This document summarizes a meeting about Pig and Hive for Hadoop. The agenda included an overview of Hadoop and MapReduce, demonstrations of Pig and Hive, and demos of exercises and projects using an on-premise Hadoop emulator and Azure HDInsight. Pig and Hive were presented as domain-specific languages that simplify writing MapReduce jobs by translating queries into the jobs. Recommendation algorithms were demonstrated in C# and Pig.

1. Pig and Hive
Csaba Toth
Central California .NET User Group
Meeting
Date: April 17th, 2014
Location: Bitwise Industries, Fresno

2. Agenda
• Little recap of Hadoop and Map-Reduce
• Pig and Hive
• Recommendation engine
• Demos 1: Exercises with on-premise Hadoop
emulator
• Demos 2: Azure HDInsight

3. Hadoop
• Hadoop is an open-source software
framework that supports data-intensive
distributed applications.
• Has two main pieces:
– Storing large amounts of data: HDFS, Hadoop
Distributed File System
– Processing large amounts of data: implementation
of the MapReduce programming model

4. Hadoop
• All of this in a cost effective way: Hadoop is
managing a cluster of commodity hardware
computers.
• The cluster is composed of a single master
node and multiple worker nodes
• It is written in Java, utilizes JVMs

5. Name node
HDFS visually
Metadata
Store
Data node Data node Data node
Node 1 Node 2
Block A Block B Block A Block B
Node 3
Block A Block B

6. Name node
Heart beat signals and
communication
Job / task management
Jobtracker
Data node Data node Data node
Tasktracker Tasktracker
Map 1 Reduce 1 Map 2 Reduce 2
Tasktracker
Map 3 Reduce 3

7. MapReduce
• Hadoop leverages the functional programming model
of map/reduce.
• Moves away from shared resources and related
synchronization and contention issues
• Thus inherently scalable and suitable for processing
large data sets, distributed computing on clusters of
computers/nodes.
• The goal of map reduce is to break huge data sets into
smaller pieces, distribute those pieces to various
worker nodes, and process the data in parallel.
• Hadoop leverages a distributed file system to store the
data on various nodes.

8. MapReduce
• It is about two functions: map and reduce
1. Map Step:
– Processes a key/value pairs and generate a set of
intermediate key/value pairs form that
2. Shuffle step:
– Groups all intermediate values associated with the
same intermediate key into one set
3. Reduce Step:
– Processes the intermediate values associated with the
same intermediate key and produces a set of values
based on the groups (usually some kind of aggregate)

9. Word count
http://blog.jteam.nl/wp-content/uploads/2009/08/MapReduceWordCountOverview1.png

10. Map, Shuffle, and Reduce
https://mm-tom.s3.amazonaws.com/blog/MapReduce.png

11. Hadoop Ecosystem and MapReduce
• Writing MapReduce Jobs in Java or C# or other
languages is useful but it is the hard way
• Several domain-specific higher level languages
and frameworks exist
– They allow phrase complicated tasks way more
simpler and shorter than Java
– These languages translate everything into Map-
Reduce jobs under the hood transparently
• We’ll see two examples: Hive and Pig

12. Hive and Pig
• Hive (http://hive.apache.org/)
– Provides SQL-like approach
– The best if the input data is at least conform to some
schema, so it can be consumed (SQL requires columnar
format, tables)
– Good for someone coming from SQL background
• Pig (http://pig.apache.org/)
– The syntax is closer to a programming language
– It defines a series of transformations, projecting one
schema into another
– Also the best if the data is not totally free form and have
some kind of schema

13. Hadoop Ecosystem / Architecture
Log Data RDBMS
Data Integration Layer
Flume Sqoop
Storage Layer (HDFS)
Computing Layer (MapReduce)
Advanced Query Engine (Hive, Pig)
Data Mining
(Pegasus,
Mahout)
Index,
Searches
(Lucene)
DB drivers
(Hive driver)
Web Browser (JS)Presentation
Layer

