From ae4e60f9665f2b096c94d70d98951c67b63d8496 Mon Sep 17 00:00:00 2001 From: Joerg Date: Wed, 21 Jan 2026 14:03:25 +0100 Subject: [PATCH] chore: remove old phase documentation and development notes Remove outdated phase markdown files and optimize.md that are no longer relevant to the active codebase. Co-Authored-By: Claude --- PHASE_1.1_COMPLETE.md | 103 -------- PHASE_1.2_COMPLETE.md | 160 ------------ PHASE_1.3_COMPLETE.md | 311 ----------------------- PHASE_1_SUMMARY.md | 182 -------------- PHASE_2.1_COMPLETE.md | 334 ------------------------- PHASE_2.2_COMPLETE.md | 427 -------------------------------- PHASE_2.4_COMPLETE.md | 491 ------------------------------------ PHASE_2_SUMMARY.md | 450 --------------------------------- optimize.md | 560 ------------------------------------------ 9 files changed, 3018 deletions(-) delete mode 100644 PHASE_1.1_COMPLETE.md delete mode 100644 PHASE_1.2_COMPLETE.md delete mode 100644 PHASE_1.3_COMPLETE.md delete mode 100644 PHASE_1_SUMMARY.md delete mode 100644 PHASE_2.1_COMPLETE.md delete mode 100644 PHASE_2.2_COMPLETE.md delete mode 100644 PHASE_2.4_COMPLETE.md delete mode 100644 PHASE_2_SUMMARY.md delete mode 100644 optimize.md diff --git a/PHASE_1.1_COMPLETE.md b/PHASE_1.1_COMPLETE.md deleted file mode 100644 index 0eda42b..0000000 --- a/PHASE_1.1_COMPLETE.md +++ /dev/null @@ -1,103 +0,0 @@ -# Phase 1.1 Complete: SQL Query Optimization - -## Summary - -Successfully optimized the `getQSOStats()` function to use SQL aggregates instead of loading all QSOs into memory. - -## Changes Made - -**File**: `src/backend/services/lotw.service.js` (lines 496-517) - -### Before (Problematic) -```javascript -export async function getQSOStats(userId) { - const allQSOs = await db.select().from(qsos).where(eq(qsos.userId, userId)); - // Loads 200k+ records into memory - const confirmed = allQSOs.filter((q) => q.lotwQslRstatus === 'Y' || q.dclQslRstatus === 'Y'); - - const uniqueEntities = new Set(); - const uniqueBands = new Set(); - const uniqueModes = new Set(); - - allQSOs.forEach((q) => { - if (q.entity) uniqueEntities.add(q.entity); - if (q.band) uniqueBands.add(q.band); - if (q.mode) uniqueModes.add(q.mode); - }); - - return { - total: allQSOs.length, - confirmed: confirmed.length, - uniqueEntities: uniqueEntities.size, - uniqueBands: uniqueBands.size, - uniqueModes: uniqueModes.size, - }; -} -``` - -**Problems**: -- Loads ALL user QSOs into memory (200k+ records) -- Processes data in JavaScript (slow) -- Uses 100MB+ memory per request -- Takes 5-10 seconds for 200k QSOs - -### After (Optimized) -```javascript -export async function getQSOStats(userId) { - const [basicStats, uniqueStats] = await Promise.all([ - db.select({ - total: sql`COUNT(*)`, - confirmed: sql`SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END)` - }).from(qsos).where(eq(qsos.userId, userId)), - - db.select({ - uniqueEntities: sql`COUNT(DISTINCT entity)`, - uniqueBands: sql`COUNT(DISTINCT band)`, - uniqueModes: sql`COUNT(DISTINCT mode)` - }).from(qsos).where(eq(qsos.userId, userId)) - ]); - - return { - total: basicStats[0].total, - confirmed: basicStats[0].confirmed || 0, - uniqueEntities: uniqueStats[0].uniqueEntities || 0, - uniqueBands: uniqueStats[0].uniqueBands || 0, - uniqueModes: uniqueStats[0].uniqueModes || 0, - }; -} -``` - -**Benefits**: -- Executes entirely in SQLite (fast) -- Only returns 5 integers instead of 200k+ objects -- Uses <1MB memory per request -- Expected query time: 50-100ms for 200k QSOs -- Parallel queries with `Promise.all()` - -## Verification - -✅ SQL syntax validated -✅ Backend starts without errors -✅ API response format unchanged -✅ No breaking changes to existing code - -## Performance Improvement Estimates - -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| Query Time (200k QSOs) | 5-10 seconds | 50-100ms | **50-200x faster** | -| Memory Usage | 100MB+ | <1MB | **100x less memory** | -| Concurrent Users | 2-3 | 50+ | **16x more capacity** | - -## Next Steps - -**Phase 1.2**: Add critical database indexes to further improve performance - -The indexes will speed up the WHERE clause and COUNT(DISTINCT) operations, ensuring we achieve the sub-100ms target for large datasets. - -## Notes - -- The optimization maintains backward compatibility -- API response format is identical to before -- No frontend changes required -- Ready for deployment (indexes recommended for optimal performance) diff --git a/PHASE_1.2_COMPLETE.md b/PHASE_1.2_COMPLETE.md deleted file mode 100644 index 9daeb36..0000000 --- a/PHASE_1.2_COMPLETE.md +++ /dev/null @@ -1,160 +0,0 @@ -# Phase 1.2 Complete: Critical Database Indexes - -## Summary - -Successfully added 3 critical database indexes specifically optimized for QSO statistics queries, bringing the total to 10 performance indexes. - -## Changes Made - -**File**: `src/backend/migrations/add-performance-indexes.js` - -### New Indexes Added - -#### Index 8: Primary User Filter -```sql -CREATE INDEX IF NOT EXISTS idx_qsos_user_primary ON qsos(user_id); -``` -**Purpose**: Speed up basic WHERE clause filtering -**Impact**: 10-100x faster for user-based queries - -#### Index 9: Unique Counts -```sql -CREATE INDEX IF NOT EXISTS idx_qsos_user_unique_counts ON qsos(user_id, entity, band, mode); -``` -**Purpose**: Optimize COUNT(DISTINCT) operations -**Impact**: Critical for `getQSOStats()` unique entity/band/mode counts - -#### Index 10: Confirmation Status -```sql -CREATE INDEX IF NOT EXISTS idx_qsos_stats_confirmation ON qsos(user_id, lotw_qsl_rstatus, dcl_qsl_rstatus); -``` -**Purpose**: Optimize confirmed QSO counting -**Impact**: Fast SUM(CASE WHEN ...) confirmed counts - -### Complete Index List (10 Total) - -1. `idx_qsos_user_band` - Filter by band -2. `idx_qsos_user_mode` - Filter by mode -3. `idx_qsos_user_confirmation` - Filter by confirmation status -4. `idx_qsos_duplicate_check` - Sync duplicate detection (most impactful for sync) -5. `idx_qsos_lotw_confirmed` - LoTW confirmed QSOs (partial index) -6. `idx_qsos_dcl_confirmed` - DCL confirmed QSOs (partial index) -7. `idx_qsos_qso_date` - Date-based sorting -8. **`idx_qsos_user_primary`** - Primary user filter (NEW) -9. **`idx_qsos_user_unique_counts`** - Unique counts (NEW) -10. **`idx_qsos_stats_confirmation`** - Confirmation counting (NEW) - -## Migration Results - -```bash -$ bun src/backend/migrations/add-performance-indexes.js -Starting migration: Add performance indexes... -Creating index: idx_qsos_user_band -Creating index: idx_qsos_user_mode -Creating index: idx_qsos_user_confirmation -Creating index: idx_qsos_duplicate_check -Creating index: idx_qsos_lotw_confirmed -Creating index: idx_qsos_dcl_confirmed -Creating index: idx_qsos_qso_date -Creating index: idx_qsos_user_primary -Creating index: idx_qsos_user_unique_counts -Creating index: idx_qsos_stats_confirmation - -Migration complete! Created 10 performance indexes. -``` - -### Verification - -```bash -$ sqlite3 src/backend/award.db "SELECT name FROM sqlite_master WHERE type='index' AND tbl_name='qsos' ORDER BY name;" - -idx_qsos_dcl_confirmed -idx_qsos_duplicate_check -idx_qsos_lotw_confirmed -idx_qsos_qso_date -idx_qsos_stats_confirmation -idx_qsos_user_band -idx_qsos_user_confirmation -idx_qsos_user_mode -idx_qsos_user_primary -idx_qsos_user_unique_counts -``` - -✅ All 10 indexes successfully created - -## Performance Impact - -### Query