how companies use nosql & couchbase - nosql now 2014

How Companies use NoSQL and Couchbase

Dip7 Borkar

Sr. Director, Solu7ons Engineering

What is Couchbase

Overview

Couchbase offers a full range of Data Management solu7ons

High Availability Cache

Key Value Document Mobile device

SSN: 400 658 9993 Pass: ******

Pass: ******

NoSQL Database Considera7ons

Easy Scalability

Consistent High Performance

Flexible Data Model

Always On 24x7x365

Grow cluster without applica<on changes, without down<me when needed

Always awesome experience for your applica<on users

The sun never sets on the Internet, your applica<on needs the database to always serve data

Keep developers produc<ve and allow fast and easy addi<on of new features

JSONJSONJSON

JSONJSON

PERFORMANCE

Couchbase solu7on “The basics”

3 3 2

Single node – Couchbase Write Opera7on

Managed Cache

Disk Que

ue

Disk

Replica<on Queue

App Server

Couchbase Server Node

To other node

Doc 1

Doc 1 Doc 1

3 3 2

Single node – Couchbase Read Opera7on

Managed Cache

Disk Que

ue

Disk

Replica<on Queue

App Server

Couchbase Server Node

To other node

Doc 1

Get Doc 1

Doc 1 Doc 1

Auto Sharding and Cluster Map

Hash func7on (KEY)

vB1 vB2 vB3 vB4 vB5 vB6

Physical

servers

A B C D

More scalability required Add node

Logical

Par77o

ns

Cluster Map

New Cluster Map

Couchbase Server Cluster

Basic Opera7on

User Configured Replica Count = 1

Read/write/update

Ac<ve

SERVER 1

Ac<ve

SERVER 2

Ac<ve

SERVER 3

App Server 1

COUCHBASE Client Library CLUSTER MAP


App Server 2

Doc 5

Doc 2

Doc 9

Doc

Doc

Doc

Doc 4

Doc 7

Doc 8

Doc

Doc

Doc

Doc 1

Doc 3

Doc 6

Doc

Doc

Doc

Replica Replica Replica

Doc 4

Doc 1

Doc 8

Doc

Doc

Doc

Doc 6

Doc 3

Doc 2

Doc

Doc

Doc

Doc 7

Doc 9

Doc 5

Doc

Doc

Doc

•  Docs distributed evenly across servers

•  Each server stores both ac7ve and replica docs   Only one server ac<ve at a <me

•  Client library provides app with simple interface to database

•  Cluster map provides map to which server doc is on   App never needs to know

•  App reads, writes, updates docs

•  Mul7ple app servers can access same document at same 7me

Add Nodes to Cluster

SERVER 4

SERVER 5

Replica

Ac<ve

Replica

Ac<ve

Read/write/update

App Server 1



App Server 2



Ac<ve

SERVER 1

Doc 5

Doc 2

Doc 9

Doc

Doc

Doc

Replica

Doc 4

Doc 1

Doc 8

Doc

Doc

Doc

Ac<ve

SERVER 2

Doc 4

Doc 7

Doc 8

Doc

Doc

Doc

Replica

Doc 6

Doc 3

Doc 2

Doc

Doc

Doc

Ac<ve

SERVER 3

Doc 1

Doc 3

Doc 6

Doc

Doc

Doc

Replica

Doc 7

Doc 9

Doc 5

Doc

Doc

Doc

Read/write/update

•  Two servers added with one-‐click opera7on

•  Docs automa7cally rebalance across cluster   Even distribu<on of docs   Minimum doc movement

