0
Scalling to 1 million users
Ido Shilon | 4/6/2014
@idoshilon
{
name: "Ido Shilon",
age: 37,
kids: [
"illy"
],
wife: "Oshrat",
Title: "Group Manager @ LivePerson (Heading th...
Data @ LP
13 TB
per month 20M
Engagements per month 1.8 B
Visits per month
VOLUME
Data stack
LiveEngage
DASHBOARD
MONITORING CHAT/VOICE
system
Batch track Real-Time track
APACHE KAFKA
STORM
COMPLEX EVENT
...
Web agent console
Enables your agents to interact with
website visitors
Improve agent efficiency
Reduce chat time
The use ...
The story - once upon a time
Visitor’s
Events
Agents console
(Java app)
Web Tier Visitors
And then the story continues
Data center 1 Data center 2
Kafka & Strom
(Event bus)
Web Agent
???
Possible solutions we considered
Why did we pick Couchbase
Always on
Linear scale
Searchable
Document store
Key Value
High throughput (R/W)
XDCR
Cassandra
Architecture
Couchbase Java SDK
Application server
Tomcat
M/R views
cluster
M/R views
cluster
XDCR
REST API
Couchbase Java...
Data stack now with Couchbase
LiveEngage
DASHBOARD
MONITORING CHAT/VOICE
system
Batch track Real-Time track
APACHE KAFKA
S...
Thank You
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Scaling to 1 million users v1

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Transcript of "Scaling to 1 million users v1"

  1. 1. Scalling to 1 million users Ido Shilon | 4/6/2014
  2. 2. @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)" ] }
  3. 3. Data @ LP 13 TB per month 20M Engagements per month 1.8 B Visits per month VOLUME
  4. 4. 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
  5. 5. Web agent console Enables your agents to interact with website visitors Improve agent efficiency Reduce chat time The use case
  6. 6. The story - once upon a time Visitor’s Events Agents console (Java app) Web Tier Visitors
  7. 7. And then the story continues Data center 1 Data center 2 Kafka & Strom (Event bus) Web Agent ???
  8. 8. Possible solutions we considered
  9. 9. Why did we pick Couchbase Always on Linear scale Searchable Document store Key Value High throughput (R/W) XDCR Cassandra
  10. 10. 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
  11. 11. 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
  12. 12. Thank You
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