Scoobi provides several traits to help building applications. ScoobiApp brings them all together:

object WordCount extends ScoobiApp {
  def run() {
    val lines = DList(repeat("hello" -> 3, "world" -> 4):_*)

    val frequencies = lines.flatMap(_.split(" "))
      .map(word => (word, 1))
      .combine((a: Int, b: Int) => a + b).materialise


  /** @return a Seq of strings where each key has been duplicated a number of times indicated by the value */
  def repeat(m: (String, Int)*): Seq[String] = m.flatMap { case (k, v) => Seq.fill(v)(k) }

When you extend the ScoobiApp trait:

  • you need to implement the run method with your Scoobi logic

  • if you create an object, you get a main(arguments: Array[String]) method to run your code from the command-line: run-main examples.WordCount

  • you inherit the command-line arguments in a variable named args

  • you inherit an implicit ScoobiConfiguration object, for persisting DLists. The ScoobiConfiguration object encapsulates a Hadoop Configuration object setup with the Hadoop command-line arguments, and is accessible through the configuration variable.


ScoobiApp provides support for passing in Hadoop-configuration and Scoobi-configuration arguments, which are stripped from the command line prior to being made available in the args variable.

The format of the command line is as follows:


where COMMAND-TO-INVOKE-PROGRAM is the invocation needed to run the program, HADOOP-ARGS are optional arguments to configure Hadoop, APPLICATION-ARGS are the arguments to the application itself, and everything else specifies arguments to configure Scoobi. Note that both the Hadoop-configuration and Scoobi-configuration arguments are optional.

A simple example that runs an application mypackage.MyApp using sbt run-main with application arguments input-dir output-dir and without any configuration-level arguments is:

sbt "run-main mypackage.MyApp input-dir output-dir"

A more complicated example is as follows:

sbt "run-main mypackage.MyApp -Dmapred.max.reduce.failures.percent=20 --by-time input-dir output-dir -- scoobi warn.times"

This command contains the following sets of arguments:

  1. The arguments -Dmapred.max.reduce.failures.percent=20 are handled by Hadoop. (They allow for 20% of the map or reduce tasks to fail and the job as a whole to still succeed.)
  2. The arguments --by-time input-dir output-dir are passed to the application itself.
  3. The arguments warn.times are handled by Scoobi. (They set the log level to WARN and cause logging of execution time.)

The various sets of arguments are accessible from the ScoobiApp with the following variables:

  1. args: The application arguments.
  2. scoobiArgs: The Scoobi-configuration arguments, split on ".".
  3. commandLineArguments: The entire set of command-line arguments.

Scoobi Configuration Arguments

The following are the possible configuration arguments for Scoobi:

Name Default value Description
verbose true if defined, log statements are displayed
quiet false if defined, log statements are not displayed
times false if defined the total execution time is logged
level info minimum level of statements to log
category .* regular expression. By default everything is logged. Use scoobi to display only Scoobi logs
local false if defined, run the Scoobi job locally
inmemory false if defined, run the Scoobi job in memory backed up by Scala collections instead of Hadoop
deletelibjars false if defined, remove jars from the libjars directory before uploading them again
nolibjars false if defined, do not upload dependent jars to the libjars directory and include the Scoobi jar in the job jar
useconfdir false if defined, use the configuration files in $HADOOP_HOME/conf or $HADOOP_CONF(useful when running apps from inside sbt)
keepfiles false if defined, temp files and working directory files are not deleted after the job execution (only for testing)

The list of possible log levels is all, fatal, info, warn, trace, off.

It is also possible to change configuration values by overriding methods in ScoobiApp.


Based on how you package your Scoobi application, Scoobi will try to determine what is the best way to find your dependencies and to add them to the classpath:

  1. if you build a "fat" jar containing the application code plus all the dependent jars (see the Deployment page), Scoobi will get all entries in this jar and add them to a "Job" jar that is distributed to all nodes on the cluster. The drawback of this method is that all the dependencies are sent to the cluster each time the application is executed

  2. if you build a "slim" jar with only the application code or execute from inside sbt, Scoobi will find the dependent jars by introspecting the classloader and try to upload them to a directory on the cluster, then to distribute them to each node. This will be done only once to speed-up the development cycle

For each of those 2 options, you can still alter Scoobi behavior by passing command-line arguments or by overriding some methods of the ScoobiApp trait

"Fat" jar

Scoobi determines if a jar is a "fat" jar by:

  1. determining which class is used to run the application (a class extending ScoobiApp)
  2. finding the jar containing this class
  3. checking if this jar contains the "scoobi" jar (in a lib directory for example if packaged using sbt-assembly) or some Scoobi classes

In this case all jar entries will be copied to a job-specific jar distributed to each node before the Scoobi job execution.

