The document outlines a battle plan for creating scalable and resilient applications with MongoDB. It will cover introductory topics on MongoDB including NoSQL, JSON/BSON, CRUD operations, indexes, replica sets, read preference, write concern, and read concern. It will then demonstrate setting up a MongoDB-based IoT project, migrating it to the cloud using Atlas, and implementing features like change streams and alerts. Advanced topics like sharded clusters, MongoDB 4.0 transactions, and Stitch will also be covered.

2. Creating scalable, resilient
applications with MongoDB

4. 4
Battle Plan
Intro
• Presentation MongoDB
– NoSQL
– BigData
– JSON/BSON
– CRUD
– Indexes
– Replica Set
– Read Preference
– Write Concern
– Read Concern
• Presentation Mongo-IOT Project
• Live coding mongo-iot server on localhost

5. 5
Battle Plan
Cloud
• Presentation MongoDB Atlas
• Migrate to Atlas
• Presentation Sharded Cluster
• Migrate mongo-iot to AWS with VPC
• Start generator
• Real Time graphs and metrics in Atlas

6. 6
Battle Plan - MongoDB 3.6
• MongoDB Compass
• Aggregation Framework in Compass
• Demo 3.6
– Retryable Writes
– Causal Consistency
– Change Streams
• Live coding analyser with change streams
• Deploy analyser on AWS
• Live coding alert-manager
• Deploy alert-manager on AWS and Slack

7. 7
Battle Plan
More
• MongoDB Stitch
• MongoDB 4.0 : Multi-document ACID Transactions.

8. 8
Document Model
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location: {
type : ‘Point’,
coordinates : [45.123,47.232]
},
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
MongoDB
RDBMS

9. 9
Developer Agility - Documents
{
first_name: ‘Paul’,
surname: ‘Miller’,
ID: 125678,
city: ‘London’,
location: {
type : ‘Point’,
coordinates : [45.123,47.232]
},
Profession: [‘banking’, ‘finance’, ‘trader’],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
Fields can contain an array
of sub-documents
Typed field values
Fields can contain
arrays
String
Number
Geo-Location
Fields

10. 10
Developer Agility – Flexible Schema
{
product_name: ‘Acme Paint’,
sku: "123-567-845",
color: [‘Red’, ‘Green’],
size_oz: [8, 32],
finish: [‘satin’, ‘eggshell’]
}
{
product_name: ‘T-shirt’,
sku : "123-589-234",
size: [‘S’, ‘M’, ‘L’, ‘XL’],
color: [‘Heather Gray’ … ],
material: ‘100% cotton’,
wash: ‘cold’,
dry: ‘tumble dry low’
}
{
product_name: ‘Mountain Bike’,
sku : "143-534-678",
brake_style: ‘mechanical disc’,
color: ‘grey’,
frame_material: ‘aluminum’,
no_speeds: 21,
package_height: ‘7.5x32.9x55’,
weight_lbs: 44.05,
suspension_type: ‘dual’,
wheel_size_in: 26
}

11. 11
CRUD & Indexes

12. 12
Developer Agility – Use Your Favourites
Page 12
Morphia
MEAN Stack

13. 13
Replica Set – Durability
• Replica set – 2 to 50 copies
• Replica sets make up a self-healing ‘shard’
• Data center awareness
• Replica sets address:
• High availability
• Data durability, consistency
• Maintenance (e.g., HW swaps)
• Disaster Recovery
Application
Driver
Primary
Secondary
Secondary
Replication

14. 14
High Availability and Data Durability – Replica Sets
SecondarySecondary
Primary

15. 15
Replica Set Creation
SecondarySecondary
Primary
Heartbeat

16. 16
Replica Set Node Failure
SecondarySecondary
Primary
No Heartbeat

17. 17
Replica Set Recovery
SecondarySecondary
Heartbeat
And Election

18. 18
New Replica Set – 2 Nodes
SecondaryPrimary
Heartbeat
And New Primary

19. 19
Replica Set Repair
SecondaryPrimary
Secondary
Rejoin and resync

20. 20
Replica Set Stable
SecondaryPrimary
Secondary
Heartbeat

21. 21
Strong Consistency
SecondarySecondary
Primary
Client Application
Client Driver
Write
Read

22. 22
Eventual Consistency
SecondarySecondary
Primary
Client Application
Client Driver
Write
Read
Read

23. Developing with MongoDB
Application
Driver
mongod
/data

24. Replica Set with MongoDB
Application
Driver
Primary
/data
Secondary
/data
Secondary
/data

25. 25
Replica Set
Read Preference
Write Concern
Read Concern

26. 26
Mongo-IOT

27. MongoDB Atlas

28. 28
Operational Agility – Atlas - Database as a
Service
Self-service, elastic,
and automated
Secure by defaultGlobal and highly
available
Continuous
backups
Real-time monitoring and
optimization
Cloud agnostic

29. Aggregation Framework

30. 30
Developer Agility – The Aggregation Framework

31. 31
Developer Agility – Compass – MongoDB GUI
Page 31
Debug & Optimize
Visualize & Explore
Insert, Modify, & Delete

32. Sharded Cluster

33. 33
Replica Set Bottlenecks
Application
Driver
Primary
/data
Secondary
/data
Secondary
/data
RAM Limits on
single server
CPU Limits on
single server
Network
Bandwidth
Disk I/O

34. 34
Reasons to Shard
Performance
Data locality
Recovery Time
Objective (RTO)

35. 35
Construction
• Build Cluster
• Identify shard key
• Sharding happens on individual collections
• To shard a collection:
sh.shardcollections( "MUGS.members",{ "members.member_id" : 1 } )

36. 36
What Is Sharding?
Application
mongos mongos mongos
Driver

37. 37
But There is More
Application
mongos mongos mongos
Driver
Config Server

38. 38
Chunk is a Section on the Range

39. 39
Chunk Splitting

40. 40
How Data Is Distributed
• Initially 1 chunk
• Default max chunk size: 64mb
• MongoDB automatically splits & migrates chunks when max
reached
Shard 1 Shard 2

41. 41
Balancing The Cluster
Shard 1 Shard 2
MongosMongosMongos
Primary
Config
Server

42. 42
Moving The Chunk
Shard 1 Shard 2
Mongos
Primary
Config
Server

43. 43
Commiting The Migration
Shard 1 Shard 2
Mongos
Primary
Config
Server

44. 44
Clean Up
Shard 1 Shard 2
Mongos
Primary
Config
Server

45. 45
Demo!

46. 46
Conclusion
@MBeugnet MaBeuLux88

47. 47
Community is awesome!
https://mongo-db.slack.com
https://mongodb.influitive.com/

48. 48
MongoDB Events
MongoDB .local Paris