A workshop on analyzing data about Facebook likes of a set of people

1. Data + Algorithms = Knowledge
Facebook Analytics
With Elastic Map/Reduce
– a Hands-on Workshop
November 12, 2012
J Singh, DataThinks.org
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2. Take-away Messages
• Map Reduce is simple, Hadoop is one implementation of MR…
– …made even simpler by services like Elastic Map Reduce
• But Map Reduce requires a different style of programming…
– …and a different set of techniques for debugging
• Facebook data can get big very quickly…
– …and storage and bandwidth costs can dominate your solution
• Analytics is an iterative (agile) process…
– …each iteration requires evaluating results, and tuning the algorithms,
possibly the acquisition of more data
© J Singh, 2012 2
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3. Signing Up for AWS
The steps required to obtain an AWS account
 Create an AWS account (http://aws.amazon.com).
– http://www.slideshare.net/AmazonWebServices/video-how-to-sign-up-for-
amazon-web-services-8700872
– Requires a valid credit card and a phone based identification.
 Sign in to the AWS Management Console
– http://aws.amazon.com/console
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4. Elastic Map Reduce Resources
• Summary of the offering
• Elastic MapReduce Training
• Getting Started Guide
• Developers Guide
© J Singh, 2012 4
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5. MapReduce Conceptual Underpinnings
• Based on Functional Programming model
– From Lisp
• (map square '(1 2 3 4)) (1 4 9 16)
• (reduce plus '(1 4 9 16)) 30
– From APL
• +/ N N  1 2 3 4
• Easy to distribute (based on each element of the vector)
• New for Map/Reduce: Nice failure/retry semantics
– Hundreds and thousands of low-end servers are running at
the same time
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6. MapReduce Flow
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7. Elastic Map Reduce – Summary
• Hadoop installed and maintained by Amazon
– We can focus on programming
– Offers a few options on map and reduce programs
• Streaming
– Map and Reduce programs
connect through stdin and
stdout
– Allows Map and Reduce to be
written in any language
• Hive, Pig
– Translates to Map/Reduce JARs
– Can cascade M/R pipelines
• Custom JAR – for special cases
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8. Elastic Map Reduce – Architecture
• Starting with data in S3
• EMR Service initiates the job
• Hadoop Master coordinates
operation
• Slave nodes are initiated and
data loaded into them
• Extra nodes can be invoked if
needed
• Results are copied back into S3
– Nodes are destroyed
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9. Elastic Map Reduce – Word Count
• Use the AWS Management Console >> Elastic MapReduce
– Define Job Flow
• Hadoop Version 1.0.3
• Run your own application
– Steaming
– Specify Parameters
• For input files,
elasticmapreduce/samples/wordcount/input
• For output files, you need to define your own S3 bucket
– In a separate browser tab, AWS Management Console >> S3
– Bucket names can include lowercase letters, numbers, period, dash
• Mapper code can be seen at http://goo.gl/EbCme
– Copy this code to one of your buckets
– Specify path <your-bucket>/wordSplitter.py
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10. Elastic Map Reduce – Word Count (p2)
• Configure EC2 Instances
• Advanced Options
– Optional: Amazon EC2 Key Pair
• To log into the master and make changes to a running job
– E.g,, add extra nodes to speed up processing
– Amazon S3 Log Path
• <your-bucket>/log-2012-11-12--19-30
• Accept all other defaults and go!
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11. Monitoring Operation
• AWS Management Console provides a view into the
operation
– These screen-shots were taken at minute 27 of a 30-minute
run
– Configuration default in this case was for 2 map slots
– First slot became available at 12:00, second around 12:10
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12. Elastic Map Reduce – Debugging
• AWS console and the log files provide clues on what went
wrong and how to fix it
• Make a change that will break the operation and examine
the AWS console to find the error you introduced
– Introduce a parsing error in the mapper program
– Uncomment these lines to have it raise an exception
import random
x = 1 / random.randint(0,1000)
– Save the file to an S3 bucket and run
– Can you find where EMR reveals what happened?
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13. Facebook Analytics – Summary
• Extend the architecture
– Import Facebook data into S3
– Change Map Reduce programs as required
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14. Facebook Analytics – Observations
• Fetching and staging data is the real challenge in putting
together an analytics solution
– For unstructured data, it requires
• An understanding of the data model at the source
• Custom code to read it
– For structured data, consider Pig/Hive (higher-level Hadoop
components)
• Pig/Hive can read/write tables formatted as CSV/TSV files in S3
– Either we need to bring files into S3
– Or point Pig/Hive at a JDBC connection
• An opportunity to rethink the ETL pipeline?
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15. Facebook Analytics – Data Collection
• The exercise is based on everyone‟s Facebook data
• Log into http://apps.facebook.com/map-reduce-workshop
– Requires permission to get
• Information about you,
• Your friends,
• Your likes, your friends‟ likes.
– Randomly selects 10 of those friends
– Randomly selects 25 of their likes
– Anonymizes your friends‟ Facebook IDs before storing into
S3
• All data, even though opaque, will be deleted at the end of
the workshop
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16. Facebook Analytics – Data Collected
Original = 75 Friends = 750 Likes = up to about 20,000
• Each user record shows anonymized user ID and their likes
– 4110002004281 ['21506845769', '345722385482735', '93433060687']
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17. Facebook Analytics – Likes Count
• Use the AWS Management Console >> Elastic MapReduce
– Define Job Flow
• Hadoop Version 1.0.3
• Run Your Own Application
– Streaming
– Specify Parameters
• For input files, use bucket datathinks-users
• For output files, you need to define your own S3 bucket
– In a separate browser tab, AWS Management Console >> S3
• Mapper: copy goo.gl/PcLK4 into a bucket you own
– Advanced options:
• Choose a fresh log file location
– Accept all other defaults and go!
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18. Viewing the Results
• The results of Data Analysis are available in S3.
– Partial example: 139784736075551 1
140413412750046 6
184331976202 3
220854914702193 1
29092950651 1
• How to interpret the results.
– Sort by frequency, then examine most frequent likes
• 140413412750046 is cryptic
• But http://www.facebook.com/pages/w/140413412750046
reveals what it is (DataThinks)
• Requires further action: what to do with the results?
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19. Algorithm Discussion
• The algorithm based on exact matches for likes may be
too restrictive
– „Ella Fitzgerald‟ != „Duke Ellington‟
– But people who like Ella Fitzgerald may be reachable the
same way as people who like Duke Ellington
– An idea to explore further:
• Is there a way to find ID‟s that we might consider equivalent?
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20. Data Collected and Embellished
Original = 75 Friends = 750 Likes = 15,000 Similar Likes = 150,000
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21. Extended Facebook Analytics – Summary
• Extend the architecture
– Get mappers to fetch “similar likes” from the internet
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22. Facebook Analytics – Showing Results
• The other challenge in putting together an analytics
solution is displaying results
– Demo of our results page
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23. Take-away Messages
• Map Reduce is simple, Hadoop is one implementation of MR…
– …made even simpler by services like Elastic Map Reduce
• But Map Reduce requires a different style of programming…
– …and a different set of techniques for debugging
• Facebook data can get big very quickly…
– …and storage and bandwidth costs can dominate your solution
• Analytics is an iterative (agile) process…
– …each iteration requires evaluating results, and tuning the algorithms,
possibly the acquisition of more data
© J Singh, 2012 23
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24. Thank you
• J Singh
– President, Early Stage IT
• Technology Services and Strategy for Startups
• DataThinks.org is a service of Early Stage IT
– “Big Data” analytics solutions
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