Scout MCP

# Scout MCP Performance Analysis - Executive Summary **Date:** 2025-11-28 **Status:** ⚠️ NOT PRODUCTION READY (P0 fixes required) **Grade:** B+ (Good with critical issues) --- ## Quick Stats | Metric | Current | Target | Status | |--------|---------|--------|--------| | Cold start | 10.47ms | <20ms | ✅ GOOD | | Warm lookup | 0.02ms | <1ms | ✅ EXCELLENT | | Single-host throughput | 2,186 req/s | >1,000 | ✅ GOOD | | Multi-host throughput | 149 req/s | >1,000 | ❌ CRITICAL | | Memory (100 conns) | 0.07 MB | <10 MB | ✅ EXCELLENT | | Lock contention | High | Low | ❌ CRITICAL | | Pool size limit | None | 100-500 | ❌ CRITICAL | --- ## Critical Issues (P0 - Must Fix) ### 🔴 Issue #1: Global Lock Serialization - **File:** `pool.py:44` - **Impact:** 10x slowdown on parallel connections (104ms vs 10ms) - **Fix:** Per-host locks instead of global lock - **Effort:** 2-4 hours ### 🔴 Issue #2: No Connection Pool Limits - **File:** `pool.py:35` - **Impact:** Memory exhaustion under load - **Fix:** Add `max_connections` parameter with semaphore - **Effort:** 1-2 hours ### 🔴 Issue #3: No Request Concurrency Limits - **File:** `server.py:36` - **Impact:** DoS vulnerability - **Fix:** Global request semaphore - **Effort:** 30 minutes --- ## Performance by Component ### Connection Pool - ✅ Cold start: 10.47ms (excellent) - ✅ Warm lookup: 0.02ms (excellent) - ❌ Multi-host parallelism: 10x slower than expected - ✅ Memory: 80 bytes/connection (excellent) - ✅ Cleanup overhead: <0.01ms (negligible) ### SSH Operations - ✅ stat_path: 1.10ms (13% overhead) - ✅ cat_file: 1.13ms (13% overhead) - ✅ ls_dir: 1.13ms (13% overhead) - ✅ Large files (1MB): 93 MB/s (7% overhead) ### Configuration - ✅ Parse 100 hosts: 1.67ms (60k hosts/s) - ✅ Parse 1000 hosts: 10.16ms (98k hosts/s) - ✅ Cached lookup: 0.011ms (excellent) ### URI Parsing - ✅ Average: 0.0011ms (<0.01% of request) - ✅ Long paths: 0.0007ms (no degradation) --- ## Scalability Limits ### Current (Broken) ``` 1 host: 10ms ✅ 10 hosts: 100ms ❌ (should be 15ms) 100 hosts: 1000ms ❌ (should be 20ms) ``` ### After Fixes ``` 1 host: 10ms ✅ 10 hosts: 15ms ✅ (6.6x improvement) 100 hosts: 20ms ✅ (50x improvement) ``` --- ## Benchmark Commands ```bash # Run all benchmarks python -m pytest benchmarks/ -v -s # Connection pool benchmarks python -m pytest benchmarks/test_connection_pool.py -v -s # End-to-end benchmarks python -m pytest benchmarks/test_end_to_end.py -v -s # CPU profiling python benchmarks/profile_cpu.py # Memory profiling python benchmarks/profile_memory.py ``` --- ## Fix Checklist ### Phase 1 (P0 - Production Blockers) - [ ] Replace global lock with per-host locks (`pool.py:39,44`) - [ ] Add connection pool size limit (`pool.py:35`) - [ ] Add request concurrency limit (`server.py:36`) - [ ] Add SSH connection timeout (`pool.py:53`) ### Phase 2 (P1 - Important) - [ ] Add pool metrics (size, hit rate, miss rate) - [ ] Add per-host connection limits - [ ] Add connection warming for frequent hosts ### Phase 3 (P2 - Nice to Have) - [ ] Adaptive timeouts based on latency - [ ] Connection health checks - [ ] Request prioritization --- ## Production Readiness **Current Status:** ❌ NOT READY **Blockers:** 1. Global lock prevents horizontal scaling 2. No resource limits (memory/connection exhaustion) 3. No timeout protection (hung connections) **After P0 Fixes:** ✅ READY for moderate load (<1000 req/s) **Total effort to production:** 4-7 hours --- ## Key Metrics to Monitor 1. **Pool hit rate** - Should be >90% for warm workloads 2. **Connection count** - Should stay within limits 3. **P95 latency** - Should be <50ms for warm requests 4. **Lock wait time** - Should be <1ms (after fix) 5. **Memory usage** - Should be linear with connections --- For detailed analysis, see `.docs/performance-analysis.md`