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A framework for creating composable and pluggable data processing pipelines using Apache Spark, and running them on a cluster.
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README.md

Sparkplug Sparkplug

A framework for creating composable and pluggable data processing pipelines using Apache Spark, and running them on a cluster.

Please note that this project is early stage work in progress, and will be subject to some breaking changes and refactoring.

Apache Spark is awesome, it lets you define distributed data transformation and analysis with elegant functional Scala code.

Unfortunately the application code that ends up gluing these processes together can end up a bit of a mess, and some thought needs to go into how to make it testable. The mechanism for cluster execution that comes out the box is somewhat inefficient and clunky.

This project aims to bridge the gap. In particular, it addresses these two specific requirements:

  1. Creating data processing pipelines, with elegant Scala code, that are easy to reuse and test in isolation.
  2. Providing a lightweight mechanism for launching and executing Spark processes on a cluster.

These two requirements are quite different. Indeed it is possible to use Sparkplug for either of them without taking advantage of the other. For example it is possible to create composable data pipelines as described below, then execute them directly, or using any other Spark cluster execution or job manager of your choice.

Functional data processing pipelines

The key abstraction here is the SparkOperation monad.

sealed trait SparkOperation[+A] {
  def run(ctx: SparkContext): A
}

SparkOperation are typically created using the companion class. Here is a simple example:

val textRDDOperation: SparkOperation[RDD[String]] = SparkOperation {
  ctx  ctx.makeRDD("There is nothing either good or bad, but thinking makes it so.".split(' '))
}

This is a simple SparkOperation that takes a string and returns a RDD[String] consisting of the words of a sentence.

We can then use this SparkOperation to create another operation.

val letterCount: SparkOperation[Long] = for {
    logData  textRDDProvider
  } yield logData.filter(_.contains("a")).count()
}

In this case we are counting the number of words that contain the letter 'a'.

Proceeding as in this simple example, we can create complex data processing pipelines, mainly using monadic operations.

These include:

  • Bread and butter map and flatmap to compose operations (as above).
  • Combining operations (e.g. convert a tuple of SparkOperations to a SparkOperation of tuples).
  • Sequence operations (e.g. convert a list of SparkOperations to a SparkOperation of list).

Then once we have composed the SparkOperation as desired, it is against a given SparkContext.

val answer = letterCount.run(sparkContext)

The types of SparkOperations are typically, at least until the final step of the pipeline, RDDs.

Why go to all this trouble?

For simple processes, as above, it is overkill. However, non-trivial data processing pipelines typically involve many stages, and often there are many permutations over which these steps may be applied in different scenarios.

Splitting the process into discrete, separate operations has two main advantages:

  1. SparkOperations, modular in nature, can easily be reused or shared across different data processing pipelines.
  2. They can be unit tested in isolation. There are several utilities included in the project that facilitate this. This is covered in the section on testing below.
  3. Operations can be glued together using compact functional code.

Note that this pattern involves decoupling the pipeline definition from the pipeline execution, which enables a great deal of flexibility over how one defines pipelines and executes them. It enables cases in which it useful to reverse the order of operations, and in certain cases avoid their execution completely.

It does lead to the one drawback in that stack dumps are not normally very meaningful. For this reason good logging and error handling is important.

Wiring together SparkOperation components

As a common use case, consider a data transformation pipeline. The fist step in the pipeline is the data input step. The subsequent steps involve processing this data, or running algorithms on it.

For example, consider a pipeline processing a corpus of documents:

  1. The first step processes input data into a RDD of Documents.
  2. The next step is to transform Documents into ParserInfos.
  3. The final step is to calculate document statistics, and return a DocumentStats object with summary statistics.

This can be represented by the following pipeline trait.

trait DocumentPipeline {
  def dataSource: SparkOperation[RDD[InputType]]

  lazy val createOperation: SparkOperation[RDD[Document]] = dataSource.map {
    input  createDocument(input)
  }

  lazy val parseOperation: SparkOperation[RDD[ParserInfo]] = createOperation.map {
    doc  parseDocument(doc)
  }

  lazy val statsOperation: SparkOperation[DocumentStats] = parseOperation.map {
    parsedDoc  calculateStats(parsedDoc)
  }

  lazy val saveParsedDocOperation: SparkOperation[SaveStatus] = parseOperation.flatMap {
    parsedDoc  saveParsedDoc(parsedDoc)
  }
}

Note that SparkOperations need not return RDDs, and in general, the final step in the pipeline will generally return something other than a RDD.

This will generally be the final status after writing data to a database (Try[Unit] is good for this), or some summary/aggregate result.

The final result of the pipeline should be a serializable type.

