Talk MongoDB - Amil

{

}

name : ‘Marcelo Cenerino’,
company: ‘Amil’,
date : ‘2013-10-30T08:30:00.000Z’

Here’s a definition:
MongoDB (from humongous) is an open-source, highperformance, scalable, general purpose database. It is

used by organizations of all sizes to power online
applications where low latency and high availability are
critical requirements of the system.

You can have at most two of these properties for
any shared-data system. Dr. Eric A. Brewer, 2000

Main characteristics
• Document based

• Schemaless
• Open source (on GitHub)

• High performance
• Horizontally scalable
• Full featured

Customers

eBay, Ericsson, EA, SAP, Telefonica, Code School, Abril...

RDBMS: data is structured as tables
id

nome

idade

genero

1

João

25

Masculino

2

Maria

30

Feminino

3

Pedro

40

Masculino

...

...
...

Document oriented
{
_id : 1,
nome : 'João',
idade : 25,
genero : 'Masculino'
}
MongoDB stores data as document in a
binary representation called BSON
(Binary JSON)

Size: up to 16 MB

RDBMS

vs.

MongoDB

Table

Collection

Row

Document

Column

Field

Index

Index

Joins

Embedded doc.

FK

Reference

Partition

Shard

Transaction Model

MongoDB guarantees atomic updates
to data at the document level.

Relational schema design
• Large ERD diagrams

• Create table statements
• ORM to map tables to objects
• Tables just to join tables together
• Lots of revision and alter table statements until we
get it just right

In a MongoDB based app we

start building our app
and let the schema evolve.

Mongo “schema” design
Article

User
name
email

title
date
text
author

Comment[]
author
date
text

Tag[]
value

Inserting a document
> user = {name : ‘marcelo’, age : 29, gender : ‘Male’}
> db.users.insert(user)
>

• No collection creation needed!

Querying a document
> db.users.findOne()
{
"_id" : ObjectId("5269d66271de67aa7c3c41b4"),
"name" : “marcelo",
"age" : 29,
“gender" : “male"
}

•
•
•
•

_id is the primary key in MongoDB
Automatically indexed
Automatically created as an ObjectId if not provided
Any unique immutable value could be used

Querying a document
> db.users.find({name : 'maria', age : {$gt : 25}})
{ "_id" : ObjectId("526f1af1dac0a62cdc152a96"), "name" : "maria", "age"
: 26 }

Operators
Group

Operators

Comparison

$gt, $gte, $in, $lt, $lte, $ne, $nin

Logical

$or, $and, $not, $nor

Element

$exists, $type

Evaluation

$mod, $regex, $where

Geospatial

$geoWithin, $geoIntersects, $near, $nearSphere

Array

$all, $elemMatch, $size

Projection

$, $elemMatch, $slice

Updating a document
> db.users.find({age : {$gt : 25}}, {_id : 0})
{ "name" : "maria", "age" : 26 }
{ "name" : "marcelo", "age" : 29 }
>
>db.users.update({age : {$gt : 25}}, {$set : {roles : ['admin', 'dev', 'operator']}})

Removing a document
> db.users.remove({name : 'maria'})
> db.users.find().pretty()
{
"_id" : ObjectId("526f1cb3dac0a62cdc152a98"),
"age" : 29,
"name" : "marcelo",

"roles" : [
"admin",
"dev",
"operator"

]
}

Querying a large collection without index
> db.estabelecimentos.count()
307929
>
> db.estabelecimentos.find({'localizacao.cidade' : 'PIACATU'}).explain()
{
"cursor" : "BasicCursor",
"isMultiKey" : false,
"n" : 5,
"nscannedObjects" : 307929,
"nscanned" : 307929,
"nscannedObjectsAllPlans" : 307929,
"nscannedAllPlans" : 307929,
"scanAndOrder" : false,
"indexOnly" : false,
"nYields" : 1,
"nChunkSkips" : 0,
"millis" : 311,
"indexBounds" : {

