Time-Series Examples

Time-series data, characterized by its chronological sequence and high generation frequency, differs from conventional data. This time-series data is crucial for forecasting and trend analysis in many financial services and Internet of Things (IoT) applications. MongoDB provides specialized features tailored for processing vast amounts of data and distinct query patterns, such as windowing, associated with time-series use cases. In this chapter, you will look at examples of how you can use aggregation pipelines to extract insight from time-series data.

In this chapter, you will learn about the following:

• Analyzing rolling windows of data
• Computing the power consumption of devices
• Tracking behavior changes of devices

IoT power consumption

In industrial IT, organizations must accurately compute the power consumption of devices to optimize energy use, reduce costs, and ensure sustainable operations. They need specialized database features to manage the vast and dynamic data generated by these devices and then offer real-time analytics for predictive modeling to maintain efficient energy management and operational continuity. In this example, you will explore how to build an aggregation pipeline to calculate the power consumption of devices.

Note

For this example, you require MongoDB version 5.0 or above. This is because you'll be using time-series collections, the $setWindowFields stage, and the $integral operator introduced in version 5.0.

Scenario

You are monitoring various air-conditioning units running in two buildings on an industrial campus. Every 30 minutes, a device in each unit sends the unit's current power consumption reading back to base, which is persisted in a...

State change boundaries

Continuing on the industrial IT theme, an organization managing a large estate of devices needs to identify operating trends and rhythms across its devices to control costs and enable proactive measures, such as predictive maintenance. In this example, you will discover how to build an aggregation pipeline to identify patterns for when devices are in use or idle.

Note

For this example, you require MongoDB version 5.0 or above. This is because you will use time-series collections, the $setWindowFields stage, and the $shift operator introduced in version 5.0.

Scenario

You are monitoring various industrial devices (e.g., heaters and fans) contained in the business locations of your clients. You want to understand the typical patterns of when these devices are on and off to help you optimize for sustainability by reducing energy costs and their carbon footprint. The source database contains periodic readings for every device, capturing whether each is...

Summary

In this chapter, you explored examples of using time-series windowing aggregations to compute summaries and trends for industrial IoT devices.

In the next chapter, you will learn how to process and manipulate array fields contained in documents without having to unwind and regroup the content of each array.