This document discusses using MapReduce on a Hadoop cluster to analyze shopping cart data similar to what Amazon analyzes. It begins with an agenda that includes deploying Hadoop and using MapReduce for machine learning. It then discusses the origins of Hadoop from the Nutch project and key facts about Hadoop architecture. Part 1 explains how to configure and deploy a Hadoop cluster. Part 2 demonstrates hands-on use of MapReduce to analyze sample data, providing example Mapper and Reducer Python scripts. It concludes with other real-world uses of MapReduce.

1. Amazon-style
shopping cart analysis
using MapReduce
on a Hadoop cluster
Dan Şerban

2. Agenda
:: Introduction
- Real-world uses of MapReduce
- The origins of Hadoop
- Hadoop facts and architecture
:: Part 1
- Deploying Hadoop
:: Part 2
- MapReduce is machine learning
:: Q&A

3. Why shopping cart analysis
is useful to amazon.com

4. Linkedin and Google Reader

5. The origins of Hadoop
:: Hadoop got its start in Nutch
:: A few enthusiastic developers were attempting
to build an open source web search engine
and having trouble managing computations
running on even a handful of computers
:: Once Google published their GoogleFS and MapReduce
whitepapers, the way forward became clear
:: Google had devised systems to solve precisely
the problems the Nutch project was facing
:: Thus, Hadoop was born

6. Hadoop facts
:: Hadoop is a distributed computing platform
for processing extremely large amounts of data
:: Hadoop is divided into two main components:
- the MapReduce runtime
- the Hadoop Distributed File System (HDFS)
:: The MapReduce runtime allows the user to submit
MapReduce jobs
:: The HDFS is a distributed file system that provides
a logical interface for persistent and redundant
storage of large data
:: Hadoop also provides the HadoopStreaming library
that leverages STDIN and STDOUT so you can write
mappers and reducers in your programming language
of choice

7. Hadoop facts
:: Hadoop is based on the principle
of moving computation to where the data is
:: Data stored on the Hadoop Distributed File System
is broken up into chunks and replicated across
the cluster providing fault tolerant parallel processing
and redundancy for both the data and the jobs
:: Computation takes the form of a job which consists
of a map phase and a reduce phase
:: Data is initially processed by map functions which run
in parallel across the cluster
:: Map output is in the form of key-value pairs
:: The reduce phase then aggregates the map results
:: The reduce phase typically happens in multiple
consecutive waves until the job is complete

8. Hadoop architecture

9. Part 1:
Configuring and deploying
the Hadoop cluster

10. Hands-on with Hadoop

11. core-site.xml - before

12. core-site.xml - after

13. hdfs-site.xml - before

14. hdfs-site.xml - after

15. mapred-site.xml - before

16. mapred-site.xml - after

17. Setting up SSH
:: hadoop@hadoop-master needs to be able to ssh* into:
- hadoop@hadoop-master
- hadoop@chunkserver-a
- hadoop@chunkserver-b
- hadoop@chunkserver-c
:: hadoop@job-tracker needs to be able to ssh* into:
- hadoop@job-tracker
- hadoop@chunkserver-a
- hadoop@chunkserver-b
- hadoop@chunkserver-c
*Passwordless-ly and passphraseless-ly

18. Hands-on with Hadoop

19. Hands-on with Hadoop

20. Hands-on with Hadoop

21. Hands-on with Hadoop

22. Hands-on with Hadoop

23. Hands-on with Hadoop

24. Hands-on with Hadoop

25. Part 2:
MapReduce
is machine learning

26. Rolling your own
self-hosted alternative to ...

27. Hands-on with MapReduce

28. Hands-on with MapReduce

29. Hands-on with MapReduce

30. mapper.py
#!/usr/bin/python
import sys
for line in sys.stdin:
line = line.strip()
IDs = line.split()
for firstID in IDs:
for secondID in IDs:
if secondID > firstID:
print '%s_%st%s' % (firstID, secondID, 1)

31. reducer.py
#!/usr/bin/python
import sys
subTotals = {}
for line in sys.stdin:
line = line.strip()
word = line.split('t')[0]
count = int(line.split('t')[1])
subTotals[word] = subTotals.get(word, 0) + count
for k, v in subTotals.items():
print "%st%s" % (k, v)

32. Hands-on with MapReduce

33. Hands-on with MapReduce

34. Hands-on with MapReduce

35. Hands-on with MapReduce

36. Hands-on with MapReduce

37. Other MapReduce use cases
:: Google Suggest
:: Video recommendations (YouTube)
:: ClickStream Analysis (large web properties)
:: Spam filtering and contextual advertising (Yahoo)
:: Fraud detection (eBay, CC companies)
:: Firewall log analysis to discover exfiltration and
other undesirable (possibly malware-related) activity
:: Finding patterns in social data, analyzing “likes” and
building a search engine on top of them (FaceBook)
:: Discovering microblogging trends and opinion leaders,
analyzing who follows who (Twitter)
:: Plain old supermarket shopping basket analysis
:: The semantic web

38. Questions / Feedback

39. Bonus slide: Making of SQLite DB