How Salesforce.com uses Hadoop
Narayan Bharadwaj
Data Science
@nadubharadwaj
Jed Crosby
Data Science
@JedCrosby
#forcewebinar
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Safe Harbor
Safe harbor statement under the Private Securities Litigation Reform Act of 1995:
This presentation may contain forward-looking statements that involve risks, uncertainties, and assumptions. If any such
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acquisitions, the immature market in which we operate, our relatively limited operating history, our ability to expand, retain,
and motivate our employees and manage our growth, new releases of our service and successful customer deployment, our
limited history reselling non-salesforce.com products, and utilization and selling to larger enterprise customers. Further
information on potential factors that could affect the financial results of salesforce.com, inc. is included in our annual report
on Form 10-K for the most recent fiscal year ended January 31, 2011 and in our quarterly report on Form 10-Q for the most
recent fiscal quarter ended October 31, 2011. These documents and others containing important disclosures are available
on the SEC Filings section of the Investor Information section of our Web site.
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not intend to update these forward-looking statements.
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Agenda
§ Hadoop use cases
§ Use case 1 - Product Metrics*
§ Technology
§ Use case 2- Collaborative Filtering*
§ Q&A
*Every time you see the elephant, we will attempt to
explain a Hadoop related concept.
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Got “Cloud Data”?
130k customers 780 million transactions/day
Millions of users Terabytes/day
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Hadoop Overview
§ Started by Doug Cutting at Yahoo!
§ Based on two Google papers
– Google File System (GFS): http://research.google.com/archive/gfs.html
– Google MapReduce: http://research.google.com/archive/mapreduce.html
§ Hadoop is an open source Apache project
– Hadoop Distributed File System (HDFS)
– Distributed Processing Framework (MapReduce)
§ Several related projects
– HBase, Hive, Pig, Flume, ZooKeeper, Mahout, Oozie, HCatalog
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Hadoop use cases
User behavior
Product Metrics Capacity planning
analysis
Monitoring Performance
Security
intelligence analysis
Ad-hoc log Collaborative
Search Relevancy
searches Filtering
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Product Metrics

Product Metrics – Problem Statement
§ Track feature usage/adoption across 130k+ customers
– Eg: Accounts, Contacts, Visualforce, Apex,…
§ Track standard metrics across all features
– Eg: #Requests, #UniqueOrgs, #UniqueUsers,
AvgResponseTime,…
§ Track features and metrics across all channels
– API, UI, Mobile
§ Primary audience: Executives, Product Managers
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Data Pipeline
Collaborate & Fancy UI
Feature (What?)
Iterate (Visualize)
Feature Metadata Daily Summary
(Instrumentation) (Output)
Crunch it
(How?)
Storage & Processing
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Product Metrics Pipeline
User Input Collaboration Reports,
(Page Layout) (Chatter) Dashboards
Formula
Workflow
Fields
Feature Metrics Trend Metrics
(Custom Object) (Custom Object)
API
API
Client Machine
Java Program
Pig script generator
Workflow
Log Pull
Hadoop
Log Files
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Feature Metrics (Custom Object)
Id Feature Name PM Instrumentation Metric1 Metric2 Metric3 Metric4 Status
F0001 Accounts John /001 #requests #UniqOrgs #UniqUsers AvgRT Dev
F0002 Contacts Nancy /003 #requests #UniqOrgs #UniqUsers AvgRT Review
F0003 API Eric A #requests #UniqOrgs #UniqUsers AvgRT Deployed
F0004 Visualforce Roger V #requests #UniqOrgs #UniqUsers AvgRT Decom
F0005 Apex Kim axapx #requests #UniqOrgs #UniqUsers AvgRT Deployed
F0006 Custom Objects Chun /aXX #requests #UniqOrgs #UniqUsers AvgRT Deployed
F0008 Chatter Jed chcmd #requests #UniqOrgs #UniqUsers AvgRT Deployed
F0009 Reports Steve R #requests #UniqOrgs #UniqUsers AvgRT Deployed
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Feature Metrics (Custom Object)
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User Input (Page Layout)
Formula
Field
Workflow
Rule
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User Input (Child Custom Object)
Child
Objects
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Apache Pig

Basic Pig script construct
-- Define UDFs
DEFINE GFV GetFieldValue(‘/path/to/udf/file’);
-- Load data
A = LOAD ‘/path/to/cloud/data/log/files’ USING PigStorage();
-- Filter data
B = FILTER A BY GFV(row, ‘logRecordType’) == ‘U’;
-- Extract Fields
C = FOREACH B GENERATE GFV(*, ‘orgId’), LFV(*. ‘userId’) ……..
