A Morning with MongoDB Barcelona: MongoDB Basic Concepts

MongoDB Basic Concepts

Norberto Leite

Senior Solutions Architect, EMEA
norberto@10gen.com
@nleite

Sunday, 21 October 12

Agenda

•Overview
•Replication
•Scalability
•Consistency & Durability
•Flexibility, Developer Experienc


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Basic Concepts
Application Document
Oriented
High { author : “steve”,
date : new Date(),
text : “About MongoDB...”,
Performance tags : [“tech”, “database”]}

Fully
Consistent

Horizontally Scalable


Tradeoff: Scale vs Functionality

• memcached
scalability & performance

•key/value

• RDBMS

depth of functionality

Replication


Why do we need replication

•Failover
•Backups
•Secondary batch jobs
•High availability


Replica Sets
Data Availability across nodes
• Data Protection
• Multiple copies of the data
• Spread across Data Centers, AZs
• High Availability
• Automated Failover
• Automated Recovery


Replica Sets

App Write
Primary
Asynchronous
Read Replication

Secondary
Read

Secondary
Read


Replica Sets

App Write
Primary
Read

Secondary
Read

Secondary
Read


Replica Sets

App
Primary

Write
Primary Automatic Election of
new Primary
Read

Secondary
Read


Replica Sets

App
Recovering

Write New primary serves
Primary data
Read

Secondary
Read


Replica Sets

App
Secondary
Read

Write
Primary
Read

Secondary
Read


Scalability


Horizontal Scalability


Sharding
Data Distribution across nodes
• Data location transparent to your code
• Data distribution is automatic
• Data re-distribution is automatic
• Aggregate system resources horizontally
• No code changes


Sharding - Range distribution

sh.shardCollection("test.tweets", {_id: 1} , false)

shard01 shard02 shard03


Sharding - Range distribution


a-i j-r s-z


Sharding - Splits


a-i ja-jz s-z
k-r


Sharding - Splits


a-i ja-ji s-z
ji-js
js-jw
jz-r


Sharding - Auto Balancing


a-i ja-ji s-z
ji-js
js-jw js-jw
jz-r jz-r


Sharding - Auto Balancing


a-i ja-ji n-z
ji-js
js-jw
jz-r


Sharding - Routed Query
ﬁnd({_id: "norberto"})


a-i ja-ji n-z
ji-js
js-jw
jz-r


Sharding - Scatter Gather
ﬁnd({email: "norberto@10gen.com"})


a-i ja-ji n-z
ji-js
js-jw
jz-r


Sharding - Caching
96 GB Mem
3:1 Data/Mem

shard01

a-i
300 GB Data

j-r
n-z

300 GB


Aggregate Horizontal Resources
96 GB Mem 96 GB Mem 96 GB Mem
1:1 Data/Mem 1:1 Data/Mem 1:1 Data/Mem


a-i j-r n-z
300 GB Data

100 GB 100 GB 100 GB


Consistency & Durability


Two choices for consistency

•Eventual consistency
•Allow updates when a system has been partitioned
•Resolve conﬂicts later
•Example: CouchDB, Cassandra

•Immediate consistency
•Limit the application of updates to a single master
node for a given slice of data
•Another node can take over after a failure is detected
•Avoids the possibility of conﬂicts
•Example: MongoDB


Durability

•For how long is my data available?
•When do I now that my data is safe?
•Where?
•Mongodb style
•Fire and Forget
•Get Last Error
•Journal Sync
•Replica Safe


Fire and Forget
Driver Primary


Fire and Forget
Driver Primary

write


Fire and Forget
Driver Primary

write

apply in memory


Get Last Error
Driver Primary


Get Last Error
Driver Primary
write

getLastError


Get Last Error
Driver Primary
write

getLastError
apply in memory


Journal Sync
Driver Primary


Journal Sync
Driver Primary
write

getLastError
apply in memory
j:true


Journal Sync
Driver Primary
write

getLastError
apply in memory
j:true
write to journal


Replicas Safe
Driver Primary Secondary


Replicas Safe

write

getLastError
w:2 apply in memory


Replicas Safe

write

getLastError
w:2 apply in memory
replicate


Flexibility


What MongoDB solves

• Applications store complex data that is easier to
Agility •
model as documents
Schemaless DB enables faster development cycles

• Relaxed transactional semantics enable easy scale
Flexibility •
out
Auto Sharding for scale down and scale up

• Cost effective operationalize abundant data
Cost (clickstreams, logs, tweets, ...)


Challenges for Databases

✓ Build a database for scaleout
• Run on clusters of 100s of commodity
machines

•… that enables agile development

•… and is usable for a broad variety of applications


Data Model

• Why JSON?
• Provides a simple, well understood
encapsulation of data
• Maps simply to the object in your OO language
• Linking & Embedding to describe relationships


Json

place1 = {

name : "10gen HQ",

address : "578 Broadway 7th Floor",

city : "New York",

zip : "10011",
tags : [ "business", "tech" ]
}

Schema Design
Relational Database


Schema Design
MongoDB embedding

linking

Schemas in MongoDB

Design documents that simply map to
your application
post = {author: "Hergé",
date: new Date(),
text: "Destination Moon",
tags: ["comic", "adventure"]}

> db.posts.save(post)


Embedding
> db.blogs.find( { author: "Hergé"} )

{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"),
author : "Hergé",
date : ISODate("2011-09-18T09:56:06.298Z"),
text : "Destination Moon",
tags : [ "comic", "adventure" ],
comments : [
! {
! ! author : "Kyle",
! ! date : ISODate("2011-09-19T09:56:06.298Z"),
! ! text : "great book"
! }
]
}


JSON & Scaleout

• Embedding removes need for
• Distributed Joins
• Two Phase commit
• Enables data to be distributed across many nodes
without penalty


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A Morning with MongoDB Barcelona: MongoDB Basic Concepts

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