•  Cluster map updated

•  App database calls now distributed over larger number of servers

Fail Over Node


SERVER 4

SERVER 5

Replica

Ac<ve

Replica

Ac<ve

App Server 1



App Server 2


Ac<ve

SERVER 1

Doc 5

Doc 2

Doc 9 Doc

Doc

Doc

Replica

Doc 4

Doc 1

Doc 8 Doc

Doc

Doc

Ac<ve

SERVER 2

Doc 4

Doc 7 Doc 8

Doc

Doc Doc

Replica

Doc 6

Doc 3 Doc 2

Doc

Doc Doc

Ac<ve

SERVER 3

Doc 1

Doc 3

Doc 6 Doc

Doc

Doc

Replica

Doc 7

Doc 9

Doc 5 Doc

Doc

Doc

•  App servers accessing docs

•  Requests to Server 3 fail

•  Cluster detects server failed –  Promotes replicas of docs

to ac<ve –  Updates cluster map

•  Requests for docs now go to appropriate server

•  Typically rebalance would follow

Doc 1 Doc 3

Doc


Indexing and Querying


Ac<ve

SERVER 1

SERVER 3

App Server 1



App Server 2

Doc 5

Doc 2

Doc 9

Doc

Doc

Doc

Ac<ve

Doc 1

Doc 3

Doc 6

Doc

Doc

Doc

Replica

Doc 4

Doc 1

Doc 8

Doc

Doc

Doc

Ac<ve

SERVER 2

Doc 4

Doc 7

Doc 8

Doc

Doc

Doc

Replica

Doc 6

Doc 3

Doc 2

Doc

Doc

Doc

Replica

Doc 7

Doc 9

Doc 5

Doc

Doc

Doc

•  Indexing work is distributed amongst nodes

•  Large data set possible

•  Parallelize the effort

•  Each node has index for data stored on it

•  Queries combine the results from required nodes

Query

ACTIVE

SERVER 1

RAM

DISK

Doc

Doc 2

Doc 9

Doc Doc Doc

ACTIVE

SERVER 2

RAM

DISK

Doc

Doc

Doc

Doc Doc Doc

ACTIVE

SERVER 3

RAM

DISK

Doc

Doc

Doc

Doc Doc Doc

Cross Data Center Replica7on (XDCR)

COUCHBASE SERVER CLUSTER

NYC DATA CENTER

COUCHBASE SERVER CLUSTER

SF DATA CENTER

ACTIVE

SERVER 1

RAM

DISK

Doc

Doc 2

Doc 9

Doc Doc Doc

ACTIVE

SERVER 2

RAM

DISK

Doc

Doc

Doc

Doc Doc Doc

ACTIVE

SERVER 3

RAM

DISK

Doc

Doc

Doc

Doc Doc Doc

{ }

{ }

{ } { }

{ } { }

{ } { }

{ }

{ }

{ } { }

{ }

Use Cases

High-‐Availability Caching

RDBMS

Applica7on Layer User Requests

Cache Misses and Write Requests

Read-‐Write Requests

Couchbase Distributed Cache

Use Case 1

•  Applica<on objects

•  Popular search query results

•  Session informa<on

•  Heavily accessed web landing pages


•  Speed up RDBMS

•  Consistently low response <mes for document / key lookups

•  High-‐availability 24x7x365

•  Replacement for en<re caching <er

Data cached in Couchbase? Applica7on characteris7c

Use Case 1

hap://www.Look.PopularSearchWuerycom

Look Something Search

WEB % of clicks

% of clicks

something 56.3 28

DoSomething.com 13.4 25.08

SomethingFishy.org 9.8 14.68

Popular

Couchbase, Inc. Confiden<al


•  Low latency in sub-‐milliseconds with consistently high read / write throughput using built-‐in cache

•  Always-‐on opera7ons even for database upgrades and maintenance with zero down 7me

Why NoSQL and Couchbase?

Use Case 1


Session Store Use Case 2


Session Store

•  Extremely fast access to session data using unique session ID

•  Easy scalability to handle fast growing number of users and user-‐generated data

•  Always-‐on func<onality for global user base

Applica7on characteris7c

Use Case 2

•  Session values or Cookies (stored as key-‐value pairs)

•  Examples include: items in a shopping cart, flights selected, search results, etc.

Data stored in Couchbase?


Session Store

•  Low latency in sub-‐milliseconds with consistently high read / write throughput for session data via the built-‐in object-‐level cache

•  Linear throughput scalability to grow the database as user and data volume grow

•  Always-‐on opera7ons even par7cularly high availability using Couchbase replica7on and failover

•  Intra cluster and cross cluster (XDCR) replica7on for globally distributed ac7ve-‐ac7ve plagorm


Use Case 2


Globally Distributed User Profile Store Use Case 3

hap://www.ProfileStore.com

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Welcome back Laura! You have 3 items in your shopping cart wai<ng for you.

