2011 mongo FR - scaling with mongodb

Eliot Horowitz @eliothorowitz MongoUK March 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 Config Servers 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

Splitting T1 T2 T3 MIN MAX LOCATION A Z shard1 MIN MAX LOCATION A G shard1 G Z shard1 MIN MAX LOCATION A D shard1 D G shard1 G S shard1 S Z shard1

Balancing
Moves chunks from one shard to another
Done online while system is running
Balancing runs in the background

Migrating T3 T4 T5 MIN MAX LOCATION A D shard1 D G shard1 G S shard1 S Z shard1 MIN MAX LOCATION A D shard1 D G shard1 G S shard1 S Z shard2 MIN MAX LOCATION A D shard1 D G shard1 G S shard2 S Z shard2

Choosing a Shard Key
Shard key determines how data is partitioned
Hard to change
Most important performance decision

Use Case: User Profiles
{ email : “ [email_address] ” ,
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 }

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2011 mongo FR - scaling with mongodb

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