Testing guide

Testing guide

Running locally

The first thing to do is to run your code locally (in non-distributed mode). This does not require you to be running on a Hadoop cluster or even to have Hadoop installed on your local machine. The way to do that is simply to run your application normally under the JVM. You can do that directly using java, but specifying the right class path is tricky; as a result, it's often easier to run using sbt run-main. For example, if your main class is called mypackage.myapp.RunMyApp, run something like the following command from the top-level directory of your SBT project:

sbt "run-main mypackage.myapp.RunMyApp input-files output"

Running on the cluster

Only when your application runs and works locally should you try running on a Hadoop cluster. In order to do so you will probably need to build an assembly, i.e. a self-contained JAR file containing all of the libraries needed. This can be done using the sbt-assembly plugin to SBT. See the Quick Start page in this User Guide for more information.

Once you have built the assembly, you can run it by logging into the job tracker and running it using hadoop, e.g. as follows:

hadoop jar ./target/MyApp-assembly-0.1.jar mypackage.myapp.RunMyApp input-files output

With specs2

Scoobi provides testing support to make your coding experience as productive as possible. It is tightly integrated with specs2 out-of-the-box, but you can reuse the testing traits with your own favorite testing library.

First of all you need to add the specs2 dependency to your build.sbt file (use the same version that Scoobi is using)

Base specification

The abstract class com.nicta.scoobi.testing.mutable.HadoopSpecification is the base class for testing Hadoop jobs. Here's an example showing how to use it:

import com.nicta.scoobi.testing.mutable._

class WordCountSpec extends HadoopSpecification {

  "Counting words frequencies must return the frequency for each word" >> { conf: ScoobiConfiguration =>
    // your Scoobi code goes here


This specification does several things for you:

  • it creates a new ScoobiConfiguration for each example so that you should be able to run your examples concurrently
  • this configuration is setup with the properties found in the $HADOOP_HOME/conf directory. If the $HADOOP_HOME variable is not set or the properties not found you will get an exception at runtime
  • because you are passed in the configuration you can change those properties if required
  • each example will be executed locally only. If you want to execute them on the cluster as well, add scoobi cluster to the command line arguments. If you want some cluster execution but no local execution, use scoobi !local.cluster.
  • all the logging is intercepted so as not to clutter the console output
  • before executing the examples, all the dependent jars, as defined by the sbt classpath, will be copied in a directory on the cluster (~/libjars by default). This upload is only done for missing jars on the cluster


You can change every step in the process above and create your own Specification trait with a different behavior:

  • the fs and jobTracker properties comes from the application.Cluster trait and you can override them with hardcoded values so that you don't depend on what's on your build server

  • you can change the execution context of the examples by overriding the context method and returning local or cluster instead of localThenCluster which is the default specs2 context. The same thing is achievable on the sbt command line by using the exclude argument: test-only *WordCount* -- exclude cluster will only run locally.

  • the directory for loading the jars is defined by the libjarsDirectory method which you can override. More generally you can change the loading and distribution of jars by overriding methods of the application.LibJars trait

Command-line arguments

The command-line arguments which you can use to execute a Scoobi application are also applicable when running specs2 specifications. Here are a few examples:

// enable logging with all the categories
sbt> test-only *WordCountSpec* -- scoobi verbose.all

// enable logging but filter the categories with a regular expression
sbt> test-only *WordCountSpec* -- scoobi verbose.all.[scoobi.impl.*]

// only run in memory
sbt> test-only *WordCountSpec* -- scoobi !local

// only run in memory and on the cluster
sbt> test-only *WordCountSpec* -- scoobi !local.cluster

// only run one example but not in memory, locally only (with all log categories)
sbt> test-only *WordCountSpec* -- -ex my first example -- scoobi !inmemory.verbose.all

// only run the examples with the tag 'amazon EC2', on the cluster
sbt> test-only *WordCountSpec* -- -include amazon EC2 -- scoobi !inmemory.!local.cluster

