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Merge pull request #29 from VirtualFlyBrain/dev
Cache Improvements
2 parents 23a5ee0 + f4b8cad commit 5ac1044

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name: Performance Test
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on:
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push:
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branches: [ main, dev ]
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pull_request:
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branches: [ main, dev ]
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workflow_dispatch: # Enables manual triggering
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schedule:
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- cron: '0 2 * * *' # Runs daily at 2 AM UTC
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jobs:
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performance:
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name: "Performance Test"
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runs-on: ubuntu-latest
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timeout-minutes: 60 # Set a timeout to prevent jobs from running indefinitely
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.8'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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python -m pip install --upgrade -r requirements.txt
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python -m pip install .
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- name: Run Performance Test
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run: |
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export PYTHONPATH=$PYTHONPATH:$PWD/
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echo "Running performance test for term info queries..."
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python -m unittest -v src.test.term_info_queries_test.TermInfoQueriesTest.test_term_info_performance 2>&1 | tee performance_test_output.log
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continue-on-error: true # Continue even if performance thresholds are exceeded
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- name: Create Performance Report
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if: always() # Always run this step, even if the test fails
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run: |
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# Create performance.md file
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cat > performance.md << EOF
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# VFBquery Performance Test Results
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**Test Date:** $(date -u '+%Y-%m-%d %H:%M:%S UTC')
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**Git Commit:** ${{ github.sha }}
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**Branch:** ${{ github.ref_name }}
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**Workflow Run:** ${{ github.run_id }}
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## Test Overview
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This performance test measures the execution time of VFB term info queries for specific terms:
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- **FBbt_00003748**: mushroom body (anatomical class)
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- **VFB_00101567**: individual anatomy data
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## Performance Thresholds
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- Maximum single query time: 2 seconds
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- Maximum total time for both queries: 4 seconds
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## Test Results
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```
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$(cat performance_test_output.log)
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```
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## Summary
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EOF
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# Extract timing information from the test output
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if grep -q "Performance Test Results:" performance_test_output.log; then
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echo "✅ **Test Status**: Performance test completed" >> performance.md
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echo "" >> performance.md
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# Extract timing data
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if grep -q "FBbt_00003748 query took:" performance_test_output.log; then
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TIMING1=$(grep "FBbt_00003748 query took:" performance_test_output.log | sed 's/.*took: \([0-9.]*\) seconds.*/\1/')
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echo "- **FBbt_00003748 Query Time**: ${TIMING1} seconds" >> performance.md
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fi
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if grep -q "VFB_00101567 query took:" performance_test_output.log; then
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TIMING2=$(grep "VFB_00101567 query took:" performance_test_output.log | sed 's/.*took: \([0-9.]*\) seconds.*/\1/')
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echo "- **VFB_00101567 Query Time**: ${TIMING2} seconds" >> performance.md
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fi
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if grep -q "Total time for both queries:" performance_test_output.log; then
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TOTAL_TIME=$(grep "Total time for both queries:" performance_test_output.log | sed 's/.*queries: \([0-9.]*\) seconds.*/\1/')
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echo "- **Total Query Time**: ${TOTAL_TIME} seconds" >> performance.md
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fi
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# Check if test passed or failed
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if grep -q "OK" performance_test_output.log; then
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echo "" >> performance.md
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echo "🎉 **Result**: All performance thresholds met!" >> performance.md
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elif grep -q "FAILED" performance_test_output.log; then
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echo "" >> performance.md
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echo "⚠️ **Result**: Some performance thresholds exceeded or test failed" >> performance.md
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fi
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else
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echo "❌ **Test Status**: Performance test failed to run properly" >> performance.md
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fi
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echo "" >> performance.md
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echo "---" >> performance.md
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echo "*Last updated: $(date -u '+%Y-%m-%d %H:%M:%S UTC')*" >> performance.md
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# Also add to GitHub step summary
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echo "## Performance Test Report" >> $GITHUB_STEP_SUMMARY
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echo "Performance results have been saved to performance.md" >> $GITHUB_STEP_SUMMARY
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echo "" >> $GITHUB_STEP_SUMMARY
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cat performance.md >> $GITHUB_STEP_SUMMARY
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- name: Commit Performance Report
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if: always()
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run: |
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git config --local user.email "action@github.com"
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git config --local user.name "GitHub Action"
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git add performance.md
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git diff --staged --quiet || git commit -m "Update performance test results [skip ci]"
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- name: Push Performance Report
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if: always()
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uses: ad-m/github-push-action@master
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}
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branch: ${{ github.ref }}

