The value of the fast growing class of NoSQL databases is the ability to handle high velocity and volumes of data while enabling greater agility with dynamic schemas. MongoDB gives you those benefits while also providing a rich querying capability and a document model for developer productivity. Arthur Viegers will outline the reasons for MongoDB's popularity in IoT applications and how you can leverage the core concepts of NoSQL to build robust and highly scalable IoT applications.

1. MongoDB and
The Internet of Things
Arthur Viegers
Senior Solutions Architect, MongoDB
MongoDB IoT City Tour 2014

2. MongoDB
*
Document
Database
Open-
Source
General
Purpose

3. Documents Are Core
Relational MongoDB
*
{
first_name: "Paul",
surname: "Miller",
city: "London",
location: [45.123,47.232],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000, … },
{ model: "Rolls Royce",
year: 1965,
value: 330000, … }
]
}

4. Documents Are Core
*
Relational MongoDB

5. MongoDB Scales Horizontally
*

6. MongoDB Replication
*

7. MongoDB Architecture
*

8. Modelling time series data
in MongoDB

9. Rexroth NEXO Cordless Nutrunner
*

10. Time series schema design goal
*
• Store event data
• Support Analytical Queries
• Find best compromise of:
- Memory utilization
- Write performance
- Read/Analytical Query Performance
• Accomplish with realistic amount of hardware

11. Modelling time series data
*
• Document per event
• Document per minute (average)
• Document per minute (second)
• Document per hour

12. Document per event
*
{
deviceId: "Test123",
timestamp: ISODate("2014-07-03T22:07:38.000Z"),
temperature: 21
}
• Relational-centric approach
• Insert-driven workload

13. Document per minute (average)
*
{
deviceId: "Test123",
timestamp: ISODate("2014-07-03T22:07:00.000Z"),
temperature_num: 18,
temperature_sum: 357
}
• Pre-aggregate to compute average per minute
more easily
• Update-driven workload
• Resolution at the minute level

14. Document per minute (by second)
*
{
deviceId: "Test123",
timestamp: ISODate("2014-07-03T22:07:00.000Z"),
temperature: { 0: 18, 1: 18, …, 58: 21, 59: 21 }
}
• Store per-second data at the minute level
• Update-driven workload
• Pre-allocate structure to avoid document moves

15. Document per hour (by second)
{
*
deviceId: "Test123",
timestamp: ISODate("2014-07-03T22:00:00.000Z"),
temperature: { 0: 18, 1: 18, …, 3598: 20, 3599: 20 }
}
• Store per-second data at the hourly level
• Update-driven workload
• Pre-allocate structure to avoid document moves
• Updating last second requires 3599 steps

16. Document per hour (by second)
{
*
deviceId: "Test123",
timestamp: ISODate("2014-07-03T22:00:00.000Z"),
temperature: {
0: { 0: 18, …, 59: 18 },
…,
59: { 0: 21, …, 59: 20 }
}
}
• Store per-second data at the hourly level with nesting
• Update-driven workload
• Pre-allocate structure to avoid document moves
• Updating last second requires 59 + 59 steps

17. Rexroth NEXO schema
*
{
assetId: "NEXO 109",
hour: ISODate("2014-07-03T22:00:00.000Z"),
status: "Online",
type: "Nutrunner",
serialNo : "100-210-ABC",
ip: "127.0.0.1",
positions: {
0: {
0: { x: "10", y:"40", zone: "itc-1", accuracy: "20” },
…,
59: { x: "15", y: "30", zone: "itc-1", accuracy: "25” }
},
…,
59: {
0: { x: "22", y: "27", zone: "itc-1", accuracy: "22” },
…,
59: { x: "18", y: "23", zone: "itc-1", accuracy: "24” }
}
}
}

18. Summary

19. Why is MongoDB a good fit for IoT?
*
• IoT processes are real-time
• Relational technologies can simply not compete
on cost, performance, scalability, and
manageability
• IoT data can come in any format, structured or
unstructured, ranging from text and numbers to
audio, picture and video
• Time series data is a natural fit
• IoT applications often require geographically
distributed systems

20. Thank you!