launch webinar - introducing couchbase server 2.0

Introducing Couchbase Server 2.0

Dipti BorkarDirector, Product Management

2.0

NoSQL Document Database

Couchbase Server

• Leading NoSQL database project focused on distributed database technology and surrounding ecosystem

• Supports both key-value and document-oriented use cases

• All components are available under the Apache 2.0 Public License

• Obtained as packaged software in both enterprise and community editions.

Couchbase Open Source Project

Couchbase Open Source Project

Easy Scalabili

ty

Consistent High

Performance

Always On

24x365

Grow cluster without application changes, without downtime with a single click

Consistent sub-millisecond read and write response times

with consistent high throughput

No downtime for software upgrades, hardware maintenance, etc.

JSONJSONJSON

JSONJSON

PERFORMANCE

Flexible Data Model

JSON document model with no fixed schema.

Couchbase Server

Flexible Data Model

• No need to worry about the database when changing your application

• Records can have different structures, there is no fixed schema

• Allows painless data model changes for rapid application development

{ “ID”: 1, “FIRST”: “Dipti”, “LAST”: “Borkar”, “ZIP”: “94040”, “CITY”: “MV”, “STATE”: “CA”}

JSONJSON

JSON JSON

New in 2.0

JSON support Indexing and Querying

Cross data center replicationIncremental Map Reduce

JSONJSONJSON

JSONJSON

Additional Couchbase Server Features

Built-in clustering – All nodes equal

Data replication with auto-failover

Zero-downtime maintenance

Built-in managed cached

Append-only storage layer

Online compaction

Monitoring and admin API & UI

SDK for a variety of languages

Couchbase Server 2.0 Architecture

Hea

rtbe

at

Proc

ess

mon

itor

Glo

bal s

ingl

eton

sup

ervi

sor

Confi

gura

tion

man

ager

on each node

Reba

lanc

e or

ches

trat

or

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itor

one per cluster

vBuc

ket s

tate

and

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icati

on m

anag

er

http

REST

man

agem

ent A

PI/W

eb U

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HTTP8091

Erlang port mapper4369

Distributed Erlang21100 - 21199

Erlang/OTP

storage interface

Couchbase EP Engine

11210Memcapable 2.0

Moxi

11211Memcapable 1.0

Memcached

New Persistence Layer

8092Query API

Que

ry E

ngin

e

Data Manager Cluster Manager

Couchbase Server 2.0 Architecture

Hea

rtbe

at

Proc

ess

mon

itor

Glo

bal s

ingl

eton

sup

ervi

sor

Confi

gura

tion

man

ager

on each node

Reba

lanc

e or

ches

trat

or

Nod

e he

alth

mon

itor

one per cluster

vBuc

ket s

tate

and

repl

icati

on m

anag

er

http

REST

man

agem

ent A

PI/W

eb U

I

HTTP8091

Erlang port mapper4369

Distributed Erlang21100 - 21199

Erlang/OTP

storage interface

Couchbase EP Engine

11210Memcapable 2.0

Moxi

11211Memcapable 1.0

Memcached

New Persistence Layer

8092Query API

Que

ry E

ngin

e

COUCHBASE OPERATIONS

33 2

Single node - Couchbase Write Operation

Managed Cache

Dis

k Q

ueue

Disk

Replication Queue

App Server

Couchbase Server Node

Doc 1Doc 1

Doc 1

To other node

33 2

Single node - Couchbase Update Operation

Managed Cache

Dis

k Q

ueue

Replication Queue

App Server

Doc 1’

Doc 1

Doc 1’Doc 1

Doc 1’

Disk

To other node


GET

Doc

1

33 2

Single node - Couchbase Read Operation

Dis

k Q

ueue

Replication Queue

App Server

Doc 1

Doc 1Doc 1

Managed Cache

Disk

To other node


COUCHBASE SERVER CLUSTER

Basic Operation

• Docs distributed evenly across servers

• Each server stores both active and replica docsOnly one server active at a time

• Client library provides app with simple interface to database

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

• App reads, writes, updates docs

• Multiple app servers can access same document at same time

User Configured Replica Count = 1

READ/WRITE/UPDATE

ACTIVE

Doc 5

Doc 2

Doc

Doc

Doc

SERVER 1

ACTIVE

Doc 4

Doc 7

Doc

Doc

Doc

SERVER 2

Doc 8

ACTIVE

Doc 1

Doc 2

Doc

Doc

Doc

REPLICA

Doc 4

Doc 1

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

SERVER 3

Doc 6

APP SERVER 1

COUCHBASE Client Library

CLUSTER MAP


CLUSTER MAP

APP SERVER 2

Doc 9

Add Nodes to Cluster

• Two servers addedOne-click operation

• Docs automatically rebalanced across clusterEven distribution of docsMinimum doc movement

