Real World Use Cases and Success Stories for In-Memory Data Grids (TIBCO ActiveSpaces, Oracle Coherence, Infinispan, IBM WebSphere eXtreme Scale, Hazelcast, Gigaspaces, GridGain, Pivotal Gemfire, not: SAP HANA)

A lot of in-memory data grid products are available. TIBCO ActiveSpaces, Oracle Coherence, Infinispan, IBM WebSphere eXtreme Scale, Hazelcast, Gigaspaces, GridGain, Pivotal Gemfire to name most of the important ones. Not SAP HANA!

The goal of my talk was not very technical. Instead, I discussed several different real world use cases and success stories for using in-memory data grids. Here is the abstract for my talk:

NoSQL is not just about different storage alternatives such as document store, key value store, graphs or column-based databases. The hardware is also getting much more important. Besides common disks and SSDs, enterprises begin to use in-memory storages more and more because a distributed in-memory data grid provides very fast data access and update. While its performance will vary depending on multiple factors, it is not uncommon to be 100 times faster than corresponding database implementations. For this reason and others described in this session, in-memory computing is a great solution for lifting the burden of big data, reducing reliance on costly transactional systems, and building highly scalable, fault-tolerant applications.The session begins with a short introduction to in-memory computing. Afterwards, different frameworks and product alternatives are discussed for implementing in-memory solutions. Finally, the main part of this session shows several different real world uses cases where in-memory computing delivers business value by supercharging the infrastructure.

