Change order of grouping columns #6936
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WalkthroughThe pull request modifies the SQL queries for counting duplicates in the FlowETL QA checks. The changes involve adjusting the grouping columns for different CDR types, specifically adding back Changes
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Auto Pull Request Review from LlamaPReview
1. Overview
1.1 PR Summary
- Business Value: Improves performance of FlowETL QA checks by reordering grouping columns to align with typical pre-sorting of tables.
- Key Components Modified: FlowETL QA checks (
count_duplicatesandcount_duplicated) - Impact Assessment: Potential performance improvement for tables pre-sorted by (
msisdn,datetime). - System Dependencies and Integration Impacts: Changes might affect downstream data processing or reporting, requiring thorough compatibility testing.
2. Detailed Technical Analysis
2.1 Code Logic Deep-Dive
Core Logic Changes
1. Improved Performance for QA Checks
-
File Path:
flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicates.sql -
Function/Class Name:
count_duplicates- Submitted PR Code:
SELECT COALESCE(sum(n_dupes), 0) FROM
(SELECT count(*) - 1 as n_dupes
FROM {{ final_table }}
GROUP BY
msisdn,
datetime,
imsi,
imei,
tac,
location_id,
{% if cdr_type == 'calls' %}
msisdn_counterpart,
outgoing,
duration,
{% elif cdr_type == 'sms' %}
msisdn_counterpart,
outgoing,
{% elif cdr_type == 'mds' %}
duration,
volume_total,
volume_upload,
volume_download,
{% elif cdr_type == 'topups' %}
type,
recharge_amount,
airtime_fee,
tax_and_fee,
pre_event_balance,
post_event_balance,
{% endif %}
operator_code,
country_code
HAVING count(*) > 1) tableWithCount-
Analysis:
- The PR changes the order of grouping columns to (
msisdn,datetime, ...), aligning with typical pre-sorting of tables in the system. - This change aims to improve performance by reducing the number of distinct groups and potentially enabling more efficient data processing.
- Potential Issue: The performance improvement should be validated through thorough testing to ensure that it does not introduce any performance regressions or other unforeseen issues.
- The PR changes the order of grouping columns to (
-
LlamaPReview Suggested Improvement:
-- Add an index on the first two columns to further improve performance
CREATE INDEX idx_count_duplicates_msisdn_datetime
ON {{ final_table }} (msisdn, datetime);- **Rationale**: Adding an index on the first two columns (`msisdn` and `datetime`) can further enhance the performance of the QA checks by enabling more efficient data retrieval and grouping. This improvement should be tested in conjunction with the reordered columns to validate its effectiveness and ensure that it does not introduce any regressions.
2. Data Consistency Checks
-
File Path:
flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicates.sql -
Function/Class Name:
count_duplicates- Submitted PR Code:
-- ... (same as above)-
Analysis:
- The reordered columns might affect the data consistency checks performed by the QA checks. It's essential to validate that the changes do not introduce any inconsistencies or incorrect data processing.
- The
HAVING count(*) > 1clause ensures that only records with duplicate counts are included in the result set, which is crucial for identifying and addressing data inconsistencies.
-
LlamaPReview Suggested Improvement:
- No improvements suggested, as the data consistency checks appear to be intact.
-
Rationale: The reordered columns should not affect the data consistency checks, as the
HAVINGclause ensures that only records with duplicate counts are considered. However, it's crucial to validate this assumption through thorough testing to ensure that the changes do not introduce any data inconsistencies.
3. Compatibility Testing
-
File Path:
flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicates.sqlandflowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicated.sql -
Function/Class Name:
count_duplicatesandcount_duplicated-
Submitted PR Code:
- See previous sections for the submitted PR code.
-
Analysis:
- The changes might have unforeseen impacts on other parts of the system that rely on these QA checks, such as downstream data processing or reporting. It's essential to test the changes with other components of the system to ensure that they continue to work as expected.
-
LlamaPReview Suggested Improvement:
- No improvements suggested, as the compatibility testing is an ongoing process that should be performed by the development team.
-
Rationale: Thorough compatibility testing is crucial for ensuring that the changes do not introduce any regressions or unforeseen issues. This testing should be performed with representative scenarios and datasets to validate the expected behavior of the system.
