LivePerson_Couchbase_SF_2013

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LivePerson_Couchbase_SF_2013

  1. 1. I get by with a little help from my friends Ido Shilon | 7/22/2013
  2. 2. Today’s programme Who is LivePerson? What was our journey? Why was Couchbase chosen? How did we build the new platform? What can you take with you?
  3. 3. @idoshilon { name: "Ido Shilon", age: 37, kids: [ "illy" ], wife: "Oshrat", Title: "Group Manager @ LivePerson (Heading the platform group)", Lived_Worked_At: [ "Silicon Wadi (Israel)", "Silicon Alley (NYC)", "Silicon Valley (Bay Area)" ] }
  4. 4. 8,500customers Creating Meaningful Customer Connections LivePerson is… SaaS pioneer since 1998 Mission Customers Technology
  5. 5. Optimize Customer Acquisition & Reduce Bounce Rate Live engagement for lingering customer Rich multimedia to drive sales closure Recommended use case
  6. 6. Technology @ LivePerson Application Stack JVM heavy - Java & Scala Private cloud based on openstack Linux on commodity servers
  7. 7. Data @ LP 13 TB per  month 20M Engagements  per  month 1.8 B Visits  per  month VOLUME
  8. 8. Data stack LiveEngage DASHBOARD MONITORING CHAT/VOICE system Batch track Real-Time track APACHE KAFKA STORM COMPLEX EVENT PROCESSING PERPETUAL STORE RT REPOSITORY Cassandra BUSINESS INTELLIGENCE ANALYTICAL DB
  9. 9. Web agent console Enables your agents to interact with website visitors Improve agent efficiency Reduce chat time The use case
  10. 10. The story - once upon a time Visitor’s Events Agents console (Java app) Web Tier Visitors
  11. 11. And then the story continues Data center 1 Data center 2 Kafka & Strom (Event bus) Web Agent ???
  12. 12. Possible solutions we considered
  13. 13. Why did we pick Couchbase Always on Linear scale Searchable Document store Key Value High throughput (R/W) XDCR Cassandra
  14. 14. Architecture Couchbase Java SDK Application server Tomcat M/R views cluster M/R views cluster XDCR REST API Couchbase Java SDK Storm Topology Couchbase Java SDK Storm Topology
  15. 15. Data flow Visitor Visitor Feed - Storm Topology Agent Kafka Couchbase Visitor Monitoring Service (1) Visitor browsing (2) Visitor events (4) Write event to visitor document (6) Return relevant visitors (7) Return relevant visitors (5) Get visitors List Every 3 sec Visitor Feed API (3) Analyze relevant events
  16. 16. Data modeling - doc Visitor type doc Document = Active visitor Contain session level attributes Multi tenant bucket Views Used for secondary indexes
  17. 17. { "accountId": "64302875", "id": 121640710013, "rtSessionId": "643028754295878498", "eventSequence": 5104, "ipAddress": { "fieldValue": "194.39.63.10", "seq": 1 }, "browser": { "fieldValue": "Chrome 27.0.1453.116", "seq": 1 }, "state": { "fieldValue": "LEFT_SITE", "seq": 5104 } ...................................... } Document structure Multi tenant DB Basic visitor information Sequence use due to Kafka
  18. 18. Cross data center replication Using Bi Directional replication (A/A) Tips : Key space is the same across the two clusters (avoid conflicts) Impact on the sizing
  19. 19. Couchbase in production
  20. 20. What did we learn till now ? Test in production Use the Couchbase sizing guidelines RAM and SSD are key factors in scalability
  21. 21. Data stack now with Couchbase LiveEngage DASHBOARD MONITORING CHAT/VOICE system Batch track Real-Time track APACHE KAFKA STORM COMPLEX EVENT PROCESSING PERPETUAL STORE RT REPOSITORY Cassandra BUSINESS INTELLIGENCE ANALYTICAL DB
  22. 22. Couchbase in LP - Strategic choice Visitor Session state Cross Session state Fast RW persistent store Generic caching layer (Memcached style)
  23. 23. Summary Who we are Our journey The problem we have encountered We are not alone - Couchbase helped us
  24. 24. Thank You

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