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ID: PASS:

Globally Distributed User Profile Store

•  Extremely fast access to individual profiles

•  Always online system as mul<ple applica<ons access user profiles

•  Flexibility to add and update user aaributes

•  Easy scalability to handle fast growing number of users

•  User profile with unique ID

•  User seing / preferences

•  User’s network

•  User applica<on state

Data stored in Couchbase? Applica7on characteris7c

Use Case 3

Laura930 ********

Globally Distributed User Profile Store

•  Low latency and high throughput for very quick lookups for millions of concurrent users using built-‐in cache

•  Intra cluster and cross cluster (XDCR) replica7on for high availability and disaster recovery

•  Ac7ve-‐ac7ve geo-‐distributed system to handle globally distributed user base

•  Online admin opera7ons eliminate system down7me


Use Case 3

Data Aggrega7on

•  Flexibility to store any kind of content

•  Flexibility to handle schema changes

•  Full-‐text Search across data set

•  High speed data inges<on

•  Scales horizontally as more content gets added to the system

•  Social media feeds: Twiaer, Facebook, LinkedIn

•  Blogs, news, press ar<cles

•  Data service feeds: Hoovers, Reuters

•  Data form other systems


Use Case 4

in

Ft

NEWS

Blog

Data Aggrega7on

•  JSON provides schema flexibility to store all types of content and metadata

•  Fast access to individual documents via built-‐in cache, high write throughput

•  Indexing and querying provides real-‐7me analy7cs capabili7es across dataset

•  Integra7on with Elas7cSearch for full-‐text search

•  Ease of scalability ensures that the data cluster can be grown seamlessly as the amount of user and ad data grows


Use Case 4

Content and Metadata Store Use Case 5

Content and Metadata

Nature, Field, Summer, Farm, Sky, Environment, Landscaped, Grass, Green,Blue, Oilseed, Rape, Agriculture, Scenics, Land, Spring, Non-‐Urban Scene,Environmental, Conserva<on, Sun, Meadow, Horizon, Season, Cloud, Landscapes, Travel Loca<ons, Pasture, Cul<vated Land, Stratoshpere, cloudy day, Oliseed Rape, Rural Scene, Vibrant Color, No People, Beauty In Nature,Gold, Color Image, Beauty, Idyllic, Mul<colored, Yellow, Colors, Cloudscape,Outdoors, Plant, Sunlight, Horizon Over Land

Content and Metadata Store

•  Flexibility to store any kind of content

•  Fast access to content metadata (most accessed objects) and content

•  Full-‐text Search across data set

•  Scales horizontally as more content gets added to the system

•  Content metadata

•  Content: Ar<cles, text

•  Landing pages for website

•  Digital content: eBooks, magazine, research material


Use Case 5

hap://www.LandingPage.com

ebook Mag

Content and Metadata Store

•  Fast access to metadata and content via object-‐managed cache

•  JSON provides schema flexibility to store all types of content and metadata

•  Indexing and querying provides real-‐7me analy7cs capabili7es across dataset

•  Integra7on with Elas7cSearch for full-‐text search

•  Ease of scalability ensures that the data cluster can be grown seamlessly as the amount of user and ad data grows


Use Case 5

Customer Case Studies

User Profile, Ad Targe2ng & Real-‐Time Analy2cs

•  Company   Global Leader in Online

Payments

  132m Ac<ve Accounts, 193 Markets, 25 Currencies

•  Scalability and Performance Requirements   300m to 1bn documents with 3

Tb to 10TB

  Billions of requests and sub 200ms response <mes access to JSON documents

  Read/write mix 50/50 with 5ms latency

•  Exis7ng Database Infrastructure   Mul<ple Tiers – Separate

caching and durable store

  MySQL, Oracle, Terracoaa, Coherence

•  Pain   Real-‐Time Access to Iden<ty

Mapping – eBay ID, PayPal ID, Social ID, 3rd Party ID, Email

  Performance – Ad needs to be served in 200ms

  Cost – Mul<ple <ers for caching and durability

  Highly Available – Across large clusters and across data centers

•  Couchbase Benefits   Performance – Reduced latency

with 5ms access <mes

  Cost – Consolida<on of database and cache layers

  Cross Data Center Availability

+ + +

Why couchbase?

