london web performance-couchbase meetup

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CouchbaseDistributed Document Database

Perry KrugSr. Solutions Architect

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Company Background

• Leading NoSQL database company

• Open source development and distribution model

• Provide easy-to-develop and -deploy, high-performance, easily scalable, document database

• Focused on internet and mobile applications and cloud computing environments

• Most mature, reliable and widely deployed solution– 1000s of production deployments worldwide

• Located in Silicon Valley (Mountain View, CA)– 80 employees including 50 in engineering/product– Round C company, VCs includes Accel partners, Mayfield, North Bridge, and Ignition

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Paid Production Deployments (partial list)

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Couchbase automatically distributes data across commodity servers. Built-in caching enables apps to read and write data with sub-millisecond latency. And with no schema to manage,

Couchbase effortlessly accommodates changing data management requirements.

Couchbase Server (a.k.a. Membase)

Simple. Fast. Elastic. NoSQL.

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RDBMS HAS DOMINATED FOR 40 YEARS BUT NO LONGER BEST SOLUTION FOR MANY APPS

Relational database technology has served us well for 40 years, and will likely continue to do so for the foreseeable future to support transactions requiring ACID guarantees. But a large, and increasingly dominant, class of software systems and data do not need those guarantees. Much of the data manipulated by Web applications have less strict transactional requirements but, for lack of a practical alternative, many IT teams continue to use relational technology, needlessly tolerating its cost and scalability limitations. For these applications and data, distributed document cache and database technologies such as Couchbase’s provide a promising alternative.

Carl OlofsonIDC Research Vice President, Information and Data Management

“

”

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Modern Interactive Software Architecture

Application Scales OutJust add more commodity web servers

Database Scales UpGet a bigger, more complex server

Expensive & disruptive sharding, doesn’t perform at web scale

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Data Layer Matches Application Logic Tier Architecture

Application Scales OutJust add more commodity web servers

Database Scales OutJust add more commodity data servers

Scaling out flattens the cost and performance curves

• Horizontally scalable with auto-sharding• High performance at web scale• Schema-less for flexibility

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Couchbase NoSQL: Simple, Fast, Elastic

• Easily scale apps to an “infinite” number of users– Simply add nodes with a single click– Never need to change your application to scale– Simple development with memcached API

• High performance with predictably low latency– Sub millisecond reads and writes– No drop in performance as app scales

• Schema-less document database– Flexibility to meet rapidly changing market requirements– Roadmap: Indexing, querying similar to RDBMS capabilities

• Low cost solution that economically scales with app

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PERFORMANCE

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Key results of Cisco and Solarflare Benchmark

Couchbase Server demonstrates

• Consistent sub-millisecond latency for mixed workload

• High throughput

• Linear scalability

http://www.cisco.com/en/US/prod/collateral/switches/ps9441/ps9670/white_paper_c11-708169.pdf

http://www.cisco.com/en/US/prod/collateral/switches/ps9441/ps9670/white_paper_c11-708169.pdf

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Your secret weapon: Sub-millisecond AND consistent latency

Object size (Bytes)

Late

ncy (

mic

ro s

econ

ds)

Consistently low latencies in microseconds for varying documents sizes with a mixed workload

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Your secret weapon: Sub-millisecond AND consistent latency

Number of servers in cluster

Op

era

tion

s p

er

secon

d

High throughput with 1.4 GB/sec data transfer rate using 4 servers

Linear throughput scalability

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SCALE

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Draw Something by OMGPOP

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Draw Something “goes viral” 3 weeks after launch

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Draw Something by OMGPOPDaily Active Users (millions)

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2

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As usage grew, game data went non-linear.

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Draw Something by OMGPOPDaily Active Users (millions)

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2

4

6

8

10

12

14

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By March 19, there were over 30,000,000 downloads of the app,

over 5,000 drawings being stored per second,over 2,200,000,000 drawings stored,

over 105,000 database transactions per second,and over 3.3 terabytes of data stored. Instagram (7.5M in 5 wks)

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The game exploded. But Couchbase did not.

Without a second of downtime, and while sustaining front-end performance, the cluster was continuously expanded to support growth, absorbing frequent server hardware failures.

Drawings/second

Total drawings

R/W latency (usec)

Servers

February

6February

13February

20February

27March

5March

12March

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0 5 12 50 500 1 2.2million million million million billion billion

6 6 6 18 54 72 90

30 40 32 31 38 29 34

0 3 50 333 1660 3000 5400

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In contrast.

