Building a Weather Data Services Platform on Riak (RICON East 2013)

Building a Weather Data Services Platform on Riak (RICON East 2013)

Presented by Sathish Gaddipati at RICON East 2013

In this talk Sathish will discuss the size, complexity and use cases surrounding weather data services and analytics, which will entail an overview of the architecture of such systems and the role of Riak in these patterns.

About Sathish

Sathish is a senior technology executive with strong entrepreneurial drive and enjoy linking technology capabilities with business needs. Hands on experience on complex technology transformation initiatives and leading large and highly capable global teams. In-depth knowledge in the state of the art technologies and its application in multiple industry settings.

E0f4dbccf64a1d37a92e224b070ee84f?s=128

Basho Technologies

May 13, 2013
Tweet

Transcript

  1. Building  Weather  Data  Services  Pla5orm  on   Riak    

      Sathish  Gaddipa+   VP  -­‐  Data  Management  
  2. The  Weather  Company  

  3. Science   Tech.   Data   The  Weather  Company’s  Core

     
  4. What  to  Expect?   Use  Cases     Architecture  

    Compu+ng   Challenges   Components   Objec+ves     RIAK     Data   Governance   API   Management   Next  Steps   With  RIAK  
  5. WDS   Insurance   Retail   Weather  Data  Service  -­‐

     Use  Cases   ü  Tornado    and  flood  forecasts   ü  Weather  warnings   ü  Historical    weather  trends     Wind   Energy   ü  Wind  Speed  Forecast   ü  Historical  Wind  Speeds   ü  Precipita+on  forecast   ü  Temperature  forecast   ü  Extreme  weather  forecast   Max.  Premium  Rate   Min.  Claims     PPC                         Maintenance                         Inventory  Management   Distribu+on    
  6. WDS   Media   Ad.   World   ü  Hourly

     Forecasts   ü  Daily  Forecast   ü  Current  condi+ons     ü  Temperature  Forecast   ü  Historical    trens   ü  Real-­‐+me  condi+ons   ü  Forecasts   ü  Customer  loca+on   Weather    data   Weather  content     Bidding                         Demand  Forecas+ng               Impression  serving   Improved  Ad.  Exchange   Energy     Exchange   Weather  Data  Service  -­‐  Use  Cases  
  7. WDS   Mobile     Apps   Hospitality   ü 

    Hourly  Forecasts   ü  Daily  Forecast   ü  Current  condi+ons     ü  Weather  Forecast   ü  Historical    Forecast   ü  Watches  and  warnings   ü  Current  condi+ons   ü  Forecasts   ü  Airline  delays/                cancella+ons   Weather    data   Weather  content     Local  weather         Na+onal  Weather             Room  rates   Revenue  op+miza+on   Government   Weather  Data  Service  -­‐  Use  Cases  
  8. WDS   ü  Hourly  Forecasts   ü  Daily  Forecast  

    ü  Current  condi+ons   ü  Historical  trends     ü  Historical    data   ü  Correla+ons    between                consumer  spent  vs.                  weather  condi+ons   ü  Air  turbulence  and                    wind  speeds   ü  Weather  forecasts   ü  Current  condi+ons   Weather    Data   Weather  Content                      Business  Impact   Consumer  Behavior             Op+mal  Routes   Flight  Schedules                         Internal     (Digital  &    Cable)   Weather   Analy+cs   Airlines   Weather  Data  Service  -­‐  Use  Cases  
  9. Weather  Data  Services  PlaYorm  

  10.     1.  Reduce  +me  to  deploy  and  market  new

     data  sets   2.  Reduce  opera+ng  cost  of  data  services   3.      Centralize  data  services  across  the  company                      -­‐  Eliminate  duplicate  data  feeds,  storage  and  APIs                      -­‐  Provide  system  of  record  for  TWC  weather  data  products     4.      Provide  visibility  of  data  access                      -­‐  Who  is  accessing  what  data  and  how  frequently                      -­‐  Metering       5.  Provides  data  governance  process  and  framework     6.  Serves  world’s  best  weather  forecast  across  all  products       7.  Low  latency,  highly  scalable  APIs     8.  Secured  access  to  data     9.  Centralized  and  scalable  architecture     10.  Consistent  “rich”  content  across  plaYorms       Weather  Data  Services  –     Top  10  Objec+ves  
  11. 1.Distribute  thousands  of  gridded  binary  files  to  mul+ple  loca+ons  

         across  globe    within  5  minutes     2.  Serve  more  than  billion  data  services  API  requests/day     3.  Metering  and  Authen+ca+on  of  API  calls  with  low  latency     4.  Process  mul+ple  TBs  of  data  every  day     5.  Ensure  business  con+nuity     6.  Leverage  data  caching       7.  Store  petabytes  of  historical  data     8.  Meshing  weather  data  with  consumer  behavior  and  derive  analy+cs     9.  Build  flexible  data  inges+on  plaYorm  to  manage  100s  of  data  feeds          from  external  par+es     10.    Maintain  above  systems  within  OPEX  budget     Weather  Data  Services    –     Top  10  Compu+ng  Challenges  
  12. Data  Governance    and  Organiza=on   (3  Months)   Fast

     to  Market  API   (6-­‐8  Months)   Current    Systems   SUN  Pla5orm   Data  Governance    and  Organiza=on   Data  service  PlaYorm  Development  Approach  
  13. 1   Top  Architecture  Considera+ons   1.  Non-­‐Blocking  Data  Inges+on

