Batch processing is one of the earliest ways of data processing, utilized by Herman Hollerith’s Tabulating Machine in 1890. Batch processing was developed to take advantage of scarce computing resources: it avoids idling these expensive resources by queueing instructions to process data without manual user intervention, and can shift workload to times when resources are less scarce¹.

Today, we can leverage modern architectural patterns like worker systems, message queues and the cloud to level-up these advantages and simplify our code. Let’s look at an example of queueing and workers using a calorie-dense metaphor: gelato.

Using our favorite local gelato shop as an example, we explore how architectural concepts like queueing and workers can affect a given task. We chose gelato because:

• Each order takes time to set up. You must examine the menu and display, choose and order.
• Each order takes time to process. The time spent preparing an order can vary based on the order size and complexity, just as job size can vary in a worker system.
• Adding additional workers helps. The queue of customers will be processed faster, in the same way that adding more workers to a particular batch processing job will make the queue shrink faster.
• Like your infrastructure, gelato must be kept cold, and is delicious when consumed².

Check it out!

¹ https://en.wikipedia.org/wiki/Batch_processing

²This is not true. Iron.io assumes no responsibility if you eat your infrastructure.