Optimize algorithm structure by separating concerns and using appropriate data structures and language features. When implementing algorithms: 1. **Separate search from processing** to improve readability and maintainability. When you need to find and process an item, first locate the item, then apply operations to it rather than mixing these steps.
Optimize algorithm structure by separating concerns and using appropriate data structures and language features. When implementing algorithms:
Separate search from processing to improve readability and maintainability. When you need to find and process an item, first locate the item, then apply operations to it rather than mixing these steps.
Use specialized data structures that match your algorithm’s needs instead of forcing operations into generic containers. Custom structures can combine related data for clearer intent and fewer traversals.
Leverage built-in operations like set intersections, comprehensions, or functional approaches instead of writing manual loops when possible.
Example of separating search from processing using functional approach:
# Instead of this:
for condition in evidence_data.conditions:
if condition["condition_result"] == target_priority:
threshold_type = fetch_threshold_type(Condition(condition["type"]))
resolve_threshold = fetch_resolve_threshold(condition["comparison"], group_status)
# More processing...
break
else:
raise ValueError("No threshold type found for metric issues")
# Do this:
try:
condition = next(
cond for cond in evidence_data.conditions
if cond["condition_result"] == target_priority
)
threshold_type = fetch_threshold_type(Condition(condition["type"]))
resolve_threshold = fetch_resolve_threshold(condition["comparison"], group_status)
# More processing...
except StopIteration:
raise ValueError("No threshold type found for metric issues")
Similarly, for set operations, use the built-in methods rather than explicit iteration:
# Instead of checking each element individually:
workflow_triggers = set()
for dcg in groups_to_fire[group.id]:
if dcg.id in trigger_group_to_dcg_model[DataConditionHandler.Group.WORKFLOW_TRIGGER]:
workflow_triggers.add(dcg)
# Do this:
workflow_triggers = groups_to_fire[group.id].intersection(
trigger_group_to_dcg_model[DataConditionHandler.Group.WORKFLOW_TRIGGER]
)
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