Execution Plans - -**Before (Full Table Scan)**: -``` -SCAN TABLE qsos USING INDEX idx_qsos_user_primary -``` - -**After (Index Seek)**: -``` -SEARCH TABLE qsos USING INDEX idx_qsos_user_primary (user_id=?) -USE TEMP B-TREE FOR count(DISTINCT entity) -``` - -### Expected Performance Gains - -| Operation | Before | After | Improvement | -|-----------|--------|-------|-------------| -| WHERE user_id = ? | Full scan | Index seek | 50-100x faster | -| COUNT(DISTINCT entity) | Scan all rows | Index scan | 10-20x faster | -| SUM(CASE WHEN confirmed) | Scan all rows | Index scan | 20-50x faster | -| Overall getQSOStats() | 5-10s | **<100ms** | **50-100x faster** | - -## Database Impact - -- **File Size**: No significant increase (indexes are efficient) -- **Write Performance**: Minimal impact (indexing is fast) -- **Disk Usage**: Slightly higher (index storage overhead) -- **Memory Usage**: Slightly higher (index cache) - -## Combined Impact (Phase 1.1 + 1.2) - -### Before Optimization -- Query Time: 5-10 seconds -- Memory Usage: 100MB+ -- Concurrent Users: 2-3 -- Table Scans: Yes (slow) - -### After Optimization -- ✅ Query Time: **<100ms** (50-100x faster) -- ✅ Memory Usage: **<1MB** (100x less) -- ✅ Concurrent Users: **50+** (16x more) -- ✅ Table Scans: No (uses indexes) - -## Next Steps - -**Phase 1.3**: Testing & Validation - -We need to: -1. Test with small dataset (1k QSOs) - target: <10ms -2. Test with medium dataset (50k QSOs) - target: <50ms -3. Test with large dataset (200k QSOs) - target: <100ms -4. Verify API response format unchanged -5. Load test with 50 concurrent users - -## Notes - -- All indexes use `IF NOT EXISTS` (safe to run multiple times) -- Partial indexes used where appropriate (e.g., confirmed status) -- Index names follow consistent naming convention -- Ready for production deployment - -## Verification Checklist - -- ✅ All 10 indexes created successfully -- ✅ Database integrity maintained -- ✅ No schema conflicts -- ✅ Index names are unique -- ✅ Database accessible and functional -- ✅ Migration script completes without errors - ---- - -**Status**: Phase 1.2 Complete -**Next**: Phase 1.3 - Testing & Validation diff --git a/PHASE_1.3_COMPLETE.md b/PHASE_1.3_COMPLETE.md deleted file mode 100644 index 5728571..0000000 --- a/PHASE_1.3_COMPLETE.md +++ /dev/null @@ -1,311 +0,0 @@ -# Phase 1.3 Complete: Testing & Validation - -## Summary - -Successfully tested and validated the optimized QSO statistics query. All performance targets achieved with flying colors! - -## Test Results - -### Test Environment -- **Database**: SQLite3 (src/backend/award.db) -- **Dataset Size**: 8,339 QSOs -- **User ID**: 1 (random test user) -- **Indexes**: 10 performance indexes active - -### Performance Results - -#### Query Execution Time -``` -⏱️ Query time: 3.17ms -``` - -**Performance Rating**: ✅ EXCELLENT - -**Comparison**: -- Target: <100ms -- Achieved: 3.17ms -- **Performance margin: 31x faster than target!** - -#### Scale Projections - -| Dataset Size | Estimated Query Time | Rating | -|--------------|---------------------|--------| -| 1,000 QSOs | ~1ms | Excellent | -| 10,000 QSOs | ~5ms | Excellent | -| 50,000 QSOs | ~20ms | Excellent | -| 100,000 QSOs | ~40ms | Excellent | -| 200,000 QSOs | ~80ms | **Excellent** ✅ | - -**Note**: Even with 200k QSOs, we're well under the 100ms target! - -### Test Results Breakdown - -#### ✅ Test 1: Query Execution -- Status: PASSED -- Query completed successfully -- No errors or exceptions -- Returns valid results - -#### ✅ Test 2: Performance Evaluation -- Status: EXCELLENT -- Query time: 3.17ms (target: <100ms) -- Performance margin: 31x faster than target -- Rating: EXCELLENT - -#### ✅ Test 3: Response Format -- Status: PASSED -- All required fields present: - - `total`: 8,339 - - `confirmed`: 8,339 - - `uniqueEntities`: 194 - - `uniqueBands`: 15 - - `uniqueModes`: 10 - -#### ✅ Test 4: Data Integrity -- Status: PASSED -- All values are non-negative integers -- Confirmed QSOs (8,339) <= Total QSOs (8,339) ✓ -- Logical consistency verified - -#### ✅ Test 5: Index Utilization -- Status: PASSED (with note) -- 10 performance indexes on qsos table -- All critical indexes present and active - -## Performance Comparison - -### Before Optimization (Memory-Intensive) -```javascript -// Load ALL QSOs into memory -const allQSOs = await db.select().from(qsos).where(eq(qsos.userId, userId)); - -// Process in JavaScript (slow) -const confirmed = allQSOs.filter((q) => q.lotwQslRstatus === 'Y' || q.dclQslRstatus === 'Y'); - -// Count unique values in Sets -const uniqueEntities = new Set(); -allQSOs.forEach((q) => { - if (q.entity) uniqueEntities.add(q.entity); - // ... -}); -``` - -**Performance Metrics (Estimated for 8,339 QSOs)**: -- Query Time: ~100-200ms (loads all rows) -- Memory Usage: ~10-20MB (all QSOs in RAM) -- Processing Time: ~50-100ms (JavaScript iteration) -- **Total Time**: ~150-300ms - -### After Optimization (SQL-Based) -```javascript -// SQL aggregates execute in database -const [basicStats, uniqueStats] = await Promise.all([ - db.select({ - total: sql`CAST(COUNT(*) AS INTEGER)`, - confirmed: sql`CAST(SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END) AS INTEGER)` - }).from(qsos).where(eq(qsos.userId, userId)), - - db.select({ - uniqueEntities: sql`CAST(COUNT(DISTINCT entity) AS INTEGER)`, - uniqueBands: sql`CAST(COUNT(DISTINCT band) AS INTEGER)`, - uniqueModes: sql`CAST(COUNT(DISTINCT mode) AS INTEGER)` - }).from(qsos).where(eq(qsos.userId, userId)) -]); -``` - -**Performance Metrics (Actual: 8,339 QSOs)**: -- Query Time: **3.17ms** ✅ -- Memory Usage: **<1MB** (only 5 integers returned) ✅ -- Processing Time: **0ms** (SQL handles everything) -- **Total Time**: **3.17ms** ✅ - -### Performance Improvement - -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| Query Time (8.3k QSOs) | 150-300ms | 3.17ms | **47-95x faster** | -| Query Time (200k QSOs est.) | 5-10s | ~80ms | **62-125x faster** | -| Memory Usage | 10-20MB | <1MB | **10-20x less** | -| Processing Time | 50-100ms | 0ms | **Infinite** (removed) | - -## Scalability Analysis - -### Linear Performance Scaling -The optimized query scales linearly with dataset size, but the SQL engine is highly efficient: - -**Formula**: `Query Time ≈ (QSO Count / 8,339) × 3.17ms` - -**Predictions**: -- 10k QSOs: ~4ms -- 50k QSOs: ~19ms -- 100k QSOs: ~38ms -- 200k QSOs: ~76ms -- 500k QSOs: ~190ms - -**Conclusion**: Even with 500k QSOs, query time remains under 200ms! - -### Concurrent User Capacity - -**Before Optimization**: -- Memory per request: ~10-20MB -- Query time: 150-300ms -- Max concurrent users: 2-3 (memory limited) - -**After Optimization**: -- Memory per request: <1MB -- Query time: 3.17ms -- Max concurrent users: 50+ (CPU limited) - -**Capacity Improvement**: 16-25x more concurrent users! - -## Database Query Plans - -### Optimized Query Execution - -```sql --- Basic stats query -SELECT - CAST(COUNT(*) AS INTEGER) as total, - CAST(SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END) AS INTEGER) as confirmed -FROM qsos -WHERE user_id = ? - --- Uses index: idx_qsos_user_primary --- Operation: Index seek (fast!) -``` - -```sql --- Unique counts query -SELECT - CAST(COUNT(DISTINCT entity) AS INTEGER) as uniqueEntities, - CAST(COUNT(DISTINCT band) AS INTEGER) as uniqueBands, - CAST(COUNT(DISTINCT mode) AS INTEGER) as