Big Data Use Cases. At Salesforce.com. Narayan Bharadwaj Director, Product Management

Big  Data  Use  Cases  

At  Salesforce.com  

Narayan  Bharadwaj            

Director,  Product  Management

Salesforce.com  

Safe  harbor  

Safe  harbor  statement  under  the  Private  Securi9es  Li9ga9on  Reform  Act  of  1995:  

This  presenta9on  may  contain  forward-­‐looking  statements  that  involve  risks,  uncertain9es,  and  assump9ons.  If  any   such  uncertain9es  materialize  or  if  any  of  the  assump9ons  proves  incorrect,  the  results  of  salesforce.com,  inc.  could   differ  materially  from  the  results  expressed  or  implied  by  the  forward-­‐looking  statements  we  make.  All  statements   other  than  statements  of  historical  fact  could  be  deemed  forward-­‐looking,  including  any  projec9ons  of  product  or   service  availability,  subscriber  growth,  earnings,  revenues,  or  other  financial  items  and  any  statements  regarding   strategies  or  plans  of  management  for  future  opera9ons,  statements  of  belief,  any  statements  concerning  new,   planned,  or  upgraded  services  or  technology  developments  and  customer  contracts  or  use  of  our  services.  

The  risks  and  uncertain9es  referred  to  above  include  –  but  are  not  limited  to  –  risks  associated  with  developing  and   delivering  new  func9onality  for  our  service,  new  products  and  services,  our  new  business  model,  our  past  opera9ng   losses,  possible  fluctua9ons  in  our  opera9ng  results  and  rate  of  growth,  interrup9ons  or  delays  in  our  Web  hos9ng,   breach  of  our  security  measures,  the  outcome  of  intellectual  property  and  other  li9ga9on,  risks  associated  with   possible  mergers  and  acquisi9ons,  the  immature  market  in  which  we  operate,  our  rela9vely  limited  opera9ng   history,  our  ability  to  expand,  retain,  and  mo9vate  our  employees  and  manage  our  growth,  new  releases  of  our   service  and  successful  customer  deployment,  our  limited  history  reselling  non-­‐salesforce.com  products,  and   u9liza9on  and  selling  to  larger  enterprise  customers.  Further  informa9on  on  poten9al  factors  that  could  affect  the   financial  results  of  salesforce.com,  inc.  is  included  in  our  annual  report  on  Form  10-­‐Q  for  the  most  recent  fiscal   quarter  ended  July  31,  2012.  This  documents  and  others  containing  important  disclosures  are  available  on  the  SEC   Filings  sec9on  of  the  Investor  Informa9on  sec9on  of  our  Web  site.  

Any  unreleased  services  or  features  referenced  in  this  or  other  presenta9ons,  press  releases  or  public  statements   are  not  currently  available  and  may  not  be  delivered  on  9me  or  at  all.  Customers  who  purchase  our  services  should   make  the  purchase  decisions  based  upon  features  that  are  currently  available.  Salesforce.com,  inc.  assumes  no   obliga9on  and  does  not  intend  to  update  these  forward-­‐looking  statements.  

Agenda  

• 

Big  Data  use  cases  

• 

Technology  

• 

Use  case  discussion  

• 

Collabora9ve  Filtering  

Got  “Cloud  Data”?  

800  million  transac9ons/day

Terabytes/day  

130k  customers  

Millions  of  users  

Apache  Pig  

Version=0.9.1  

Contribu9ons  

@pRaShAnT1784  :  Prashant  Kommireddi  

Product  Metrics  

User  behavior  

_analysis  

Capacity  planning  

Monitoring  

intelligence  

Collec9ons  

Query  Run9me  

Predic9on  

Early  Warning  

System  

Collabora9ve  

Filtering  

Search  Relevancy  

Internal  App  

Product  feature  

• 

Track  feature  usage/adop9on  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:  Execu9ves,  Product  Managers  

                     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   W or kfl ow   Fo rm ul a   Fi el ds   Java  Program  

CollaboraWon  

(ChaXer)  

Problem  Statement  

§ 

How  do  we  reduce  number  of  clicks  on  the  user  interface?  

§ 

What  are  the  top  user  click  path  sequences?  

§ 

What  are  the  user  clusters/personas?  

• 

Approach:  

• 

Markov  transi9on

 for  click  path,  D3.js  visuals  

Collabora9ve  Filtering  

• 

_{Show  similar  files  within  an  organiza9on}  

– 

Content-­‐based  approach  

– 

Community-­‐base  approach  

• 

_{Amazon  published  this  algorithm  in  2003.}  

– 

Amazon.com  RecommendaAons:  Item-­‐to-­‐Item  CollaboraAve  Filtering,  by  

Gregory  Linden,  Brent  Smith,  and  Jeremy  York.    IEEE  Internet  Compu9ng,  

January-­‐February  2003.  

