Task distribution patterns
In Chapter 11, Advanced Recipes, you learned how to delegate costly tasks to multiple local processes. Even though this was an effective approach, it’s inherently limited to the capacity of a single machine. Sooner or later, you’ll hit a hard scalability ceiling, whether due to CPU saturation, memory constraints, or I/O bottlenecks. In this section, we’ll explore how to apply a similar pattern in a distributed architecture by using remote workers that can run anywhere in the network. This opens the door to a much more scalable solution, where work can be distributed across dozens, hundreds, or even thousands of machines running independently and in parallel.
As part of his cloud consulting work at fourTheorem (nodejsdp.link/4t), one of the authors of this book (Luciano) contributed to a large-scale modernization project in the financial sector. By applying many of the techniques covered in this chapter (including asynchronous...
