipeline.spark.api.SparkTransformer abstract class, implementing the transform(JavaRDD<Record> recordRDD) and, optionally, init(JavaSparkContext context, List<String> parameters) and destroy() methods.

Here's a minimal implementation that simply returns its input as its output:

package com.streamsets.spark.scala

import com.streamsets.pipeline.api.Record
import com.streamsets.pipeline.spark.api.SparkTransformer
import com.streamsets.pipeline.spark.api.TransformResult
import org.apache.spark.api.java.JavaPairRDD
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.api.java.JavaSparkContext

import java.io.Serializable
import java.util

class CustomTransformer extends SparkTransformer with Serializable {
  var emptyRDD: JavaRDD[(Record, String)] = _

  override def init(javaSparkContextInstance: JavaSparkContext, params: util.List[String]): Unit = {
    // Create an empty JavaPairRDD to return as 'errors'
    emptyRDD = javaSparkContextInstance.emptyRDD
  }

  override def transform(recordRDD: JavaRDD[Record]): TransformResult = {
    val rdd = recordRDD.rdd

    val errors = emptyRDD

    // Apply a function to the incoming records
    val result = rdd.map((record)=> record)

    // return result
    new TransformResult(result.toJavaRDD(), new JavaPairRDD[Record, String](errors))
  }
}

Create a new directory for your Spark Transformer project, and save the above code there as src/main/scala/CustomTransformer.scala. You will also need a build.sbt file in the project directory itself. Change the version of streamsets-datacollector-spark-api to match your Data Collector version, and change the version of org.apache.spark to match the stage library you will be using. In this tutorial, I'm using CDH 5.15; looking in the $SDC_HOME/streamsets-libs/streamsets-datacollector-cdh_5_15-lib/lib/ directory, I can see that the Spark core jar file is spark-core_2.11-2.1.0.cloudera1.jar. We don't need to reference the Cloudera-specific version, so we set the dependency as "org.apache.spark" % "spark-core_2.11" % "2.1.0". If you are using a different stage library, you will need to change these values to match your Spark core jar file.

name := "spark_transformer"

version := "1.0"

scalaVersion := "2.10.6"

resolvers ++= Seq(
  "cloudera" at "https://repository.cloudera.com/artifactory/cloudera-repos/"
)

libraryDependencies ++= Seq(
  "com.streamsets" % "streamsets-datacollector-spark-api" % "3.4.2",
  "org.apache.spark" % "spark-core_2.11" % "2.1.0"
)

Now build the project with sbt clean package:

$ sbt clean package
[info] Loading project definition from /Users/pat/Downloads/spark_transformer/project
[info] Set current project to spark_transformer (in build file:/Users/pat/Downloads/spark_transformer/)
[success] Total time: 1 s, completed Feb 20, 2017 10:03:48 AM
[info] Updating {file:/Users/pat/Downloads/spark_transformer/}spark_transformer...
[info] Resolving org.fusesource.jansi#jansi;1.4 ...
[info] Done updating.
[info] Compiling 1 Scala source to /Users/pat/Downloads/spark_transformer/target/scala-2.10/classes...
[info] Packaging /Users/pat/Downloads/spark_transformer/target/scala-2.10/spark_transformer_2.10-1.0.jar ...
[info] Done packaging.
[success] Total time: 16 s, completed Feb 20, 2017 10:04:04 AM

Note - you may see some warnings of version conflicts as the JAR is built. These are due to the various Hadoop and Spark jar files using different versions of common libraries. These version conflicts will not cause a problem at runtime, since the correct libraries are packaged into the Data Collector stage libraries.

You should see a jar file in the target/scala-2.10 directory:

$ ls target/scala-2.10
classes       spark_transformer_2.10-1.0.jar

Creating a Pipeline Using a Spark Transformer

Since the skeleton Spark Transformer simply passes records through unchanged, you could drop it into any pipeline to test it. Since we'll be extending the skeleton to operate on credit card data, we'll build a simple pipeline to read in a CSV file based on the New York City taxi transaction dataset.

  1. Download the sample CSV file from here.

  2. In the Data Collector home screen, click the Create New Pipeline button, enter a suitable name and description and click Save.

  3. In the Properties panel, click the Error Records tab; for the Error Records property, select Write to File. This writes error records to a file so you can deal with error records without having to stop the pipeline.

  4. Click the Error Records - Write to File tab and set Directory to an appropriate location on your machine. Note - this directory must exist for the pipeline to be started.

  5. Click Select Origin > Directory, or, in the stage library, click the Directory origin to add a Directory origin to the pipeline.

  6. In the Properties panel, click the Files tab and configure the following properties. Use the defaults for properties that aren't listed:

Files Property Value
Files Directory Directory where you saved the sample CSV file.
File Name Pattern nyc_taxi_data.csv
  1. Click the Data Format tab and configure the following. Use the defaults for properties that aren't listed:
Data Format Property Value
Data Format Delimited
Header Line With Header Line

  1. Click Select Processor > Spark Evaluator, or, in the stage library, click the Spark Evaluator processor, then in the General tab, set the stage library to CDH 5.15.0, or whichever stage library you installed.

