From Zero to Hadoop: a tutorial for getting started writing Hadoop jobs on Amazon Elastic MapReduce


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Hadoop is everywhere these days, but it can seem like a complex, intimidating ecosystem to those who have yet to jump in.

In this hands-on workshop, Alex Dean, co-founder of Snowplow Analytics, will take you "from zero to Hadoop", showing you how to run a variety of simple (but powerful) Hadoop jobs on Elastic MapReduce, Amazon's hosted Hadoop service. Alex will start with a no-nonsense overview of what Hadoop is, explaining its strengths and weaknesses and why it's such a powerful platform for data warehouse practitioners. Then Alex will help get you setup with EMR and Amazon S3, before leading you through a very simple job in Pig, a simple language for writing Hadoop jobs. After this we will move onto writing a more advanced job in Scalding, Twitter's Scala API for writing Hadoop jobs. For our final job, we will consolidate everything we have learnt by building a more sophisticated job in Scalding.

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From Zero to Hadoop: a tutorial for getting started writing Hadoop jobs on Amazon Elastic MapReduce

  1. 1. Workshop: From Zero to _ Budapest DW Forum 2014
  2. 2. Agenda today 1. Some setup before we start 2. (Back to the) introduction 3. Our workshop today 4. Part 2: a simple Scalding job on EMR
  3. 3. Some setup before we start
  4. 4. There is a lot to copy and paste – so let’s all join a Google Hangout chat • If I forget to paste some content into the chat room, just shout out and remind me
  5. 5. First, let’s all download and setup Virtualbox and Vagrant dex.html
  6. 6. Now let’s setup our development environment $ vagrant plugin install vagrant-vbguest If you have git already installed: $ git clone --recursive If not: $ wget environment/archive/ $ unzip $ wget playbooks/archive/ $ unzip
  7. 7. Now let’s setup our development environment $ cd dev-environment $ vagrant up $ vagrant ssh
  8. 8. Final step for now, let’s install some software $ ansible-playbook /vagrant/ansible- playbooks/aws-tools.yml --inventory- file=/home/vagrant/ansible_hosts -- connection=local $ ansible-playbook /vagrant/ansible- playbooks/scala-sbt.yml --inventory- file=/home/vagrant/ansible_hosts -- connection=local
  9. 9. (Back to the) introduction
  10. 10. Snowplow is an open-source web and event analytics platform, built on Hadoop • Co-founders Alex Dean and Yali Sassoon met at OpenX, the open-source ad technology business in 2008 • We released Snowplow as a skunkworks prototype at start of 2012: • We built Snowplow on top of Hadoop from the very start
  11. 11. We wanted to take a fresh approach to web analytics • Your own web event data -> in your own data warehouse • Your own event data model • Slice / dice and mine the data in highly bespoke ways to answer your specific business questions • Plug in the broadest possible set of analysis tools to drive value from your data Data warehouseData pipeline Analyse your data in any analysis tool
  12. 12. And we saw the potential of new “big data” technologies and services to solve these problems in a scalable, low-cost manner These tools make it possible to capture, transform, store and analyse all your granular, event-level data, to you can perform any analysis Amazon EMRAmazon S3CloudFront Amazon Redshift
  13. 13. Our Snowplow event processing flow runs on Hadoop, specifically Amazon’s Elastic MapReduce hosted Hadoop service Website / webapp Snowplow Hadoop data pipeline CloudFront- based event collector Scalding- based enrichment on Hadoop JavaScript event tracker Amazon Redshift / PostgreSQL Amazon S3 or Clojure- based event collector
