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How Salesforce.com Uses Hadoop
Some Data Science Use Cases
Narayan Bharadwaj Jed Crosby
salesforce.com salesforce.com
@nadubharadwaj @JedCrosby
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Agenda
• Technology
• Hadoop use cases
• Use case discussion
• Product Metrics
• User Behavior Analysis
• Collaborative Filtering
• Q&A
Every time you see the elephant, we will attempt to explain a
Hadoop related concept.
Got “Cloud Data”?
800 million transactions/day
Terabytes/day
130k customers
Millions of users
Technology
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
Our Hadoop Ecosystem
Apache Pig
Contributions
@pRaShAnT1784 : Prashant Kommireddi
Lars Hofhansl @thefutureian : Ian Varley
Use Cases
Product Metrics User behavior
analysis Capacity planning
Monitoring intelligence Collections Query Runtime
Prediction
Early Warning System Collaborative Filtering Search Relevancy
Internal App Product feature
Hadoop Use Cases
Product Metrics
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
Product Metrics – Problem Statement
Feature Metadata
(Instrumentation)
Daily Summary
(Output)
Crunch it
(How?)
Storage & Processing
Feature (What?) Fancy UI
(Visualize) Collaborate & Iterate
Data Pipeline
Feature Metrics
(Custom Object)
Trend Metrics
(Custom Object)
Client Machine
Pig script generator
Hadoop
Log Files
Lo
g P
ull
User Input
(Page Layout) Reports, Dashboards
AP
I
AP
I
Wo
rkfl
ow
Fo
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Fie
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Java Program
Collaboration
(Chatter)
Wo
rkfl
ow
Product Metrics Pipeline
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
Feature Metrics (Custom Object)
Feature Metrics (Custom Object)
User Input (Page Layout)
Formula
Field
Workflow
Rule
User Input (Child Custom Object)
Child
Objects
Apache Pig
-- 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’;
Basic Pig Script Construct
Java Pig Script Generator (Client)
Id Date #Requests #Unique Orgs #Unique Users Avg 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>
Trend Metrics (Custom Object)
Upload to Trend Metrics (Custom Object)
Visualization (Reports & Dashboards)
Visualization (Reports & Dashboards)
Collaborate, Iterate (Chatter)
Feature Metrics
(Custom Object)
Trend Metrics
(Custom Object)
Client Machine
Pig script generator
Hadoop
Log Files
Lo
g P
ull
User Input
(Page Layout) Reports, Dashboards
AP
I
AP
I
Wo
rkfl
ow
Fo
rmu
la
Fie
lds
Java Program
Collaboration
(Chatter)
Wo
rkfl
ow
Recap
User Behavior Analysis
Problem Statement
How do we reduce number of clicks on the user interface?
Need to understand top user click paths. What are they typically trying to do?
What are the user clusters/personas?
Approach:
• Markov transition for click path, D3.js visuals
• K-means (unsupervised) clustering for user groups
Markov Transitions for "Setup" Pages
K-means clustering of "Setup" Pages
Collaborative Filtering
Jed Crosby
Show similar files within an organization
Content-based approach
Community-base approach
Collaborative Filtering – Problem Statement
Popular File
Related File
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.
We found this relationship using item-to-item collaborative
filtering
Annual Report Vision Statement
Dilbert Comic
Darth Vader Cartoon
Disk Usage Report
Example: CF on 5 files
Annual
Report
Vision
Statement
Dilbert
Cartoon
Darth Vader
Cartoon
Disk Usage
Report
Miranda
(CEO) 1 1 1 0 0
Bob (CFO) 1 1 1 0 0
Susan
(Sales) 0 1 1 1 0
Chun (Sales) 0 0 1 1 0
Alice (IT) 0 0 1 1 1
View History Table
Annual Report
Disk Usage
Report
Darth Vader
Cartoon Dilbert Cartoon
Vision Statement
Relationships Between the Files
Annual Report
Disk Usage
Report
Darth Vader
Cartoon Dilbert Cartoon
Vision Statement 2
2
0
0
3 1
0
3
1 1
Relationships Between the Files
Annual
Report
Vision
Statement
Dilbert
Cartoon
Darth Vader
Cartoon
Disk Usage
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.
Sorted Relationships for Each File
Annual Report
Disk Usage
Report
Darth Vader
Cartoon Dilbert Cartoon
Vision Statement .82
.63 0
0
.77 .33
0
.77
.45 .58
Normalized Relationships Between the Files
Annual Report Vision
Statement
Dilbert
Cartoon
Darth Vader
Cartoon
Disk Usage
Report
Vision Stmt.
(.82)
Annual Report
(.82)
Darth Vader
(.77) Dilbert (.77)
Darth Vader
(.58)
Dilbert (.63) Dilbert (.77) Vision Stmt.
(.77)
Disk Usage
(.58)
Dilbert
(.45)
Darth Vader
(.33)
Annual Report
(.63)
Vision Stmt.
(.33)
Disk Usage
(.45)
High relationship tallies AND similar popularity values now drive closeness.
Sorted relationships for each file, normalized by file popularities
1) Compute file popularities
2) Compute relationship tallies and divide by file popularities
3) Sort and store the results
The Item-to-Item CF Algorithm
MapReduce Overview Map Shuffle Reduce
(adapted from http://code.google.com/p/mapreduce-framework/wiki/MapReduce)
<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.
1. Compute File Popularities
(Miranda, Dilbert), (Bob, Dilbert), (Susan, Dilbert), (Chun, Dilbert), (Alice, Dilbert)
Inverse identity map
<Dilbert, {Miranda, Bob, Susan, Chun, Alice}>
Reduce
(Dilbert, 5)
Example: File popularity for Dilbert
<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.
2a. Compute Relationship Tallies − Find All Relationships in View History Table
(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)>
Example 2a: Miranda’s (CEO) File Relationship Votes
<(file1, file2), Integer(1)>
<(file1, file2), List<Integer(1)>
Identity map
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.
2b. Tally the Relationship Votes − Just a Word Count, Where Each
Relationship Occurrence is a Word
<(Dilbert, Vader), Integer(1)>,
<(Dilbert, Vader), Integer(1)>,
<(Dilbert, Vader), Integer(1)>
<(Dilbert, Vader), {1, 1, 1}>
Identity map
Reduce: count and divide
by popularities
<Dilbert, (Vader, sqrt(3/5))>, <Vader, (Dilbert, sqrt(3/5))>
Example 2b: the Dilbert/Darth Vader Relationship
<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
3. Sort and Store Results
<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
Example 3: Sorting the Results for Dilbert
Cosine formula and normalization trick to avoid the distributed
cache
Mahout has CF
Asymptotic order of the algorithm is O(M*N2) in worst case, but
is helped by sparsity.
cosqAB =A · B
A B=A
A·B
B
Appendix
Narayan Bharadwaj
Director, Product Management
@nadubharadwaj
Jed Crosby
Data Scientist
@JedCrosby