social gaming & gambling summit - london chris anderson

@jchrisChris Anderson

NoSQL Landscape& Grid Compu7ng

1Saturday, October 6, 12

• 2.2 Billion internet users• 50% Americans use smartphones

• Your app can grow overnight

• Are you ready?

2

Growth is the New Reality

Saturday, October 6, 12

Instagrowth: Android Launch

• Instagram gained nearly 1 million users overnight when they expanded to Android

Example


1 Instagram=

7.5M MAU*

4

Instagrowth: Android Launch

Example


Draw Something -‐ Social Game

5

35 million monthly active users in 1 monthabout 5 Instagrams

(Instagram today is waaaay more than 1 Instagram)


Goes Viral 3 Weeks aOer Launch

6

191715131197533/12826242220181614121082/6

Draw Something by OMGPOPDaily Ac)ve Users (millions)

21

2

4

6

8

10

12

14

16

35+M MAUat peak


By Contrast, at 1/2 an Instagram

7

The Simpson’s: Tapped OutDaily Ac)ve Users (millions)


GETTING IT RIGHT


Scalable Data Layer

9

●On-‐demand cluster sizing●Grow or shrink with workload

●Easy node provisioning●All nodes are the same

●MulA-‐master Cross-‐Datacenter ReplicaAon●For a fast and reliable user experience worldwide

●EffecAve Auto-‐sharding●Should avoid cluster hot spots


Old School Hits a Scale Wall

10

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


Tradi^onal MySQL + Memcached Architecture

11

●Run as many MySQL machines as you need

●Data sharded evenly across the machines using client code

●Memcached used to provide faster response time for users and reduce load on the database Memcached Tier

MySQL Tier

App Servers

www.example.com


Limitaôns of MySQL + Memcached

12

● To scale you need to start using MySQL more simply

● Scale by hand

● Replication / Sharding is a black art

● Code overhead to manage keeping memcache and mysql in sync

● Lots of components to deploy

Learn From Others -‐ This Scenario Costs Time and Money. Scaling SQL is potenâlly disastrous when going Viral: very risky ^me for major code changes and migraôns... you have no Time when skyrocke^ng up.


NoSQL Architectural Promise

13

Couchbase Database Servers

App Servers

www.example.com

•High Performance data access

• Scale Up/Down Horizontally

• 24x7x365 Always-On Availability

• Flexible Schema Document Model


14

NOSQL TAXONOMY


15

The Key-Value Store – the foundation of NoSQL

Key

101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101101100101000100010011101

OpaqueBinaryValue


16

Memcached – the NoSQL precursor

Key

101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101101100101000100010011101

OpaqueBinaryValue

memcached

In-‐memory onlyLimited set of opera^onsBlob Storage: Set, Add, Replace, CASRetrieval: GetStructured Data: Append, Increment

“Simple and fast.”

Challenges: cold cache, disrup^ve elas^city


17

Redis – More “Structured Data” commands

Key

101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101101100101000100010011101

“Data Structures”BlobListSetHash…

redis

In-‐memory onlyVast set of opera^onsBlob Storage: Set, Add, Replace, CASRetrieval: Get, Pub-‐SubStructured Data: Strings, Hashes, Lists, Sets,Sorted lists

Example opera7ons for a SetAdd, count, subtract sets, intersec^on, is member?, atomic move from one set to another


18

NoSQL catalog

Key-‐Value

memcached redis

Data Structure Document Column Graph

Cache

(mem

ory on

ly)


19

Membase – From key-‐value cache to database

Disk-‐based with built-‐in memcached cacheCache refill on restartMemcached compa^ble (drop in replacement)Highly-‐available (data replica^on)Add or remove capacity to live cluster

“Simple, fast, elas^c.”

membaseKey

101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101

101100101000100010011101101100101000100010011101101100101000100010011101

OpaqueBinaryValue


20

NoSQL catalog

Key-‐Value

memcached

membase

redis


Cache

(mem

ory on

ly)

Database

(mem

ory/disk)


21

Couchbase – document-‐oriented database

Key

{ “string” : “string”, “string” : value, “string” : { “string” : “string”, “string” : value }, “string” : [ array ]}

Auto-‐shardingDisk-‐based with built-‐in memcached cacheCache refill on restartMemcached compa^ble (drop in replace)Highly-‐available (data replica^on)Add or remove capacity to live cluster