-
2.2 Implementation Quality
- Code Organization and Structure: The PR code is well-structured and follows the existing project conventions.
- Design Patterns Usage: The PR uses appropriate SQL queries and grouping logic to perform the required data analysis.
- Error Handling Approach: The PR includes error handling using the
HAVINGclause to ensure that only records with duplicate counts are considered. - Resource Management: The PR does not introduce any new resources or modify existing resource management.
3. Critical Findings
3.1 Potential Issues
-
🔴 Critical Issues
- Issue: Potential performance regression due to insufficient testing of the reordered columns.
- Impact: The changes might introduce performance regressions if not thoroughly tested.
- Recommendation: Conduct thorough performance testing before and after the changes to validate the expected improvement and ensure that no regressions are introduced.
- Issue: Potential performance regression due to insufficient testing of the reordered columns.
-
🟡 Warnings
- Warning: Lack of tests for the new additions in the PR.
- Potential Risks: Without appropriate tests, the changes might introduce regressions or unforeseen issues.
- Suggested Improvements:
- Add unit tests to validate the functionality of the QA checks before and after the changes.
- Consider adding integration tests to ensure that the changes do not introduce any regressions in the overall system behavior.
- Warning: Lack of tests for the new additions in the PR.
3.2 Code Quality Concerns
- Maintainability Aspects: The PR code is well-documented and follows the existing project conventions, ensuring maintainability.
- Readability Issues: The PR code is concise and easy to read, with clear variable names and comments.
- Performance Bottlenecks: The PR aims to improve performance by reordering grouping columns and adding an index. However, it's crucial to validate these improvements through thorough testing.
4. Security Assessment
- Authentication/Authorization Impacts: The changes do not affect authentication or authorization mechanisms.
- Data Handling Concerns: The PR does not introduce any new data handling mechanisms or modify existing ones.
- Input Validation: The PR does not introduce any new input validation mechanisms or modify existing ones.
- Security Best Practices: The PR follows best practices for SQL queries and data handling.
- Potential Security Risks: The changes do not introduce any new security risks.
- Mitigation Strategies: No additional mitigation strategies are required, as the changes do not introduce any new security risks.
- Security Testing Requirements: Thorough security testing should be performed to validate the overall system security.
5. Testing Strategy
5.1 Test Coverage
- Unit Test Analysis: No unit tests are provided in the PR. It's crucial to add unit tests to validate the functionality of the QA checks before and after the changes.
- Integration Test Requirements: Integration tests should be added to ensure that the changes do not introduce any regressions in the overall system behavior.
- Edge Cases Coverage: Edge cases should be considered in the testing strategy to ensure that the changes do not introduce any unforeseen issues.
5.2 Test Recommendations
Suggested Test Cases
# Sample unit test for count_duplicates function
def test_count_duplicates():
# Arrange
final_table = "test_table"
cdr_type = "calls"
# Act
result = count_duplicates(final_table, cdr_type)
# Assert
assert result == expected_result
# Sample integration test for QA checks
def test_qa_checks_integration():
# Arrange
# ... (set up test data and expected results)
# Act
# ... (perform QA checks)
# Assert
# ... (validate the results)- Coverage Improvements: Add unit tests to validate the functionality of the QA checks before and after the changes.
- Performance Testing Needs: Conduct thorough performance testing before and after the changes to validate the expected improvement and ensure that no regressions are introduced.
6. Documentation & Maintenance
- Documentation Updates Needed: Update the FlowETL QA checks documentation to reflect the changes in grouping column order and the addition of an index.
- Long-Term Maintenance Considerations: The changes should not introduce any long-term maintenance concerns, as they align with the typical pre-sorting of tables in the system.
- Technical Debt and Monitoring Requirements: No additional technical debt or monitoring requirements are introduced by the changes.
7. Deployment & Operations
- Deployment Impact and Strategy: The changes should be deployed as part of the regular FlowKit release process, with thorough testing and monitoring to ensure a smooth transition.
- Key Operational Considerations: No additional operational considerations are introduced by the changes, as they align with the typical data processing workflow in FlowKit.