  2. Click the Spark tab and configure the following. Use the defaults for properties that aren't listed:

Data Format Property Value
Parallelism For best performance, set this to the number of CPU cores in your machine
Spark Transformer Class com.streamsets.spark.scala.CustomTransformer
  1. Click the External Libraries tab on the left and click the upload icon upload. Click browse and select the jar file you created in the previous step:

install stage library

Note that your Spark Transformer is now available wherever you use the Spark Evaluator - not just in this pipeline.

Your pipeline should look like this:

pipeline spark configuration

Don't worry about the fact that the Spark Evaluator has an open output stream; you can still preview the pipeline interactively to check that the Spark Transformer is correctly loaded. Click the preview icon above the pipeline canvas, click Run Preview, and then click the Spark Evaluator stage. Open up the first record in the preview panel and you will see the unmodified data:

preview pipeline

Troubleshooting

SPARK_01 - Specified class: 'com.streamsets.spark.scala.CustomTransformer' was not found in classpath

If you see the above error message, check all the steps in Installing a Spark Transformer above. If it still doesn't work, feel free to reach out for help on the sdc-user Google Group or our community Slack team.

SPARK_07 - Spark job failed with error: null

java.lang.InterruptedException

If you see either of the above error messages, the most likely problem is that Spark is taking too long to start. Increase Preview Timeout and try again.

Extending the Spark Transformer

Now that we have a very basic 'do nothing' sample working, we can extend it. We'll reimplement the Jython script from the 'Taxi Transactions' tutorial as a Spark Transformer in Scala. The script examines the value in the credit_card field and sets a credit_card_type field according to a set of credit card issuer prefixes - '4' for Visa, '51', '52', '53', '54' or '55' for Mastercard etc.

Let's start with a basic implementation. Add this import near the top of CustomTransformer.scala:

import com.streamsets.pipeline.api.Field

Add a 'companion object' above the class definition:

object CustomTransformer {
  val VALUE_PATH = "/credit_card"
  val RESULT_PATH = "/credit_card_type"

  // Return true if creditCard starts with one of prefixList
  def ccPrefixMatches(creditCard: String, prefixList: Array[String]) : Boolean = {
    return !(prefixList.filter(creditCard.startsWith(_)).isEmpty)
  }
}

Then add the following code at the top of the CustomTransformer class, just before the emptyRDD member variable:

val ccTypes = collection.immutable.ListMap(
  "Visa" -> Array("4"),
  "Mastercard" -> Array("51","52","53","54","55"),
  "AMEX" -> Array("34","37"),
  "Diners Club" -> Array("300","301","302","303","304","305","36","38"),
  "Discover" -> Array("6011","65"),
  "JCB" -> Array("2131","1800","35"),
  "Other" -> Array("")
)

Now replace the transform() method with the following:

override def transform(recordRDD: JavaRDD[Record]): TransformResult = {
  val errors = JavaPairRDD.fromJavaRDD(emptyRDD)

  // Apply a function to the incoming records
  val result: JavaRDD[Record] = recordRDD.rdd.map(record => {
    val creditCard: String = record.get(CustomTransformer.VALUE_PATH).getValueAsString
    val matches = ccTypes.filter((ccType) => CustomTransformer.ccPrefixMatches(creditCard, ccType._2))
    record.set(CustomTransformer.RESULT_PATH, Field.create(matches.head._1))
    record
  })

  // return result
  new TransformResult(result.toJavaRDD(), new JavaPairRDD[Record, String](errors))
}

Your code should look like this when you're done.

Rebuild the jar file, reinstall it, and restart Data Collector.

Preview the pipeline again and you will see that, this time, a new field has been added to each record, containing the credit card type:

preview pipeline

Validating Input Records and Reporting Errors

If you look at each of the first ten records, you will notice that some are cash transactions (payment_type is CSH) and do not contain credit card numbers. Since the non-existent number doesn't match any of the defined prefixes, the credit card type is set to Other. This isn't quite right. Let's stipulate that the transformer should only be called on records with a credit card number, and flag the record as an error if the credit card number is not present.

Add the following import to the top of CustomTransformer.scala

import org.apache.spark.rdd.RDD

Add the following method to the CustomTransformer companion object, after the constant definitions:

def validateRecord(record: Record) : Boolean = {
  // We need a field to operate on!
  Option(record.get(VALUE_PATH)).exists(_.getValueAsString.length > 0)
}

Now replace the line of code that sets the errors value, near the top of transform(), with the following:

var rdd = recordRDD.rdd

// Validate incoming records
val errors: RDD[(Record, String)] = rdd.mapPartitions(iterator => {
  iterator.filterNot(CustomTransformer.validateRecord(_)).map((_, "Credit card number is missing"))
})

This code creates an RDD of records and error messages. The validateRecord method is called on each record and, if it returns false, the record is flagged as an error, with an appropriate message.

