Ajug april 2011

transcript

Introduction to MapReduce

Christopher Curtin

About Me

• 20+ years in Technology• Background in Factory Automation,

Warehouse Management and Food Safety system development before Silverpop

• CTO of Silverpop• Silverpop is a leading marketing

automation and email marketing company

Contrived Example

What is MapReduce“MapReduce is a programming model and an

associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. “

http://labs.google.com/papers/mapreduce.html

Back to the example

• I need to know:–# of each color M&M– Average weight of each color– Average width of each color

Traditional approach

• Initialize data structure• Read CSV• Split each row into parts• Find color in data structure• Increment count, add width, weight• Write final result

ASSume with me

• Determining weight is a CPU intensive step

• 8 core machine• 5,000,000,000 pieces per shift to

process• Files ‘rotated’ hourly

Thread It!

• Write logic to start multiple threads, pass each one a row (or 1000 rows) to evaluate

Issues with threading

• Have to write coordination logic• Locking of the color data structure• Disk/Network I/O becomes next

bottleneck

• As volume increases, cost of CPUs/Disks isn’t linear

Ideas to solve these problems?

• Put it a database• Multiple machines, each processes a

MapReduce

• Map– Parse the data into name/value pairs– Can be fast or expensive

• Reduce– Collect the name/value pairs and

perform function on each ‘name’

• Framework makes sure you get all the distinct ‘names’ and only one per invocation

Distributed File System

• System takes the files and makes copies across all the machines in the cluster

• Often files are broken apart and spread around

Move processing to the data!

• Rather than copying files to the processes, push the application to the machine where the data lives!

• System pushes jar files and launches JVMs to process

Runtime Distribution © Concurrent 2009

Hadoop

• Apache’s MapReduce implementation• Lots of third party support– Yahoo– Cloudera– Others announcing almost daily

Example

Issues with example

• /ajug/output can’t exist!• What’s with all the ‘Writable’

classes?• Data Structures have a lot of coding

overhead• What if I want to do multiple things

off the source?• What if I want to do something after

the Reduce?

Cascading

• Layer on top of Hadoop• Introduces Pipes to abstract when

mappers or reducers are needed• Can easily string together logic steps• No need to think about when to map,

when to reduce• No need for intermediate data

structures

Sample Example in Cascading

Multiple Output example in Cascading

Unit testing

• Kind of hard without some upfront thought

• Separate business logic from hadoop/cascading specific parts

• Try to use domain objects or primitives in business logic, not Tuples or Hadoop structures

• Cascading has a nice testing framework to implement

Other testing

• Known sets of data is critical at volume

Common Use Cases

• Evaluation of large volumes of data at a regular frequency

• Algorithms that take a single pass through the data

• Sensor data, log files, web analytics, transactional data

• First pass ‘what is going on’ evaluation before building/paying for ‘real’ reports

Things it is not good for

• Ad-hoc queries (though there are some tools on top of Hadoop to help)

• Fast/real-time evaluations• OLTP• Well known analysis may be better

off in a data wharehouse

Issues to watch out for

• Lots of small files• Default scheduler is pretty poor• Users need shell-level access?!?

Getting started

• Download latest from Cloudera or Apache

• Setup local only cluster (really easy to do)

• Download Cascading• Optional download Karmasphere if

using Eclipse (http://www.karmasphere.com/)

• Build some simple tests/apps• Running locally is almost the same as

in the cluster

Elastic Map Reduce

• Amazon EC2-based Hadoop• Define as many servers as you want• Load the data and go• 60 CENTS per hour per machine for a

decent size

So ask yourself

What could I do with 100 machines in an hour?

Ask yourself again …

What design/ architecture do I have because I didn’t have a good way to store the data?

What have I shoved into an RDBMS because I had one?