1.
BIG DATA INFRASTRUCTURE –
INTRODUCTION TO HADOOP WITH
MAP REDUCE, PIG, AND HIVE
Gil Benghiat
Eric Estabrooks
Chris Bergh
O P E N
D A T A
S C I E N C E
C O N F E R E N C E
BOSTON 2015
@opendatasci

2.
Agenda
Introductions
Hadoop Overview &
Comparisons
What do I use when?
AWS EMR
Hive
Pig
Impala Hive
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Doing
Presentation

3.
Introductions

4.
Meet DataKitchen
Chris Bergh
(Head Chef)
4
Gil Benghiat
(VP Product)
Eric Estabrooks
(VP Cloud and
Data Services)
Software development and executive experience delivering
enterprise software focused on Marketing and Health Care
sectors.
Deep Analytic Experience: Spent past decade solving
analytic challenges
New Approach To Data Preparation and Production:
focused on the Data Analysts and Data Scientists

5.
5
Analysts And Their Teams Are Spending
60-80% Of Their Time
On Data Preparation And Production

6.
This creates an expectation gap
6
Analyze
Prepare Data
C
Analyze
Prepare Data
Business Customer
Expectation
Analyst
Reality
Communicate
The business does not
think that Analysts are
preparing data
Analysts don’t want to
prepare data

7.
7
DataKitchen is on a mission to
integrate and organize data to
make analysts and
data scientists
super-powered.

8.
Meet the Audience: A few questions
• Who considers themselves
• Data scientist
• Data analyst
• Programmer / Scripter
• On the Business side
• Who knows SQL – can write a select statement?
• Who used AWS before today?
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9.
Hadoop Overview

10.
What Is Apache Hadoop?
• Software framework
• Distributed processing of large scale datasets
• Cluster of commodity hardware
• Promise of lower cost
• Has many frameworks, modules and projects
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http://hadoop.apache.org/

11.
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Mark Grover http://radar.oreilly.com/2015/02/processing-frameworks-for-hadoop.html
Hadoop ecosystem frameworks
*** *
*Covered in talk
Hands on*
*
(HDFS, Cassandra, HBase, S3)

12.
Hadoop has been evolving
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Map Reduce
Impala
Hadoop Pig
2005 2007 2009 2011 2013 2015
Google Trends
“Big Data”

13.
What is Hadoop good for?
• Problems that are huge, and can be run in
parallel over immutable data
• NOT OLTP
(e.g. backend to e-commerce site)
• Providing frameworks to build software
• Map Reduce
• Spark
• Tez
• A backend for visualization tools
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14.
Map Reduce
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http://www.cs.berkeley.edu/~matei/talks/2010/amp_mapreduce.pdf

15.
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16.
Test your system in the small
1. Make a small data set
2. Test like this:
$ cat data.txt | map | sort | reduce
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17.
You can write map reduce jobs in your favorite language
Streaming Interface
• Lets you specify mappers and
reducer
• Supports
• Java
• Python
• Ruby
• Unix Shell
• R
• Any executable
Map Reduce “generators”
• Results in map reduce jobs
• PIG
• Hive
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18.
Applications that lend themselves to map reduce
• Word Count
• PDF Generation (NY Times 11,000,000 articles)
• Analysis of stock market historical data (ROI and standard deviation)
• Geographical Data (Finding intersections, rendering map files)
• Log file querying and analysis
• Statistical machine translation
• Analyzing Tweets
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19.
Pig
• Pig Latin - the scripting language
• Grunt – Shell for executing Pig Commands
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http://www.slideshare.net/kevinweil/hadoop-pig-and-twitter-nosql-east-2009

20.
This is what it would be in Java
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http://www.slideshare.net/kevinweil/hadoop-pig-and-twitter-nosql-east-2009

21.
Hive
You write SQL! Well, almost, it is HiveQL
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SELECT *
FROM user
WHERE active = 1;
JDBC
SQL
Workbench
HUE
AWS
S3

22.
Impala
• Uses SQL very similar to HiveQL
• Runs 10-100x faster than Hive Map Reduce
• Runs in memory so it may not scale up as well
• Some batch jobs may run faster on Impala than Hive
• Great for developing your code on a small data set
• Can use interactively with Tableau and other BI tools
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23.
• Had a version of SQL called Shark
• Shark has been replaced by Spark SQL
• Hive on Spark is under development
• Spark SQL is faster than Shark
• Runs 100x faster than Hive Map Reduce
• Can use interactively with Tableau and other BI tools
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24.
Performance Comparisons

25.
Performance comparison (3. Join Query Feb 2014)
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Source: https://amplab.cs.berkeley.edu/benchmark/
What’s this?
(inSeconds)

26.
Performance comparison (TPC-DS April 2015)
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Source:

27.
Performance comparison (Single User Sep 2014)
6/1/2015 27 Source:

28.
Amazon EMR

29.
Today, we will use EMR to run Hadoop
• EMR = Elastic Map Reduce
• Amazon does almost all of the work to create a cluster
• Offers a subset of modules and projects
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OR

30.
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m3.xlarge

31.
What to use when

32.
6/1/2015 32
WhatTypeofDatabaseto
Use?
Capturing
Transactions?
Use RDMS
Capturing Logs? Use File System
Back End To
Website?
NoSQL Database
(Mongodb)
Cache (Redis)
Doing Analytics?
Small Data?
Desktop Tools
(Excel, Tableau)
Building Models?
R, Python, SAS
Miner
Big-ish Data?
Columnar Database
(Redshift)
‘Big Data’ Database
(like Hadoop)