Transcript

1. In-Memory Computing   “Real  World  Use  Cases”   Kai Wähner Technical Lead kwaehner@tibco.com @KaiWaehner www.kai-waehner.de LinkedIn / Xing à Please connect!
2. Kai Wähner Consulting Developing Coaching Speaking Writing Selling Main Tasks Requirements Engineering Enterprise Architecture Management Business Process Management Architecture and Development of Applications Service-oriented Architecture Integration of Legacy Applications Cloud Computing Big Data Contact Email: kontakt@kai-waehner.de Blog: www.kai-waehner.de/blog Twitter: @KaiWaehner Social Networks: LinkedIn, Xing
3. Disclaimer !     These  opinions  are  my  own  and  do  not   necessarily  represent  my  employer  
4. Key Messages In-Memory Computing is used for Acting in Real-Time! In-Memory is NOT just for Caching and Storing – A Data Grid offers much more! Eventing and Fault-Tolerance move In-Memory Computing to another Level!
5. © Copyright 2000-2014 TIBCO Software Inc. 5   Agenda •  Introduction to In-Memory Computing •  Use Cases / Customer Success Stories
6. © Copyright 2000-2014 TIBCO Software Inc. 6   Agenda •  Introduction to In-Memory Computing •  Use Cases / Customer Success Stories
7. Time   Business Value Business Event Data Ready for Analysis Analysis Completed Decision Made $$$$   $$$   $$   $   Action Taken In-Memory Computing and Event Processing speed action and increase business value by seizing opportunities while they matter Business Value of Events over Time
8. © Copyright 2000-2014 TIBCO Software Inc. 8   •  Hardware costs declining •  Data Processing Requirements exploding •  Traditional Approaches not scaling –  Relational Databases –  Clustered Databases –  In-Memory Caches –  Messaging Systems Drivers for In-Memory Computing
9. © Copyright 2000-2014 TIBCO Software Inc. 9   •  Two parallel responses to the 21st century data processing needs •  NoSQL Databases –  Disk based with some in-memory caching –  Horizontal Scalability on Commodity Hardware –  Alternatives to Relational Databases and SQL –  Basically Available Soft-state Eventually (BASE) –  No ACID (transactions / concurrency control) •  In-Memory Data Grid Technology –  Memory for data storage –  Pooling Memory from multiple machines –  Use database for persistence –  ACID Properties –  Eventing – Notifications, Continuous Queries New Categories of Technology
10. © Copyright 2000-2014 TIBCO Software Inc. 10   Database Landscape in 2014 h;p://blogs.the451group.com/   informaDon_management/2014/03/18/   updated-­‐data-­‐plaIorms-­‐landscape-­‐   map-­‐february-­‐2014/   SAP  HANA  is  not  an   In-­‐Memory  Data  Grid!  
11. Product Example: TIBCO ActiveSpaces Distributed In-memory System of Record Stores platform / language independent key-value data structures in memory with the option to persist data in parallel on local disks on a cluster of elastic horizontally scalable commodity hardware High Performance ACID compliant NoSQL Data Grid Offers all benefits of NoSQL databases and immediate consistency with full ACID compliance for transactions and concurrency control Minimal configuration and easy-to-use APIs (Java, C, .NET, “TIBCO Products”) Uses proprietary consistent hashing algorithm that that ensures a single network hop for fetching data. No need for partitioning, no complex XML configuration files Querying Data can be queried using an SQL-like language and queries can be accelerated through full indexing capabilities such as composite indexes and tree or hash index types. Best of both Worlds (NoSQL + InMemory)!  
12. © Copyright 2000-2014 TIBCO Software Inc. 12   Agenda •  Introduction to In-Memory Computing •  Use Cases / Customer Success Stories
13. LOADER       Caching for Fast Data Access •  Cache  to  slower  systems   •  Read-­‐only   •  Not  the  system  of  record     •  No  persistence  required   •  Side  benefit:  Backend  load   is  reduced  
14.     SERVICE   Caching + Dynamic Load •  Dynamically  loaded  into   Memory  when  the  data  is   first  accessed  by  a  client   applicaDon   •  Service  can  present  a   standard  interface     •  Client  applicaDons  are  not   required  to  implement  any   In-­‐Memory  specific  code   (1)  Check  Cache   (2)  Load  from  DB  if  not  in  Cache  
15. Routing Messages to Back-Office Applications •  Receive  a  common  data  feed  that  needs  to  be  parsed  and   routed  to  several  back-­‐office  applicaDons  can  use     •  In-­‐Memory  holding  reference  informaDon  for  the  rouDng   applicaDon.  The  router  can  quickly  determine  where  to   send  the  data.     •  Examples:  Bank  payments,  insurance  claims  processing  
16. Off-loading expensive systems Expensive  in  terms  of  response  Dme  and  /  or  transacDon  costs!  
17. Personalized Customer Experience “With  38  million  fans,  MGM  knows  how  to  put  its  customers   first,  it  takes  more  than  a  smile  too.  Customers  want  a   personalized,  tailored  experience,  one  that  knows  their   name  and  can  anDcipate  their  needs.  With  the  help  of  TIBCO   technologies  that  leverage  big  data  and  give  customers  a   digital  idenDty,  MGM  can  send  personalized  offers  directly   to  customers,  save  them  a  seat,  and  have  their  favorite  drink   on  the  way.  With  mulDple  customer  touch  points  and   channels,  MGM  can  reach  customers  in  more  ways,  and  in   more  places,  than  ever  before.”     h;ps://www.youtube.com/watch?v=X-­‐7S3kCOx9k   Latency  Problems:   •  Several  Legacy  Systems   •  Processing  via  ERP,  CRM,  Host,  etc.     In-­‐Memory:   •  Events  and  CorrelaDons   •  Enable  Real  Time   •  Only  customers  that  have  checked  in  