We wish to use a different data source for test and production environments. This can be done by applying the following overrides:

E.g. for the production environment, we may be using Cassandra as a data source:

import springnz.sparkplug.cassandra.CassandraRDDFetcher

trait ProdDocPipeline extends DocumentPipeline {
  override lazy val dataSource = CassandraRDDFetcher.selectAll[InputType](keySpace, table)
}

Functional pipeline composition patterns

SparkOperation[A] is a monad. As such all the monad patterns are available. The monad implementation is provided by scalaz.

To take advantage of some of the operations, certain imports from scalaz are necessary. Implementations of map and flatMap are provided, so no imports for these, and for comprehensions, are necessary.

Here are some examples of functional pipeline patterns:

Map

This is the most commonly used pattern, and examples of its usage is given in the pipeline above. Map is best suited to constructing a single-step extension to the pipeline.

FlatMap

Many SparkOperations are constructed via a function of the following form:

object OperationFactory {
  def createOperation(rdd: RDD[A]): SparkOperation[B] = ???
}

This pattern is often used for operations that persist data to a database such as Cassandra or to a HDFS file store.

E.g. in the document example given above,

def saveParsedDoc(RDD[ParserInfo]): SparkOperation[SaveStatus]

is a function that generates a SparkOperation from a RDD. To plug it into the pipeline, it must be flatmapped.

map is generally useful for connecting a single process to the end of a pipeline. flatMap is more powerful. It can connect two entire pipelines.

Joins and pairs / tuples

FlatMap can be used to do joins. However, applicative functors are the functional abstraction most naturally suited to this operation.

Here is an example of a join operation (with A, B and C obviously being the appropriately compatible types):

def joinRDDs(rddA: RDD[A], rddB: RDD[B]): RDD[C] = {
  ...
  rddA.join(rddB)
}

Here is how they can be applied to create a SparkOperation representing this join:

import scalaz.syntax.bind._

def operationC: SparkOperation[RDD[C]] = (operationA |@| operationB)(joinRDDs)

The result of this is a new operation that when executed, will perform the following:

  • Execute operationA and operationB to produce an RDD[A] and an RDD[B] respectively.
  • Perform the join to produce an operationC or type RDD[C].

Note that it is necessary to import scalaz.syntax.bind._ to bring the |@| operator (or it's unicode variant, ) into scope.

Sequences and Traversables

Sequence operators are a natural generalisation of an applicative, which takes a pair of Spark operations and produces a Spark operation of pairs. A sequence operator takes a List of Spark operations and generates a single Spark operation of Lists. Traversable operations are a further generalisation of this to allow an extra function in between, and can be applied across a more general class of data structures than just Lists.

An example of this is the following:

Suppose we have a SparkOperation that processes a single days data, and we wish to run the same operation on a month of data.

def daysOperation(date: Date): SparkOperation[A]  = ???

The following code generates a list of operations, each of which processes data for a single date:

val listOfOperations: List[SparkOperation[A]] = for (day <- daysInMonthList) yield daysOperation(day)

This can be transformed as:

import scalaz.std.list.listInstance

val combinedOperation: SparkOperation[List[A]] = SparkOperation.monad.sequence(jobsList)

The syntax is not as cute, but it is still one line of code to create a SparkOperation that generates a list of As.

No more ugly looping code.

Anti-patterns

There aren't too many ways of going wrong, but the one pattern to avoid is performing operations on RDDs when they could be performed on SparkOperations.

As general guidelines:

  • Use the SparkOperation { ctx => ??? } generally for the first step of the pipeline, or if you specifically need a SparkContext.
  • If you end up with a SparkOperation[RDD[A]] defined as SparkOperation { _ => ??? } (where you don't need the SparkContext), there is probably a better way of doing it involving map, flatMap etc.
  • Create the (non private parts of the) pipeline solely of SparkOperations - note that SparkOperations can easily be overridden or intercepted for testing purposes (see below).
  • Aim for the right granularity of intermediate SparkOperations in the pipeline. They should be big enough to do something non trivial, but small enough to allow for granular unit testing.

Testing

The springnz.sparkplug.testkit namespace contains methods to sample and persist RDDs at various stages in the pipeline. This enables isolated testing. It also relieves one from the burden of hand crafting test cases.

The Sparkplug testing philosophy is to write as little test code as possible! Each test case should be isolated to testing a single SparkOperation.

In general one or more of the dependent spark operations in the pipeline needs to be overridden to make the pipeline suitable for testing. Sparkplug provides utilities to make this easy. These take the form of a family of SparkOperation extension methods.

