Distributed Lists

Distributed Lists


Scoobi is centered around the idea of a distributed collection, which is implemented by the DList (distributed list) class. In a lot of ways, DList objects are similar to normal Scala List objects: they are parametrised by a type and they provide methods that can be used to produce new DList objects, often parameterised by higher-order functions. For example:

// Converting a List[Int] to a List[String] keeping only evens
val stringList = intList filter { _ % 2 == 0 } map { _.toString }

// Converting a DList[Int] to a DList[String] keeping only evens
val stringDList = intDList filter { _ % 2 == 0 } map { _.toString }

However, unlike a Scala List object, the contents of DList objects are not stored on the JVM heap but stored elsewhere, typically HDFS. Secondly, calling DList methods will not immediately result in data being generated in HDFS. This is because, behind the scenes, Scoobi implements a staging compiler. The purpose of DList methods are to construct a graph of data transformations. Then, the act of persisting a DList triggers the compilation of the graph into one or more MapReduce jobs and their execution.

In summary, DList objects essentially provide two abstractions:

  1. The contents of a DList object abstracts the storage of data and files in HDFS;
  2. Calling methods on DList objects to transform and manipulate them abstracts the mapper, combiner, reducer and sort-and-shuffle phases of MapReduce.

Word count decomposed

Let's take a step-by-step look at the simple word count example from above. The complete application for word count looks like this:

import com.nicta.scoobi.Scoobi._

object WordCount extends ScoobiApp {
  def run() {
    val lines: DList[String] = fromTextFile(args(0))

    val counts: DList[(String, Int)] = lines.flatMap(_.split(" "))
                                            .map(word => (word, 1))


Our word count example is implemented by the object WordCount, wich extends a ScoobiApp. This is a convience in Scoobi to avoid having to create configuration objects, as well as automatically handling arguments intended for hadoop. The remaining arguments are available as args

Within the implementation guts, the first task is to construct a DList representing the data located at the input directory. In this situation, because the input data are simple text files, we can use the fromTextFile method that takes our input directory as an argument and returns a DList[String] object. Here our DList object is a distributed collection where each collection element is a line from the input data and is assigned to lines.

The second task is to compute a DList of word counts given all the lines of text from our input data. This is implemented in four steps:

  1. A flatMap is performed on lines. Like List's flatMap, a parameterizing function is supplied which will take as its input a given line (a String) and will return 0 or more Strings as its result. In this case, that function is the method split which will split the input string (a line) into a collection of words based on the occurrence of whitespace. The result of the flatMap then is another DList[String] representing a distributed collection of words.

  2. A map is performed on the distributed collection of words. Like List's map, a parameterizing function is supplied which takes as its input a given word (a String) and will return another value. In this case the supplied function takes the input word and returns a pair: the word and the value 1. The resulting object is a new distributed collection of type DList[(String, Int)].

  3. A groupByKey is performed on the (String, Int) distributed collection. groupByKey has no direct counterpart in List (although there is a groupBy defined on DLists). groupByKey must be called on a key-value DList object else the program will not type check. The effect of groupByKey is to collect all distributed collection values with the same key. In this case the DList object is of type (String, Int) so a new DList object will be returned of type (String, Iterable[Int]). That is, the counts for the same words will be grouped together.

  4. To get the total count for each word, a combine is performed. combine also has no counterpart in List but its semantics are to take a DList[(K, Iterable[V])] and return a DList[(K, V)] by reducing all the values. It is parametrised by a function of type (V, V) => V that must be associative. In our case we are simply performing addition to sum all the counts.

The final task is to take the counts object, which represents counts for each word, and persist it. In this case we will simply persist it as a text file, whose path is specified by the second command line argument, using toTextFile.

Until persist is called, our application will only be running on the local client. The act of calling persist, along with the DList(s) to be persisted, will trigger Scoobi's staging compiler to take the sequence of DList transformations and turn them into one or more Hadoop MapReduce jobs. In this example Scoobi will generate a single MapReduce job that would be executed:

  • The functionality associated with the flatMap and map will become part of a mapper tasks;
  • The transformation associated with groupByKey will be occur as a consequence of the sort-and-shuffle phase;
  • The functionality of the combine will become part of both a combiner and reducer task.

The word count example is one of a number of examples included with Scoobi. The top level directory examples contains a number of self-contained tutorial-like examples, as well as a guide to building and deploying them. This is an additional starting point for learning and using scoobi.

