Hw09 Counting And Clustering And Other Data Tricks

Cheap Parlor Tricks, Counting, and Clustering Derek Gottfrid The New York Times October 2009

Evolution of Hadoop @ NYTimes.com

Early Days - 2007
Solution looking for a problem

Solution
Wouldn’t it be cool to use lots of EC2 instances
(it’s cheap; nobody will notice)
Wouldn’t it be cool to use Hadoop
(MapReduce Google style is awesome)

Found a Problem
Freeing up historical archives of NYTimes.com 1851-1922

Problem Bits
Articles are served as PDFs
Really need PDFs from 1851-1981
PDFs are dynamically generated
Free = more traffic
Real deadline

Background What goes into making a PDF of a NYTimes.com article?
Each article is made up of many different pieces - multiple columns, different sized headings, multiple pages, photos.

Simple Answer
Pre-generate all 11 million PDFs and serve them statically.

Solution
Copy all the source data to S3
Use a bunch of EC2 instances and some Hadoop code to generate all the PDFs
Store the output PDFs in S3
Serve the PDFs out of S3 w/ a signed query string

A Few Details
Limited HDFS - everything loaded in and out of S3
Reduce = 0 - only used for some stats and error reporting

Breakdown
4.3 TB of source data into S3
11M PDFS - 1.5 TB output
$240 for EC2 - 24hrs x 100 machines

TimesMachine http://timesmachine.nytimes.com

Currently - 2009
All that darn data - Web Analytics

Data
Registration / Demographic
Articles 1851 - today
Usage Data / Web Logs

Counting
Classic cookie tracking - let’s add it up
Total PV
Total unique users
PV per user

A Few Details
Using EC2 - 20 Machines
Hadoop 0.20.0
12+TB of data
Straight MR in Java

Usage Data July 2009 ???M Page Views ??M Unique Users

Merging Data
Usage data combined with demographic data.

Twitter Click Backs By Age Group July 2009

Merging Data
Usage data with article meta data

Usage Data combined with Article Data July 2009 40 Articles

Products
Coming soon...

Clustering
Moving beyond simple counting and joining
Join usage data, demographic information, and article meta data
Apply simple k-means clustering

Clustering

Conclusion
Large scale computing is transformative for NYTimes.com.

Questions? [email_address] @derekg http://open.nytimes.com/

Hw09 Counting And Clustering And Other Data Tricks

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