ref(workflow_engine): Refactor the process_data_condition_group method
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… composable. moving the processing methods and separating from pure logic evaluation
…is will allow us to reuse the condition processor and allow other areas to define the data coming in. this is especially useful with delayed processing
…ta_conditions -- seems like a really good break down
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Scope: Backend
…sults to match pattern where processors are expected to be used more externally
cathteng
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Feb 7, 2025
| ) -> DataConditionGroupResult: | ||
| # TODO - @saponifi3d - refactor the DataConditionGroup.Type (logic_type) so we can use it as a mypy type as well. | ||
| def evaluate_data_conditions( | ||
| conditions_to_evaluate: list[tuple[DataCondition, T]], |
cathteng
approved these changes
Feb 7, 2025
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| return evaluate_condition_group(group, value) | ||
| return (logic_result, condition_results), remaining_conditions |
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what's the reason for returning a tuple here? because ProcessedDataConditionResult is already a type and we're just returning something in addition to that (remaining_conditions)?
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yep! i found that super helpful in all the places we're using the processor method, i could drop results as needed for different areas and it would allow us to easily add features like enqueuing slow conditions for detector triggers in the future.
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Description
Refactor
process_data_condition_groupto be the method that handles fast / slow splitting and gathering conditions.Introduces a new method,
evaluate_data_conditionsthat takes a list of tuples; the data condition and the value to evaluate against. This method will iterate through each of the conditions + values, and then apply logic gates accordingly. This method will be reused indelayed_processingwhere each condition has different query results to evaluate for the larger data condition group.