14. Simple recommendation engine
• Sites such as Amazon.com and Netflix.com use
complex algorithms
• But the underlying concepts are simple:
finding correlation between data
• Pearson Coefficient, Excel CORREL function

15. Pearson coefficient
Pearson product-moment correlation
coefficient value
Comments
-1 Perfectly correlated data, but as one rises
the other decreases
0 Uncorrelated data
+1 Perfectly correlated data
• DEMO

16. Ratings data
Name The Lord of the Rings The Chronicles of Narnia
Jack 2 3
Mark 4 4.5
Albert 4 3.5
John 5 5
• Pearson Correlation Coefficient: 0.8705715

17. Ratings data
Name of
movie critic
Name of movie Rating
Lisa Rose Lady in the Water 2.5
Lisa Rose Snakes on a Plane 3.5
Lisa Rose Just My Luck 3
Lisa Rose Superman Returns 3.5
Lisa Rose You Me and Dupree 2.5
Lisa Rose The Night Listener 3
Gene Seymour Lady in the Water 3
Gene Seymour Snakes on a Plane 3.5
Gene Seymour Just My Luck 1.5
Gene Seymour Superman Returns 5
Gene Seymour The Night Listener 3

18. Simple recommendation
• DEMO
– C# implementation
– Simple naïve algorithm
– Non parallel, not Hadoop

19. Pig
• A data-flow language
• Express the processing as a series of
transformations
• Steps are translated into Map Reduce jobs
• We can look at it like LINQ
• We’ll learn it by example
– Pig’s command line shell: grunt
– Pig’s language: Pig Latin

20. Pig
• Load and store:
– can load/store data from/to HDFS
• Relations
– The transformations are performed on ‘relations’ – Pig
calls the collections like that, don not confuse with
traditional relational DB terminology!
– Think of it as a table with rows and columns of data
– When grouped relations can contain associative key-
values

21. Pig
• Joins
– Can accomplish joins in an conceptually intuitive
manner (using a common key)
• Filter
– Can apply filters to data. A predicate should be
specified
• Projection
– Can project from an existing collection = form a new
collection in a way like an SQL select does. That is the
“GENERATE” command of Pig

22. Pig
• Grouping
– Can group data by one or more keys. Once grouped,
you can maintain the hierarchical structure in the
relation throughout the transformations. Projections
can be made, or sometimes you can flatten some of
the hierarchy.
• Dump
– DUMP statement outputs a contents of a relation onto
the console. Useful when fooling around in the Pig
shell
• Extensible
– UDFs: User Defined Functions

23. Simple recommendation
• DEMO
– Pig implementation
– Local pseudo cluster (HDInsight on-premise)

24. Hive DEMO
• Analyzing previous meeting’s wordcount
(warpeace) results
• Analyzing the recommendation engine results

25. HDInsight
• Microsoft’s Hadoop PaaS solution in the cloud
• Hortonworks Hadoop implementation
• New Azure portal is coming, in preview:
http://azure.microsoft.com/en-us/services/preview/
• Build conference: 40 new functions in Azure
• Latest addition: ISS, Azure Intelligent Systems
Service (https://connect.microsoft.com/site1132/)
• Analytics Platform System (APS): evolutionary
combination of SQL Server PDW and Hadoop

26. References
• Daniel Jebaraj: Ignore HDInsight at Your Own
Peril: Everything You Need to Know
• Tom White: Hadoop: The Definitive Guide, 3rd
Edition, Yahoo Press
• Lynn Langit’s various presentations and YouTube
videos
• Dattatrey Sindol: Big Data Basics - Part 1 -
Introduction to Big Data
• Bruno Terkaly’s presentations (for example
Hadoop on Azure: Introduction)

27. Thanks for your attention!

28. Hadoop vs RDBMS
Hadoop / MapReduce RDBMS
Size of data Petabytes Gigabytes
Integrity of data Low High (referential, typed)
Data schema Dynamic Static
Access method Interactive and Batch Batch
Scaling Linear Nonlinear (worse than
linear)
Data structure Unstructured Structured
Normalization of data Not Required Required
Query Response Time Has latency (due to batch
processing)
Can be near immediate