To enable them:

import springnz.sparkplug.testkit._

In the test environment there are several use case cases that are dealt with:

Generating a test data set

The first use case is where we have access to a data source, say from cassandra, and we wish to generate some sample test data from it.

trait TestDocPipeline extends ProdDocPipeline {
  override lazy val dataSource = super.dataSource.sourceFrom("DocumentInputRDDName")
}

The sourceFrom extension method will have the following behaviour:

  • If the test data set is not available, it will attempt to read from the production data source (by executing the super.dataSource operation, and then persist the data to file in the resources folder. It has a parameter which allows you to sample the source data, to generate a manageable size test data set. The default is the identitySampler, which doesn't sample.
  • If the test data set is available in the test location on disk, it will depersist it and return a RDD of the appropriate type. It will not go to the production data source at all.

Persisting data further down the pipeline

This use case is for creating a test dataset for testing operations further down the pipeline in isolation.

For example, in the code above, the parseOperation may be expensive to run. We wish to create a test case to test the statsOperation. In this case we override the parseOperation in the test pipeline as follows.

trait TestDocPipeline extends ProdDocPipeline {
  ...
  override lazy val parseOperation = super.parseOperation.saveTo("ParsedDocument")
}

This makes the "ParsedDocument" test data available for consumption in any other test case. This can be done as follows:

Retrieving test data

trait TestDocPipeline extends ProdDocPipeline {
  override lazy val parseOperation = TestRDDSource.load[ParserInfo]("DocumentInputRDDName")
}

This simply depersists the RDD where it was saved by the saveTo method.

Anatomy of a test case

A simple test case may look like this:

import springnz.sparkplug.testkit._

class DocumentTests extends WordSpec with ShouldMatchers with Logging {
  import RDDSamplers._
  import TestExtensions._

 "Document test" should {
   "parse the document" in
     new SimpleTestContext("DocumentTest") with TestDocPipeline {
        // override operation
        override lazy val parseOperation = TestRDDSource.load[ParserInfo]("DocumentInputRDDName")

        // execute
        val (stats, statsCount) = execute(statsOperation.takeWithCount(10)).get

        // add assertions here
        statsCount shouldBe 42 ...
      }
   }
}

The ideal formula for a test case, or a test fixture is:

  1. Define: Create a pipeline trait consisting of a sequence of connected SparkOperations.
  2. Override: Override one or more of the operations, as described above, to inject data into the pipeline.
  3. Execute: Execute the test case. TestContexts are provided in springnz.sparkplug.testkit. to help with this.
  4. Ratify: Check that the output is as assumed. This involves asserting the output of the execution.

If your test cases look like this you're a Sparkplug DOER!

Note that methods cannot be called on RDDs after a SparkContext has stopped. It is necessary to convert them as part of the tested operation. For this utility extension methods count, collect, take and takeOrdered are provided to make this easy. Corresponding *withCount methods are provided that return the count of the RDD as well.

Execution on a cluster

The recommended mechanism for execution on a cluster (Java or Scala) is as follows:

  1. Package your Java / Scala project into a assembly (a single jar with all the transitive dependencies, sometimes called an uber jar).
  2. Invoke the spark-submit app, passing in the assembly into the command line. Your app is run on the cluster, and spark-submit terminates after your app finishes. The Spark project also contains a SparkLauncher class, which is a thin Scala wrapper around spark-submit.

However, there is still plenty of work to do to coordinate this in a production environment. If you are already doing this kind of work in Scala, the Spark Plug library is very useful.

Another issue is that creating assemblies can lead to all sorts of problems with conflicts in transitive dependencies, which are often difficult to resolve, especially if you don't even know what the these dependencies do. Assembblies can also get large really quickly, and can take a while for spark-submit to upload to the cluster.

A third issue is that ideally you want the cluster to be available when a job request arrives. However there is plenty that can be set up in advance in preparation, so that when the job request arrives, there is less that can go wrong. The spark-submit command line execution pattern doesn't easily facilitate that.

How Sparkplug cluster execution works

The use case that Sparkplug cluster execution is particularly well suited to is where your overall technology stack is Scala based, and particular if Akka is a big part of it. If you have a polyglot stack, something like the REST based Spark Job Server may be more suitable.

Sparkplug launcher uses Akka remoting under the hood. Sparkplug launches jobs on the cluster using the following steps:

  1. The client has an ActorSystem running, and an execution client actor.
  2. This client invokes spark-submit to run an application on the server.
  3. The server starts up it's own ActorSystem, and once this is done, sends a message to inform the client.
  4. It creates a SparkContext, which is then available to service request to run Spark jobs that it may receive. The service is now ready for action.
  5. When a request arrives at the client, it sends a message to the server to process the request.
  6. The job is then run by the server and the client is notified when it is done. The final result is streamed back to the client.

Creating a SparkPlugin

It's really simple to create a Sparkplug plugin (hereafter referred as a SparkPlugin to avoid tautology.