Parallel operations

We have already seen a number of DList methods - map, flatMap. These methods are parallel operations in that they are performed in parallel by Hadoop across the disributed data set. The DList trait implements parallel operations for many of the methods that you would find in the standard Scala collections:

  • map
  • flatMap
  • filter
  • filterNot
  • collect
  • partition
  • flatten
  • distinct
  • ++

All of these methods are built upon the primitive parallel operation parallelDo. Unlike the other DList methods, parallelDo provides a less functional interface and requires the user to implement a DoFn object:

def parallelDo[B](dofn: DoFn[A, B]): DList[B]

trait DoFn[A, B] {
  def setup(): Unit
  def process(input: A, emitter: Emitter[B]): Unit
  def cleanup(emitter: Emitter[B]): Unit

Because the DoFn object has an interface that is closely aligned to the Hadoop mapper and reducer task APIs it allows greater flexibility and control beyond what the collections-style APIs provide. For example, a DoFn object can maintain state where the other APIs can not:

// Fuzzy top 10 - each mapper task will only emit the top 10 integers it processes
val ints: DList[Int] = ???

val top10ints: DList[Int] = ints.parallelDo(new DoFn[Int, Int] {
  val top = scala.collection.mutable.Set.empty[Int]
  def setup() {}
  def process(input: Int, emitter: Emitter[Int]) {
    if (top.size < 10) {
      top += input
    else if (input > top.min) {
      top -= top.min
      top += input
  def cleanup(emitter: Emitter[Int]) { top foreach { emitter.emit(_) } }

Whilst the parallelDo and DoFn APIs provide greater flexibility, it is best practice to use the collections-style APIs where possible.

Counters and Heartbeat

Similarly, you can use parallelDo as a lower-level API to access Hadoop's counters and "progress" functionality

val list = DList(1, 2, 3).parallelDo((input: Int, counters: Counters) => {
  counters.incrementCounter("group1", "counter1", 1)
  input + 1

When you execute the DList with a ScoobiConfiguration object you will be able to access the counters values at the end of the run on this object:

// a list using counters
val list: DList[Int] = ???
val sc: ScoobiConfiguration = ???

// retrieve counter1 in group1

You can also use the same API to invoke the "Heartbeat" functionality, when you have long-running jobs to indicate that the job is still running

val list = DList(1, 2, 3).parallelDo((input: Int, beat: Heartbeat) => {
  input + 1
Data counters

Two counters can be enabled in order to monitor the number of values going through each mapper or each reducer

val sc: ScoobiConfiguration = ???


// return a list of counters where each counter has the number of values for a given mapper

// return a list of counters where each counter has the number of values for a given reducer

*Important note: * counting values per Mapper/Reducer will create one counter per mapper and one per reducer. However there is a limit on the number of counters which can be created. You can increase this number by changing the mapreduce.job.counters.limit property.


We have already seen the groupByKey method. DList also has a groupBy method that allows you to specicfy how the key is determined:

case class Person(name: String, age: Int)
val people: DList[Person] = ...
val peoplebyAge: DList[(Int, Person)] = people.groupBy(_.age)

The grouping methods abstract Hadoop's sort-and-shuffle phase. As such it is possible to have more control over this phase using the Grouping type class. This allows functionality such as secondary sorting to be implemented. Refer to the Grouping section for a more detailed explanation.


The combine method is Scoobi's abstraction of Hadoop's combiner functionality. For best results, combine should be
called immediately after a groupBy or groupByKey method, as in the Word Count example.

The combine method accepts an argument of the type Reduction[V] where V represents the type of value in the key/value pair. A reduction denotes a binary operation on a closed set ((V, V) => V). The Reduction class and object provide combinators for constructing reductions from existing ones.

For example, to obtain a reduction that performs append on a list of strings (Reduction[List[String]]):

val red: Reduction[List[String]] = Reduction.string.list

Another example performs a reduction on a pair of integer addition and string append, then appending that pair in Option:

val red: Reduction[Option[(Int, String)]] = (Reduction.Sum.int zip Reduction.string).option

The API for Reduction provides many more functions for combining and building reduction values. API documentation is provided for each.

The acceptance tests for Reduction provide more usage examples with documentation (com.nicta.scoobi.acceptance.ReductionSpec). The automated tests for Reduction provide a specification for the algebraic program properties that reductions satisfy (com.nicta.scoobi.core.ReductionSpec).

Creating and persisting DLists

DList objects are merely nodes in a graph describing a series of data transformations we want to perform. However, at some point we need to specify what the inputs and outputs to that computation are. We have already seen this in the previous example with fromTextFile(...) and persist(toTextFile(...)). The former is an example of loading data and the latter is an example of persisting data.

There are many ways of creating a new DList by loading data. Data can be loaded from files or various formats (e.g. text, sequence files, Avro files). Similarly, there are many ways in which a DList can be persisted. The Input and Output section provides a complete listing of all loading and persisting mechanisms.

There are two parts to persisting:

  1. Specifying how the DList object is to be persisted;
  2. Calling persist, which triggers the evaluation of the DList objects computation.

For example, to persist a DList to a text file we could write:

val rankings: DList[(String, Int)] = ...
persist(toTextFile(rankings, "hdfs://path/to/output"))

It is important to note that a call to persist is the mechansim for triggering the computation of a DList and all its dependencies. Until persist is called, a DList is simply a specification for some distributed computation. persist can of course bundle together multiple DLists allowing it to be aware of any shared computations:

val rankings: DList[(String, Int)] = ...
val rankings_reverse: DList[(Int, String)] = rankings.map(_.swap)
val rankings_example: DList[(Int, String)] = rankings_reverse.groupByKey.map{ case (ranking, items) => (ranking, items.head) }

persist(toTextFile(rankings,         "hdfs://path/to/output"),
        toTextFile(rankings_reverse, "hdfs://path/to/output-reverse"),
        toTextFile(rankings_example, "hdfs://path/to/output-example"))