How Salesforce.com uses Hadoop

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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


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.


Got “Cloud Data”?

780 million transactions/day Terabytes/day

130k customers Millions of users


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


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


§  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

Log

Pull

User Input (Page Layout)

Reports, Dashboards

AP

I

AP

I

Wor

kflo

w

Form

ula

Fiel

ds

Java Program

Collaboration (Chatter)

Wor

kflo

w

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



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>








Upload to Trend Metrics (Custom Object)


Visualization (Reports & Dashboards)


Collaborate, Iterate (Chatter)




Client Machine

Pig script generator

Hadoop Log Files

Log

Pull

User Input (Page Layout)

Reports, Dashboards

AP

I

AP

I

Wor

kflo

w

Form

ula

Fiel

ds

Java Program

Collaboration (Chatter)

Wor

kflo

w

Recap

Technology


Apache Hadoop Version=0.20.2

Hadoop ecosystem


Contributions

@pRaShAnT1784 : Prashant Kommireddi

Lars Hofhansl @thefutureian : Ian Varley


Apache Pig Version=0.9.1

Data Science tools ecosystem

Collaborative Filtering


§  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

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 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. €

cosθAB =A • BA B

=AA

•BB

Appendix


Summary

Hadoop Cloud Data

Hadoop + Force.com = Recommendation algorithms


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hadoopsurvey

Narayan Bharadwaj Jed Crosby Prashant Kommireddi Santosh Rau @nadubharadwaj @JedCrosby @pRaShAnT1784 @santoshrau

@SalesforceEng

How Salesforce.com uses Hadoop

Technology

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