When values are JSON objects (“documents”):Create indices, views and query against the views

JSONOBJECT

(“DOCUMENT”)

Couchbase


22

NoSQL catalog

Key-‐Value

memcached

membase

redis


Cache

(mem

ory on

ly)

Database

(mem

ory/disk)

membase couchbase


23

MongoDB – Document-‐oriented database

Key

{ “string” : “string”, “string” : value, “string” : { “string” : “string”, “string” : value }, “string” : [ array ]}

Disk-‐based with in-‐memory “caching”BSON (“binary JSON”) format and wire protocolMaster-‐slave replica^onAuto-‐shardingValues are BSON objectsSupports ad hoc queries – best when indexed

BSONOBJECT

(“DOCUMENT”)

MongoDB


24

NoSQL catalog

Key-‐Value

memcached

membase

redis


mongoDB

couchbase

Cache

(mem

ory on

ly)

Database

(mem

ory/disk)


25

Cassandra – Column overlays

Disk-‐based systemClustered External caching required for low-‐latency reads“Columns” are overlaid on the dataNot all rows must have all columnsSupports efficient queries on columnsRestart required when adding columnsGood cross-‐datacenter support

CassandraColumn 1

Column 2

Column 3 (not present)


26

NoSQL catalog

Key-‐Value

memcached

membase

redis


mongoDB

couchbase cassandra

Cache

(mem

ory on

ly)

Database

(mem

ory/disk)


27

Neo4j – Graph database

Disk-‐based systemExternal caching required for low-‐latency readsNodes, rela^onships and pathsProper^es on nodesDelete, Insert, Traverse, etc.

Neo4j


28

NoSQL catalog

Key-‐Value

memcached

membase

redis


mongoDB

couchbase cassandra

Cache

(mem

ory on

ly)

Database

(mem

ory/disk)

Neo4j


The Landscape

29

Speed

ScaleCouchbase

Redis

S3

Cassandra

MongoDB

Riak

HBase

CouchDB

Neo4j

SimpleDB

memcached

RDBMSDatomic


Datomic -‐ immutable func^onal data


Hello Couchbase Server 2.0


Couchbase Server 2.0 beta


33

Couchbase handles real world scale


(Really) High Performance

34

Latencyless than 1/2 ms

Throughputgrows linearly with cluster size

5 Nodes -- 1.75M operations per second

Cisco and Solarflare benchmark of Couchbase Server


How fast?

35hrp://www.slideshare.net/renatko/couchbase-‐performance-‐benchmarking


http://www.slideshare.net/renatko/couchbase-performance-benchmarking

http://www.slideshare.net/renatko/couchbase-performance-benchmarking

Latency


COMPLEXITY IS THE ENEMY


38

Couchbase Server Basic Opera^on

COUCHBASE CLIENT LIBRARY

§Docs distributed evenly across servers in the cluster

§Each server stores both ac)ve & 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§ Mul^ple App Servers can access same document at same ^me

Doc 2

Doc 5

SERVER 1

Doc 4

SERVER 2

Doc 1

SERVER 3


Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

Ac^ve Docs Ac^ve Docs Ac^ve Docs

CLUSTER MAP CLUSTER MAP

APP SERVER 1 APP SERVER 2

COUCHBASE SERVER CLUSTER


38








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

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

Doc 9

Doc 5

DOC

DOC

DOC

Doc 1

Doc 8 Doc 2

Replica Docs Replica Docs Replica Docs






38








Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2

Doc 7

Doc 1

SERVER 3

Doc 3

Read/Write/Update


Read/Write/Update

Doc 9

Doc 7

Doc 8 Doc 6

Doc 3

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

Doc 9

Doc 5

DOC

DOC

DOC

Doc 1

Doc 8 Doc 2







39

Add Nodes to the Cluster

§ Two servers added to cluster§ One-‐click opera^on

§ Docs automa^cally rebalanced across cluster§ Even distribu^on of docs§ Minimum doc movement

§ Cluster map updated§ App database calls now distributed over larger # of servers