We'll also need to filter the invalid records out of the RDD on which transform is operating (unfortunately, there is no way of creating an RDD of errors and operating on the records in one shot). Replace these lines of code:

val result: JavaRDD[Record] = recordRDD.rdd.map(record => {
  val creditCard: String = record.get(CustomTransformer.VALUE_PATH).getValueAsString
  val matches = ccTypes.filter((ccType) => CustomTransformer.ccPrefixMatches(creditCard, ccType._2))
  record.set(CustomTransformer.RESULT_PATH, Field.create(matches.head._1))
  record
})

With these lines:

val result = rdd.mapPartitions(iterator => {
  iterator.filter(CustomTransformer.validateRecord(_)).map(record => {
    val creditCard: String = record.get(CustomTransformer.VALUE_PATH).getValueAsString
    val matches = ccTypes.filter((ccType) => CustomTransformer.ccPrefixMatches(creditCard, ccType._2))
    record.set(CustomTransformer.RESULT_PATH, Field.create(matches.head._1))
    record
  })
})

The transform() method should now look like this:

override def transform(recordRDD: JavaRDD[Record]): TransformResult = {
  var rdd = recordRDD.rdd

  // Validate incoming records
  val errors: RDD[(Record, String)] = rdd.mapPartitions(iterator => {
    iterator.filterNot(CustomTransformer.validateRecord(_)).map((_, "Credit card number is missing"))
  })

  // Apply a function to the incoming records
  val result = rdd.mapPartitions(iterator => {
    iterator.filter(CustomTransformer.validateRecord(_)).map(record => {
      val creditCard: String = record.get(CustomTransformer.VALUE_PATH).getValueAsString
      val matches = ccTypes.filter((ccType) => CustomTransformer.ccPrefixMatches(creditCard, ccType._2))
      record.set(CustomTransformer.RESULT_PATH, Field.create(matches.head._1))
      record
    })
  })

  new TransformResult(result.toJavaRDD(), new JavaPairRDD[Record, String](errors))
}

The file as a whole should now look like this.

Repeat the build, copy, restart cycle, and preview the pipeline. This time, you should see 7 errors reported by the Spark Evaluator stage. Click on the Spark Evaluator and you will see the error message reported against records with missing credit card numbers:

error records

Configuring the Spark Transformer

Now that our transformer is validating its input, there's one final enhancement we can make. Right now, the mapping of credit card type to prefix is stored in the Scala code. This means that, if we want to add another prefix or type, we have to recompile the transformer jar and copy it to all Data Collector instances that use it. It would be much better to have the list of prefixes in the pipeline configuration.

If you look at the Spark tab for the Spark Evaluator stage, you'll see the Init Method Arguments property. This is a list of strings that is passed to the transformer's init() method when the pipeline starts. We can populate this list with our mappings, and parse it in init().

Add the following import to the top of CustomTransformer.scala

import scala.collection.JavaConversions._

Remove the definition of ccTypes, and the static initializer block, and replace it with the following:

val ccTypes = collection.mutable.LinkedHashMap[String, Array[String]]()

Now replace init() with:

override def init(javaSparkContextInstance: JavaSparkContext, params: util.List[String]): Unit = {
  for (param <- params) {
    val keyValue = param.split("=")
    ccTypes += (keyValue(0) -> (if (keyValue.size > 1) keyValue(1).split(",") else Array("")))
  }
}

The CustomTransformer.scala file should now look like this.

Since ccTypes is still a map of strings to arrays of strings, we don't need to modify the transform() method at all. Repeat the build, copy, restart process but, before you preview the pipeline, click the Spark Evaluator, and select the Spark tab in the configuration panel. Under Init Method Arguments, click Switch to bulk edit mode then paste in the following JSON:

[
  "Visa=4",
  "Mastercard=51,52,53,54,55",
  "AMEX=34,37",
  "Diners Club=300,301,302,303,304,305,36,38",
  "Discover=6011,65",
  "JCB=2131,1800,35",
  "Other="
]

credit card type configuration

This is the data that will be passed to the transformer's init() method at pipeline startup.

Preview the pipeline and you should see that the credit card type is correctly computed, as before. Cancel the preview, click into the Spark Evaluator's Spark tab, and change Mastercard to MC. Preview again, and you should see that the credit card type for the second record is now MC:

modified credit card type

Going Further

If you've done the NYC Taxi Transactions tutorial in the Data Collector documentation, you might have realized that this transformer is a drop-in replacement for the Jython script in that pipeline. Try duplicating that pipeline and replacing the Jython Evaluator with a Spark Evaluator, configuring it as above. Since this pipeline uses Stream Selector to split card from cash transactions, you should not see any errors when previewing the first 10 records. If you run the pipeline on the entire input data set, however, you will see that some records have the 'CRD' payment type but no credit card number. The transformer is correctly flagging erroneous records.

modified credit card type