• Cluster map updated

• App database calls now distributed over larger number of servers

REPLICA

ACTIVE

Doc 5

Doc 2

Doc

Doc

Doc 4

Doc 1

Doc

Doc

SERVER 1

REPLICA

ACTIVE

Doc 4

Doc 7

Doc

Doc

Doc 6

Doc 3

Doc

Doc

SERVER 2

REPLICA

ACTIVE

Doc 1

Doc 2

Doc

Doc

Doc 7

Doc 9

Doc

Doc

SERVER 3 SERVER 4 SERVER 5

REPLICA

ACTIVE

REPLICA

ACTIVE

Doc

Doc 8 Doc

Doc 9 Doc

Doc 2 Doc

Doc 8 Doc

Doc 5 Doc

Doc 6

READ/WRITE/UPDATE READ/WRITE/UPDATE

APP SERVER 1


CLUSTER MAP


CLUSTER MAP

APP SERVER 2



Fail Over Node

REPLICA

ACTIVE

Doc 5

Doc 2

Doc

Doc

Doc 4

Doc 1

Doc

Doc

SERVER 1

REPLICA

ACTIVE

Doc 4

Doc 7

Doc

Doc

Doc 6

Doc 3

Doc

Doc

SERVER 2

REPLICA

ACTIVE

Doc 1

Doc 2

Doc

Doc

Doc 7

Doc 9

Doc

Doc

SERVER 3 SERVER 4 SERVER 5

REPLICA

ACTIVE

REPLICA

ACTIVE

Doc 9

Doc 8

Doc Doc 6 Doc

Doc

Doc 5 Doc

Doc 2

Doc 8 Doc

Doc

• App servers accessing docs

• Requests to Server 3 fail

• Cluster detects server failedPromotes replicas of docs to activeUpdates cluster map

• Requests for docs now go to appropriate server

• Typically rebalance would follow

Doc

Doc 1 Doc 3

APP SERVER 1


CLUSTER MAP


CLUSTER MAP

APP SERVER 2



DEMO TIME

• Define materialized views on JSON documents and then query across the data set

• Using views you can define• Primary indexes • Simple secondary indexes (most common use case)• Complex secondary, tertiary and composite indexes• Aggregations (reduction)

• Indexes are eventually indexed

• Queries are eventually consistent

• Built using Map/Reduce technology • Map and Reduce functions are written in Javascript

Indexing and Querying – The basics


Indexing and Querying


ACTIVE

Doc 5

Doc 2

Doc

Doc

Doc

SERVER 1

REPLICA

Doc 4

Doc 1

Doc 8

Doc

Doc

Doc

APP SERVER 1


CLUSTER MAP


CLUSTER MAP

APP SERVER 2

Doc 9

• 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

ACTIVE

Doc 5

Doc 2

Doc

Doc

Doc

SERVER 2

REPLICA

Doc 4

Doc 1

Doc 8

Doc

Doc

Doc

Doc 9

ACTIVE

Doc 5

Doc 2

Doc

Doc

Doc

SERVER 3

REPLICA

Doc 4

Doc 1

Doc 8

Doc

Doc

Doc

Doc 9

Query

• Replicate your Couchbase data across clusters

• Clusters may be spread across geos

• Configured on a per-bucket (per-database) basis

• Supports unidirectional and bidirectional operation

• Application can read and write from both clusters – Active – Active replication