uniqueModes -FROM qsos -WHERE user_id = ? - --- Uses index: idx_qsos_user_unique_counts --- Operation: Index scan (efficient!) -``` - -### Index Utilization -- `idx_qsos_user_primary`: Used for WHERE clause filtering -- `idx_qsos_user_unique_counts`: Used for COUNT(DISTINCT) operations -- `idx_qsos_stats_confirmation`: Used for confirmed QSO counting - -## Validation Checklist - -- ✅ Query executes without errors -- ✅ Query time <100ms (achieved: 3.17ms) -- ✅ Memory usage <1MB (achieved: <1MB) -- ✅ All required fields present -- ✅ Data integrity validated (non-negative, logical consistency) -- ✅ API response format unchanged -- ✅ Performance indexes active (10 indexes) -- ✅ Supports 50+ concurrent users -- ✅ Scales to 200k+ QSOs - -## Test Dataset Analysis - -### QSO Statistics -- **Total QSOs**: 8,339 -- **Confirmed QSOs**: 8,339 (100% confirmation rate) -- **Unique Entities**: 194 (countries worked) -- **Unique Bands**: 15 (different HF/VHF bands) -- **Unique Modes**: 10 (CW, SSB, FT8, etc.) - -### Data Quality -- High confirmation rate suggests sync from LoTW/DCL -- Good diversity in bands and modes -- Significant DXCC entity count (194 countries) - -## Production Readiness - -### Deployment Status -✅ **READY FOR PRODUCTION** - -**Requirements Met**: -- ✅ Performance targets achieved (3.17ms vs 100ms target) -- ✅ Memory usage optimized (<1MB vs 10-20MB) -- ✅ Scalability verified (scales to 200k+ QSOs) -- ✅ No breaking changes (API format unchanged) -- ✅ Backward compatible -- ✅ Database indexes deployed -- ✅ Query execution plans verified - -### Recommended Deployment Steps -1. ✅ Deploy SQL query optimization (Phase 1.1) - DONE -2. ✅ Deploy database indexes (Phase 1.2) - DONE -3. ✅ Test in staging (Phase 1.3) - DONE -4. ⏭️ Deploy to production -5. ⏭️ Monitor for 1 week -6. ⏭️ Proceed to Phase 2 (Caching) - -### Monitoring Recommendations - -**Key Metrics to Track**: -- Query response time (target: <100ms) -- P95/P99 query times -- Database CPU usage -- Index utilization (should use indexes, not full scans) -- Concurrent user count -- Error rates - -**Alerting Thresholds**: -- Warning: Query time >200ms -- Critical: Query time >500ms -- Critical: Error rate >1% - -## Phase 1 Complete Summary - -### What We Did - -1. **Phase 1.1**: SQL Query Optimization - - Replaced memory-intensive approach with SQL aggregates - - Implemented parallel queries with `Promise.all()` - - File: `src/backend/services/lotw.service.js:496-517` - -2. **Phase 1.2**: Critical Database Indexes - - Added 3 new indexes for QSO statistics - - Total: 10 performance indexes on qsos table - - File: `src/backend/migrations/add-performance-indexes.js` - -3. **Phase 1.3**: Testing & Validation - - Verified query performance: 3.17ms for 8.3k QSOs - - Validated data integrity and response format - - Confirmed scalability to 200k+ QSOs - -### Results - -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| Query Time (200k QSOs) | 5-10s | ~80ms | **62-125x faster** | -| Memory Usage | 100MB+ | <1MB | **100x less** | -| Concurrent Users | 2-3 | 50+ | **16-25x more** | -| Table Scans | Yes | No | **Index seek** | - -### Success Criteria Met - -✅ Query time <100ms for 200k QSOs (achieved: ~80ms) -✅ Memory usage <1MB per request (achieved: <1MB) -✅ Zero bugs in production (ready for deployment) -✅ User feedback: "Page loads instantly" (anticipate positive feedback) - -## Next Steps - -**Phase 2: Stability & Monitoring** (Week 2) - -1. Implement 5-minute TTL cache for QSO statistics -2. Add performance monitoring and logging -3. Create cache invalidation hooks for sync operations -4. Add performance metrics to health endpoint -5. Deploy and monitor cache hit rate (target >80%) - -**Estimated Effort**: 1 week -**Expected Benefit**: Cache hit: <1ms response time, 80-90% database load reduction - ---- - -**Status**: Phase 1 Complete ✅ -**Performance**: EXCELLENT (3.17ms vs 100ms target) -**Production Ready**: YES -**Next**: Phase 2 - Caching & Monitoring diff --git a/PHASE_1_SUMMARY.md b/PHASE_1_SUMMARY.md deleted file mode 100644 index a10c40b..0000000 --- a/PHASE_1_SUMMARY.md +++ /dev/null @@ -1,182 +0,0 @@ -# Phase 1 Complete: Emergency Performance Fix ✅ - -## Executive Summary - -Successfully optimized QSO statistics query performance from 5-10 seconds to **3.17ms** (62-125x faster). Memory usage reduced from 100MB+ to **<1MB** (100x less). Ready for production deployment. - -## What We Accomplished - -### Phase 1.1: SQL Query Optimization ✅ -**File**: `src/backend/services/lotw.service.js:496-517` - -**Before**: -```javascript -// Load 200k+ QSOs into memory -const allQSOs = await db.select().from(qsos).where(eq(qsos.userId, userId)); -// Process in JavaScript (slow) -``` - -**After**: -```javascript -// SQL aggregates execute in database -const [basicStats, uniqueStats] = await Promise.all([ - db.select({ - total: sql`CAST(COUNT(*) AS INTEGER)`, - confirmed: sql`CAST(SUM(CASE WHEN confirmed THEN 1 ELSE 0 END) AS INTEGER)` - }).from(qsos).where(eq(qsos.userId, userId)), - // Parallel queries for unique counts -]); -``` - -**Impact**: Query executes entirely in SQLite, parallel processing, only returns 5 integers - -### Phase 1.2: Critical Database Indexes ✅ -**File**: `src/backend/migrations/add-performance-indexes.js` - -Added 3 critical indexes: -- `idx_qsos_user_primary` - Primary user filter -- `idx_qsos_user_unique_counts` - Unique entity/band/mode counts -- `idx_qsos_stats_confirmation` - Confirmation status counting - -**Total**: 10 performance indexes on qsos table - -### Phase 1.3: Testing & Validation ✅ - -**Test Results** (8,339 QSOs): -``` -⏱️ Query time: 3.17ms (target: <100ms) ✅ -💾 Memory usage: <1MB (was 10-20MB) ✅ -📊 Results: total=8339, confirmed=8339, entities=194, bands=15, modes=10 ✅ -``` - -**Performance Rating**: EXCELLENT (31x faster than target!) - -## Performance Comparison - -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| **Query Time (200k QSOs)** | 5-10 seconds | ~80ms | **62-125x faster** | -| **Memory Usage** | 100MB+ | <1MB | **100x less** | -| **Concurrent Users** | 2-3 | 50+ | **16-25x more** | -| **Table Scans** | Yes | No | **Index seek** | - -## Scalability Projections - -| Dataset | Query Time | Rating | -|---------|------------|--------| -| 10k QSOs | ~5ms | Excellent | -| 50k QSOs | ~20ms | Excellent | -| 100k QSOs | ~40ms | Excellent | -| 200k QSOs | ~80ms | **Excellent** ✅ | - -**Conclusion**: Scales efficiently to 200k+ QSOs with sub-100ms performance! - -## Files Modified - -1. **src/backend/services/lotw.service.js** - - Optimized `getQSOStats()` function - - Lines: 496-517 - -2. **src/backend/migrations/add-performance-indexes.js** - - Added 3 new indexes - - Total: 10 performance indexes - -3. **Documentation Created**: - - `optimize.md` - Complete optimization plan - - `PHASE_1.1_COMPLETE.md` - SQL query optimization details - - `PHASE_1.2_COMPLETE.md` - Database indexes details - - `PHASE_1.3_COMPLETE.md` - Testing & validation results - -## Success Criteria - -✅ **Query time <100ms for 200k QSOs** - Achieved: ~80ms -✅ **Memory usage <1MB per request** - Achieved: <1MB -✅ **Zero bugs in production** - Ready for deployment -✅ **User feedback expected** - "Page loads instantly" - -## Deployment Checklist - -- ✅ SQL query optimization implemented -- ✅ Database indexes created and verified -- ✅ Testing completed (all tests passed) -- ✅ Performance targets exceeded (31x faster than target) -- ✅ API response format unchanged -- ✅ Backward compatible -- ⏭️ Deploy to production -- ⏭️ Monitor for 1 week - -## Monitoring Recommendations - -**Key Metrics**: -- Query response time (target: <100ms) -- P95/P99 query times -- Database CPU usage -- Index utilization -- Concurrent user count -- Error rates - -**Alerting**: -- Warning: Query time >200ms -- Critical: Query time >500ms -- Critical: Error rate >1% - -## Next Steps - -**Phase 2: Stability & Monitoring** (Week 2) - -1. **Implement 5-minute TTL cache** for QSO statistics - - Expected benefit: Cache hit <1ms response time - - Target: >80% cache hit rate - -2. **Add performance monitoring** and logging - - Track query performance over time - - Detect performance regressions early - -3. **Create cache invalidation hooks** for sync operations - - Invalidate cache after LoTW/DCL syncs - -4. **Add performance metrics** to health endpoint - - Monitor system health in production - -**Estimated Effort**: 1 week -**Expected Benefit**: 80-90% database load reduction, sub-1ms cache hits - -## Quick Commands - -### View Indexes -```bash -sqlite3 src/backend/award.db "SELECT name FROM sqlite_master WHERE type='index' AND tbl_name='qsos' ORDER BY name;" -``` - -### Test Query Performance -```bash -# Run the backend -bun run src/backend/index.js - -# Test the API endpoint -curl http://localhost:3001/api/qsos/stats -``` - -### Check Database Size -```bash -ls -lh src/backend/award.db -``` - -## Summary - -**Phase 1 Status**: ✅ **COMPLETE** - -**Performance Results**: -- Query time: 5-10s → **3.17ms** (62-125x faster) -- Memory usage: 100MB+ → **<1MB** (100x less) -- Concurrent capacity: 2-3 → **50+** (16-25x more) - -**Production Ready**: ✅ **YES** - -**Next Phase**: Phase 2 - Caching & Monitoring - ---- - -**Last Updated**: 2025-01-21 -**Status**: Phase 1 Complete - Ready for Phase 2 -**Performance**: EXCELLENT (31x faster than target) diff --git a/PHASE_2.1_COMPLETE.md b/PHASE_2.1_COMPLETE.md deleted file mode 100644 index 8a98220..0000000 --- a/PHASE_2.1_COMPLETE.md +++ /dev/null @@ -1,334 +0,0 @@ -# Phase 2.1 Complete: Basic Caching Layer - -## Summary - -Successfully implemented a 5-minute TTL caching layer for QSO statistics, achieving **601x faster** query performance on cache hits (12ms → 0.02ms). - -## Changes Made - -### 1. Extended Cache Service -**File**: `src/backend/services/cache.service.js` - -Added QSO statistics caching functionality alongside existing award progress caching: - -**New Features**: -- `getCachedStats(userId)` - Get cached stats with hit/miss tracking -- `setCachedStats(userId, data)` - Cache statistics data -- `invalidateStatsCache(userId)` - Invalidate stats cache for a user -- `getCacheStats()` - Enhanced with stats cache metrics (hits, misses, hit rate) - -**Cache Statistics Tracking**: -```javascript -// Track hits and misses for both award and stats caches -const awardCacheStats = { hits: 0, misses: 0 }; -const statsCacheStats = { hits: 0, misses: 0 }; - -// Automatic tracking in getCached functions -export function recordStatsCacheHit() { statsCacheStats.hits++; } -export function recordStatsCacheMiss() { statsCacheStats.misses++; } -``` - -**Cache Configuration**: -- **TTL**: 5 minutes (300,000ms) -- **Storage**: In-memory Map (fast, no external dependencies) -- **Cleanup**: Automatic expiration check on each access - -### 2. Updated QSO Statistics Function -**File**: `src/backend/services/lotw.service.js:496-517` - -Modified `getQSOStats()` to use caching: - -```javascript -export async function getQSOStats(userId) { - // Check cache first - const cached = getCachedStats(userId); - if (cached) { - return cached; // <1ms cache hit - } - - // Calculate stats from database (3-12ms cache miss) - const [basicStats, uniqueStats] = await Promise.all([...]); - - const stats = { /* ... */ }; - - // Cache results for future queries - setCachedStats(userId, stats); - - return stats; -} -``` - -### 3. Cache Invalidation Hooks -**Files**: `src/backend/services/lotw.service.js`, `src/backend/services/dcl.service.js` - -Added automatic cache invalidation after QSO syncs: - -**LoTW Sync** (`lotw.service.js:385-386`): -```javascript -// Invalidate award and stats cache for this user since QSOs may have changed -const deletedCache = invalidateUserCache(userId); -invalidateStatsCache(userId); -logger.debug(`Invalidated ${deletedCache} cached award entries and stats cache for user ${userId}`); -``` - -**DCL Sync** (`dcl.service.js:413-414`): -```javascript -// Invalidate award cache for this user since QSOs may have changed -const deletedCache = invalidateUserCache(userId); -invalidateStatsCache(userId); -logger.debug(`Invalidated ${deletedCache} cached award entries and stats cache for user ${userId}`); -``` - -## Test Results - -### Test Environment -- **Database**: SQLite3 (src/backend/award.db) -- **Dataset Size**: 8,339 QSOs -- **User ID**: 1 (test user) -- **Cache TTL**: 5 minutes - -### Performance Results - -#### Test 1: First Query (Cache Miss) -``` -Query time: 12.03ms -Stats: total=8339, confirmed=8339 -Cache hit rate: 0.00% -``` - -#### Test 2: Second Query (Cache Hit) -``` -Query time: 0.02ms -Cache hit rate: 50.00% -✅ Cache hit! Query completed in <1ms -``` - -**Speedup**: 601.5x faster than database query! - -#### Test 3: Data Consistency -``` -✅ Cached data matches fresh data -``` - -#### Test 4: Cache Performance -``` -Cache hit rate: 50.00% (2 queries: 1 hit, 1 miss) -Stats cache size: 1 -``` - -#### Test 5: Multiple Cache Hits (10 queries) -``` -10 queries: avg=0.00ms, min=0.00ms, max=0.00ms -Cache hit rate: 91.67% (11 hits, 1 miss) -✅ Excellent average query time (<5ms) -``` - -#### Test 6: Cache Status -``` -Total cached items: 1 -Valid items: 1 -Expired items: 0 -TTL: 300 seconds -✅ No expired cache items (expected) -``` - -### All Tests Passed ✅ - -## Performance Comparison - -### Query Time Breakdown - -| Scenario | Time | Speedup | -|----------|------|---------| -| **Database Query (no cache)** | 12.03ms | 1x (baseline) | -| **Cache Hit** | 0.02ms | **601x faster** | -| **10 Cached Queries** | ~0.00ms avg | **600x faster** | - -### Real-World Impact - -**Before Caching** (Phase 1 optimization only): -- Every page view: 3-12ms database query -- 10 page views/minute: 30-120ms total DB time/minute - -**After Caching** (Phase 2.1): -- First page view: 3-12ms (cache miss) -- Subsequent page views: <0.1ms (cache hit) -- 10 page views/minute: 3-12ms + 9×0.02ms = ~3.2ms total DB time/minute - -**Database Load Reduction**: ~96% for repeated stats requests - -### Cache Hit Rate Targets - -| Scenario | Expected Hit Rate | Benefit | -|----------|-----------------|---------| -| Single user, 10 page views | 90%+ | 90% less DB load | -| Multiple users, low traffic | 50-70% | 50-70% less DB load | -| High traffic, many users | 70-90% | 70-90% less DB load | - -## Cache Statistics API - -### Get Cache Stats -```javascript -import { getCacheStats } from './cache.service.js'; - -const stats = getCacheStats(); -console.log(stats); -``` - -**Output**: -```json -{ - "total": 1, - "valid": 1, - "expired": 0, - "ttl": 300000, - "hitRate": "91.67%", - "awardCache": { - "size": 0, - "hits": 0, - "misses": 0 - }, - "statsCache": { - "size": 1, - "hits": 11, - "misses": 1 - } -} -``` - -### Cache Invalidation -```javascript -import { invalidateStatsCache } from './cache.service.js'; - -// Invalidate stats cache after QSO sync -await invalidateStatsCache(userId); -``` - -### Clear All Cache -```javascript -import { clearAllCache } from './cache.service.js'; - -// Clear all cached items (for testing/emergency) -const clearedCount = clearAllCache(); -``` - -## Cache Invalidation Strategy - -### Automatic Invalidation - -Cache is automatically invalidated when: -1. **LoTW sync completes** - `lotw.service.js:386` -2. **DCL sync completes** - `dcl.service.js:414` -3. **Cache expires** - After 5 minutes (TTL) - -### Manual Invalidation - -```javascript -// Invalidate specific user's stats -invalidateStatsCache(userId); - -// Invalidate all user's cached data (awards + stats) -invalidateUserCache(userId); // From existing code - -// Clear entire cache (emergency/testing) -clearAllCache(); -``` - -## Benefits - -### Performance -- ✅ **Cache Hit**: <0.1ms (601x faster than DB) -- ✅ **Cache Miss**: 3-12ms (no overhead from checking cache) -- ✅ **Zero Latency**: In-memory cache, no network calls - -### Database Load -- ✅ **96% reduction** for repeated stats requests -- ✅ **50-90% reduction** expected in production (depends on hit rate) -- ✅ **Scales linearly**: More cache hits = less DB load - -### Memory Usage -- ✅ **Minimal**: 1 cache entry per active user (~500 bytes) -- ✅ **Bounded**: Automatic expiration after 5 minutes -- ✅ **No External Dependencies**: Uses JavaScript Map - -### Simplicity -- ✅ **No Redis**: Pure JavaScript, no additional infrastructure -- ✅ **Automatic**: Cache invalidation built into sync operations -- ✅ **Observable**: Built-in cache statistics for monitoring - -## Success Criteria - -✅ **Cache hit time <1ms** - Achieved: 0.02ms (50x faster than target) -✅ **5-minute TTL** - Implemented: 300,000ms TTL -✅ **Automatic invalidation** - Implemented: Hooks in LoTW/DCL sync -✅ **Cache statistics** - Implemented: Hits/misses/hit rate tracking -✅ **Zero breaking changes** - Maintained: Same API, transparent caching - -## Next Steps - -**Phase 2.2**: Performance Monitoring -- Add query performance tracking to logger -- Track query times over time -- Detect slow queries automatically - -**Phase 2.3**: (Already Complete - Cache Invalidation Hooks) -- ✅ LoTW sync invalidation -- ✅ DCL sync invalidation -- ✅ Automatic expiration - -**Phase 2.4**: Monitoring Dashboard -- Add performance metrics to health endpoint -- Expose cache statistics via API -- Real-time monitoring - -## Files Modified - -1. **src/backend/services/cache.service.js** - - Added stats cache functions - - Enhanced getCacheStats() with stats metrics - - Added hit/miss tracking - -2. **src/backend/services/lotw.service.js** - - Updated imports (invalidateStatsCache) - - Modified getQSOStats() to use cache - - Added cache invalidation after sync - -3. **src/backend/services/dcl.service.js** - - Updated imports (invalidateStatsCache) - - Added cache invalidation after sync - -## Monitoring Recommendations - -**Key Metrics to Track**: -- Cache hit rate (target: >80%) -- Cache size (active users) -- Cache hit/miss ratio -- Response time distribution - -**Expected Production Metrics**: -- Cache hit rate: 70-90% (depends on traffic pattern) -- Response time: <1ms (cache hit), 3-12ms (cache miss) -- Database load: 50-90% reduction - -**Alerting Thresholds**: -- Warning: Cache hit rate <50% -- Critical: Cache hit rate <25% - -## Summary - -**Phase 2.1 Status**: ✅ **COMPLETE** - -**Performance Improvement**: -- Cache hit: **601x faster** (12ms → 0.02ms) -- Database load: **96% reduction** for repeated requests -- Response time: **<0.1ms** for cached queries - -**Production Ready**: ✅ **YES** - -**Next**: Phase 2.2 - Performance Monitoring - ---- - -**Last Updated**: 2025-01-21 -**Status**: Phase 2.1 Complete - Ready for Phase 2.2 -**Performance**: EXCELLENT (601x faster on cache hits) diff --git a/PHASE_2.2_COMPLETE.md b/PHASE_2.2_COMPLETE.md deleted file mode 100644 index a8d53b5..0000000 --- a/PHASE_2.2_COMPLETE.md +++ /dev/null @@ -1,427 +0,0 @@ -# Phase 2.2 Complete: Performance Monitoring - -## Summary - -Successfully implemented comprehensive performance monitoring system with automatic slow query detection, percentiles, and performance ratings. - -## Changes Made - -### 1. Performance Service -**File**: `src/backend/services/performance.service.js` (new file) - -Created a complete performance monitoring system: - -**Core Features**: -- `trackQueryPerformance(queryName, fn)` - Track query execution time -- `getPerformanceStats(queryName)` - Get statistics for a specific query -- `getPerformanceSummary()` - Get overall performance summary -- `getSlowQueries(threshold)` - Get queries above threshold -- `checkPerformanceDegradation(queryName)` - Detect performance regression -- `resetPerformanceMetrics()` - Clear all metrics (for testing) - -**Performance Metrics Tracked**: -```javascript -{ - count: 11, // Number of executions - totalTime: 36.05ms, // Total execution time - minTime: 2.36ms, // Minimum query time - maxTime: 11.75ms, // Maximum query time - p50: 2.41ms, // 50th percentile (median) - p95: 11.75ms, // 95th percentile - p99: 11.75ms, // 99th percentile - errors: 0, // Error count - errorRate: "0.00%", // Error rate percentage - rating: "EXCELLENT" // Performance rating -} -``` - -**Performance Ratings**: -- **EXCELLENT**: Average < 50ms -- **GOOD**: Average 50-100ms -- **SLOW**: Average 100-500ms (warning threshold) -- **CRITICAL**: Average > 500ms (critical threshold) - -**Thresholds**: -- Slow query: > 100ms -- Critical query: > 500ms - -### 2. Integration with QSO Statistics -**File**: `src/backend/services/lotw.service.js:498-527` - -Modified `getQSOStats()` to use performance tracking: - -```javascript -export async function getQSOStats(userId) { - // Check cache first - const cached = getCachedStats(userId); - if (cached) { - return cached; // <0.1ms cache hit - } - - // Calculate stats from database with performance tracking - const stats = await trackQueryPerformance('getQSOStats', async () => { - const [basicStats, uniqueStats] = await Promise.all([...]); - return { /* ... */ }; - }); - - // Cache results - setCachedStats(userId, stats); - - return stats; -} -``` - -**Benefits**: -- Automatic query time tracking -- Performance regression detection -- Slow query alerts in logs - -## Test Results - -### Test Environment -- **Database**: SQLite3 (src/backend/award.db) -- **Dataset Size**: 8,339 QSOs -- **Queries Tracked**: 11 (1 cold, 10 warm) -- **User ID**: 1 (test user) - -### Performance Results - -#### Test 1: Single Query Tracking -``` -Query time: 11.75ms -✅ Query Performance: getQSOStats - 11.75ms -✅ Query completed in <100ms (target achieved) -``` - -#### Test 2: Multiple Queries (Statistics) -``` -Executed 11 queries -Avg time: 3.28ms -Min/Max: 2.36ms / 11.75ms -Percentiles: P50=2.41ms, P95=11.75ms, P99=11.75ms -Rating: EXCELLENT -✅ EXCELLENT average query time (<50ms) -``` - -**Observations**: -- First query (cold): 11.75ms -- Subsequent queries (warm): 2.36-2.58ms -- Cache invalidation causes warm queries -- 75% faster after first query (warm DB cache) - -#### Test 3: Performance Summary -``` -Total queries tracked: 11 -Total time: 36.05ms -Overall avg: 3.28ms -Slow queries: 0 -Critical queries: 0 -✅ No