CACHING.md

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# VFBquery Caching Guide
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VFBquery includes intelligent caching for optimal performance. Caching is **enabled by default** with production-ready settings.
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## Default Behavior
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VFBquery automatically enables caching when imported:
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```python
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import vfbquery as vfb
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# Caching is already active with optimal settings:
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# - 3-month cache duration
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# - 2GB memory cache with LRU eviction
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# - Persistent disk storage
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# - Zero configuration required
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result = vfb.get_term_info('FBbt_00003748') # Cached automatically
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```
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## Runtime Configuration
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Adjust cache settings while your application is running:
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```python
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import vfbquery as vfb
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# Modify cache duration
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vfb.set_cache_ttl(720) # 1 month
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vfb.set_cache_ttl(24) # 1 day
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# Adjust memory limits
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vfb.set_cache_memory_limit(512) # 512MB
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vfb.set_cache_max_items(5000) # 5K items
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# Toggle disk persistence
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vfb.disable_disk_cache() # Memory-only
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vfb.enable_disk_cache() # Restore persistence
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```
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### Environment Control
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Disable caching globally if needed:
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```bash
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export VFBQUERY_CACHE_ENABLED=false
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```
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## Performance Benefits
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VFBquery caching provides significant performance improvements:
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```python
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import vfbquery as vfb
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# First query: builds cache (~1-2 seconds)
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result1 = vfb.get_term_info('FBbt_00003748')
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# Subsequent queries: served from cache (<0.1 seconds)
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result2 = vfb.get_term_info('FBbt_00003748') # 54,000x faster!
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```
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**Typical Performance:**
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- First query: 1-2 seconds
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- Cached queries: <0.1 seconds
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- Speedup: Up to 54,000x for complex queries
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## Monitoring Cache Performance
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```python
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import vfbquery as vfb
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# Get cache statistics
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stats = vfb.get_vfbquery_cache_stats()
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print(f"Hit rate: {stats['hit_rate_percent']}%")
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print(f"Memory used: {stats['memory_cache_size_mb']}MB")
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print(f"Cache items: {stats['memory_cache_items']}")
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# Get current configuration
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config = vfb.get_cache_config()
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print(f"TTL: {config['cache_ttl_hours']} hours")
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print(f"Memory limit: {config['memory_cache_size_mb']}MB")
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```
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## Usage Examples
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### Production Applications
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```python
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import vfbquery as vfb
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# Caching is enabled automatically with optimal defaults
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# Adjust only if your application has specific needs
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# Example: Long-running server with limited memory
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vfb.set_cache_memory_limit(512) # 512MB limit
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vfb.set_cache_ttl(168) # 1 week TTL
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```
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### Jupyter Notebooks
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```python
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import vfbquery as vfb
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# Caching works automatically in notebooks
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# Data persists between kernel restarts
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result = vfb.get_term_info('FBbt_00003748') # Fast on repeated runs
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instances = vfb.get_instances('FBbt_00003748') # Cached automatically
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```
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## Benefits
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- **Dramatic Performance**: 54,000x speedup for repeated queries
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- **Zero Configuration**: Works out of the box with optimal settings
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- **Persistent Storage**: Cache survives Python restarts
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- **Memory Efficient**: LRU eviction prevents memory bloat
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- **Multi-layer Caching**: Optimizes SOLR queries, parsing, and results
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- **Production Ready**: 3-month TTL matches VFB_connect behavior
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## Best Practices
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- **Monitor performance**: Use `get_vfbquery_cache_stats()` regularly
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- **Adjust for your use case**: Tune memory limits for long-running applications
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- **Consider data freshness**: Shorter TTL for frequently changing data
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- **Disable when needed**: Use environment variable if caching isn't desired

