MongoDB IoT City Tour EINDHOVEN: Managing the Database Complexity
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The document discusses the use of MongoDB in Internet of Things (IoT) applications, highlighting its advantages such as horizontal scalability, cost-effectiveness, and capability to handle various data formats. It emphasizes the modeling of time series data with different document schema designs to optimize memory and performance. Additionally, it illustrates how MongoDB supports real-time IoT processes that require distributed systems.
![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, … }
]
}](https://image.slidesharecdn.com/05arthur-viegersmongodb01-140917104131-phpapp02/85/MongoDB-IoT-City-Tour-EINDHOVEN-Managing-the-Database-Complexity-3-320.jpg)
MongoDB IoT City Tour EINDHOVEN: Managing the Database Complexity
- 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
- 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!