Lazy loading for processing large data sets


This is part of a series of articles where we describe the way the Meniscus Analytics Platform (MAP) works. Theses articles jump into the features that make MAP different to other analytics applications by providing an Integrated Analytics Stack delivering real time analytics.

This article investigate the benefits of lazy loading of data and why this is important in MAP

What is lazy loading of data?

Quite simply, it means only loading the part of the data that is required to deliver the information requested. In terms of how MAP works then this principle is used to limit the data input and output from the the underlying MongoDB database into MAP. Whilst this may sound like quite a simple and obvious principle to apply it isn’t always used. Many developers will know the principle when developing dashboard and user interfaces but it is more important when considering the back end database operation.

Lazy loading is a design pattern commonly used in computer programming to defer initialization of an object until the point at which it is needed. It can contribute to efficiency in the program’s operation if properly and appropriately used. The opposite of lazy loading is eager loading. This makes it ideal in use cases where network content is accessed and initialization times are to be kept at a minimum, such as in the case of web pages.


Why is lazy loading relevant in MAP?

MAP ingests and processes very large volumes of near real time data, specifically data associated with weather. More importantly, MAP holds historic data so that we can deliver historic analytics as used in our MAP Rain solution.

This means data IO is a key factor in delivering the lighting fast calculation speeds that MAP delivers. So, anything that can improve these IO times is of huge importance to MAP. Lazy loading reduces data volumes extracted and then written back to the database and so improves data IO times.

About MAP

MAP is an Integrated Analytics Stack providing a framework for users to create and deploy calculations at scale using any source of raw data. MAP is based on IOT principles and uses Items as the underlying building blocks to store either RAW or CALCulated data. So, users create an Entity Template or Thing using these Items and then replicate this template hundreds of thousands of times using an ItemFactory.

For more information on MAP then click here