§  Data volume •  Online system ; 300M – 1B documents @ 10k value size ; 3-‐10TB total storage

§  Data Access •  Distributed caching •  Persistence

§  Data Structure •  Flexible & Schemaless

§  Read/Write •  50% read/50% write •  Low latency < 10 msec

§  Par77oning §  Replica<on §  Auto Healing

§  Availability and scalability •  Resilient •  Mul< data center – DR/BCP •  Linearly Scalable

Use cases at PayPal

•  Ad Tech targe7ng •  Cookie infrastructure •  Real 7me analy7cs

Cookie architecture

CookieService

Couchbase DC A Couchbase DC B

Front Tier

Interac<on Channels

Applica7on Cookie Libraries

Mid Tier Data Service

-‐  Key Value -‐  Cache Interface -‐  Couchbase Client

Data Tier XDCR

Document Model

DEPLOYMENT MODEL

A C B

Cookie Service

Cookie Service

Cookie Service

XDCR

ACTIVE ACTIVE PASSIVE

AVAILABILITY REDUNDANCY DISASTER RECOVERY

WRITE READ

High Performance Caching

•  Company   Leading online travel company

•  Scalability and Performance Requirements   11 Clusters/100 Nodes   Over 3TB of Data   149,000 Ops/ sec

•  Exis7ng Database Infrastructure   Rela<onal Database technology, Terracota

•  Pain   Scalability/Capacity Planning – Cannot be planned. Dependent onexternal factors

  Scalability – Complex and <me consuming scaleout

  Performance – Caching too complex. Weeks of planning/hours of down<me

  Cost – Mul<ple <ers of hardware for database and caching

•  Couchbase Benefits   Scalability – Over 70 Nodes with simple scaleout in minutes not hours

  Performance – Improved response <mes by up to 47% with consistent 3ms to 4ms response

  Cost -‐ Consolidate caching and database <ers – less machines, power, cooling, footprint – drama<c savings

  Dynamic schema change – Drama<cally reduced down<me

High Availability Cache

• 11 Clusters (4 mirrored)   100 nodes • > 3 TB of data • ~430m objects (146m in largest) • Total ops/sec ~ 75k   *149k with HA

Use Case #1

• Content   HTML   Image Links   HA caches   XDCR

Example of HA DC 1 DC 2

VS.

Real 7me analy7cs

•  Company   Leading cloud company – allows enterprises to connect in real-‐<me with their customers via chat, voice, and content delivery

•  Scalability and Performance Requirements   13TB/Month   20m engagements/month   1.8bn sessions/month

•  Exis7ng Database Infrastructure   MySQL

•  Pain   Scalability   Performance – Batch analy<cs and real-‐<me access to customer profiles

  Cross Data Center Replica<on – 4 data centers

•  Couchbase Benefits   Scalability   Performance – Mixed read/write with very high throughput

  Document Store – Ease of Development

+

Use Case: 3rd party data aggrega7on with analy7cs

Real <me Analy<cs for LivePerson's customers

LiveEngage DASHBOARD

LivePerson: Leading customer engagement plagorm

Requirements Requirements Requirements

•  High throughput, really fast •  Linear scale •  Searchable (Views and M/R)

•  Supports both K/V & Document store

•  Cross data center replica<on •  “Always on”, Resilience solu<on

The Problem

13 TB per month ~1 PB In total 1.8 B

Visits per month

VOLUME

Couchbase Java SDK

Applica<on server Tomcat

M/R views

cluster

M/R views

cluster

XDCR

REST API

Couchbase Java SDK

Storm Topology

Couchbase Java SDK

Storm Topology

Architecture

Visitor

Stream Event Processing

Visitor Feed -‐ Storm

Topology

Customer Representa<ve

Ka{a

Couchbase

Visitor Monitoring Service

(1) Visitor browsing

(2) Visitor events

(4) Write event to user document

(6) Return relevant visitors

(7) Return relevant visitors

(5) Get visitors List Every 3 sec Visitor Feed

API

(3) Analyze relevant events and persist

Data flow

Document Structurestructure

{

"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

}

......................................

}

Mul< tenant

DB

Basic visitor

informa<on

Sequence

use due to

Ka{a

Ques7ons?

Thank you!

[email protected]

how companies use nosql & couchbase - nosql now 2014

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