8. Summary & Recommendations
8.1 Key Action Items
- Critical changes required: Conduct thorough performance testing before and after the changes to validate the expected improvement and ensure that no regressions are introduced.
- Important improvements suggested: Add unit tests to validate the functionality of the QA checks before and after the changes. Consider adding integration tests to ensure that the changes do not introduce any regressions in the overall system behavior.
- Best practices to implement: Follow the suggested improvement to add an index on the first two columns (
msisdnanddatetime) to further enhance the performance of the QA checks. Ensure that the changes do not introduce any data inconsistencies or regressions in other parts of the system by performing thorough compatibility testing.
8.2 Future Considerations
- Technical evolution path: The changes align with the typical pre-sorting of tables in the system, ensuring a smooth technical evolution path.
- Business capability evolution: The changes should not introduce any significant barriers to business capability evolution, as they focus on improving the performance of existing QA checks.
- System integration impacts: The changes might have unforeseen impacts on other parts of the system that rely on these QA checks. It's crucial to test the changes with other components of the system to ensure that they continue to work as expected and do not introduce any regressions.
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Actionable comments posted: 0
🧹 Nitpick comments (1)
flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicates.sql (1)
Line range hint
1-35: Consider documenting the performance optimisation rationale.The changes improve performance for pre-sorted tables, but this assumption isn't documented. Consider adding a comment explaining this optimisation.
Add a comment at the start of both SQL files:
+-- Optimised GROUP BY column ordering for tables sorted by msisdn, datetime SELECT COALESCE(sum(n_dupes), 0) FROM
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flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicated.sql(1 hunks)flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicates.sql(1 hunks)
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🔇 Additional comments (3)
flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicated.sql (2)
12-18: Logical grouping structure maintained for different CDR types.The grouping maintains a clear structure with core identifiers followed by CDR-specific fields, whilst preserving the necessary distinctions between calls and SMS records.
5-10: Optimised grouping order aligns with table sorting.The reordering of GROUP BY columns with
msisdnanddatetimefirst is a sensible optimisation for tables already sorted by these columns.Let's verify the table sorting assumption:
flowetl/flowetl/flowetl/qa_checks/qa_checks/count_duplicates.sql (1)
5-10: Consistent optimisation applied across both duplicate counting queries.The identical reordering of GROUP BY columns maintains consistency with
count_duplicated.sql, ensuring uniform performance benefits.
jc-harrison
changed the title
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Actionable comments posted: 0
🧹 Nitpick comments (1)
CHANGELOG.md (1)
12-12: Consider expanding the changelog entry with more details.While the entry correctly references the issue, it would be helpful to specify:
- Which columns were reordered
- The performance impact/benefit
- Any backwards compatibility considerations
This helps users better understand the impact of the changes.
-- Modified FlowETL `count_duplicates` and `count_duplicated` QA check queries to improve performance, without changing the results. [#6935](https://github.com/Flowminder/FlowKit/issues/6935) ++ Modified FlowETL `count_duplicates` and `count_duplicated` QA check queries by reordering grouping columns (msisdn, datetime first) to improve performance for pre-sorted tables, without changing the results. [#6935](https://github.com/Flowminder/FlowKit/issues/6935)
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🔇 Additional comments (1)
CHANGELOG.md (1)
Line range hint
1-1022: LGTM! The changelog structure follows best practices.The file maintains:
- Clear version sections
- Proper categorization of changes
- Consistent formatting
- Well-maintained version links
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| Branch Review |
optimise-duplicate-checks
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| Run status |
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| Run duration | 00m 49s |
| Commit |
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| Committer | James Harrison |
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #6936 +/- ##
==========================================
- Coverage 92.19% 91.02% -1.18%
==========================================
Files 276 276
Lines 10761 10761
Branches 1298 1298
==========================================
- Hits 9921 9795 -126
- Misses 690 806 +116
- Partials 150 160 +10 ☔ View full report in Codecov by Sentry. |
Closes #6935
I have:
Description
Changes the order of grouping fileds in the FlowETL
count_duplicatesandcount_duplicatedchecks, to hopefully improve performance on tables that are pre-sorted by (msisdn,datetime).Summary by CodeRabbit
These changes optimise the performance of duplicate detection across various telecommunications record types, ensuring more precise data quality checks.