MongoDB and the Internet of Things

Massimo Brignoli
Senior Solutions Architect
MongoDB Inc.
massimo@mongodb.com
@massimobrignoli
MongoDB and
The Internet of Things

Who Am I?
• Solutions Architect/Evangelist in MongoDB Inc.
• 25 years of experience in databases and software
development
• Former MySQL and MariaDB employee
• Previous life: web, web, web

Document Data Model
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, … }
]
}

Document Model Benefits
• Agility and flexibility
– Data models can evolve easily
– Companies can adapt to changes quickly
• Intuitive, natural data representation
– Developers are more productive
– Many types of applications are a good fit
• Reduces the need for joins, disk seeks
– Programming is more simple
– Performance can be delivered at scale

Developers are more productive

The Problem
• If you're thinking about designing an ideal data
structure for your Internet of Things application, then
here's what you should do:
don't do it.

The Problem
• The Internet of Things requires a huge deal of
flexibility.
Why?
• Because there are billions of heterogeneus objects
that will begin interacting with each other in ways we
can't predict.
• The structured and rigid tables offered by traditional
databases won't help us because they require a pre-
defined set of properties and tables, which again,
we can't predict.

The Problem
• Let's say we want to measure water levels in a large
number of wells. A simplified data architecture for
this application would look like this:

The Problem
• This looks just fine and should work perfectly using
a relational database. But then, 2 years after the
system has been up and running, someone has an
idea:
"Hey, now that we bought these new Internet-enabled
diesel generators to power the water pumps, let's see
their live data!”

The Problem
• To make this change, we would have to add a new
table called "Power Plants" and a new column to the
table "Wells”:

The Solution
• A great way of handling IoT data is the document-
oriented approach
• Instead of fixed tables, columns, and rows, you have
documents describing each object.

MongoDB
Document
Database
Open-
Source
General
Purpose

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, … }
]
}

Modeling time series data
in MongoDB

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

Modeling time series data
• Document per event
• Document per minute (average)
• Document per minute (second)
• Document per hour

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

Document per minute (average)
• Pre-aggregate to compute average per minute
more easily
• Update-driven workload
• Resolution at the minute level
{
temperature_num: 18,
temperature_sum: 357
}

Document per minute (by second)
• Store per-second data at the minute level
• Pre-allocate structure to avoid document moves
{
temperature: { 0: 18, 1: 18, …, 58: 21, 59: 21 }
}

Document per hour (by second)
• Store per-second data at the hourly level
• Updating last second requires 3599 steps
{
temperature: { 0: 18, 1: 18, …, 3598: 20, 3599: 20 }
}

Document per hour (by second)
• Store per-second data at the hourly level with nesting
• Updating last second requires 59 + 59 steps
{
temperature: {
0: { 0: 18, …, 59: 18 },
…,
59: { 0: 21, …, 59: 20 }
}
}

Scaling Out
First Edition (1771)
3 Volumes
Fifteenth Edition (2010)
32 Volumes

Shards and Shard Keys
Shard
Shard key
range

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

MongoDB and the Internet of Things

MongoDB and the Internet of Things

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