Workshop: From Zero. Budapest DW Forum 2014

Transcription

1 Workshop: From Zero to _ Budapest DW Forum 2014

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 Some setup before we start

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 First, let s all download and setup Virtualbox and Vagrant dex.html

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 $ unzip temp.zip $ wget $ unzip temp.zip

7 Now let s setup our development environment $ cd dev-environment $ vagrant up $ vagrant ssh

8 Final step for now, let s install some software $ ansible-playbook /vagrant/ansibleplaybooks/aws-tools.yml --inventoryfile=/home/vagrant/ansible_hosts -- connection=local $ ansible-playbook /vagrant/ansibleplaybooks/scala-sbt.yml --inventoryfile=/home/vagrant/ansible_hosts -- connection=local

9 (Back to the) introduction

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: github.com/snowplow/snowplow We built Snowplow on top of Hadoop from the very start

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 pipeline Data warehouse Analyse your data in any analysis tool

12 And we saw the potential of new big data technologies and services to solve these problems in a scalable, low-cost manner CloudFront Amazon S3 Amazon EMR Amazon Redshift These tools make it possible to capture, transform, store and analyse all your granular, event-level data, to you can perform any analysis

13 Our Snowplow event processing flow runs on Hadoop, specifically Amazon s Elastic MapReduce hosted Hadoop service Website / webapp Snowplow Hadoop data pipeline Amazon S3 JavaScript event tracker CloudFrontbased event collector or Clojurebased event collector Scaldingbased enrichment on Hadoop Amazon Redshift / PostgreSQL

14 Why did we pick Hadoop? Scalability We have customers processing 350m Snowplow events a day in Hadoop runs in <2 hours Easy to reprocess data If business rules change, we can fire up a large cluster and re-process all historical raw Snowplow events Highly testable 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 And why Amazon s Elastic MapReduce (EMR)? No need to run our own cluster Running your own Hadoop cluster is a huge pain not for the fainthearted. By contrast, EMR just works (most of the time!) Elastic 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 Interop with other AWS services EMR works really well with Amazon S3 as a file store. We are big fans of Amazon Redshift (hosted columnar database) too

16 Our workshop today

17 Hadoop is complicated

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

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 Part 1: a simple Pig Latin job on EMR

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 Cascading Crunch Hive Pig Java Hadoop MapReduce Hadoop DFS

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 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 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 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 Let s get our EMR command-line tools installed (1/2) $ /vagrant/emr-cli/elastic-mapreduce $ rvm install ruby head $ rvm use $ alias emr=/vagrant/emr-cli/elasticmapreduce

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 Let s get our EMR command-line tools installed (2/2) // This should work fine now: $ emr --list <no output>

29 Let s do some local file work $ mkdir -p ~/zero2hadoop/part1/pig $ cd ~/zero2hadoop/part1/pig $ wget ean/d8371cebdf ae/raw/cb3030a6c48b85d1 01e296ccf df3288d/wordcount.pig // The original bdf ae

30 Now upload to S3 $ aws s3 cp wordcount.pig s3://zero2hadoopjobs-yourname/part1/ $ aws s3 ls --recursive s3://zero2hadoopjobs-yourname/part1/ :10: part1/wordcount.pig

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 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 and also in the UI

34 Okay let s check the output of our job! (1/2) $ aws s3 ls --recursive s3://zero2hadoop-out- YOURNAME/part :57:53 0 part1/_success :57:50 26 part1/part-r

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 _SUCCESS $ cat part-r world 1 Hello 1 Goodbye

36 Part 2: a simple Scalding job on EMR

37 What is Scalding? Scalding is a Scala API over Cascading, the Java framework for building data processing pipelines on Hadoop: Scalding Cascalog PyCascading cascading. jruby Cascading Pig Java Hadoop MapReduce Hadoop DFS

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

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 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 Okay let s get started! Head to

42 Let s get this code down locally and build it $ mkdir -p ~/zero2hadoop/part2 $ cd ~/zero2hadoop/part2 $ git clone git://github.com/snowplow/scalding-exampleproject.git $ cd scalding-example-project $ sbt assembly

43 Here is our MapReduce code

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/scaldingexample-project jar s3://zero2hadoopjobs-yourname/part2/ // If that doesn t work: $ aws cp s3://snowplow-hosted-assets/thirdparty/scalding-example-project jar s3://zero2hadoop-jobs-yourname/part2/ $ aws s3 ls s3://zero2hadoop-jobs- YOURNAME/part2/

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 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 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 and also in the UI

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 goodbye 1 hello 1 world 2

49 Part 3: a more complex Scalding job on EMR

50 Let s explore another tutorial together

51 Questions? To talk on Twitter or