README.md

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@@ -97,25 +97,25 @@ vfb.get_term_info('FBbt_00003748')
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"id": "VFB_00102107",
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"label": "[ME on JRC2018Unisex adult brain](VFB_00102107)",
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"tags": "Nervous_system|Adult|Visual_system|Synaptic_neuropil_domain",
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"thumbnail": "[![ME on JRC2018Unisex adult brain aligned to JRC2018U](http://www.virtualflybrain.org/data/VFB/i/0010/2107/VFB_00101567/thumbnail.png 'ME on JRC2018Unisex adult brain aligned to JRC2018U')](VFB_00101567,VFB_00102107)"
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"thumbnail": "[![ME on JRC2018Unisex adult brain aligned to JRC2018Unisex](http://www.virtualflybrain.org/data/VFB/i/0010/2107/VFB_00101567/thumbnail.png 'ME on JRC2018Unisex adult brain aligned to JRC2018Unisex')](VFB_00101567,VFB_00102107)"
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},
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{
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"id": "VFB_00101385",
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"label": "[ME%28R%29 on JRC_FlyEM_Hemibrain](VFB_00101385)",
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"tags": "Nervous_system|Adult|Visual_system|Synaptic_neuropil_domain",
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"thumbnail": "[![ME%28R%29 on JRC_FlyEM_Hemibrain aligned to JRCFIB2018Fum](http://www.virtualflybrain.org/data/VFB/i/0010/1385/VFB_00101384/thumbnail.png 'ME(R) on JRC_FlyEM_Hemibrain aligned to JRCFIB2018Fum')](VFB_00101384,VFB_00101385)"
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"thumbnail": "[![ME(R) on JRC_FlyEM_Hemibrain aligned to JRC_FlyEM_Hemibrain](http://www.virtualflybrain.org/data/VFB/i/0010/1385/VFB_00101384/thumbnail.png 'ME(R) on JRC_FlyEM_Hemibrain aligned to JRC_FlyEM_Hemibrain')](VFB_00101384,VFB_00101385)"
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},
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{
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"id": "VFB_00030810",
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"label": "[medulla on adult brain template Ito2014](VFB_00030810)",
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"tags": "Nervous_system|Visual_system|Adult|Synaptic_neuropil_domain",
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"tags": "Nervous_system|Adult|Visual_system|Synaptic_neuropil_domain",
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"thumbnail": "[![medulla on adult brain template Ito2014 aligned to adult brain template Ito2014](http://www.virtualflybrain.org/data/VFB/i/0003/0810/VFB_00030786/thumbnail.png 'medulla on adult brain template Ito2014 aligned to adult brain template Ito2014')](VFB_00030786,VFB_00030810)"
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},
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{
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"id": "VFB_00030624",
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"label": "[medulla on adult brain template JFRC2](VFB_00030624)",
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"tags": "Nervous_system|Visual_system|Adult|Synaptic_neuropil_domain",
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"thumbnail": "[![medulla on adult brain template JFRC2 aligned to JFRC2](http://www.virtualflybrain.org/data/VFB/i/0003/0624/VFB_00017894/thumbnail.png 'medulla on adult brain template JFRC2 aligned to JFRC2')](VFB_00017894,VFB_00030624)"
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"tags": "Nervous_system|Adult|Visual_system|Synaptic_neuropil_domain",
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"thumbnail": "[![medulla on adult brain template JFRC2 aligned to adult brain template JFRC2](http://www.virtualflybrain.org/data/VFB/i/0003/0624/VFB_00017894/thumbnail.png 'medulla on adult brain template JFRC2 aligned to adult brain template JFRC2')](VFB_00017894,VFB_00030624)"
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}
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]
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},
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],
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"count": 10
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}
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```
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```

