How Salesforce.com Uses HadoopHow Salesforce.com Uses HadoopSome 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/dayTerabytes/day

130k customersMillions 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 AppInternal App Product featureProduct 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)

Data Pipeline

Feature Metrics (Custom Object) Feature Metrics (Custom Object)

Trend Metrics(Custom Object)Trend Metrics(Custom Object)

Client Machine Client Machine

Pig script generatorPig script generator

HadoopHadoop Log FilesLog Files

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User Input(Page Layout)

User Input(Page Layout) Reports, DashboardsReports, Dashboards

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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) Feature Metrics (Custom Object)

Trend Metrics(Custom Object)Trend Metrics(Custom Object)

Client Machine Client Machine

Pig script generatorPig script generator

HadoopHadoop Log FilesLog Files

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User Input(Page Layout)

User Input(Page Layout) Reports, DashboardsReports, Dashboards

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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 ReportVision 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 Statement2

2

0

0

31

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 CartoonDilbert Cartoon

Vision Statement.82

.630

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 OverviewMap 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.

Appendix

Narayan BharadwajNarayan BharadwajDirector, Product Management

@nadubharadwaj

Jed CrosbyJed CrosbyData Scientist@JedCrosby