-- Group
G = GROUP C BY ……
-- Compute output metrics
O = FOREACH G {
orgs = C.orgId; uniqueOrgs = DISTINCT orgs;
}
-- Store or Dump results
STORE O INTO ‘/path/to/user/output’;
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Java Pig Script Generator (Client)
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Trend Metrics (Custom Object)
#Unique #Unique Avg
Id Date #Requests
Orgs Users ResponseTime
F0001 06/01/2012 <big> <big> <big> <little>
F0002 06/01/2012 <big> <big> <big> <little>
F0003 06/01/2012 <big> <big> <big> <little>
F0001 06/02/2012 <big> <big> <big> <little>
F0002 06/02/2012 <big> <big> <big> <little>
F0003 06/03/2012 <big> <big> <big> <little>
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Upload to Trend Metrics (Custom Object)
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Visualization (Reports & Dashboards)
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Visualization (Reports & Dashboards)
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Collaborate, Iterate (Chatter)
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Recap
User Input Collaboration Reports,
(Page Layout) (Chatter) Dashboards
Formula
Workflow
Fields
Feature Metrics Trend Metrics
(Custom Object) (Custom Object)
API
API
Client Machine
Java Program
Pig script generator
Workflow
Log Pull
Hadoop
Log Files
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Technology

Hadoop ecosystem
Apache Hadoop
Version=0.20.2
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Contributions
@pRaShAnT1784 : Prashant Kommireddi
Lars Hofhansl @thefutureian : Ian Varley
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Data Science tools ecosystem
Apache Pig
Version=0.9.1
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Collaborative Filtering

Collaborative Filtering – Problem Statement
§ Show similar files within an organization
– Content-based approach
– Community-base approach
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Popular File
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Related File
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We found this relationship using item-to-item collaborative
filtering
§ Amazon published this algorithm in 2003.
– Amazon.com Recommendations: Item-to-Item Collaborative Filtering,
by Gregory Linden, Brent Smith, and Jeremy York. IEEE Internet
Computing, January-February 2003.
§ At Salesforce, we adapted this algorithm for Hadoop,
and we use it to recommend files to view and users to
follow.
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Example: CF on 5 files
Vision Statement
Annual Report
Dilbert Comic
Darth Vader Cartoon
Disk Usage Report
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View History Table
Annual Vision Dilbert Darth Disk
Report Statement Cartoon Vader Usage
Cartoon Report
Miranda 1 1 1 0 0
(CEO)
Bob (CFO) 1 1 1 0 0
Susan 0 1 1 1 0
(Sales)
Chun 0 0 1 1 0
(Sales)
Alice (IT) 0 0 1 1 1
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Relationships between the files
Annual Report Vision Statement
Darth Vader
Cartoon
Dilbert
Cartoon
Disk Usage
Report
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Relationships between the files
Annual
Report 2 Vision Statement
0 1
3
2
0 Darth Vader
0 Cartoon
Dilbert
Cartoon 3
1
1
Disk Usage
Report
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Sorted relationships for each file
Annual Vision Dilbert Darth Vader Disk Usage
Report Statement Cartoon Cartoon Report
Dilbert (2) Dilbert (3) Vision Stmt. (3) Dilbert (3) Dilbert (1)
Vision Stmt. (2) Annual Rpt. (2) Darth Vader (3) Vision Stmt. (1) Darth Vader (1)
Darth Vader (1) Annual Rpt. (2) Disk Usage (1)
Disk Usage (1)
The popularity problem: notice that Dilbert appears first in every list.
This is probably not what we want.
The solution: divide the relationship tallies by file popularities.
