Presentation I gave at work about MongoDB.

1. MongoDB (for Java Developers)
Anthony Slabinck

2. Who am I?
• Internship at Provikmo
• 3 years 6 months
• Competitive cyclist

3. What is MongoDB?
• The leading NoSQL database (http://db-engines.com/en/)
• Open source
• Non-relational JSON document store
• BSON (Binary JSON)
• Dynamic schema
• Agile
• Scalable through replicaton and sharding
3

4. The leading NoSQL database
4
• LinkedIn Job Skills
• Google Search
• Indeed.com Trends

5. MongoDB relative to relational databases
5

6. Who uses MongoDB?
6

7. By use case
• Single View
• Internet of Things
• Mobile
• Real-Time Analytics
• Personalization
• Content Management
• Catalog
7

8. From relational databases to MongoDB
8
{
first_name: "Anthony",
surname: "Slabinck",
city: "Bruges",
location: [45.123,47.232],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000 },
{ mode: "Rolls Royce",
year: 1965,
value: 330000 } ]
}

9. MongoDB is full featured
9

10. MongoDB CRUD Operations
10
Documents

11. MongoDB CRUD Operations
11
Collections

12. MongoDB CRUD Operations
12
Read operations

13. MongoDB CRUD Operations
13
Read operations

14. MongoDB CRUD Operations
14
Write operations - insert

15. MongoDB CRUD Operations
15
Write operations - update

16. MongoDB CRUD Operations
16
Write operations - remove

17. Installation
• Download MongoDB from http://www.mongodb.org/downloads
• Download the Java Driver (maven)
• mongod
• Daemon process
• mongo
• Interactive JavaScript shell interface
• Robomongo
• Cross-platform management tool
17

18. Getting started with MongoDB
18
Demo

19. Data Models
• Flexible schema
• Collections do not enforce document structure
• Consider how applications will use your database
• No foreign keys, no joins
• Relationships between data
• Embedded documents
• References
• Documents require a unique _id field that acts as a primary key
19

20. Data Models
• Denormalized
• Better read performance
• Single atomic write operation
• Document growth
• Dot notation
20
Embedded Data Models

21. Data Model
• One-to-One Relationship
21
Embedded Data Models
{
_id: "infasla",
name: "Anthony Slabinck",
address: {
street: "123 Fake Street",
city: "Faketon",
state: "MA",
zip: "12345"
}
}

22. Data Model
• One-to-Many Relationship
22
Embedded Data Models
{
_id: "infasla",
name: "Anthony Slabinck",
addresses: [
{ street: "123 Fake Street",
city: "Faketon",
state: "MA",
zip: "12345" },
{ street: "1 Other Street",
city: "Boston",
state: "MA",
zip: "12345"
}
]
}

23. Data Model
• Normalized
• Duplication of data
• Complex many-to-many
relationships
• Follow-up queries
23
References

24. Data Model
• One-to-Many Relationship
{ _id: "oreilly",
name: "O'Reilly Media",
founded: 1980,
location: "CA"
}
{ _id: 123456789,
title: "MongoDB: The Definitive Guide",
author: [ "Kristina Chodorow", "Mike Dirolf" ],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
publisher_id: "oreilly"
}
{ _id: 234567890,
title: "50 Tips and Tricks for MongoDB Developer",
author: "Kristina Chodorow",
published_date: ISODate("2011-05-06"),
pages: 68,
language: "English",
publisher_id: "oreilly"
}
24
References

25. Model Tree Structures
• Parent references
• Child references
• Array of Ancestors
• Materialized Paths
db.categories.insert( { _id: "MongoDB", parent: "Databases" } )
db.categories.insert( { _id: "dbm", parent: "Databases" } )
db.categories.insert( { _id: "Databases", parent: "Programming" } )
db.categories.insert( { _id: "Languages", parent: "Programming" } )
db.categories.insert( { _id: "Programming", parent: "Books" } )
db.categories.insert( { _id: "Books", parent: null } )
25