        2.  Pull  and  Push  data  service     3.  Load  balanced  data  processing    across    data  centers     4.  Use  memory  based  data  storage  for  real  +me  data  systems     5.  Easily  scalable,  highly  available  and  easy  to  maintain  large  historical            data  sets.       6.  Data  caching  to  achieve  low  latency     7.  To  ensure  business  con+nuity,  parallel  process  between    two          geographical  loca+ons     8.  Use  COTS  based  API  management  for  authen+ca+on,  metering  and            developer  on  boarding.       9.  Data  Replica+on  to  mul+ple  loca+ons  from  one  loca+on  within                  60+GB  data  within  5  mins  
  14. 1   Historical  data  service  PlaYorm  -­‐  RIAK    

    §  Easy  administra+on   §  Data  center  to  data  center  replica+on   §  Ease  of  scaling   §  High  availability   §  Text  and  numeric  data   §  KV  Store   §  More  reads  than  writes  
  15. RIAK  Test  Environment   node1   node2   node3  

    node4   node5   node6   Load   Node   Load   Node   Load   Balancer   Riak  Cluster   M1.xlarge   4  cores   15  GB  RAM   4  EBS  1000  IOP     Volumes     RAID  10   C1.medium   2  cores   1.7  GB  RAM   C1.medium   2  cores   1.7  GB  RAM   Zone  #  1   Zone  #  2   Load   Node   C1.medium   2  cores   1.7  GB  RAM  
  16. 1   RIAK  Test  Results  –  600  Concurrent  User  Tests

      Test  configura+on  -­‐  Apache  bench  -­‐n  20000  c  100   6  Terminal  sessions  running  the  above,   So  concurrent  user  load  is  100  *  6  (c  *  #terminals)  =  600     Concurrent User Load Request per Second(mean) Response Time(mean) CPU Utilization 600 Riak1- 979 Riak2- 891 Riak3 - 881 Riak4- 906 Riak5 -968 Riak6- 984 ----------------------------- Total - 5609/sec Riak 1- 102 ms Riak 2- 112 ms Riak 3- 113 ms Riak 4 - 110 ms Riak 5 - 103 ms Riak 6 - 101 ms ------------------------ Average - 106 ms Riak 1- 25- 30% Riak 2- 35- 40% Riak 3- 35-40% Riak 4- 20-25% Riak 5- 35- 40% Riak 6- 25- 30% ----------------------- Well below 60%
  17. Mobile     Apps   Hospitality   RIAK  Test  Results

     –  1800  Concurrent  User  Tests   Test  configura+on  -­‐  Apache  bench  -­‐n  20000  c  300   6  Terminal  sessions  running  the  above,   So  concurrent  user  load  is  300  *  6  (c  *  #terminals)  =  1800   Concurrent User Load Request per Second(mean) Response Time(mean) CPU Utilization 1800 Riak1- 923 Riak2- 896 Riak3 - 907 Riak4- 939 Riak5 -964 Riak6- 965 ----------------------------- Total - 5594/sec Riak 1- 324 ms Riak 2- 334 ms Riak 3- 330 ms Riak 4 - 319 ms Riak 5 - 311ms Riak 6 - 310 ms ------------------------ Average - 324 ms Riak 1- 35-40% Riak 2- 35- 40% Riak 3- 35-40% Riak 4- 35-40% Riak 5- 35- 40% Riak 6- 35-40% ----------------------- Well below 60%
  18. RIAK    Read  Test  Results  

  19. Data  Services  -­‐  Prerequisites       Data   services

      Data     Organiza+on   Process  &   Governance   Technology   SUN  PlaYorm   Data  Services  Org.   Processes  and  Governance  around   Exis+ng  and  New  Data  Services  Opportuni+es   Data  Gaps   Data  Quality  
  20. Data  Services  Governance   B2B  Data  Requests   B2C  Data

     Policy   Requests   Weather  FX  Data   Requests   Other  Data  Requests   Data     Services   Org.   Data   Acquisi+on   Data  API   Develop.   IT  Capacity   Security  &   Privacy   Cross   Channel   Impact   Cost   Es+mates  
  21. Data  Services  Governance  (DSG)     DSG   DG  Sponsor

      Data   Stakeholders     Data   Steward   Facilitator   Data  Services   Organiza+on   COO   Divisional  Heads   Data  Enthusiasts   Data  Stakeholders   Data  Experts    
  22. Ø  Metering   Ø  Authen+ca+on   Ø  Developer  Onboarding  

    Ø  Billing  Interface   Ø  User  Analy+cs   q Mashery   q Layer  7   q WSO2   q Oracle   API  Management  
  23. Insurance   Retail   Next  Steps  With  Riak   q Replica+on

     Tests     q Caching  on  top  op  Riak  
  24. Ques+ons?