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 the

platform group)",

Lived_Worked_At: [

"Silicon Wadi (Israel)",

"Silicon Alley (NYC)",

"Silicon Valley (Bay Area)"

]

}

Data @ LP

13 TBper month 20M

Engagements per month 1.8 BVisits per month

VOLUME

Data stack

LiveEngage DASHBOARD

MONITORING CHAT/VOICEsystem

Batch track Real-Time trackAPACHE KAFKA

STORM

COMPLEX EVENT PROCESSING

PERPETUAL STORE

RT REPOSITORY

CassandraBUSINESS INTELLIGENCE

ANALYTICAL DB

Web agent console

Enables your agents to interact with

website visitors

Improve agent efficiency

Reduce chat time

The use case

The story - once upon a time

Visitor’sEvents

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 serverTomcat

M/R views

cluster

M/R views

cluster

XDCR

REST API

Couchbase Java SDK

Storm Topology

Couchbase Java SDK

Storm Topology

Data stack now with Couchbase

LiveEngage DASHBOARD

MONITORING CHAT/VOICEsystem

Batch track Real-Time trackAPACHE KAFKA

STORM

COMPLEX EVENT PROCESSING

PERPETUAL STORE

RT REPOSITORY

CassandraBUSINESS INTELLIGENCE

ANALYTICAL DB

Thank You