Dependencies uploading

Dependencies are being uploaded to the cluster if Scoobi determines that you are not using a "fat" jar:

  1. the dependent jars are taken from the context classloader and are only loaded if they come from a .ivy2 or .m2 directory (the Hadoop jars are not included)

  2. the jars are uploaded to a directory on the cluster. By default this directory is named libjars and only new jars are uploaded.

  3. all those dependencies are added to the mapred.classpath Hadoop configuration variable

If you don't want to use this facility at all (or if Scoobi fails to recognize your jar as a "fat" jar), you can override the upload method and set it to false or pass scoobi nolibjars on the command line.

LibJars usage

The functionalities for managing dependencies reside in methods of the LibJars trait which can be reused independently from the ScoobiApp trait, or overidden:

  1. jars defines the list of jars to upload
  2. uploadLibJarFiles uploads the new jars to the libjars directory
  3. libJarsDirectory defines the name of the directory containing the dependent jars. The default value is libjars/ but you can change it by passing the -Dscoobi.libjarsdir property
  4. deleteJars removes all existing jars in the libjars directory if one of your dependencies has changed (but kept the same name)

Here is an example on how to use these methods:

package mypackage

object MyApp extends ScoobiApp {
  def run() {
    // remove the existing jars on the cluster
    // upload the dependencies to the cluster

    // define and execute a Scoobi job
    val lines = ...


The ScoobiApp trait can be used in 2 different contexts:

  1. with the hadoop jar command

    In that case, hadoop finds the cluster location by either using the configuration files in its conf directory or by using the fs and jt arguments

  2. within sbt

    In that case the cluster location can be either defined by using the useconfdir command line argument to get the configuration files found in $HADOOP_HOME/conf or the $HADOOP_CONF directory
    You can also override the fs and jobTracker methods, and get those values from your own properties file.

Local execution

For testing, it can useful to run Scoobi applications locally only:

  • if you are using the hadoop jar command, you can specify a local configuration by either using the fs/jt arguments
  • if you are using sbt, you can pass the "scoobi local" argument for a local execution
  • if you are using sbt, you can pass the "scoobi inmemory" argument for a fast execution backed up by Scala collections instead of Hadoop


By default, when extending the Hadoop trait, Hadoop and Scoobi logs will be shown in the console at the "INFO" level. However for debugging failed jobs you may want to change the log level or the log categories:

  • show some log levels: run-main mypackage.MyApp [ARGS] -- scoobi warn (you can also override the level method). The log levels are the ones from the Apache commons logging library: ALL, FATAL, INFO, WARN, TRACE

  • show some log categories: run-main mypackage.MyApp [ARGS] -- scoobi warn.(hadoop|scoobi) will only display the log lines where the category matches .*(hadoop|scoobi).*. Note that you can visually separate this regular expression for log categories with brackets to help the reading: run mypackage.MyApp -- scoobi.warn.[(hadoop|scoobi)].times

  • you can additionally show the execution times, locally and on the cluster: run-main mypackage.MyApp -- scoobi times (or override the showTimes method)

  • finally you can combine those flags: run-main mypackage.MyApp [ARGS] -- scoobi warn.times

Note that logs can be turned off by using the 'quiet' argument: run-main mypackage.MyApp [ARGS] -- scoobi quiet (you can also override the quiet method to return true)


A special kind of application is the REPL. In order to use the Scoobi REPL you need to create a Java script with the classpath of all the jars you are using invoking the ScoobiRepl class:

java -cp [all your jars here] com.nicta.scoobi.application.ScoobiRepl

Once the REPL is initialized, you can start jobs by simply running DLists:

scoobi> DList(1, 2, 3).run

The default execution mode is using the Cluster with the configuration found in $HADOOP_HOME/conf (if any). It is possible to switch between different execution modes by invoking inmemory, local at the prompt (and cluster to come back to the cluster execution mode). Note also that the ScoobiConfiguration is accessible with the configuration variable.