Fine tuning

Implicit configuration

By default, all the examples of a specification are executed concurrently, which is why each example needs to be passed its own ScoobiConfiguration instance. If you prefer having a sequential execution (with the sequential specs2 argument) you can omit the explicit passing of the ScoobiConfiguration object:

class WordCountSpec extends HadoopSpecification {
  "Counting words frequencies must return the frequency for each word" >> {
    // your Scoobi code goes here
Cluster properties

If you only have one cluster for your testing you can hardcode the fs and jobTracker properties by overriding the corresponding methods:

class WordCountSpec extends HadoopSpecification {
  override def fs         = "hdfs://svm-hadoop1.ssrg.nicta.com.au"
  override def jobTracker = "svm-hadoop1.ssrg.nicta.com.au:8021"

This will be especially useful if you execute your specifications on a build server where Hadoop is not installed or configured.


The display of Hadoop and Scoobi logs can be controlled by passing command-line arguments. By default logs are turned off (contrary to a ScoobiApp) but they can be turned on by using the verbose arguments. See the Application in this User Guide to learn how to set log levels and log categories.

Type alias

Passing the configuration to each example is a bit verbose so you can use a type alias to shorten it:

class WordCountSpec extends HadoopSpecification {
  type SC = ScoobiConfiguration

  "Counting words frequencies must return the frequency for each word" >> { implicit conf: SC =>
    // your Scoobi code goes here

Simple jobs

The HadoopSpecification class allows to create any kind of job and execute them either locally or on the cluster. The SimpleJobs trait is an additional trait which you can use to:

  • write some strings to a temporary input text file and get back a DList representing this data
  • execute some transformations based on the DList API
  • "run" the DList and get the results as a Seq[T]

    class WordCountSpec extends HadoopSpecification with SimpleJobs {
      "getting the size of words" >> { implicit c: SC =>
        val list: List[String] = fromInput("hello", "world")
        list.map(_.size).run must_== Seq("5", "5")

fromInput creates a temporary file and a new DList from a TextInput. Then the run method persists the DList and retrieves the results. At the end of the tests the temporary files are deleted unless the keepFiles argument is set on the command line

Other jobs might be slightly more complex and require inputs coming from several files:

"Numbers can be partitioned into even and odd numbers" >> { implicit sc: SC =>
  val numbers = fromInput((1 to count).map(i => r.nextInt(count * 2).toString):_*).map((_:String).toInt)
  val (evens, odds) = numbers.partition(_ % 2 == 0).run

  forall(evens.map(_.toInt))(i => i must beEven)
  forall(odds.map(_.toInt))(i => i must beOdd)

With your own library

Some of the functionalities described above has been extracted into traits in the application package which you can reuse with your own test framework:

  • ScoobiAppConfiguration provides a ScoobiConfiguration configured from the HADOOP_HOME/conf files

  • LocalHadoop provides the onLocal method to execute Hadoop code locally

  • Hadoop extends the LocalHadoop with the onCluster method to execute a Hadoop job on the cluster

  • ScoobiUserArgs parses command line arguments to extract meaningful values (quiet, showTimes,...) for the Hadoop trait

  • LibJars distributes the dependent jars to the cluster

Debugging tips

Here are a few tips to help you in debugging your application:

  • don't use shared state! Since Hadoop is executing distributed computations, there is no way to share the content of variables across different JVMs. So if one of the functions that you use on a DList updates a variable somewhere, you will get incorrect results or NullPointerExceptions. This also goes for external libraries. Some of them can store some local state which will cause issues in a distributed mode
  • allow some tasks to fail without the entire job failing by using the settings mapred.max.map.failures.percent and mapred.max.reduce.failures.percent. For example, the following command

    hadoop jar ./myapp.jar mypackage.RunMyApp -Dmapred.max.map.failures.percent=20 -Dmapred.max.reduce.failures.percent=20 input output

    will allow up to 20% of map and reduce tasks to fail while still allowing the operation as a whole to proceed

  • Scoobi logs additional debugging information at the DEBUG logging level. Unfortunately, Apache Commons Logging provides no means of programmatically changing the logging level. To do this you have to reach down directly into log4j, the underlying logging implementation used in Hadoop, as follows:

    import org.apache.log4j.{Level=>JLevel,_}