  14. 14. Why did we pick Hadoop? Scalability Easy to reprocess data Highly testable We have customers processing 350m Snowplow events a day in Hadoop – runs in <2 hours If business rules change, we can fire up a large cluster and re-process all historical raw Snowplow events We write unit and integration tests for our jobs and run them locally, giving us confidence that our jobs will run correctly at scale on Hadoop
  15. 15. And why Amazon’s Elastic MapReduce (EMR)? No need to run our own cluster Elastic Interop with other AWS services Running your own Hadoop cluster is a huge pain – not for the fainthearted. By contrast, EMR just works (most of the time !) Snowplow runs as a nightly (sometimes more frequent) batch job. We spin up the EMR cluster to run the job, and shut it down straight after EMR works really well with Amazon S3 as a file store. We are big fans of Amazon Redshift (hosted columnar database) too
  16. 16. Our workshop today
  17. 17. Hadoop is complicated…
  18. 18. … for our workshop today, we will stick to using Elastic MapReduce and try to avoid any unnecessary complexity
  19. 19. … and we will learn by doing! • Lots of books and articles about Hadoop and the theory of MapReduce • We will learn by doing – no theory unless it’s required to directly explain the jobs we are creating • Our priority is to get you up-and-running on Elastic MapReduce, and confident enough to write your own Hadoop jobs
  20. 20. Part 1: a simple Pig Latin job on EMR
  21. 21. What is Pig (Latin)? • Pig is a high-level platform for creating MapReduce jobs which can run on Hadoop • The language you write Pig jobs in is called Pig Latin • For quick-and-dirty scripts, Pig just works Hadoop DFS Hadoop MapReduce Crunch Hive Pig Java Cascading
  22. 22. Let’s all come up with a unique name for ourselves • Lowercase letters, no spaces or hyphens or anything • E.g. I will be alexsnowplow – please come up with a unique name for yourself! • It will be visible to other participants so choose something you don’t mind being public  • In the rest of this workshop, wherever you see YOURNAME, replace it with your unique name
  23. 23. Let’s restart our Vagrant and do some setup $ mkdir zero2hadoop $ aws configure // And type in: AWS Access Key ID [None]: AKIAILD6DCBTFI642JPQ AWS Secret Access Key [None]: KMVdr/bsq4FDTI5H143K3gjt4ErG2oTjd+1+a+ou Default region name [None]: eu-west-1 Default output format [None]:
  24. 24. Let’s create some buckets in Amazon S3 – this is where our data and our apps will live $ aws s3 mb s3://zero2hadoop-in-YOURNAME $ aws s3 mb s3://zero2hadoop-out-YOURNAME $ aws s3 mb s3://zero2hadoop-jobs-YOURNAME // Check those worked $ aws s3 ls
  25. 25. Let’s get some source data uploaded $ mkdir -p ~/zero2hadoop/part1/in $ cd ~/zero2hadoop/part1/in $ wget alding-example-project/master/data/hello.txt $ cat hello.txt Hello world Goodbye world $ aws s3 cp hello.txt s3://zero2hadoop-in- YOURNAME/part1/hello.txt
  26. 26. Let’s get our EMR command-line tools installed (1/2) $ /vagrant/emr-cli/elastic-mapreduce $ rvm install ruby-1.8.7-head $ rvm use 1.8.7 $ alias emr=/vagrant/emr-cli/elastic- mapreduce
  27. 27. Let’s get our EMR command-line tools installed (2/2) Add this file: { "access_id": "AKIAI55OSYYRLYWLXH7A", "private_key": "SHRXNIBRdfWuLPbCt57ZVjf+NMKUjm9WTknDHPTP", "region": "eu-west-1" } to: /vagrant/emr-cli/credentials.json (sudo sntp -s
  28. 28. Let’s get our EMR command-line tools installed (2/2) // This should work fine now: $ emr --list <no output>
  29. 29. Let’s do some local file work $ mkdir -p ~/zero2hadoop/part1/pig $ cd ~/zero2hadoop/part1/pig $ wget ean/d8371cebdf00064591ae/raw/cb3030a6c48b85d1 01e296ccf27331384df3288d/wordcount.pig // The original bdf00064591ae
  30. 30. Now upload to S3 $ aws s3 cp wordcount.pig s3://zero2hadoop- jobs-YOURNAME/part1/ $ aws s3 ls --recursive s3://zero2hadoop- jobs-YOURNAME/part1/ 2014-06-06 09:10:31 674 part1/wordcount.pig
