Here is the situation. There is a top-level module (see designs below) containing code, that as the name suggests, manages an ETL pipeline. A directory is created called etl_helpers that organizes several modules responsible for making up the pipeline. The discussion concerns the Python language, which supports OOP as well as Structural/Functional approaches to programming.
I am interested in opinions on which design adheres best to standard architectural practices and the SOLID principles. I understand that this is one of those topics where people may have strong opinions one way or the other. I am interested in those opinions. Allow me to give my thoughts. First, I don't think there would be much difference if I was using OOP for the functionality, or using a structural paradigm. A structural paradigm in my opinion, along the lines of Rich Hickey's comments on simple versus complex, would be a simpler implementation. In this case there is no reason to create a construct with state. So let's assume the code is structural and not OOP. I would go with Design I. Succinctly stated, Design I supports readability and maintainability at least as well, if not better than the other designs. The goal of the SOLID principles are the creation of mid-level software structures that (Software Architecture: SA Martin). I think Design I best adheres to these principles of: ---- Tolerate change, ---- Are easy to understand, and ---- Are the basis of components that can be used in many software systems. I could point to the Single Responsibility Principle which is defined as (SA Martin): a module should be responsible to one, and only one, actor. It should satisfy the Liskov Substitution Principle as well. Further, each module in the etl_helpers directory is at the same level of abstraction. I could also mention that as Dijkstra stressed, at every level, from the smallest function to the largest component, software is like a science and, therefore, is driven by falsifiability. Software architects strive to define modules, components, and services that are easily falsifiable (testable). To do so, they employ restrictive disciplines similar to structured programming, albeit at a much higher level (SA Martin). One can point to multiple reasons why Design I might be preferred, but what are the compelling reasons, if there are any, that would suggest another design was superior. Finally, let me reference an interesting research paper I read recently that seems to support the other designs as anti-patterns: Architecture_Anti-patterns_Automatically.pdf ---- (https://www.cs.drexel.edu/~yfcai/papers/2019/tse2019.pdf) SEVERAL DESIGNS FOR COMPARISON DESIGN I: ---- manage_the_etl_pipeline.py ---- etl_helpers ---- extract.py ---- transform.py ---- load.py Of course one could also DESIGN II: ---- manage_the_etl_pipeline.py ---- etl_helpers ---- extract_transform_load.py or probably even: DESIGN III: ---- manage_the_etl_pipeline.py ---- extract_transform_load.py -- https://mail.python.org/mailman/listinfo/python-list