slow or critical queries detected -``` - -#### Test 4: Slow Query Detection -``` -Found 0 slow queries (>100ms avg) -✅ No slow queries detected -``` - -#### Test 5: Top Slowest Queries -``` -Top 5 slowest queries: - 1. getQSOStats: 3.28ms (EXCELLENT) -``` - -#### Test 6: Detailed Query Statistics -``` -Query name: getQSOStats -Execution count: 11 -Average time: 3.28ms -Min time: 2.36ms -Max time: 11.75ms -P50 (median): 2.41ms -P95 (95th percentile): 11.75ms -P99 (99th percentile): 11.75ms -Errors: 0 -Error rate: 0.00% -Performance rating: EXCELLENT -``` - -### All Tests Passed ✅ - -## Performance API - -### Track Query Performance -```javascript -import { trackQueryPerformance } from './performance.service.js'; - -const result = await trackQueryPerformance('myQuery', async () => { - // Your query or expensive operation here - return await someDatabaseOperation(); -}); - -// Automatically logs: -// ✅ Query Performance: myQuery - 12.34ms -// or -// ⚠️ SLOW QUERY: myQuery took 125.67ms -// or -// 🚨 CRITICAL SLOW QUERY: myQuery took 567.89ms -``` - -### Get Performance Statistics -```javascript -import { getPerformanceStats } from './performance.service.js'; - -// Stats for specific query -const stats = getPerformanceStats('getQSOStats'); -console.log(stats); -``` - -**Output**: -```json -{ - "name": "getQSOStats", - "count": 11, - "avgTime": "3.28ms", - "minTime": "2.36ms", - "maxTime": "11.75ms", - "p50": "2.41ms", - "p95": "11.75ms", - "p99": "11.75ms", - "errors": 0, - "errorRate": "0.00%", - "rating": "EXCELLENT" -} -``` - -### Get Overall Summary -```javascript -import { getPerformanceSummary } from './performance.service.js'; - -const summary = getPerformanceSummary(); -console.log(summary); -``` - -**Output**: -```json -{ - "totalQueries": 11, - "totalTime": "36.05ms", - "avgTime": "3.28ms", - "slowQueries": 0, - "criticalQueries": 0, - "topSlowest": [ - { - "name": "getQSOStats", - "count": 11, - "avgTime": "3.28ms", - "rating": "EXCELLENT" - } - ] -} -``` - -### Find Slow Queries -```javascript -import { getSlowQueries } from './performance.service.js'; - -// Find all queries averaging >100ms -const slowQueries = getSlowQueries(100); - -// Find all queries averaging >500ms (critical) -const criticalQueries = getSlowQueries(500); - -console.log(`Found ${slowQueries.length} slow queries`); -slowQueries.forEach(q => { - console.log(` - ${q.name}: ${q.avgTime} (${q.rating})`); -}); -``` - -### Detect Performance Degradation -```javascript -import { checkPerformanceDegradation } from './performance.service.js'; - -// Check if recent queries are 2x slower than overall average -const status = checkPerformanceDegradation('getQSOStats', 10); - -if (status.degraded) { - console.warn(`⚠️ Performance degraded by ${status.change}`); - console.log(` Recent avg: ${status.avgRecent}`); - console.log(` Overall avg: ${status.avgOverall}`); -} else { - console.log('✅ Performance stable'); -} -``` - -## Monitoring Integration - -### Console Logging - -Performance monitoring automatically logs to console: - -**Normal Query**: -``` -✅ Query Performance: getQSOStats - 3.28ms -``` - -**Slow Query (>100ms)**: -``` -⚠️ SLOW QUERY: getQSOStats - 125.67ms -``` - -**Critical Query (>500ms)**: -``` -🚨 CRITICAL SLOW QUERY: getQSOStats - 567.89ms -``` - -### Performance Metrics by Query Type - -| Query Name | Avg Time | Min | Max | P50 | P95 | P99 | Rating | -|------------|-----------|------|------|-----|-----|-----|--------| -| getQSOStats | 3.28ms | 2.36ms | 11.75ms | 2.41ms | 11.75ms | 11.75ms | EXCELLENT | - -## Benefits - -### Visibility -- ✅ **Real-time tracking**: Every query is automatically tracked -- ✅ **Detailed metrics**: Min/max/percentiles/rating -- ✅ **Slow query detection**: Automatic alerts >100ms -- ✅ **Performance regression**: Detect 2x slowdown - -### Operational -- ✅ **Zero configuration**: Works out of the box -- ✅ **No external dependencies**: Pure JavaScript -- ✅ **Minimal overhead**: <0.1ms tracking cost -- ✅ **Persistent tracking**: In-memory, survives requests - -### Debugging -- ✅ **Top slowest queries**: Identify bottlenecks -- ✅ **Performance ratings**: EXCELLENT/GOOD/SLOW/CRITICAL -- ✅ **Error tracking**: Count and rate errors -- ✅ **Percentile calculations**: P50/P95/P99 for SLA monitoring - -## Use Cases - -### 1. Production Monitoring -```javascript -// Add to cron job or monitoring service -setInterval(() => { - const summary = getPerformanceSummary(); - if (summary.criticalQueries > 0) { - alertOpsTeam(`🚨 ${summary.criticalQueries} critical queries detected`); - } -}, 60000); // Check every minute -``` - -### 2. Performance Regression Detection -```javascript -// Check for degradation after deployments -const status = checkPerformanceDegradation('getQSOStats'); -if (status.degraded) { - rollbackDeployment('Performance degraded by ' + status.change); -} -``` - -### 3. Query Optimization -```javascript -// Identify slow queries for optimization -const slowQueries = getSlowQueries(100); -slowQueries.forEach(q => { - console.log(`Optimize: ${q.name} (avg: ${q.avgTime})`); - // Add indexes, refactor query, etc. -}); -``` - -### 4. SLA Monitoring -```javascript -// Verify 95th percentile meets SLA -const stats = getPerformanceStats('getQSOStats'); -if (parseFloat(stats.p95) > 100) { - console.warn(`SLA Violation: P95 > 100ms`); -} -``` - -## Performance Tracking Overhead - -**Minimal Impact**: -- Tracking overhead: <0.1ms per query -- Memory usage: ~100 bytes per unique query -- CPU usage: Negligible (performance.now() is fast) - -**Storage Strategy**: -- Keeps last 100 durations per query for percentiles -- Automatic cleanup of old data -- No disk writes (in-memory only) - -## Success Criteria - -✅ **Query performance tracking** - Implemented: Automatic tracking -✅ **Slow query detection** - Implemented: >100ms threshold -✅ **Critical query alert** - Implemented: >500ms threshold -✅ **Performance ratings** - Implemented: EXCELLENT/GOOD/SLOW/CRITICAL -✅ **Percentile calculations** - Implemented: P50/P95/P99 -✅ **Zero breaking changes** - Maintained: Works transparently - -## Next Steps - -**Phase 2.3**: Cache Invalidation Hooks (Already Complete) -- ✅ LoTW sync invalidation -- ✅ DCL sync invalidation -- ✅ Automatic expiration - -**Phase 2.4**: Monitoring Dashboard -- Add performance metrics to health endpoint -- Expose cache statistics via API -- Real-time monitoring UI - -## Files Modified - -1. **src/backend/services/performance.service.js** (NEW) - - Complete performance monitoring system - - Query tracking, statistics, slow detection - - Performance regression detection - -2. **src/backend/services/lotw.service.js** - - Added performance service imports - - Wrapped getQSOStats in trackQueryPerformance - -## Monitoring Recommendations - -**Key Metrics to Track**: -- Average query time (target: <50ms) -- P95/P99 percentiles (target: <100ms) -- Slow query count (target: 0) -- Critical query count (target: 0) -- Performance degradation (target: none) - -**Alerting Thresholds**: -- Warning: Avg > 100ms OR P95 > 150ms -- Critical: Avg > 500ms OR P99 > 750ms -- Regression: 2x slowdown detected - -## Summary - -**Phase 2.2 Status**: ✅ **COMPLETE** - -**Performance Monitoring**: -- ✅ Automatic query tracking -- ✅ Slow query detection (>100ms) -- ✅ Critical query alerts (>500ms) -- ✅ Performance ratings (EXCELLENT/GOOD/SLOW/CRITICAL) -- ✅ Percentile calculations (P50/P95/P99) -- ✅ Performance regression detection - -**Test Results**: -- Average query time: 3.28ms (EXCELLENT) -- Slow queries: 0 -- Critical queries: 0 -- Performance rating: EXCELLENT - -**Production Ready**: ✅ **YES** - -**Next**: Phase 2.4 - Monitoring Dashboard - ---- - -**Last Updated**: 2025-01-21 -**Status**: Phase 2.2 Complete - Ready for Phase 2.4 -**Performance**: EXCELLENT (3.28ms average) diff --git a/PHASE_2.4_COMPLETE.md b/PHASE_2.4_COMPLETE.md deleted file mode 100644 index 97edc2f..0000000 --- a/PHASE_2.4_COMPLETE.md +++ /dev/null @@ -1,491 +0,0 @@ -# Phase 2.4 Complete: Monitoring Dashboard - -## Summary - -Successfully implemented monitoring dashboard via health endpoint with real-time performance and cache statistics. - -## Changes Made - -### 1. Enhanced Health Endpoint -**File**: `src/backend/index.js:6, 971-981` - -Added performance and cache monitoring to `/api/health` endpoint: - -**Updated Imports**: -```javascript -import { getPerformanceSummary, resetPerformanceMetrics } from './services/performance.service.js'; -import { getCacheStats } from './services/cache.service.js'; -``` - -**Enhanced Health Endpoint**: -```javascript -.get('/api/health', () => ({ - status: 'ok', - timestamp: new Date().toISOString(), - uptime: process.uptime(), - performance: getPerformanceSummary(), - cache: getCacheStats() -})) -``` - -**Note**: Due to module-level state, performance metrics are tracked per module. For cross-module monitoring, consider implementing a shared state or singleton pattern in future enhancements. - -### 2. Health Endpoint Response Structure - -**Complete Response**: -```json -{ - "status": "ok", - "timestamp": "2025-01-21T06:37:58.109Z", - "uptime": 3.028732291, - "performance": { - "totalQueries": 0, - "totalTime": 0, - "avgTime": "0ms", - "slowQueries": 0, - "criticalQueries": 0, - "topSlowest": [] - }, - "cache": { - "total": 0, - "valid": 0, - "expired": 0, - "ttl": 300000, - "hitRate": "0%", - "awardCache": { - "size": 0, - "hits": 0, - "misses": 0 - }, - "statsCache": { - "size": 0, - "hits": 0, - "misses": 0 - } - } -} -``` - -## Test Results - -### Test Environment -- **Server**: Running on port 3001 -- **Endpoint**: `GET /api/health` -- **Testing**: Structure validation and field presence - -### Test Results - -#### Test 1: Basic Health Check -``` -✅ All required fields present -✅ Status: ok -✅ Valid timestamp: 2025-01-21T06:37:58.109Z -✅ Uptime: 3.03 seconds -``` - -#### Test 2: Performance Metrics Structure -``` -✅ All performance fields present: - - totalQueries - - totalTime - - avgTime - - slowQueries - - criticalQueries - - topSlowest -``` - -#### Test 3: Cache Statistics Structure -``` -✅ All cache fields present: - - total - - valid - - expired - - ttl - - hitRate - - awardCache - - statsCache -``` - -#### Test 4: Detailed Cache Structures -``` -✅ Award cache structure valid: - - size - - hits - - misses - -✅ Stats cache structure valid: - - size - - hits - - misses -``` - -### All Tests Passed ✅ - -## API Documentation - -### Health Check Endpoint - -**Endpoint**: `GET /api/health` - -**Response**: -```json -{ - "status": "ok", - "timestamp": "ISO-8601 timestamp", - "uptime": "seconds since server start", - "performance": { - "totalQueries": "total queries tracked", - "totalTime": "total execution time (ms)", - "avgTime": "average query time", - "slowQueries": "queries >100ms avg", - "criticalQueries": "queries >500ms avg", - "topSlowest": "array of slowest queries" - }, - "cache": { - "total": "total cached items", - "valid": "non-expired items", - "expired": "expired items", - "ttl": "cache TTL in ms", - "hitRate": "cache hit rate percentage", - "awardCache": { - "size": "number of entries", - "hits": "cache hits", - "misses": "cache misses" - }, - "statsCache": { - "size": "number of entries", - "hits": "cache hits", - "misses": "cache misses" - } - } -} -``` - -### Usage Examples - -#### 1. Basic Health Check -```bash -curl http://localhost:3001/api/health -``` - -**Response**: -```json -{ - "status": "ok", - "timestamp": "2025-01-21T06:37:58.109Z", - "uptime": 3.028732291 -} -``` - -#### 2. Monitor Performance -```bash -watch -n 5 'curl -s http://localhost:3001/api/health | jq .performance' -``` - -**Output**: -```json -{ - "totalQueries": 125, - "avgTime": "3.28ms", - "slowQueries": 0, - "criticalQueries": 0 -} -``` - -#### 3. Monitor Cache Hit Rate -```bash -watch -n 10 'curl -s http://localhost:3001/api/health | jq .cache.hitRate' -``` - -**Output**: -```json -"91.67%" -``` - -#### 4. Check for Slow Queries -```bash -curl -s http://localhost:3001/api/health | jq '.performance.topSlowest' -``` - -**Output**: -```json -[ - { - "name": "getQSOStats", - "avgTime": "3.28ms", - "rating": "EXCELLENT" - } -] -``` - -#### 5. Monitor All Metrics -```bash -curl -s http://localhost:3001/api/health | jq . -``` - -## Monitoring Use Cases - -### 1. Health Monitoring - -**Setup Automated Health Checks**: -```bash -# Check every 30 seconds -while true; do - response=$(curl -s http://localhost:3001/api/health) - status=$(echo $response | jq -r '.status') - - if [ "$status" != "ok" ]; then - echo "🚨 HEALTH CHECK FAILED: $status" - # Send alert (email, Slack, etc.) - fi - - sleep 30 -done -``` - -### 2. Performance Monitoring - -**Alert on Slow Queries**: -```bash -#!/bin/bash -threshold=100 # 100ms - -while true; do - health=$(curl -s http://localhost:3001/api/health) - slow=$(echo $health | jq -r '.performance.slowQueries') - critical=$(echo $health | jq -r '.performance.criticalQueries') - - if [ "$slow" -gt 0 ] || [ "$critical" -gt 0 ]; then - echo "⚠️ Slow queries detected: $slow slow, $critical critical" - # Investigate: check logs, analyze queries - fi - - sleep 60 -done -``` - -### 3. Cache Monitoring - -**Alert on Low Cache Hit Rate**: -```bash -#!/bin/bash -min_hit_rate=80 # 80% - -while true; do - health=$(curl -s http://localhost:3001/api/health) - hit_rate=$(echo $health | jq -r '.cache.hitRate' | tr -d '%') - - if [ "$hit_rate" -lt $min_hit_rate ]; then - echo "⚠️ Low cache hit rate: ${hit_rate}% (target: ${min_hit_rate}%)" - # Investigate: check cache TTL, invalidation logic - fi - - sleep 300 # Check every 5 minutes -done -``` - -### 4. Uptime Monitoring - -**Track Server Uptime**: -```bash -#!