  31. 31. And now we run our Pig script $ emr --create --name "part1 YOURNAME" --set-visible-to-all-users true --pig-script s3n://zero2hadoop-jobs- YOURNAME/part1/wordcount.pig --ami-version 2.0 --args "-p,INPUT=s3n://zero2hadoop-in- YOURNAME/part1, -p,OUTPUT=s3n://zero2hadoop-out- YOURNAME/part1"
  32. 32. Let’s check out the jobs running in Elastic MapReduce – first at the console $ $ emr --list j-1HR90SWPP40M4 STARTING part1 YOURNAME PENDING Setup Pig PENDING Run Pig Script
  33. 33. and also in the UI
  34. 34. Okay let’s check the output of our job! (1/2) $ aws s3 ls --recursive s3://zero2hadoop-out- YOURNAME/part1 2014-06-06 09:57:53 0 part1/_SUCCESS 2014-06-06 09:57:50 26 part1/part-r- 00000
  35. 35. Okay let’s check the output of our job! $ mkdir -p ~/zero2hadoop/part1/out $ cd ~/zero2hadoop/part1/out $ aws s3 cp --recursive s3://zero2hadoop-out- YOURNAME/part1 . $ ls part-r-00000 _SUCCESS $ cat part-r-00000 2 world 1 Hello 1 Goodbye
  36. 36. Part 2: a simple Scalding job on EMR
  37. 37. What is Scalding? • Scalding is a Scala API over Cascading, the Java framework for building data processing pipelines on Hadoop: Hadoop DFS Hadoop MapReduce Cascading Pig … Java Scalding Cascalog PyCascading cascading. jruby
  38. 38. Cascading has a “plumbing” abstraction over vanilla MapReduce which should be quite comfortable to DW practitioners
  39. 39. Scalding improves further on Cascading by reducing boilerplate and making more complex pipelines easier to express • Scalding written in Scala – reduces a lot of boilerplate versus vanilla Cascading. Easier to look at a job in its entirety and see what it does • Scalding created and supported by Twitter, who use it throughout their organization • We believe that data pipelines should be as strongly typed as possible – all the other DSLs/APIs on top of Cascading encourage dynamic typing
  40. 40. Strongly typed data pipelines – why? • Catch errors as soon as possible – and report them in a strongly typed way too • Define the inputs and outputs of each of your data processing steps in an unambiguous way • Forces you to formerly address the data types flowing through your system • Lets you write code like this:
  41. 41. Okay let’s get started! • Head to
  42. 42. Let’s get this code down locally and build it $ mkdir -p ~/zero2hadoop/part2 $ cd ~/zero2hadoop/part2 $ git clone git:// project.git $ cd scalding-example-project $ sbt assembly
  43. 43. Here is our MapReduce code
  44. 44. Good, tests are passing, now let’s upload this to S3 so it’s available to our EMR job $ aws s3 cp target/scala-2.10/scalding- example-project-0.0.5.jar s3://zero2hadoop- jobs-YOURNAME/part2/ // If that doesn’t work: $ aws cp s3://snowplow-hosted-assets/third- party/scalding-example-project-0.0.5.jar s3://zero2hadoop-jobs-YOURNAME/part2/ $ aws s3 ls s3://zero2hadoop-jobs- YOURNAME/part2/
  45. 45. And now we run it! $ emr --create --name ”part2 YOURNAME" --set-visible-to-all-users true --jar s3n://zero2hadoop-jobs- YOURNAME/part2/scalding-example-project- 0.0.5.jar --arg com.snowplowanalytics.hadoop.scalding.WordCou ntJob --arg --hdfs --arg --input --arg s3n://zero2hadoop-in- YOURNAME/part1/hello.txt --arg --output --arg s3n://zero2hadoop-out- YOURNAME/part2
  46. 46. Let’s check out the jobs running in Elastic MapReduce – first at the console $ emr --list j-1M62IGREPL7I STARTING scalding-example-project PENDING Example Jar Step
  47. 47. and also in the UI
  48. 48. Okay let’s check the output of our job! $ aws s3 ls --recursive s3://zero2hadoop-out- YOURNAME/part2 $ mkdir -p ~/zero2hadoop/part2/out $ cd ~/zero2hadoop/part2/out $ aws s3 cp --recursive s3://zero2hadoop-out- YOURNAME/part2 . $ ls $ cat part-00000 goodbye 1 hello 1 world 2
  49. 49. Part 3: a more complex Scalding job on EMR
  50. 50. Let’s explore another tutorial together
  51. 51. Questions? @snowplowdata To talk offline – @alexcrdean on Twitter or