26. GridFS
• BSON-document size limit of 16MB
• Divides a file into parts, or chunks and stores each of those chunks as
a separate document
• Two collections
• File chunks
• File metadata
• Reassemble chunks as needed
26

27. Capped Collections
• Fixed-size collections
• Insert and retrieve documents based on insertion order
• Automatically removes the oldest document
• Ideal for logging
27

28. Aggregation
• Operations that process data records and return computed results
• Simplifies application code
• Limits resource requirements
• Aggregation modalities
• Aggregation pipelines
• Map-Reduce
• Single purpose aggregation operations
28

29. Aggregation
• Stages
• Preferred method
29
Aggregation pipelines

30. Aggregation
• Two phases
• JavaScript functions
• Less efficient and more
complex than the aggregation
pipeline
30
Map-Reduce

31. Aggregation
• Simple
• Count
• Distinct
• Grouping
31
Single purpose aggregation operations

32. Indexes
• Efficient execution of queries
• Data structure
• Stores the value of a specific
field or set of fields, ordered by
value the field
• Create indexes that support
your common and user-facing
queries
32

33. Indexes
• Default _id
• Single Field
• Compound Index
• Multikey Index
• Geospatial Index
• Text Indexes
• Hashed Indexes
33
Types

34. Indexes
• Unique Indexes
• Sparse Indexes
• TTL Indexes
34
Properties

35. Indexes
• db.people.ensureIndex( { zipcode: 1 } )
• db.people.ensureIndex( { zipcode: 1 }, { background: true } )
• db.people.ensureIndex( { zipcode: 1 }, { background: true, sparse: true } )
• db.accounts.ensureIndex( { username: 1 }, { unique: true, dropDups: true } )
35
Creation

36. Replication
• What?
• Synchronizing data across multiple servers
• Purpose?
• Provides redundancy and increases data availability
36

37. Replication
• A group of mongod instances
that host the same data set
• Primary receives all write
operations
• Primary logs all changes in its
oplog
• Secondaries apply operations
from the primary
37
Replica set

38. Replication
• Arbiter
• Does not maintain a data set
• Only exits to vote
38
Replica set

39. Replication
39
Replica set
• Automatic failover

40. Replication
• Additional features:
• Read preference
• Priority
• Hidden members
• Delayed members
40
Replica set

41. Sharding
• What?
• Storing data across multiple machines
• When?
• High query rates 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
41

42. Sharding
• Adds more CPU and storage
42
Vertical scaling – scale up
Scale
Price

43. Sharding
• Distributes the data
43
Horizontal scaling – scale outPrice
Scale

44. Sharding
• Shards store the data
• Query Routers interface with
client applications and direct
operations
• Config servers store the
cluster’s metadata
44
Sharded cluster

45. Sharding
• Collection level
• Shard key
• Indexed field or an indexed
compound field that exists in
every document
• Chunks
• Range based partitioning
• Hash based partitioning
• Automatic balancing
45
Data partitioning

46. MongoDB Architecture
46

47. MongoDB at scale
• Cluster scale
• Distributing across 100+ nodes in multiple data centers
• Performance scale
• 100K+ database reads and writes per second while maintaining strict SLAs
• Data scale
• Storing 1B+ documents in the database
47
Metrics

48. Lower TCO
• Dev/Ops savings
• Ease of use
• Fast, iterative development
• Hardware savings
• Commodity hardware
• Scale out
• Software/Support savings
• No upfront licence
48
Relational database

49. POJO Mappers
• Morphia
• Spring Data MongoDB
• Hibernate OGM
49

50. Resources
• http://docs.mongodb.org/manual/
• https://university.mongodb.com/
• M101J: MongoDB for Java Developers
• M102: MongoDB for DBAs
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51. Building an App with MongoDB
51
Demo

52. Questions?
52