Eliot Horowitz@eliothorowitz

MongoUKMarch 21, 2011

Scaling with MongoDB

Scaling

•Storage needs only go up

•Operations/sec only go up

•Complexity only goes up

Horizontal Scaling

•Vertical scaling is limited

•Hard to scale vertically in the cloud

•Can scale wider than higher

Read Scaling

•One master at any time

•Programmer determines if read hits master or a slave

•Pro: easy to setup, can scale reads very well

•Con: reads are inconsistent on a slave

•Writes don’t scale

One Master, Many Slaves

•Custom Master/Slave setup

•Have as many slaves as you want

•Can put them local to application servers

•Good for 90+% read heavy applications (Wikipedia)

Replica Sets

•High Availability Cluster

•One master at any time, up to 6 slaves

•A slave automatically promoted to master if failure

•Drivers support auto routing of reads to slaves if programmer allows

•Good for applications that need high write availability but mostly reads (Commenting System)

•Many masters, even more slaves

•Can scale in two dimensions

•Add Shards for write and data size scaling

•Add slaves for inconsistent read scaling and redundancy

Sharding

Sharding Basics

•Data is split up into chunks

•Shard: Replica sets that hold a portion of the data

•Config Servers: Store meta data about system

•Mongos: Routers, direct direct and merge requests

Architecture

client

mongos ...mongos

mongod

mongodddd ...

Shards

mongod

mongod

mongod

ConfigServers

mongod

mongod

mongodddd

mongod

mongod

mongodddd

mongod

client client client

Common Setup

•A common setup is 3 shards with 3 servers per shard: 3 masters, 6 slaves

•Can add sharding later to an existing replica set with no down time

•Can have sharded and non-sharded collections

Range Based

•collection is broken into chunks by range

•chunks default to 64mb or 100,000 objects

MIN MAX LOCATION

A F shard1

F M shard1

M R shard2

R Z shard3

Config Servers

•3 of them

•changes are made with 2 phase commit

•if any are down, meta data goes read only

•system is online as long as 1/3 is up

mongos

•Sharding Router

•Acts just like a mongod to clients

•Can have 1 or as many as you want

•Can run on appserver so no extra network traffic

•Cache meta data from config servers

Writes

•Inserts : require shard key, routed

•Removes: routed and/or scattered

•Updates: routed or scattered

Queries

•By shard key: routed

•sorted by shard key: routed in order

•by non shard key: scatter gather

•sorted by non shard key: distributed merge sort

Splitting

•Take a chunk and split it in 2

•Splits on the median value

•Splits only change meta data, no data change

SplittingMIN MAX LOCATION

A Z shard1

T1

MIN MAX LOCATION

A G shard1

G Z shard1

T2

MIN MAX LOCATION

A D shard1

D G shard1

G S shard1

S Z shard1

T3

Balancing

•Moves chunks from one shard to another

•Done online while system is running

•Balancing runs in the background

MigratingMIN MAX LOCATION

A D shard1

D G shard1

G S shard1

S Z shard1

T3

MIN MAX LOCATION

A D shard1

D G shard1

G S shard1

S Z shard2

T4

MIN MAX LOCATION

A D shard1

D G shard1

G S shard2

S Z shard2

T5

Choosing a Shard Key

•Shard key determines how data is partitioned

•Hard to change

•Most important performance decision

Use Case: User Profiles

{ email : “eliot@10gen.com” ,

addresses : [ { state : “NY” } ]

}

•Shard by email

•Lookup by email hits 1 node

•Index on { “addresses.state” : 1 }

Use Case: Activity Stream

{ user_id : XXX, event_id : YYY , data : ZZZ }

•Shard by user_id

•Looking up an activity stream hits 1 node

•Writing even is distributed

•Index on { “event_id” : 1 } for deletes

Use Case: Photos

{ photo_id : ???? , data : <binary> }

What’s the right key?

•auto increment

•MD5( data )

•now() + MD5(data)

•month() + MD5(data)

Use Case: Logging

{ machine : “app.foo.com” , app : “apache” ,

when : “2010-12-02:11:33:14” , data : XXX }

Possible Shard keys

•{ machine : 1 }

•{ when : 1 }

•{ machine : 1 , app : 1 }

•{ app : 1 }

Download MongoDBhttp://www.mongodb.org

and let us know what you think@eliothorowitz @mongodb

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