This directory contains comprehensive performance analysis tools, reports, and optimizations for the neuView system, with a focus on the neuview pop command optimization.

performance/
├── README.md                    # This file
├── scripts/                     # Performance analysis and profiling scripts
│   ├── analyze_pop_performance.py       # Comprehensive pop command analysis
│   ├── profile_pop_command.py           # Basic pop command profiling
│   ├── profile_pop_detailed.py          # Detailed instrumented profiling
│   ├── profile_bulk_generation.py       # Bulk generation performance analysis
│   ├── profile_realistic_bulk.py        # Realistic bulk scenario profiling
│   ├── profile_soma_cache.py             # Soma cache performance analysis
│   └── performance_comparison.py        # Performance comparison utilities
├── reports/                     # Analysis reports and documentation
│   ├── NEUVIEW_POP_PERFORMANCE_OPTIMIZATION_REPORT.md  # Main optimization report
│   ├── SOMA_CACHE_OPTIMIZATION_REPORT.md                 # Soma cache analysis
│   └── OPTIMIZATION_IMPLEMENTATION_COMPLETE.md           # Implementation status
└── data/                        # Performance data and logs
    ├── pop_performance_analysis.json      # Latest comprehensive analysis
    ├── detailed_pop_performance_report.json
    ├── pop_performance_report.json
    └── pop_detailed_profile.log

• Throughput: 0.16 operations/second
• Average Execution Time: 6.26 seconds per operation
• Queue Processing Time: 19.6 hours for 11,287 files
• Database Queries: 21 per operation
• Cache Hit Rate: 100% (existing cache)

1. 🔴 CRITICAL: Sequential processing architecture
2. 🔴 CRITICAL: Excessive database queries (21 per operation)
3. 🟡 MEDIUM: Service initialization overhead (4.1%)
4. 🟡 MEDIUM: Redundant cache I/O operations

• Soma Cache Optimization: 50% reduction in cache I/O (IMPLEMENTED)
• Batch Processing: 3-5x throughput improvement
• Database Connection Pooling: 15-25% query overhead reduction

Expected Result: 0.16 → 1.0 ops/sec (6x improvement)

• Service Daemon Mode: Eliminate command overhead
• Advanced Caching: Query result caching
• Async Pipeline: Convert to async/await architecture

Expected Result: 1.0 → 2.5 ops/sec (2.5x additional improvement)

• Pre-computation: Off-peak data processing
• Incremental Updates: Smart regeneration
• Memory Optimization: Template and data caching

Expected Result: 2.5 → 5.0 ops/sec (2x additional improvement)

# Comprehensive pop command analysis
cd neuview
python performance/scripts/analyze_pop_performance.py

# Basic profiling
python performance/scripts/profile_pop_command.py

# Detailed instrumented profiling
python performance/scripts/profile_pop_detailed.py

# Ensure queue files exist
python -m neuview fill-queue

# Verify queue status
ls -la output/.queue/*.yaml | wc -l

• Status: Implementation complete and validated
• Impact: 50% reduction in cache I/O operations
• Files Affected: src/neuview/neuprint_connector.py
• Validation: 100% data consistency, zero regressions
• Cleanup: scripts/cleanup_redundant_cache.py available

• Priority: HIGH
• Expected Impact: 3-5x throughput improvement
• Implementation: Add --batch N option to pop command
• Status: Design complete, implementation pending

1. Database Connection Pooling (Priority: HIGH)
2. Service Daemon Mode (Priority: MEDIUM)
3. Async Processing Pipeline (Priority: MEDIUM)
4. Pre-computation System (Priority: LOW)

# Throughput monitoring
watch -n 60 'find output/.queue -name "*.yaml" | wc -l'

# Performance benchmarking
time python -m neuview pop

# Cache performance
grep "cache.*hit\|cache.*miss" logs/*.log | wc -l

Phase 1 Targets:

• Throughput ≥ 0.8 ops/sec
• Average time ≤ 1.5s per operation
• Cache hit rate ≥ 95%
• Zero functionality regressions

Long-term Targets:

• Throughput ≥ 4.0 ops/sec
• Queue processing ≤ 1 hour (11K+ files)
• Database queries ≤ 5 per operation

# Run comprehensive analysis on 15 operations
python performance/scripts/analyze_pop_performance.py

# View results
cat performance/data/pop_performance_analysis.json | jq '.statistics.timing'

# Profile specific number of operations
python performance/scripts/profile_pop_command.py --count 25

# Profile with custom timeout
python performance/scripts/profile_pop_detailed.py --timeout 600

# Verify soma cache optimization is working
python scripts/verify_optimization.py

# Compare performance before/after optimization
python performance/scripts/performance_comparison.py

No queue files available:

python -m neuview fill-queue

Performance regression:

# Check optimization status
python scripts/verify_optimization.py

# Revert to fallback if needed
git checkout HEAD~1 src/neuview/neuprint_connector.py

High memory usage:

# Monitor memory during profiling
python performance/scripts/profile_pop_detailed.py --memory-monitoring

1. Create script in performance/scripts/
2. Output data to performance/data/
3. Update this README with usage instructions
4. Add to .gitignore if generating large data files

Include:

• System information (Python version, OS)
• Queue size and characteristics
• Performance metrics (throughput, timing)
• Error logs if applicable

• Main Optimization Report
• Soma Cache Analysis
• Implementation Status

Last Updated: January 2025
Performance Baseline: 0.16 ops/sec
Optimization Target: 5.0 ops/sec