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The Simpson’s: Tapped OutDaily Active Users (millions)

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#2 Free app on iPad#3 Free app on iPhone

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NO SCHEMA

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Document database

{"_id": "brewery_Cleveland_ChopHouse_and_Brewery","_rev": "1-00000061480b50910000000000000000","city": "Cleveland","updated": "2010-07-22 20:00:20","code": "44113","name": "Cleveland ChopHouse and Brewery","country": "United States","phone": "1-216-623-0909","state": "Ohio","address": [

"824 West St.Clair Avenue"],"geo": {

"loc": ["-81.6994","41.4995"],"accuracy": "ROOFTOP"

},"$expiration": 0,"$flags": 0

}

• Json objects• Flexible schema

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COUCHBASE SOLUTION“THE BASICS”

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COUCHBASE CLIENT LIBRARY

Basic Operation – scale out

Docs distributed evenly across servers in the cluster

Each server stores both active & replica docs Only one server active at a time

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

Multiple App Servers can access same document at same time

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3

User Configured Replica Count = 1

Read/Write/Update


Read/Write/Update

Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

DOC

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Doc 9

Doc 5

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Doc 1

Doc 8 Doc 2

Replica Docs Replica Docs Replica Docs

Active Docs Active Docs Active Docs

CLUSTER MAP CLUSTER MAP

APP SERVER 1 APP SERVER 2

COUCHBASE SERVER CLUSTER

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Add Nodes

Two servers added to cluster One-click operation

Docs automatically rebalanced across cluster Even distribution of

docs Minimum doc

movement Cluster map updated

App database calls now distributed over larger # of servers


Read/Write/Update Read/Write/Update

Doc 7

Doc 9

Doc 3

Active Docs

Replica Docs

Doc 6


CLUSTER MAP

APP SERVER 1


CLUSTER MAP

APP SERVER 2

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3

Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

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Doc 9

Doc 5

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Doc 1

Doc 8 Doc 2



SERVER 4 SERVER 5

Active Docs Active Docs

Replica Docs Replica Docs


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Fail Over Node

App servers happily accessing docs on Server 3

Server fails App server requests to server 3 fail Cluster detects server has failed

Promotes replicas of docs to active Updates cluster map

App server requests for docs now go to appropriate server

Typically rebalance would follow


Doc 7

Doc 9

Doc 3

Active Docs

Replica Docs

Doc 6


CLUSTER MAP

APP SERVER 1


CLUSTER MAP

APP SERVER 2

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3

Doc 9

Doc 7 Doc 8

Doc 6

Doc 3

DOC

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DOCDOC

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DOC DOC

DOC

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DOC DOC

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Doc 9

Doc 5DOC

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Doc 1

Doc 8

Doc 2



SERVER 4 SERVER 5

Active Docs Active Docs

Replica Docs Replica Docs


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Couchbase Server 2.0

• Next major release of Couchbase Server• Currently in Developer Preview, approaching Beta and GA.

What’s new:• New storage engine technology (Append only b-tree)• Indexing and Querying• Incremental Map Reduce• Cross Data Center Replication• Better memory management, large data sets, and other

technological improvments• Fully backwards compatible with existing Couchbase Server

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storage interface

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

httpRE

ST m

anag

emen

t API

/Web

UI

8092Couch View

CouchStoreAuto compaction

Memcached

Couchbase Server 2.0 Architecture

Couc

h AP

I

Membase EP Engine

11210Memcapable 2.0

Moxi

11211Memcapable 1.0

HTTP8091

Erlang port mapper4369

Distributed Erlang21100 - 21199

Membase

Erlang/OTP

Distributed Indexing

CouchBase

Cluster ManagerData Manager

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storage interface

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

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one per cluster

vBuc

ket s

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and

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on m

anag

er

httpRE

ST m

anag

emen

t API

/Web

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8092Couch View



Couc

h AP

I

Membase EP Engine

11210Memcapable 2.0

Moxi

11211Memcapable 1.0

HTTP8091



Membase

Erlang/OTP


CouchBase

Cluster Manager

Memcached Interface

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storage interface

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

httpRE

ST m

anag

emen

t API

/Web

UI

8092Couch View


Memcached Interface


Couc

h AP

I

EP Engine

11210Memcapable 2.0

Moxi

11211Memcapable 1.0

HTTP8091



Erlang/OTP


CouchBase

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Partitioning The Data – vbucket map