18. Fault Tolerance and Disaster Recovery Enabling Active-Active Fault Tolerance in Applications: In-­‐Memory  CompuDng  is   reliable,  scalable  and   fault-­‐tolerant!  
19. Fault Tolerance and Disaster Recovery Multisite Data Replication: In-­‐Memory  CompuDng  is   reliable,  scalable  and   fault-­‐tolerant!  
20. Handling temporary spikes on a slow ‘system of record’ •  An  In-­‐Memory  event  listener  gets  noDfied  whenever  a  data  value  is  changed  and  sends  updates  through  a   message  queue  for  updaDng  the  master  system  of  record.   •  The  back  office  system  can  also  be  updated  through  other  channels.   •  Examples:  Christmas  Shopping  in  E-­‐Commerce,  Ticket  Sales,  Online  Bekng  
21. à  In-­‐Memory  as  “system  of  record”   Operational Data Store (Local File System)
22. •  Low-­‐latency,  high-­‐throughput  operaDonal  data   –  Customer  data:  e.g.  account  status  and  balance,   purchase  history:  real-­‐Dme  loyalty  (promoDons,     cross-­‐selling),  fraud  detecDon,  ...   –  Market  data:  e.g.  risk  assessment,  porIolio  mgmt,   producDon  output  opDmizaDon,  buyer-­‐seller  matching   –  Sensor  data:  e.g.  smart  metering  /  grid,  public  transport  safety   –  Track  and  trace:  e.g.  barcode  scans,  RFID:  logisDcs,  airlines   •  Why  In-­‐Memory?   –  Much  faster  than  tradiDonal  DB,  especially  many  small  transacDons  (XTP)   –  State  /  data  management  not  addressed  by  messaging  soluDons   –  EvenDng  is  a  first  class  feature,  changes  can  be  ‘pushed’  in  real-­‐Dme  to  interested  parDes   (subscribe  to  changes,  conDnuous  queries)   –  Provides  for  distributed  process  synchronizaDon   –  Integrated  with  CEP  engine  (TIBCO  BusinessEvents)   Operational Data Store (Local File System)
23. Situation •  Master data management system stores over 800 million customer records across more than 30 enterprise apps. •  Stores real-time inventory data to enable ‘Buy online and pick-up at store’ and ‘Smart fulfillment’ features Problem •  Due to lack of correlation between Point of Sale data and inventory, the website contained outdated inventory data. Products were listed as out of stock when there was actually inventory. •  Need to leverage store inventory as well as inventory located fulfillment centers Solution •  In-Memory stores real-time inventory data for the website, the fulfillment application, and other applications that need access to inventory data Business Impact •  Reduction in customer churn •  Intelligent fulfillments leading to greater customer satisfaction •  Improved overall efficiency of fulfillment centers and store inventory Retailer: Inventory Management
24. Distribution of Rapidly Changing Data à   Examples  are  monitoring  data  for  a  power  plant,  stock  market  data,  telemetry  data  for  a   complex  system  (example,  a  satellite),  or  the  status  and  locaDon  of  packages  for  a  major   logisDcs  or  shipping  company.    
25. Telco: Real-Time Offer Generation and Fulfillment by Different Subcontractors Reload Give 100 free SMS to subscriber who tops-up Total: 12 mio top-up / day Peak: 300 top-up per sec Purchase 3G Package Cross-sell Voice/SMS package to subscriber who purchases 3G Mobile Package Total: 3 mio / day Peak: 50 events per sec Voice Call Give discount VOIP package to subscriber who makes a IDD call Total: 200 mio / day Peak: 12,000 events per sec SMS Usage Give discounted SMS package to subscriber who sends SMS more than 10 times a day Total: 750 mio / day Peak: 27,000 events per sec Event Cloud Purchase BB Package Reload Voice Call IDD Call OnNet Call SMS Usage Event Handling and Processing Touchpoint Integration Billing, Offer Fulfilled Fulfill SMS Package Fulfill 3G Package Fulfill Voice Package Fulfill SMS Package 46.7 million subscribers 2,000 SMS notifications per seconds 500 offer fulfillments per second Offer Message Reminder Message Fulfillment Message
26. State-­‐full   Data   Storing State-full Data for Enterprise Applications
27. Super Fast Compute Grid for Intermediary Calculations for Analytics
28. •  Technical  issues  in  distributed  grid  compuDng  with  large  scale  data   –  Work  load  distribuDon   –  Process  synchronizaDon   –  Data  transfer   •  Examples   –  Risk  assessment  and  management   –  OpDmizaDon  problems:  scheduling,  cargo  assignment,  load  distribuDon  in   power  network  /  grid   •  Why  In-­‐Memory?   –  Many  useful  synchronizaDon  features  (e.g.  atomic  “take”)   –  LocaDon  transparency  and  fault-­‐tolerance   –  Real-­‐Dme  instead  of  nightly  /  weekly  /  ...  Data-­‐Warehousing  approach   Super Fast Compute Grid for Intermediary Calculations for Analytics
29. Eventing and Fault-Tolerance move In-Memory Computing to another Level! In-Memory is NOT just for Caching and Storing – A Data Grid offers much more! In-Memory Computing is used for Acting in Real-Time! Key Messages
30. Questions? Kai Wähner kwaehner@tibco.com @KaiWaehner www.kai-waehner.de LinkedIn / Xing à Please connect!