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Normalized relationships between the files
Annual Report Vision Statement
.82
0 .33
.77
.63
0
0 Darth Vader
Cartoon
Dilbert
Cartoon .77
.45 .58
Disk Usage
Report
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Sorted relationships for each file, normalized by file popularities
Annual Report Vision Dilbert Darth Vader Disk Usage
Statement Cartoon Cartoon Report
Vision Stmt. Annual Report Darth Vader Dilbert (.77) Darth Vader
(.82) (.82) (.77) (.58)
Dilbert (.63) Dilbert (.77) Vision Stmt. Disk Usage Dilbert
(.77) (.58) (.45)
Darth Vader Annual Report Vision Stmt.
(.33) (.63) (.33)
Disk Usage
(.45)
High relationship tallies AND similar popularity values now drive closeness.
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The item-to-item CF algorithm
1) Compute file popularities
2) Compute relationship tallies and divide by file
popularities
3) Sort and store the results
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MapReduce Overview
Map Shuffle Reduce
(adapted from http://code.google.com/p/mapreduce-framework/wiki/MapReduce)
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1. Compute File Popularities
<user, file>
Inverse identity map
<file, List<user>>
Reduce
<file, (user count)>
Result is a table of (file, popularity) pairs that you store in the Hadoop distributed cache.
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Example: File popularity for Dilbert
(Miranda, Dilbert), (Bob, Dilbert), (Susan, Dilbert), (Chun, Dilbert), (Alice, Dilbert)
Inverse identity map
<Dilbert, {Miranda, Bob, Susan, Chun, Alice}>
Reduce
(Dilbert, 5)
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2a. Compute relationship tallies - find all relationships in view
history table
<user, file>
Identity map
<user, List<file>>
Reduce
<(file1, file2), Integer(1)>,
<(file1, file3), Integer(1)>,
…
<(file(n-1), file(n)), Integer(1)>
Relationships have their file IDs in alphabetical order
to avoid double counting.
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Example 2a: Miranda’s (CEO) file relationship votes
(Miranda, Annual Report), (Miranda, Vision Statement), (Miranda, Dilbert)
Identity map
<Miranda, {Annual Report, Vision Statement, Dilbert}>
Reduce
<(Annual Report, Dilbert), Integer(1)>,
<(Annual Report, Vision Statement), Integer(1)>,
<(Dilbert, Vision Statement), Integer(1)>
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2b. Tally the relationship votes - just a word count, where each
relationship occurrence is a word
<(file1, file2), Integer(1)>
Identity map
<(file1, file2), List<Integer(1)>
Reduce: count and
divide by popularities
<file1, (file2, similarity score)>, <file2, (file1, similarity score)>
Note that we emit each result twice, one for each file that belongs to a
relationship.
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Example 2b: the Dilbert/Darth Vader relationship
<(Dilbert, Vader), Integer(1)>,
<(Dilbert, Vader), Integer(1)>,
<(Dilbert, Vader), Integer(1)>
Identity map
<(Dilbert, Vader), {1, 1, 1}>
Reduce: count and
divide by popularities
<Dilbert, (Vader, sqrt(3/5))>, <Vader, (Dilbert, sqrt(3/5))>
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3. Sort and store results
<file1, (file2, similarity score)>
Identity map
<file1, List<(file2, similarity score)>>
Reduce
<file1, {top n similar files}>
Store the results in your location of choice
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Example 3: Sorting the results for Dilbert
<Dilbert, (Annual Report, .63)>,
<Dilbert, (Vision Statement, .77)>,
<Dilbert, (Disk Usage, .45)>,
<Dilbert, (Darth Vader, .77)>
Identity map
<Dilbert, {(Annual Report, .63), (Vision Statement, .77), (Disk Usage, .45), (Darth Vader, .77)}>
Reduce
<Dilbert, {Darth Vader, Vision Statement}> (Top 2 files)
Store results
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Appendix
§ Cosine formula and normalization trick to avoid the
distributed cache
A• B A B
cosθ AB = = •
A B A B
§ Mahout has CF
§ Asymptotic order of the algorithm is O(M*N2) in worst
€
case, but is helped by sparsity.
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Summary
Hadoop Cloud Data
Hadoop + Force.com = Recommendation algorithms
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Q&A
http://bit.ly/
hadoopsurvey
Narayan Bharadwaj Jed Crosby Prashant Kommireddi Santosh Rau
@nadubharadwaj @JedCrosby @pRaShAnT1784 @santoshrau
@SalesforceEng
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