Doc 7

Doc 9

Doc 3

Ac^ve Docs

Replica Docs

Doc 6

COUCHBASE CLIENT LIBRARYCLUSTER MAP

APP SERVER 1


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

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

Doc 9

Doc 5

DOC

DOC

DOC

Doc 1

Doc 8 Doc 2





39





Doc 7

Doc 9

Doc 3

Ac^ve Docs

Replica Docs

Doc 6


APP SERVER 1


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

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

DOC

Doc 9

Doc 5

DOC

DOC

DOC

Doc 1

Doc 8 Doc 2



SERVER 4 SERVER 5

Ac^ve Docs Ac^ve Docs

Replica Docs Replica Docs



39





Doc 7

Doc 9

Doc 3

Ac^ve Docs

Replica Docs

Doc 6


APP SERVER 1


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

DOC

DOCDOC

DOC DOC

DOC

DOC

DOC

DOC

DOC DOC

DOC

DOC

DOC

Doc 9

Doc 5DOC

DOC

DOC

Doc 1

Doc 8

Doc 2



SERVER 4 SERVER 5





39





Read/Write/Update Read/Write/Update

Doc 7

Doc 9

Doc 3

Ac^ve Docs

Replica Docs

Doc 6


APP SERVER 1


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

DOC

DOCDOC

DOC DOC

DOC

DOC

DOC

DOC

DOC DOC

DOC

DOC

DOC

Doc 9

Doc 5DOC

DOC

DOC

Doc 1

Doc 8

Doc 2



SERVER 4 SERVER 5





40

Fail Over Node



Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2 SERVER 3

Doc 3

Doc 9

Doc 7 Doc 8

Doc 6DOC

DOC

DOCDOC

DOC

DOC

DOC

DOC

DOC DOC

DOCDoc 5DOC

DOC

DOC

Doc 1

Doc 8

Doc 2



SERVER 4 SERVER 5




§ 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 ac)ve§ Updates cluster map

§ App server requests for docs now go to appropriate server

§ Typically rebalance would follow

Doc 7

Doc 9

Doc 3

Ac^ve Docs

Replica Docs

Doc 6

Doc 7

Doc 1

Doc 3

DOC

DOCDoc 9

DOC

DOC


40

Fail Over Node



Doc 4

Doc 2

Doc 5

SERVER 1

Doc 6

Doc 4

SERVER 2 SERVER 3

Doc 3

Doc 9

Doc 7 Doc 8

Doc 6DOC

DOC

DOCDOC

DOC

DOC

DOC

DOC

DOC DOC

DOCDoc 5DOC

DOC

DOC

Doc 1

Doc 8

Doc 2



SERVER 4 SERVER 5




§ 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 ac)ve§ Updates cluster map

§ App server requests for docs now go to appropriate server

§ Typically rebalance would follow


41

●Suddenly, disk writes all began to time out●Many services experienced outages:●FourSquare, Reddit, Quora, among others

●With memory buffered writes, a scalable data layer keeps working●When EBS came back online, Couchbase wrote all the updated data to disk without missing a beat.

War Story: EBS Outage


42

Cross Data Center Replica^on

§Data close to users§Mul^ple loca^ons for disaster recovery§Independently managed clusters serving local data

US DATA CENTER EUROPE DATA CENTER ASIA DATA CENTER

Replica7on Replica7on

Replica7on


43

Built for Produc^on


JSON DOCUMENT DATABASE


45

Document Database as Aggregate Database

hrp://mar^nfowler.com/bliki/AggregateOrientedDatabase.html


http://martinfowler.com/bliki/AggregateOrientedDatabase.html

http://martinfowler.com/bliki/AggregateOrientedDatabase.html

46

Document Database

This synergy between the programming model and the distribution model is very valuable. It allows the database to use its knowledge of how the application programmer clusters the data to help performance across the cluster.

hrp://mar^nfowler.com/bliki/AggregateOrientedDatabase.html

o::1001{

uid: ji22jd,customer: Ann,line_items: [

{ sku: 0321293533, quan: 3, unit_price: 48.0 },{ sku: 0321601912, quan: 1, unit_price: 39.0 },{ sku: 0131495054, quan: 1, unit_price: 51.0 }

],payment: { type: Amex, expiry: 04/2001,

last5: 12345 }}


Developers <3 JSON


48

LET’S GET POST-‐RELATIONAL!