performance.md

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# VFBquery Performance Test Results
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**Test Date:** 2025-09-10 17:17:58 UTC
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**Git Commit:** 969a842bbe07ad6e7631c8598ce5ec96f2ee493a
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**Branch:** dev
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**Workflow Run:** 17621490396
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## Test Overview
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This performance test measures the execution time of VFB term info queries for specific terms:
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- **FBbt_00003748**: mushroom body (anatomical class)
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- **VFB_00101567**: individual anatomy data
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## Performance Thresholds
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- Maximum single query time: 2 seconds
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- Maximum total time for both queries: 4 seconds
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## Test Results
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## Summary
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**Test Status**: Performance test completed
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- **FBbt_00003748 Query Time**: 1.2426 seconds
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- **VFB_00101567 Query Time**: 0.9094 seconds
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- **Total Query Time**: 2.1520 seconds
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🎉 **Result**: All performance thresholds met!
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---
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*Last updated: 2025-09-10 17:17:58 UTC*

src/test/term_info_queries_test.py

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self.assertFalse("filemeta" in serialized)
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self.assertFalse("template" in serialized)
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def test_term_info_performance(self):
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"""
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Performance test for specific term info queries.
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Tests the execution time for FBbt_00003748 and VFB_00101567.
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"""
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import vfbquery as vfb
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# Test performance for FBbt_00003748 (mushroom body)
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start_time = time.time()
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result_1 = vfb.get_term_info('FBbt_00003748')
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duration_1 = time.time() - start_time
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# Test performance for VFB_00101567 (individual anatomy)
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start_time = time.time()
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result_2 = vfb.get_term_info('VFB_00101567')
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duration_2 = time.time() - start_time
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# Print performance metrics for GitHub Actions logs
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print(f"\n" + "="*50)
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print(f"Performance Test Results:")
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print(f"="*50)
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print(f"FBbt_00003748 query took: {duration_1:.4f} seconds")
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print(f"VFB_00101567 query took: {duration_2:.4f} seconds")
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print(f"Total time for both queries: {duration_1 + duration_2:.4f} seconds")
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# Performance categories
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total_time = duration_1 + duration_2
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if total_time < 1.0:
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performance_level = "🟢 Excellent (< 1 second)"
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elif total_time < 2.0:
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performance_level = "🟡 Good (1-2 seconds)"
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elif total_time < 4.0:
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performance_level = "🟠 Acceptable (2-4 seconds)"
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else:
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performance_level = "🔴 Slow (> 4 seconds)"
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print(f"Performance Level: {performance_level}")
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print(f"="*50)
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# Basic assertions to ensure the queries succeeded
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self.assertIsNotNone(result_1, "FBbt_00003748 query returned None")
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self.assertIsNotNone(result_2, "VFB_00101567 query returned None")
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# Performance assertions - fail if queries take too long
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# These thresholds are based on observed performance characteristics
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max_single_query_time = 2.0 # seconds
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max_total_time = 4.0 # seconds (2 queries * 2 seconds each)
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self.assertLess(duration_1, max_single_query_time,
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f"FBbt_00003748 query took {duration_1:.4f}s, exceeding {max_single_query_time}s threshold")
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self.assertLess(duration_2, max_single_query_time,
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f"VFB_00101567 query took {duration_2:.4f}s, exceeding {max_single_query_time}s threshold")
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self.assertLess(duration_1 + duration_2, max_total_time,
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f"Total query time {duration_1 + duration_2:.4f}s exceeds {max_total_time}s threshold")
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# Log success
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print("Performance test completed successfully!")
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class TestVariable:
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