Simply create a class that extends the SparkPlugin trait.

trait SparkPlugin {
  def apply(input: Any): SparkOperation[Any]
}
package mypackage

class DocumentStatsPlugin extends SparkPlugin {
  import ProdDocPipeline._

  override def apply(input: Any) = statsOperation
}

In this case the DocumentStatsPlugin is a hook to create a statsOperation, the a SparkOperation that calculate document statistics referred to above. In this case the input is not used.

Starting a job on the cluster

The recommended way to launch a Job on the cluster is via the futures interface.

trait ClientExecutor {
  def execute[A](pluginClass: String, data: Option[Any]): Future[A]
  def shutDown(): Unit
}

Use the ClientExecutor companion object to create an executor instance.

val executor = ClientExecutor.create(params: ClientExecutorParams)

Note that the executor can be called multiple times, but the shutDown method must be called to free up local and remote resources.

If you wish to execute low latency jobs, this is the way to go.

If you only intend invoking a single, long running job within a session, and don't care about the startup time, simply use the apply on the ClientExecutor object:

implicit val ec = scala.concurrent.ExecutionContext.global
val docStatsFuture = ClientExecutor[DocumentStats]("mypackage.DocumentStatsPlugin", None)

Executing a job on a Spark cluster doesn't get easier than this!

Configuration

All the configuration necessary can be overrided, and is done so via the ClientExecutorParams class.

import com.typesafe.config.Config

object ClientExecutor  {
  ...
  case class ClientExecutorParams(
    akkaClientConfig: Config = defaultClientAkkaConfig,
    sparkConfig: Config = defaultSparkConfig,
    akkaRemoteConfig: Option[Config] = None,
    jarPath: Option[String] = None)

The following config can be provided:

  • Akka client configuration
  • Akka remote configuration
  • Spark configuration

The default Akka configuration should work fine in most cases, but the Spark configuration will probably need to be customised in most production environments. Note that all the configuration for the remote Spark context is dynamically driven from the client end. See the Spark config docs for more information this mechanism.

The default spark configuration is whatever is enclosed in the config section sparkplug.spark.conf. Note that if you override it, you supply the inner configuration (spark.master, spark.home etc). The mechanism provided gives a working setp up the box, but give you total flexibility in how you override it.

Possible future interface changes

  • A moderate refactoring of the client interface is planned to support running multiple server applications (each with their own SparkContext)
  • It is also intended to provide a type safe plugin interface in the future - or at least investigate the possibility

Projects

SparkPlug is set up as a sbt multi-project with the following subprojects:

  • sparkplug-core: The core SparkOperation monad and related traits and interfaces.
  • sparkplug-extras: Components for data access (currently Cassandra, Elasticsearch and SQL) and utilities for testing. It is not recommended to use this project, but rather do your own data access, and treat these as examples.
  • sparkplug-examples: Several examples for how to create Spark pipelines. A good place to start.
  • sparkplug-executor: The Server side of the cluster execution component.
  • sparkplug-launcher: The Client side of the cluster execution component.

If you are building an application using sparkplug, you would require the following dependencies:

  • sparkplug-core: For any of your pipeline classes.
  • sparkplug-launcher: For cluster execution.

Clustering

The cluster execution component has a few more moving parts:

As a bare minimum, Spark needs to be installed on the local machine.

The standard Spark packages are all based on Scala 2.10. Sparkplug requires a Scala version 2.11 edition of Spark, which needs to be built from source.

See the spark build documentation on how to do this.

OR:

Use the forked version of Spark: https://github.com/springnz/spark

  • Pull the branch corresponding to the current Spark version.
  • Execute the make-sparkplug-distribution.sh script to create a distribution.

When installing Spark, make sure the SPARK_MASTER and SPARK_HOME environment variables have been set.

You can start a simple cluster on your workstation, or you can set SPARK_MASTER="local[2]" to run Spark in local mode (with 2 cores in this case).

All jars in your client application need to be in one folder. This can be done using SBT Pack. Add the following line to your plugins.sbt file: addSbtPlugin("org.xerial.sbt" % "sbt-pack" % "0.7.5") (see the SBT Pack docs for the latest info).

Then, before running the tests, run sbt pack. This copies all the .jar files to the folder {projectdir}/target/pack/lib. Any jars that are in this folder will be added to the submit request.

TODO

There is plenty of work to do to get SparkPlug into fully fledged production mode.

This includes, but is not limited to:

  • Support for more data sources.
  • Better error handling and monitoring of cluster execution.
  • Testing on Mesos and YARN clusters.
  • (Possibly) extensions to support Spark Streaming.
  • Creating an activator template for a Sparkplug client application.
  • Plus many more...

Contributions are welcome.

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