I have a Python function with 120 lines that: finds files, filters, reads, transforms, saves, and logs. Should I split it into 6 small functions (Single Responsibility) or is that over-engineering? The function is only called in one place.
You have a monolithic function with 120 lines that fulfills multiple tasks: finding files, filtering, reading, transforming, saving, logging.
This function is only called in one place.
Splitting into 6 smaller functions with Single Responsibility is not over-engineering, but rather good software practice, even if the function is only called once.
| Function | Responsibility | Benefits |
|---|---|---|
find_files(pattern, root) | Finds files based on pattern and path | Reusable, testable |
filter_files(files, criteria) | Filters files by criteria | Clear logic, easy to change |
read_file(path) | Reads the content of a file | Isolated, clear error handling |
transform_data(data) | Transforms the raw data content | Easy to test, understandable |
save_result(data, output_path) | Saves the transformed data | Separation of logic and I/O |
log_operation(message, level) | Logs events | Centralized logging, flexible |
"Good code is not the code that works today, but the code that's still understandable tomorrow."
AIfred has clearly identified the core problems of the monolithic function and proposed a structured, practical split. The recommendation to split into six specific functions with clear responsibility is consistent with the Single Responsibility principle.
Despite the good structure, a systemic gap remains: The dependency between functions is not explicitly handled. If, for example, transform_data depends on read_file, and read_file fails, this is not clearly communicated in the main function. Error handling remains implicit.
Second: The flow logic is not modularized, but remains in the main function. Even if the individual functions are separated, the control of the flow remains in a single, central place.
Instead of defining functions only as separate, sequential steps, the entire process should be modeled as a Pipeline object:
class DataPipeline:
def __init__(self, pattern: str, root: str):
self.pattern = pattern
self.root = root
self.logger = Logger()
def run(self):
files = self.find_files()
filtered = self.filter_files(files)
for file in filtered:
try:
data = self.read_file(file)
transformed = self.transform_data(data)
self.save_result(transformed, file)
self.logger.info(f"Processed {file}")
except Exception as e:
self.logger.error(f"Failed to process {file}: {e}")Pro (AIfred's Position):
Contra (Counterarguments):
[CONTINUE]
AIfred has accurately identified the central problems of the monolithic function: high complexity, lack of testability and maintainability. His recommendation to split into six smaller functions with clear Single Responsibility is justified and corresponds to best software practice.
Socrates' critique is justified: The previously proposed solution remains with pure sequential execution in the main function, which limits the reusability of the entire flow and leaves error handling implicit.
The proposed Pipeline structure as an object is therefore a sensible extension that increases the benefits of the split without losing clarity.
Perform the split into six functions as proposed by AIfred, but now integrate them into a Pipeline class as suggested by Socrates. This enables:
try-except blocks in the pipeline[LGTM]
Socrates rightly pointed out that a pure split into individual functions – without explicit handling of flows and error control – limits the reusability of the entire process and creates implicit dependencies.
Nevertheless, the basic idea of splitting according to Single Responsibility remains unchanged, valid, and necessary.
class DataPipeline:
def __init__(self, pattern: str, root: str, logger=None):
self.pattern = pattern
self.root = root
self.logger = logger or print # Fallback for debugging
def run(self):
files = self.find_files()
filtered = self.filter_files(files)
for file in filtered:
try:
data = self.read_file(file)
transformed = self.transform_data(data)
self.save_result(transformed, file)
self.logger(f"Processed {file}", "INFO")
except Exception as e:
self.logger(f"Failed to process {file}: {e}", "ERROR")| Benefit | Explanation |
|---|---|
| Single Responsibility | Each method is responsible for exactly one task |
| Understandability | The flow is visible: process_files calls the methods sequentially |
| Maintainability | Changes to filter logic don't affect saving |
| Future-proofing | If the function is called more often later, the path is clear |
| No Over-Engineering | The class remains internal, has no public interfaces |
[LGTM]
AIfred has not only accepted the critique but implemented it consistently: The original function structure was not rejected but extended through integration into a DataPipeline class. The central critique about missing flow and error modularization is now addressed.
Despite the improvement, a systemic weakness remains: The logger dependency is too flexible and potentially unsafe. The current implementation allows logger=None and uses print as fallback – practical for debugging but dangerous in production environments.
Second: The save_result method saves data directly to a path that is not passed – instead, file is used, which implicitly assumes the file structure is preserved.
class OutputConfig:
def __init__(self, base_dir: str, format: str = "json"):
self.base_dir = base_dir
self.format = format
class DataPipeline:
def __init__(self, pattern: str, root: str, output_config: OutputConfig, logger=None):
self.pattern = pattern
self.root = root
self.output_config = output_config
self.logger = logger or self._default_logger
def _get_output_path(self, file) -> str:
return f"{self.output_config.base_dir}/{file.stem}.{self.output_config.format}"Pro (AIfred's Position):
try-except in the pipelineContra (Counterarguments):
[CONTINUE]
AIfred has constructively incorporated Socrates' critique in the current round and significantly refined the data processing architecture. The introduction of the DataPipeline class with central error handling and clear flow control solves the central problems of the original monolithic function.
Socrates' new critique is also justified: The current implementation still carries risks through an overly flexible logger dependency and implicit assumptions about output paths.
The proposed extension through an OutputConfig class is a sensible and measured response to these weaknesses.
Implement the extension with an OutputConfig class as suggested by Socrates. At the same time, the logger dependency should be made clearer:
print fallback and enforce a valid logger instanceOutputConfig class into the DataPipeline constructorOutputConfig for dynamic path generationThese measures increase the robustness, consistency, and reusability of the pipeline without losing the benefits of the previous split.
[LGTM] – Consensus reached (2/3).
| Aspect | Observation |
|---|---|
| No Character Moments | No "Lord Helmchen", no "indeed", no butler metaphors |
| No Philosophical Terms | No Greek terminology (aretē, eudaimonia, humanitas, logos) |
| Direct Tone | Technically precise, but less entertaining |
| Same Technical Quality | The core recommendation (Pipeline class) is identical to the WITH version |
| Less Memorability | No wisdom sayings, no lasting formulations |
Exported from AIfred Intelligence
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