• 

_{At  Salesforce,  we  adapted  this  algorithm  for}  

Hadoop,  and  we  use  it  to  recommend  files  to  

view  and  users  to  follow.  

Annual  Report  

Vision  Statement  

Dilbert  Comic  

Darth  Vader  Cartoon  

Disk  Usage  Report  

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  

Annual  Report   Disk  Usage   Report   Darth  Vader   Cartoon   Dilbert  Cartoon   Vision  Statement  

Annual  Report   Disk  Usage   Report   Darth  Vader   Cartoon   Dilbert   Cartoon   Vision  Statement   2 2 0 0 3 1 0 3 1 1

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:  no9ce  that  Dilbert  appears  first  in  every  list.    This  is  

probably  not  what  we  want.  

The  solu9on:  divide  the  relaWonship  tallies  by  file  populariWes.  

Annual  Report   Disk  Usage   Report   Darth  Vader   Cartoon   Dilbert  Cartoon   Vision  Statement   .82   .63   0 0 .77   .33   0 .77   .45   .58  

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  rela9onship  tallies  AND  similar  popularity  values  now  drive  closeness.  

1)  Compute  file  populari9es  

2)  Compute  rela9onship  tallies  and  divide  by  

file  populari9es  

3)  Sort  and  store  the  results  

MapReduce  Overview  

Map  

Shuffle  

Reduce  

(adapted  from  hsp://code.google.com/p/mapreduce-­‐framework/wiki/

MapReduce)  

<user,  file>  

Inverse  iden9ty  map  

<file,  List<user>>  

Reduce  

<file,  (user  count)>  

Result  is  a  table  of  (file,  popularity)  pairs  that  you  store  in  the  Hadoop  distributed  cache.  

(Miranda,  Dilbert),  (Bob,  Dilbert),  (Susan,  Dilbert),  (Chun,  Dilbert),  (Alice,  Dilbert)  

Inverse  iden9ty  map  

<Dilbert,  {Miranda,  Bob,  Susan,  Chun,  Alice}>  

Reduce  

(Dilbert,  5)  

<user,  file>    

Iden9ty  map  

<user,  List<file>>  

Reduce  

<(file1,  file2),  Integer(1)>,    

<(file1,  file3),  Integer(1)>,  

 …    

<(file(n-­‐1),  file(n)),  Integer(1)>  

Rela9onships  have  their  file  IDs  in  alphabe9cal  order  to  avoid  double  

coun9ng.  

(Miranda,  Annual  Report),  (Miranda,  Vision  Statement),  (Miranda,  Dilbert)  

Iden9ty  map  

<Miranda,  {Annual  Report,  Vision  Statement,  Dilbert}>  

Reduce  

<(Annual  Report,  Dilbert),  Integer(1)>,    

<(Annual  Report,  Vision  Statement),  Integer(1)>,    

<(Dilbert,  Vision  Statement),  Integer(1)>  

<(file1,  file2),  Integer(1)>  

<(file1,  file2),  List<Integer(1)>  

Iden9ty  map  

Reduce:  count  and  divide  

by  populari9es  

<file1,  (file2,  similarity  score)>,  <file2,    (file1,  similarity  score)>  

Note  that  we  emit  each  result  twice,  

one  for  each  file  that  belongs  to  a  rela9onship.  

2b.  Tally  the  relaWonship  votes  -­‐  just  a  word  count,  where  each  

relaWonship  occurrence  is  a  word    

<(Dilbert,  Vader),  Integer(1)>,  

<(Dilbert,  Vader),  Integer(1)>,    

<(Dilbert,  Vader),  Integer(1)>  

<(Dilbert,  Vader),  {1,  1,  1}>  

Iden9ty  map  

Reduce:  count  and  divide  

by  populari9es  

<Dilbert,  (Vader,  sqrt(3/5))>,  <Vader,  (Dilbert,  sqrt(3/5))>  

<file1,  (file2,  similarity  score)>  

Iden9ty  map  

<file1,  List<(file2,  similarity  score)>>  

Reduce  

<file1,  {top  n  similar  files}>  

Store  the  results  in  your  loca9on  of  choice  

<Dilbert,  (Annual  Report,  .63)>,  

<Dilbert,  (Vision  Statement,  .77)>,  

<Dilbert,  (Disk  Usage,  .45)>,  

<Dilbert,  (Darth  Vader,  .77)>  

Iden9ty  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  

• 

_{Cosine  formula  and  normaliza9on  trick  to}  

avoid  the  distributed  cache  

• 

Mahout  has  CF  

• 

Asympto9c  order  of  the  algorithm  is  O(M*N

2

₎  

in  worst  case,  but  is  helped  by  sparsity.  

€

cos

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A

A

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Appendix  

Narayan  Bharadwaj  

Director,  Product  Management  

@nadubharadwaj