fix: AutompleteSelect when options is already filled and analytics StackedBarsNormalized with selected implementation groups #1493
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🧹 Nitpick comments (2)
frontend/src/lib/components/Forms/AutocompleteSelect.svelte (2)
98-98: Simplify the boolean expression.The ternary operator can be simplified to a direct boolean expression.
-let optionsLoaded = options.length ? true: false; +let optionsLoaded = Boolean(options.length);
147-156: LGTM! Consider adding error state handling.The logic for setting initial selection is now correctly decoupled from the fetch success, allowing it to work with both pre-filled and fetched options. The special case handling for single required options is also well implemented.
Consider adding error state handling to inform users when options couldn't be fetched:
} catch (error) { console.error(`Error fetching ${optionsEndpoint}:`, error); + dispatch('error', { message: `Failed to fetch options from ${optionsEndpoint}` }); } finally {
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🔇 Additional comments (1)
frontend/src/lib/components/Forms/AutocompleteSelect.svelte (1)
1-350: Fix code style issues with Prettier.The pipeline reports code style issues. Please run Prettier to format the code:
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Mohamed-Hacene
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backend/core/models.py (1)
2967-2969: Optimize database query with proper indexing.The query filters on
compliance_assessmentandrequirement__assessable, which could benefit from a composite index for better performance.Consider adding a composite index:
class Meta: indexes = [ models.Index(fields=['compliance_assessment', 'requirement__assessable']), ]
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🔇 Additional comments (2)
backend/core/models.py (2)
2961-2965: LGTM! Efficient initialization of implementation groups set.The code efficiently converts selected implementation groups to a set for faster lookups, with proper handling of the None case.
2971-2984: Verify the performance impact of in-memory filtering.The implementation uses in-memory filtering when implementation groups are selected, which could be inefficient for large datasets. Consider the following points:
- For large datasets, memory usage could be a concern as all requirements are loaded into memory.
- The intersection operation (
&) between sets is performed in memory.Let's verify the potential performance impact:
❌ Verification inconclusive
🏁 Script executed:
#!/bin/bash # Description: Check for large requirement sets that might cause memory issues # Count total requirements per compliance assessment echo "Requirements per compliance assessment:" rg -A 2 "requirement_assessments = \[" # Check for any performance-related comments or issues echo "Checking for performance-related comments:" rg -i "performance|slow|memory|optimization"Length of output: 116123
Performance Impact of In-Memory Filtering May Be a Concern on Very Large Datasets
The code moves all requirement objects into memory and then iterates to perform set intersections. While this is acceptable for moderate-sized datasets, if the number of requirements is very high the following could occur:
- Increased memory usage since the entire set of requirements is loaded into memory.
- Additional CPU overhead due to iterating over the requirements and computing set intersections in Python.
Our repository search and documentation (e.g. in the data-model docs) suggest that performance considerations have been taken into account elsewhere, but nothing specific in the code or logs conclusively confirms that this in-memory approach won’t become a bottleneck under large loads.
If the system is expected to work with very large datasets, it may be worth benchmarking this section or exploring database-side filtering to offload the work.
Summary by CodeRabbit
Bug Fixes
Refactor