Overview das principais features do MongoDB.

1. {
}
name : ‘Marcelo Cenerino’,
company: ‘Amil’,
date : ‘2013-10-30T08:30:00.000Z’

2. What is MongoDB?

3. 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.

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

5. Main characteristics
• Document based
• Schemaless
• Open source (on GitHub)
• High performance
• Horizontally scalable
• Full featured

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

7. MongoDB vs. RDBMS

8. RDBMS: data is structured as tables
id
nome
idade
genero
1
João
25
Masculino
2
Maria
30
Feminino
3
Pedro
40
Masculino
...
...
...

9. Document oriented???

10. 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

11. RDBMS
vs.
MongoDB
Table
Collection
Row
Document
Column
Field
Index
Index
Joins
Embedded doc.
FK
Reference
Partition
Shard

12. Transaction Model
MongoDB guarantees atomic updates
to data at the document level.

13. Relational schema design

14. 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

15. In a MongoDB based app we
start building our app
and let the schema evolve.

16. Mongo “schema” design
Article
User
name
email
title
date
text
author
Comment[]
author
date
text
Tag[]
value

17. Getting started with
MongoDB

19. > mongod
> mongo

20. Basic CRUD operations

21. Inserting a document
> user = {name : ‘marcelo’, age : 29, gender : ‘Male’}
> db.users.insert(user)
>
• No collection creation needed!

22. 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

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

24. 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

25. 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']}})

26. Removing a document
> db.users.remove({name : 'maria'})
> db.users.find().pretty()
{
"_id" : ObjectId("526f1cb3dac0a62cdc152a98"),
"age" : 29,
"name" : "marcelo",
"roles" : [
"admin",
"dev",
"operator"
]
}

27. Indexing

28. 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"

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

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

31. 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"
}

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

33. 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/

34. Aggregation Framework

35. Aggregation Framework
Pipeline Operators: $project, $match, $limit, $skip, $unwind, $group, $sort, $geoNear

36. 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
}
>

37. Mongo Driver for Java

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

39. Replica Sets

40. 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

41. Replica Sets

42. Replica Sets

43. Sharding

44. 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

45. 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

46. Sharded clusters

47. Range Based Sharding
• Supports more efficient range queries
• Results in an uneven distribution of data
• Monotonically increasing keys should be avoided

48. Hash Based Sharding
Ensures an even distribution of data at the expense of efficient range queries

49. https://education.mongodb.com/

50. Books

51. db.audience.find({‘question’ : true})

52. Thanks everyone!
Hope you’ve enjoyed it.