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Indexing and querying

• Build in incremental map reduce

• Map functions are written and executed on Java Script (V8)

• Index is built incrementally as mutation streams in

• Query in a scatter/gather fashion

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Incremental Map reduce using javascript

{"_id": "brewery_Cleveland_ChopHouse_and_Brewery","_rev": "1-00000061480b50910000000000000000","city": "Cleveland","updated": "2010-07-22 20:00:20","code": "44113","name": "Cleveland ChopHouse and Brewery","country": "United States","phone": "1-216-623-0909","state": "Ohio","address": [

"824 West St.Clair Avenue"],"geo": {

"loc": ["-81.6994","41.4995"],"accuracy": "ROOFTOP"

},"$expiration": 0,"$flags": 0

}

• Document from our sample built in beer database

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Map function

function (doc) { if (doc.country, doc.state, doc.city) { emit([doc.country, doc.state, doc.city], 1); } else if (doc.country, doc.state) { emit([doc.country, doc.state], 1); } else if (doc.country) { emit([doc.country], 1); }}

• Map functions

REST call: http://db1.couchbase.com:8092/beer-sample/_design/dev_beer/_view/by_location?limit=10

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Reduce functions

• Built in reduce functions• _count • _sum• _stats ({“sum”: 1411, “count”: 1411, “min”: 1, “max”: 1, “sumsqr”:1411})

• Developing procedure• Develop against a subset of the data• Built the index on the entire cluster• Promote a dev_ view to production

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APP SERVER 1


Indexing and Querying

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

CLUSTER MAP

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3


APP SERVER 2


CLUSTER MAP

Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

DOC

DOC

DOC

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Doc 9

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COUCHBASE CLIENT LIBRARY COUCHBASE CLIENT LIBRARY

CLUSTER MAP CLUSTER MAP

APP SERVER 1 APP SERVER 2

QueryResponse

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Cross Data Center Replication

Data close to users Multiple locations for disaster recovery Independently managed clusters serving local data

US DATA CENTER

EUROPE DATA CENTER

ASIA DATA CENTERReplication Replication

Replication

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Integration to Analytics systems

Use the cross data center interface

Agnostic to topology changes De-duplication Effective changes feed of the

entire cluster

Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3


Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

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CROSS DATA CENTER CONNETROR

Changes feed to consumed byAny destination

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• Support large-scale analytics on application data by streaming data from Couchbase to Hadoop– Real-time integration using Flume– Batch integration using Sqoop

• Examples– Various game statistics (e.g., monthly / daily / hourly rankings)– Analyze game patterns from users to enhance various game metrics

Couchbase and Hadoop Integration

memcachedprotocol listener/sender

engine interface

Couchbase Storage Engine

TAPSqoop

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Couchbase Client SDKs

Java Client SDK

.Net SDK

PHP SDK

Ruby SDK

Python SDK

spymemcachedConnection

HTTP couchDB connection

Java client API

User Code

Couchbase Server

CouchbaseClient cb = new CouchbaseClient(listURIs,"aBucket", "letmein");// this is all the same as beforecb.set("hello", 0, "world");cb.get("hello");Map<String, Object> manyThings =cb.getBulk(Collection<String> keys);/* accessing a view View view = cb.getView("design_document", "my_view");Query query = new Query();query.getRange("abegin", "theend");

http://www.couchbase.org/code

http://www.couchbase.org/code

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Couchbase Demonstration

• Couchbase ServerTemplate Demo– Starting with one database

node under load– Dynamically scaling to two

database nodes– Easy management and

monitoring– Not possible any other

database technology Couchbase Servers

In the EC2 or Datacenter

Web application server

Application user

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THANK YOU

COUCHBASE SIMPLE, FAST, ELASTIC NOSQL

QUESTIONS?

@couchbase [email protected]

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COUCHBASE CUSTOMERS

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Paid Production Deployments – Social Gaming

iki

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Paid Production Deployments – Key Segments

Ad Platforms

Social Networks

http://www.spotme.com/home

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Production Deployments – Key Industry Segments

Online Biz Services

Online Media

E-Commerce

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HealthCare

Military/Government

Communications

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Online Education

Web Design

FinancialServices

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Software

Security

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Production Deployments – Enterprises

london web performance-couchbase meetup

Technology

commodity data servers

game data

data managementrdbms

terabytes of data

app scales schema

database transactions

commodity servers

document database flexibility