Workshop: From Zero

to _Budapest DW Forum 2014

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

Some setup before we start

There is a lot to copy and paste – so let’s all join a Google Hangout chat

http://bit.ly/1xgSQId• If I forget to paste some content into the chat room, just shout

out and remind me

First, let’s all download and setup Virtualbox and Vagrant

http://docs.vagrantup.com/v2/installation/index.html

https://www.virtualbox.org/wiki/Downloads

Now let’s setup our development environment$ vagrant plugin install vagrant-vbguest

If you have git already installed:

$ git clone --recursive https://github.com/snowplow/dev-environment.git

If not:

$ wget https://github.com/snowplow/dev-environment/archive/temp.zip

$ unzip temp.zip

$ wget https://github.com/snowplow/ansible-playbooks/archive/temp.zip

$ unzip temp.zip

Now let’s setup our development environment

$ cd dev-environment

$ vagrant up

$ vagrant ssh

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

(Back to the) introduction

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:

github.com/snowplow/snowplow

• We built Snowplow on top of Hadoop from the very start

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

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

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

collectorScalding-

based enrichment on Hadoop

JavaScript event tracker

Amazon Redshift /

PostgreSQL

Amazon S3

or

Clojure-based event

collector

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

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

Our workshop today

Hadoop is complicated…

… for our workshop today, we will stick to using Elastic MapReduce and try to avoid any unnecessary complexity

… 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

Part 1: a simple Pig Latin job on EMR

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

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

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]:

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

Let’s get some source data uploaded

$ mkdir -p ~/zero2hadoop/part1/in

$ cd ~/zero2hadoop/part1/in

$ wget https://raw.githubusercontent.com/snowplow/scalding-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

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

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 24.56.178.140)

Let’s get our EMR command-line tools installed (2/2)

// This should work fine now:

$ emr --list

<no output>

Let’s do some local file work

$ mkdir -p ~/zero2hadoop/part1/pig

$ cd ~/zero2hadoop/part1/pig

$ wget https://gist.githubusercontent.com/alexanderdean/d8371cebdf00064591ae/raw/cb3030a6c48b85d101e296ccf27331384df3288d/wordcount.pig

// The original

https://gist.github.com/alexanderdean/d8371cebdf00064591ae

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

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"

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

and also in the UI

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

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

Part 2: a simple Scalding job on EMR

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

Cascading has a “plumbing” abstraction over vanilla MapReduce which should be quite comfortable to DW practitioners

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

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:

Okay let’s get started!

• Head to https://github.com/snowplow/scalding-example-project

Let’s get this code down locally and build it

$ mkdir -p ~/zero2hadoop/part2

$ cd ~/zero2hadoop/part2

$ git clone git://github.com/snowplow/scalding-example-project.git

$ cd scalding-example-project

$ sbt assembly

Here is our MapReduce code

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/

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.WordCountJob \

--arg --hdfs \

--arg --input --arg s3n://zero2hadoop-in-YOURNAME/part1/hello.txt \

--arg --output --arg s3n://zero2hadoop-out-YOURNAME/part2

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

and also in the UI

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

Part 3: a more complex Scalding job on EMR

Let’s explore another tutorial together

https://github.com/sharethrough/scalding-emr-tutorial

Questions?

http://snowplowanalytics.comhttps://github.com/snowplow/snowplow

@snowplowdata

To talk offline – @alexcrdean on Twitter or alex@snowplowanalytics.com