33.
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WhichToolShouldIUse?
Project Goal
Want Experience In
Coolest Tech?
Spark is Hot Tech
now
Just Want To Get
Job Done?
Choose Hadoop
Distributions
Mainly Structured
Data?
Want Fast
Response?
SQL / Impala
SQL / Redshift
Mainly
Unstructured Data?
Developer?
Write Map-Reduce
Job
Not Developer? SQL/HIVE

34.
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HowShouldIUseIt?
Use Case
Development
Use Cloud
Use Virtual
Machine
Production
Fixed Workload
Do ROI on buying
up front
Use Cloud
Variable Workload Use Cloud

35.
Hands on

36.
Form groups of 3
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37.
Let’s Do This!
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What do we need?
• AWS Account
• Key (.pem file)
• The data file in the S3 bucket
What will we do?
• Start Cluster
• MR Hive
• MR Pig
• Impala
• Sum county level
census data by state.
Prerequisites and scripts are
located at
http://www.datakitchen.io/blog

38.
AWS Console
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• Just google “aws console”
• Log in

39.
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Click Here
Where’s EMR?

40.
Create Cluster
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OR

41.
Cluster Options
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Cluster Configuration mod
Tags defaults
Software Configuration mod
File System Configuration defaults
Hardware Configuration mod
Security and Access mod
IAM Roles defaults
Bootstrap Actions defaults
Steps defaults

42.
Cluster Configuration
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mod

43.
Tags
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defaults

44.
Software Configuration
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Pick Impala here!
Hopefully we’ll have time to get to this.
mod
Don’t for get to click add!

45.
File System Configuration
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defaults

46.
Hardware Configuration
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$ 0.35 / hour
Set Core and Task to 0
mod

47.
Security and Access
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Finally we get to use our keys!
mod

48.
IAM Roles
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Just defaults, please
More JSON in here
defaults

49.
Bootstrap Actions
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defaults
• Tweak configuration
• Install custom application
(Apache Drill, Mahout, etc.)
• Shell scriptsCan use this to set up
Spark

50.
Steps
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defaults

51.
Steps
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52.
Steps: Hive Program
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53.
Provisioning
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54.
Bootstrapping
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55.
Monitor
Startup
Progress
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56.
Instructions to Connect
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Here’s your hostname
SSH Info
We’ll follow these
instructions

57.
Post ODSC Update: An easier way to access Hue
(foxyproxy slowed us down)
For Windows, Unix, and Mac, use ssh to establish a tunnel
$ ssh -i datakitchen-training.pem -L 8888:localhost:8888 hadoop@ec2-54-
152-244-88.compute-1.amazonaws.com
From the browser, go to
http://localhost:8888
You may need to fix the permissions on the .pem file:
$ chmod 400 datakitchen-training.pem
With the cygwin version of ssh, you may have to fix the group of the .pem file before the chmod
command.
$ chgrp Users datakitchen-training.pem
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58.
Post ODSC Update: On Windows, you can use
putty to establish a tunnel
1. Download PuTTY.exe to your computer from:
http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html
2. Start PuTTY.
3. In the Category list, click Session
4. In the Host Name field, type hadoop@ec2-54-152-244-88.compute-1.amazonaws.com
5. In the Category list, expand Connection > SSH > Auth
6. For Private key file for authentication, click Browse and select the private key file (datakitchen-training.ppk) used
to launch the cluster.
7. In the Category list, expand Connection > SSH, and then click Tunnels.
8. In the Source port field, type 8888.
9. In the Destination type localhost:8888
10. Verify the Local and Auto options are selected.
11. Click Add.
12. Click Open.
13. Click Yes to dismiss the security alert.
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Now this will work
http://localhost:8888

59.
Setup Web Connection – Linux/Mac
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60.
Port Forwarding (Mac/Linux)
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ssh -i ~/.ec2/emr-training.pem -L 8888:localhost:8888 hadoop@ec2-54-173-219-
156.compute-1.amazonaws.com

61.
Setup Web Connection – Windows
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62.
Setup
Web
Connection
- Chrome
(Windows
and Mac are
Identical)
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63.
Setup
Web
Connection
- Firefox
(Windows
and Mac are
Identical)
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64.
Start Hue, in browser type
http://master public DNS:8888
http://ec2-52-5-91-114.compute-1.amazonaws.com:8888
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Note: no
hadoop@

65.
Sign in
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First time Other times

66.
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67.
HIVE: Load Data from S3
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Familiar SQL
Describe file format
Pull from S3 bucket
UPDATE with your
bucket name

68.
HIVE: Run the summary interactively
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69.
HIVE: Export Our Data
6/1/2015 69
Define CSV
output
Write out data
You can look at the data in s3
UPDATE with
your bucket
name

70.
PIG: Load Data from S3
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Readable
syntax
Describe file format
Pull from S3 bucket
UPDATE with your
bucket name

71.
PIG: Transform the data
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72.
PIG Export Our Data
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UPDATE with
your bucket
name

73.
IMPALA: From the shell window
Type: impala-shell
>invalidate metadata
>show tables;
>
> quit
You can type “pig” or “hive” at the command line and run the scripts
here, without Hue.
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74.
Terminate!
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75.
Remember to shut down your clusters

76.
Recap
Presentation
• Hadoop is an evolving ecosystem of projects
• It is well suited for big data
• Use something else for medium or small data
Doing
• Started a Hadoop cluster via the AWS Console (Web UI)
• Loaded Data
• Wrote some queries
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77.
77
Thank you!
To continue the discussion,
contact us at
info@datakitchen.io
gil@datakitchen.io
eestabrooks@datakitchen.io
cbergh@datakitchen.io