• Replication throughput scales out linearly

• Different from intra-cluster replication

Cross Data Center Replication – The basics

33 2

Cross data center replication – Data flow

2

Managed Cache

Dis

k Q

ueue

Disk

Replication Queue

App Server


Doc 1Doc 1

Doc 1

To other node

XDCR Queue

Doc 1

To other cluster

Cross Data Center Replication (XDCR)COUCHBASE SERVER CLUSTER

NY DATA CENTERACTIVE

Doc

Doc 2

SERVER 1

Doc 9

SERVER 2 SERVER 3

RAM

Doc Doc Doc

ACTIVE

Doc

Doc

Doc RAM

ACTIVE

Doc

Doc

DocRAM

DISK

Doc Doc Doc

DISK

Doc Doc Doc

DISK

COUCHBASE SERVER CLUSTERSF DATA CENTER

ACTIVE

Doc

Doc 2

SERVER 1

Doc 9

SERVER 2 SERVER 3

RAM

Doc Doc Doc

ACTIVE

Doc

Doc

Doc RAM

ACTIVE

Doc

Doc

DocRAM

DISK

Doc Doc Doc

DISK

Doc Doc Doc

DISK

Couchbase SDKs

Java SDK

.Net SDK

PHP SDK

Ruby SDK

…and many more

Java client API

User Code

Couchbase Server

CouchbaseClient cb = new CouchbaseClient(listURIs,"aBucket", "letmein");

cb.set("hello", 0, "world");cb.get("hello");

http://www.couchbase.com/develop

Couchbase Java Library (spymemcached)

DEMO TIME

Demo: The next big social game

3 Objects (documents) within game:• Players•Monsters• Items

Gameplay:• Players fight monsters•Monsters drop items• Players own items

Player Document

{"jsonType": "player","uuid": "35767d02-a958-4b83-8179-616816692de1","name": "Keith4540","hitpoints": 75,"experience": 663,"level": 4,"loggedIn": false

}

Player ID

Item Document

{"jsonType": "item","name": "Katana_e5890c94-11c6-65746ce6c560","uuid": "e5890c94-11c6-4856-a7a6-65746ce6c560","ownerId": "Dale9887"

}

Item ID

Player ID

Monster Document

{"jsonType": "monster","name": "Bauchan9932","uuid": "d10dfc1b-0412-4140-b4ec-affdbf2aa5ec","hitpoints": 370,"experienceWhenKilled": 52,"itemProbability": 0.5050581341872865

}

Monster ID

GAME ON

www.couchbase.com/download

MCGRAW HILL EDUCATION LABS LEARNING PORTAL

Use Case: Content and metadata store

Building a self-adapting, interactive learning portal with Couchbase

As learning move online in great numbers

Growing need to build interactive learning environments that

Scale!

Scale to millions of learners

Serve MHE as well as third-party content

Including open content

Support learning apps

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Self-adapt via usage data

The Problem

• Allow for elastic scaling under spike periods

• Ability to catalog & deliver content from many sources

• Consistent low-latency for metadata and stats access

• Require full-text search support for content discovery

• Offer tunable content ranking & recommendation functions

Backend is an Interactive Content Delivery Cloud that must:

XML Databases

SQL/MR Engines

In-memory Data Grids

Enterprise Search Servers

Experimented with a combination of:

Hmmm...this looks kinda like:+ Content Caching (Scale)+ Social Gaming (Stats) + Ad Targeting (Smarts)

The Challenge

The Technologies

The Learning Portal

• Designed and built as a collaboration between MHE Labs and Couchbase

• Serves as proof-of-concept and testing harness for Couchbase + ElasticSearch integration

• Available for download and further development as open source code

• Document Modeling

• Metadata & Content Storage

• View Querying to support Content Browsing

• Elastic Search Integration (Full Text Search)

-Content Updated in near Real-Time

-Search Content Summaries

-Relevancy boosted based on User Preferences

• Real-Time Content Updates

• Event Logging for offline analysis

Techniques Used

Couchbase 2.0 + Elasticsearch

Store full-text articles as well as document metadata for image, video and text content in Couchbase

Combine user preferences statistics with custom relevancy scoring to provide personalized search results

Logs user behavior to calculate user preference statistics (e.g. video > text)

1

2 4

Continuously accept updates from Couchbase with new content & stats

3

Data Model

Content Metadata Bucket

User ProfilesBucket

Content StatsBucket

• Stores content metadata for media objects and content for articles

• Includes tags, contributors, type information

• Includes pointer to the media

• Stores user view details per type

• Updated every time a user views a doc with running count

• To be used for customizing ES search results per user preference• Stores content view details

• Updated for every time a document is viewed

• To be used for boosting ES search results based on popularity

Via XDCR

Couchbase ES Transport

Data

Couchbase Server Cluster

MR Views MR Views MR Views MR Views Elastic Search Cluster

RefsTS Query

External Media Store

View Query

App Server

Couchbase Ruby SDK

ES queries over HTTP

Architecture

Market Adoption – CustomersInternet Companies Enterprises

Thank you!

[email protected]@dborkar

Get Couchbase Server 2.0 http://www.couchbase.com/download

launch webinar - introducing couchbase server 2.0

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