How Salesforce.com uses Hadoop · Salesforce.com, inc. assumes no obligation and does not intend to...
Transcript of How Salesforce.com uses Hadoop · Salesforce.com, inc. assumes no obligation and does not intend to...
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How Salesforce.com uses Hadoop
Narayan Bharadwaj
Data Science
@nadubharadwaj
Jed Crosby
Data Science
@JedCrosby
#forcewebinar
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Safe Harbor
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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”?
780 million transactions/day
Terabytes/day
130k customers
Millions of users
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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
Product Metrics User behavior
analysis Capacity planning
Monitoring intelligence
Performance analysis
Security
Ad-hoc log searches
Collaborative Filtering
Search Relevancy
Product Metrics
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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
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Feature Metadata
(Instrumentation)
Daily Summary
(Output)
Crunch it
(How?)
Storage & Processing
Feature (What?) Fancy UI
(Visualize)
Collaborate &
Iterate
Data Pipeline
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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
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Java Program
Collaboration
(Chatter)
Wo
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Product Metrics Pipeline
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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)
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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
Apache Pig
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-- 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
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Java Pig Script Generator (Client)
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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)
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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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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
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Recap
Technology
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Contributions
@pRaShAnT1784 : Prashant Kommireddi
Lars Hofhansl @thefutureian : Ian Varley
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Apache Pig
Version=0.9.1
Data Science tools ecosystem
Collaborative Filtering
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Show similar files within an organization
– Content-based approach
– Community-base approach
Collaborative Filtering – Problem Statement
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Popular File
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Related File
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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
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Annual Report Vision Statement
Dilbert Comic
Darth Vader Cartoon
Disk Usage Report
Example: CF on 5 files
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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
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Annual Report
Disk Usage
Report
Darth Vader
Cartoon
Dilbert
Cartoon
Vision Statement
Relationships between the files
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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
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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
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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
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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
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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
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MapReduce Overview Map Shuffle Reduce
(adapted from http://code.google.com/p/mapreduce-framework/wiki/MapReduce)
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<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
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(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
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<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
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(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
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<(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
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<(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
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<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
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<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
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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.
cosAB A B
A BA
AB
B
Appendix
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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