}
>

},
"server" : "Cenerino-PC:27017"

Showing collection’s indexes
> db.estabelecimentos.getIndexes()
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"ns" : "mapa-servicos.estabelecimentos",
"name" : "_id_"
}
]
>

Creating an index
> // creating an index
> db.estabelecimentos.ensureIndex({'localizacao.cidade' : 1})
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_"
},
{
"v" : 1,
"key" : {
"localizacao.cidade" : 1
},
"name" : "localizacao.cidade_1"
}
]
>

Same query, now using index
> db.estabelecimentos.find({'localizacao.cidade' : 'PIACATU'}).explain()
{
"cursor" : "BtreeCursor localizacao.cidade_1",
"isMultiKey" : false,
"n" : 5,
"nscannedObjects" : 5,
"nscanned" : 5,
"nscannedObjectsAllPlans" : 5,
"nscannedAllPlans" : 5,
"scanAndOrder" : false,
"indexOnly" : false,
"nYields" : 0,
"nChunkSkips" : 0,
"millis" : 0,
"indexBounds" : {
"localizacao.cidade" : [
[
"PIACATU",
"PIACATU"
]
]
},
"server" : "Cenerino-PC:27017"
}

Geospatial index
> db.estabelecimentos.ensureIndex({'localizacao.coordenadas' : '2dsphere'})
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_"
},
{
"v" : 1,
"key" : {
"localizacao.cidade" : 1
},
"name" : "localizacao.cidade_1"
},
{
"v" : 1,
"key" : {
"localizacao.coordenadas" : "2dsphere"
},
"name" : "localizacao.coordenadas_2dsphere"
}
]

Geospatial index
> lng = -46.80208830000004
> lat = -23.515985699999998
> distance = 30 / 6378.137
>
> db.estabelecimentos.find({ "localizacao.coordenadas" : { "$nearSphere" : [lng , lat] ,
"$maxDistance" : distance}}).limit(50)

http://mapa-servicos-publicos.herokuapp.com/

Aggregation Framework

Pipeline Operators: $project, $match, $limit, $skip, $unwind, $group, $sort, $geoNear

Aggregation Framework
> db.estabelecimentos.aggregate([{$match : {'localizacao.uf' : 'SP'}}, {$group : {_id :
'$localizacao.cidade', qtd : {$sum : 1}}}, {$sort : {qtd : -1}}, {$limit : 3}])
{
"result" : [
{
"_id" : "SAO PAULO",
"qtd" : 6930
},
{
"_id" : "CAMPINAS",
"qtd" : 881
},
{
"_id" : "GUARULHOS",
"qtd" : 666
}
],
"ok" : 1
}
>

Spring Data MongoDB
http://projects.spring.io/spring-data-mongodb/

Replica Sets
• A replica set is a group of mongod instances that host the
same data set
• Replication provides redundancy and increases data
availability
• The primary accepts all write operations from clients (only
one primary allowed)
• Replication can be used to increase read capacity
• Asynchronous replication
• Automatic failover

Sharding
Issues of scaling:
• High query rates can exhaust the CPU capacity of the server
• Larger data sets exceed the storage capacity of a single

machine
• Working set sizes larger than the system’s RAM stress the I/O
capacity of disk drives

Vertical scaling X Sharding

Sharding

Horizontally Scalable

• Sharding is the process of storing data across multiple machines
• Each shard is an independent database, and collectively, the shards make up a
single logical database

Range Based Sharding

• Supports more efficient range queries
• Results in an uneven distribution of data
• Monotonically increasing keys should be avoided

Hash Based Sharding

Ensures an even distribution of data at the expense of efficient range queries

https://education.mongodb.com/

db.audience.find({‘question’ : true})

Thanks everyone!
Hope you’ve enjoyed it.

Talk MongoDB - Amil

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Talk MongoDB - Amil (20)

Recently uploaded (20)

Talk MongoDB - Amil