MongoDB (for Java Developers)

Anthony Slabinck

Who am I?

• Internship at Provikmo

• 3 years 6 months

• Competitive cyclist

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

The leading NoSQL database

4

• LinkedIn Job Skills

• Google Search

• Indeed.com Trends

MongoDB relative to relational databases

5

Who uses MongoDB?

6

By use case

• Single View

• Internet of Things

• Mobile

• Real-Time Analytics

• Personalization

• Content Management

• Catalog

7

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 } ]

}

MongoDB is full featured

9

MongoDB CRUD Operations

10

Documents

MongoDB CRUD Operations

11

Collections

MongoDB CRUD Operations

12

Read operations

MongoDB CRUD Operations

13

Read operations

MongoDB CRUD Operations

14

Write operations - insert

MongoDB CRUD Operations

15

Write operations - update

MongoDB CRUD Operations

16

Write operations - remove

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

Getting started with MongoDB

18

Demo

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

Data Models

• Denormalized

• Better read performance

• Single atomic write operation

• Document growth

• Dot notation

20

Embedded Data Models

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"

}

}

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"

}

]

}

Data Model

• Normalized

• Duplication of data

• Complex many-to-many

relationships

• Follow-up queries

23

References

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

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 } )

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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

Capped Collections

• Fixed-size collections

• Insert and retrieve documents based on insertion order

• Automatically removes the oldest document

• Ideal for logging

27

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

Aggregation

• Stages

• Preferred method

29

Aggregation pipelines

Aggregation

• Two phases

• JavaScript functions

• Less efficient and more

complex than the aggregation

pipeline

30

Map-Reduce

Aggregation

• Simple

• Count

• Distinct

• Grouping

31

Single purpose aggregation operations

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

Indexes

• Default _id

• Single Field

• Compound Index

• Multikey Index

• Geospatial Index

• Text Indexes

• Hashed Indexes

33

Types

Indexes

• Unique Indexes

• Sparse Indexes

• TTL Indexes

34

Properties

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

Replication

• What?

• Synchronizing data across multiple servers

• Purpose?

• Provides redundancy and increases data availability

36

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

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Replica set

Replication

• Arbiter

• Does not maintain a data set

• Only exits to vote

38

Replica set

Replication

39

Replica set

• Automatic failover

Replication

• Additional features:

• Read preference

• Priority

• Hidden members

• Delayed members

40

Replica set

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

Sharding

• Adds more CPU and storage

42

Vertical scaling – scale up

Scale

Pri

ce

Sharding

• Distributes the data

43

Horizontal scaling – scale outP

ric

e

Scale

Sharding

• Shards store the data

• Query Routers interface with

client applications and direct

operations

• Config servers store the

cluster’s metadata

44

Sharded cluster

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

MongoDB Architecture

46

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

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

POJO Mappers

• Morphia

• Spring Data MongoDB

• Hibernate OGM

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Resources

• http://docs.mongodb.org/manual/

• https://university.mongodb.com/

• M101J: MongoDB for Java Developers

• M102: MongoDB for DBAs

50

Building an App with MongoDB

51

Demo

Questions?

52