Scalding Example Project 
Introduction
This is Twitter's [WordCountJob] wordcount example for Scalding scalding adapted to run on Hadoop and Amazon Elastic MapReduce as a standalone job - i.e. without requiring scald.rb etc. It also includes Specs2 tests.
This was built as a Scala SBT project by the [Snowplow Analytics] snowplow team, as a proof of concept for porting our ETL process to Scalding to run on [Amazon Elastic MapReduce] emr.
For a much fuller Scalding example, see the Snowplow [Hadoop Enrich] snowplow-hadoop-enrich project.
See also: [Spark Example Project] spark-example-project
Building
Assuming you already have SBT installed:
$ git clone git://github.com/snowplow/scalding-example-project.git
$ cd scalding-example-project
$ sbt assembly
The 'fat jar' is now available as:
target/scalding-example-project-0.0.5.jar
Unit testing
The assembly command above runs the test suite - but you can also run this manually with:
$ sbt test
<snip>
[info] + A WordCount job should
[info] + count words correctly
[info] Passed: : Total 2, Failed 0, Errors 0, Passed 2, Skipped 0
Running on Amazon EMR
Prepare
Assuming you have already assembled the jarfile (see above), now upload the jar to Amazon S3.
Next, upload the data file [data/hello.txt] hello-txt to S3.
Run
Finally, you are ready to run this job using the [Amazon Ruby EMR client] emr-client:
$ elastic-mapreduce --create --name "scalding-example-project" \
--jar s3n://{{JAR_BUCKET}}/scalding-example-project-0.0.5.jar \
--arg com.snowplowanalytics.hadoop.scalding.WordCountJob \
--arg --hdfs \
--arg --input --arg s3n://{{IN_BUCKET}}/hello.txt \
--arg --output --arg s3n://{{OUT_BUCKET}}/results
Replace {{JAR_BUCKET}}, {{IN_BUCKET}} and {{OUT_BUCKET}} with the appropriate paths.
Inspect
Once the output has completed, you should see a folder structure like this in your output bucket:
results
|
+- _SUCCESS
+- part-00000
Download the part-00000 file and check that it contains:
goodbye 1
hello 1
world 2
Running on your own Hadoop cluster
If you are trying to run this on a non-Amazon EMR environment, you may need to edit:
project/BuildSettings.scala
And comment out the Hadoop jar exclusions:
// "hadoop-core-0.20.2.jar", // Provided by Amazon EMR. Delete this line if you're not on EMR
// "hadoop-tools-0.20.2.jar" // "
Next steps
Fork this project and adapt it into your own custom Scalding job.
To invoke/schedule your Scalding job on EMR, check out:
- [Spark Plug] spark-plug for Scala
- Elasticity elasticity for Ruby
- Boto boto for Python
- Lemur lemur for Clojure
Roadmap
Nothing planned currently.
Copyright and license
Copyright 2012-2014 Snowplow Analytics Ltd, with significant portions copyright 2012 Twitter, Inc.
Licensed under the [Apache License, Version 2.0] license (the "License"); you may not use this software except in compliance with the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.