Update fix/clean up #4192
Update fix/clean up #4192
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📝 WalkthroughWalkthroughThe pull request introduces modifications to data extraction and processing workflows across multiple files. The key changes involve enhancing the Changes
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Actionable comments posted: 0
🧹 Nitpick comments (1)
src/workflows/airqo_etl_utils/airqo_utils.py (1)
95-95: Clean implementation of daily frequency supportThe addition of
Frequency.DAILYmapping follows the existing pattern and enables the centralized data extraction approach. This change effectively supports the refactoring seen in the DAGs.Consider adding a docstring update to reflect the new frequency support.
""" Args: frequency(Frequency): The frequency of the data to be extracted, e.g., RAW or HOURLY. + frequency(Frequency): The frequency of the data to be extracted, e.g., RAW, HOURLY, or DAILY. """
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📒 Files selected for processing (4)
src/workflows/airqo_etl_utils/airqo_utils.py(1 hunks)src/workflows/airqo_etl_utils/daily_data_utils.py(0 hunks)src/workflows/dags/daily_measurements.py(4 hunks)src/workflows/dags/weather_measurements.py(3 hunks)
💤 Files with no reviewable changes (1)
- src/workflows/airqo_etl_utils/daily_data_utils.py
⏰ Context from checks skipped due to timeout of 90000ms (2)
- GitHub Check: Analyze (python)
- GitHub Check: Analyze (javascript)
🔇 Additional comments (7)
src/workflows/dags/daily_measurements.py (5)
29-29: Increased retry delay improves resilienceThe retry delay increase from 5 to 10 minutes provides more time for transient issues to resolve, potentially reducing cascading failures.
108-111: Well-maintained consistency in data extractionThe implementation maintains consistency with the realtime DAG by using the same extraction pattern and frequency. This uniformity in approach is commendable.
4-5: Consider consolidating utility importsBoth
DailyDataUtilsandAirQoDataUtilsare imported, but based on the changes, we're moving towards usingAirQoDataUtilsfor data extraction. Consider documenting which functionalities fromDailyDataUtilsare still needed.Let's verify the remaining usage of DailyDataUtils:
71-74: Confirm frequency choice for realtime dataUsing
Frequency.HOURLYfor realtime measurements suggests that data is first collected at hourly granularity before being aggregated to daily. This approach is logical but should be documented.Let's verify the data flow:
✅ Verification successful
Frequency choice is correctly implemented
The use of
Frequency.HOURLYis the intended design, confirmed by:
- Dedicated BigQuery tables for different frequencies (raw, hourly, daily)
- Documentation explicitly specifying hourly frequency for realtime data
- Consistent pattern across multiple data processing workflows
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for data aggregation after hourly collection rg "aggregate.*hourly" -A 5Length of output: 29
Script:
#!/bin/bash # Search for Frequency enum usage and implementation ast-grep --pattern 'class Frequency' rg "Frequency\." -A 2 # Look for realtime data processing rg "realtime.*data" -A 3 rg "extract_data_from_bigquery" -A 3 # Examine AirQoDataUtils implementation ast-grep --pattern 'class AirQoDataUtils'Length of output: 16699
37-40: Verify data consistency with new extraction methodThe switch to
AirQoDataUtils.extract_data_from_bigquerywithFrequency.DAILYrepresents a significant change in data extraction. Please ensure that:
- The data format remains consistent with downstream processing
- The performance characteristics are maintained or improved
Let's check for any related data processing code:
✅ Verification successful
Data consistency verified with new extraction method
The switch to
Frequency.DAILYis safe because:
- The extraction method maintains DataFrame consistency across all frequencies
- Existing error handling and retry mechanisms are preserved
- The cleanup DAG appropriately uses daily frequency for its purpose
- Other DAGs (realtime and historical) continue using hourly data with explicit resampling
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for data processing patterns after extraction ast-grep --pattern 'extract_data_from_bigquery($$$).format($$$)'Length of output: 66
Script:
#!/bin/bash # Find all occurrences of extract_data_from_bigquery rg "extract_data_from_bigquery" -A 5 # Find the AirQoDataUtils class implementation ast-grep --pattern 'class AirQoDataUtils { $$$ }' # Look for data processing in the same file cat src/workflows/dags/daily_measurements.pyLength of output: 6692
Script:
#!/bin/bash # Check DailyDataUtils implementation ast-grep --pattern 'class DailyDataUtils { $$$ }' # Look for cleanup_and_reload method ast-grep --pattern 'cleanup_and_reload($$$) { $$$ }'Length of output: 110
src/workflows/dags/weather_measurements.py (2)
Line range hint
107-119: Good standardization of task configurationsThe addition of consistent retry configurations and context provision across tasks improves reliability and maintainability. The standardized approach of:
- 3 retries
- 5-minute delay
- Context provision
is a good practice.
143-150: Consistent error handling across task typesThe standardization of retry configurations has been appropriately applied to both extract and load tasks, ensuring uniform error handling throughout the pipeline.
Description
This PR improves data cleaning and removes duplicate/redundant code.
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New Features
Refactor
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