49

JSON Documents

• Maps more closely to external API• CRUD Opera^ons, lightweight schema

• Stored under an iden^fier key

{ “fields” : [“with basic types”, 3.14159, true], “like” : “your favorite language”}

client.set(“mydocumentid”, myDocument);mySavedDocument = client.get(“mydocumentid”);


Meta + Document Body

50

{ "brewery": "New Belgium Brewing", "name": "1554 Enlightened Black Ale", "abv": 5.5, "descrip7on": "Born of a flood...", "category": "Belgian and French Ale", "style": "Other Belgian-‐Style Ales", "updated": "2010-‐07-‐22 20:00:20"}

{ "id" : "beer_Enlightened_Black_Ale”, ...{

Documentuser data,

can be anything

unique ID

Metadataidentifier,

expiration, etc

“vintage” date format from an SQL dump >_<


{ "brewery": "New Belgium Brewing", "name": "1554 Enlightened Black Ale", "abv": 5.5, "descrip7on": "Born of a flood...", "category": "Belgian and French Ale", "style": "Other Belgian-‐Style Ales", "updated": "2010-‐07-‐22 20:00:20", “ra7ngs” : { “525” : 5, “30” : 4, “1044” : 2 }, “comment_ids” : [ “f1e62”, “6ad8c” ]}

Add comments to the beer

{ "type": "comment", "about_id": "beer_Enlightened_Black_Ale", "user_id": 525, "text": "tastes like college!", "updated": "2010-‐07-‐22 20:00:20"}

link to comments

link to beer

{ "id": "f1e62"}


52

How to: look up comments from a beer

• SERIALIZED LOOP

figure hrp://www.ibm.com/developerworks/webservices/library/ws-‐sdoarch/

beer = client.get(“beer:A_cold_one”);beer.comment_ids.each { |id| comments.push(client.get(id));}

• FAST MULTI-‐KEY LOOKUPbeer = client.get(“beer:A_cold_one”);comments = client.multiGet(beer.comment_ids)

• ASYNC VIEW QUERYcomments = client.query(“myapp”,“by_comment_on”, {:key => “beer:A_cold_one”});


http://www.ibm.com/developerworks/webservices/library/ws-sdoarch/

http://www.ibm.com/developerworks/webservices/library/ws-sdoarch/

53

Emergent Schema

JSON.org

Github API

Twiqer API

"Capture the user's intent"

• The database can handle it• Your app controls the schema


Audience par^cipa^on! *that means you

54

npm install twitterfightnpm start twitterfight


@jchrishrp://www.couchbase.com/

Chris Anderson

Thank You!


http://www.couchbase.com


56

INCREMENTAL MAP-‐REDUCEFOR REALTIME ANALYTICS


What do you mean “Incremental?”

like:CREATE INDEX city ON brewery city;


58

QUERY PATTERN:FIND BY ATTRIBUTE


59

Find documents by a specific arribute

• Lets find beers by brewery_id!


60

The index defini^on


61

The result set: beers keyed by brewery_id


62

QUERY PATTERN:BASIC AGGREGATIONS


63

Use a built-‐in reduce func^on with a group query

• Lets find average abv for each brewery!


64

We are reducing doc.abv with _stats


65

Group reduce (reduce by unique key)


66

QUERY PATTERN:TIME-‐BASED ROLLUPSWITH KEY GROUPING


group_level=3 -‐ daily results -‐ great for graphing

67

• Daily, hourly, minute or second rollup all possible with the same index.

• hrp://crate.im/posts/couchbase-‐views-‐reddit-‐data/


http://crate.im/posts/couchbase-views-reddit-data/

http://crate.im/posts/couchbase-views-reddit-data/

68

GEO INDEX & FULL TEXT INTEGRATION


69

GeoCouch R-‐Tree Index

• Op^mized for bulk loading of large data sets• Simplified query model (bounding box, nearest neighbor)


Elas^c Search Adapter

70

• Elas^c Search is good for ad-‐hoc queries and faceted browsing• Our adapter is aware of changing Couchbase topology• Indexed by Elas^c Search aOer stored to disk in Couchbase


@jchrishrp://www.couchbase.com/

Chris Anderson

Thank You!




social gaming & gambling summit - london chris anderson

Documents

ons of mysql

available data replica

scalable data layer

mysql machines

keyvalue cache

internet users

onal mysql

nosql taxonomy14saturday