/bin/bash - -while true; do - health=$(curl -s http://localhost:3001/api/health) - uptime=$(echo $health | jq -r '.uptime') - - # Convert to human-readable format - hours=$((uptime / 3600)) - minutes=$(((uptime % 3600) / 60)) - - echo "Server uptime: ${hours}h ${minutes}m" - - sleep 60 -done -``` - -### 5. Dashboard Integration - -**Frontend Dashboard**: -```javascript -// Fetch health status every 5 seconds -setInterval(async () => { - const response = await fetch('/api/health'); - const health = await response.json(); - - // Update UI - document.getElementById('status').textContent = health.status; - document.getElementById('uptime').textContent = formatUptime(health.uptime); - document.getElementById('cache-hit-rate').textContent = health.cache.hitRate; - document.getElementById('query-count').textContent = health.performance.totalQueries; - document.getElementById('avg-query-time').textContent = health.performance.avgTime; -}, 5000); -``` - -## Benefits - -### Visibility -- ✅ **Real-time health**: Instant server status check -- ✅ **Performance metrics**: Query time, slow queries, critical queries -- ✅ **Cache statistics**: Hit rate, cache size, hits/misses -- ✅ **Uptime tracking**: How long server has been running - -### Monitoring -- ✅ **RESTful API**: Easy to monitor from anywhere -- ✅ **JSON response**: Machine-readable, easy to parse -- ✅ **No authentication**: Public endpoint (consider protecting in production) -- ✅ **Low overhead**: Fast query, minimal data - -### Alerting -- ✅ **Slow query detection**: Automatic slow/critical query tracking -- ✅ **Cache hit rate**: Monitor cache effectiveness -- ✅ **Health status**: Detect server issues immediately -- ✅ **Uptime monitoring**: Track server availability - -## Integration with Existing Tools - -### Prometheus (Optional Future Enhancement) - -```javascript -import { register, Gauge, Counter } from 'prom-client'; - -const uptimeGauge = new Gauge({ name: 'app_uptime_seconds', help: 'Server uptime' }); -const queryCountGauge = new Gauge({ name: 'app_queries_total', help: 'Total queries' }); -const cacheHitRateGauge = new Gauge({ name: 'app_cache_hit_rate', help: 'Cache hit rate' }); - -// Update metrics from health endpoint -setInterval(async () => { - const health = await fetch('http://localhost:3001/api/health').then(r => r.json()); - uptimeGauge.set(health.uptime); - queryCountGauge.set(health.performance.totalQueries); - cacheHitRateGauge.set(parseFloat(health.cache.hitRate)); -}, 5000); - -// Expose metrics endpoint -// (Requires additional setup) -``` - -### Grafana (Optional Future Enhancement) - -Create dashboard panels: -- **Server Uptime**: Time series of uptime -- **Query Performance**: Average query time over time -- **Slow Queries**: Count of slow/critical queries -- **Cache Hit Rate**: Cache effectiveness over time -- **Total Queries**: Request rate over time - -## Security Considerations - -### Current Status -- ✅ **Public endpoint**: No authentication required -- ⚠️ **Exposes metrics**: Performance data visible to anyone -- ⚠️ **No rate limiting**: Could be abused with rapid requests - -### Recommendations for Production - -1. **Add Authentication**: -```javascript -.get('/api/health', async ({ headers }) => { - // Check for API key or JWT token - const apiKey = headers['x-api-key']; - if (!validateApiKey(apiKey)) { - return { status: 'unauthorized' }; - } - // Return health data -}) -``` - -2. **Add Rate Limiting**: -```javascript -import { rateLimit } from '@elysiajs/rate-limit'; - -app.use(rateLimit({ - max: 10, // 10 requests per minute - duration: 60000, -})); -``` - -3. **Filter Sensitive Data**: -```javascript -// Don't expose detailed performance in production -const health = { - status: 'ok', - uptime: process.uptime(), - // Omit: performance details, cache details -}; -``` - -## Success Criteria - -✅ **Health endpoint accessible** - Implemented: `GET /api/health` -✅ **Performance metrics included** - Implemented: Query stats, slow queries -✅ **Cache statistics included** - Implemented: Hit rate, cache size -✅ **Valid JSON response** - Implemented: Proper JSON structure -✅ **All required fields present** - Implemented: Status, timestamp, uptime, metrics -✅ **Zero breaking changes** - Maintained: Backward compatible - -## Next Steps - -**Phase 2 Complete**: -- ✅ 2.1: Basic Caching Layer -- ✅ 2.2: Performance Monitoring -- ✅ 2.3: Cache Invalidation Hooks (part of 2.1) -- ✅ 2.4: Monitoring Dashboard - -**Phase 3**: Scalability Enhancements (Month 1) -- 3.1: SQLite Configuration Optimization -- 3.2: Materialized Views for Large Datasets -- 3.3: Connection Pooling -- 3.4: Advanced Caching Strategy - -## Files Modified - -1. **src/backend/index.js** - - Added performance service imports - - Added cache service imports - - Enhanced `/api/health` endpoint with metrics - -## Monitoring Recommendations - -**Key Metrics to Monitor**: -- Server uptime (target: continuous) -- Average query time (target: <50ms) -- Slow query count (target: 0) -- Critical query count (target: 0) -- Cache hit rate (target: >80%) - -**Alerting Thresholds**: -- Warning: Slow queries > 0 OR cache hit rate < 70% -- Critical: Critical queries > 0 OR cache hit rate < 50% - -**Monitoring Tools**: -- Health endpoint: `curl http://localhost:3001/api/health` -- Real-time dashboard: Build frontend to display metrics -- Automated alerts: Use scripts or monitoring services (Prometheus, Datadog, etc.) - -## Summary - -**Phase 2.4 Status**: ✅ **COMPLETE** - -**Health Endpoint**: -- ✅ Server status monitoring -- ✅ Uptime tracking -- ✅ Performance metrics -- ✅ Cache statistics -- ✅ Real-time updates - -**API Capabilities**: -- ✅ GET /api/health -- ✅ JSON response format -- ✅ All required fields present -- ✅ Performance and cache metrics included - -**Production Ready**: ✅ **YES** (with security considerations noted) - -**Phase 2 Complete**: ✅ **ALL PHASES COMPLETE** - ---- - -**Last Updated**: 2025-01-21 -**Status**: Phase 2 Complete - All tasks finished -**Next**: Phase 3 - Scalability Enhancements diff --git a/PHASE_2_SUMMARY.md b/PHASE_2_SUMMARY.md deleted file mode 100644 index 8d52857..0000000 --- a/PHASE_2_SUMMARY.md +++ /dev/null @@ -1,450 +0,0 @@ -# Phase 2 Complete: Stability & Monitoring ✅ - -## Executive Summary - -Successfully implemented comprehensive caching, performance monitoring, and health dashboard. Achieved **601x faster** cache hits and complete visibility into system performance. - -## What We Accomplished - -### Phase 2.1: Basic Caching Layer ✅ -**Files**: `src/backend/services/cache.service.js`, `src/backend/services/lotw.service.js`, `src/backend/services/dcl.service.js` - -**Implementation**: -- Added QSO statistics caching (5-minute TTL) -- Implemented cache hit/miss tracking -- Added automatic cache invalidation after LoTW/DCL syncs -- Enhanced cache statistics API - -**Performance**: -- Cache hit: 12ms → **0.02ms** (601x faster) -- Database load: **96% reduction** for repeated requests -- Cache hit rate: **91.67%** (10 queries) - -### Phase 2.2: Performance Monitoring ✅ -**File**: `src/backend/services/performance.service.js` (new) - -**Implementation**: -- Created complete performance monitoring system -- Track query execution times -- Calculate percentiles (P50/P95/P99) -- Detect slow queries (>100ms) and critical queries (>500ms) -- Performance ratings (EXCELLENT/GOOD/SLOW/CRITICAL) - -**Features**: -- `trackQueryPerformance(queryName, fn)` - Track any query -- `getPerformanceStats(queryName)` - Get detailed statistics -- `getPerformanceSummary()` - Get overall summary -- `getSlowQueries(threshold)` - Find slow queries -- `checkPerformanceDegradation()` - Detect 2x slowdown - -**Performance**: -- Average query time: 3.28ms (EXCELLENT) -- Slow queries: 0 -- Critical queries: 0 -- Tracking overhead: <0.1ms per query - -### Phase 2.3: Cache Invalidation Hooks ✅ -**Files**: `src/backend/services/lotw.service.js`, `src/backend/services/dcl.service.js` - -**Implementation**: -- Invalidate stats cache after LoTW sync -- Invalidate stats cache after DCL sync -- Automatic expiration after 5 minutes - -**Strategy**: -- Event-driven invalidation (syncs, updates) -- Time-based expiration (TTL) -- Manual invalidation support (for testing/emergency) - -### Phase 2.4: Monitoring Dashboard ✅ -**File**: `src/backend/index.js` - -**Implementation**: -- Enhanced `/api/health` endpoint -- Added performance metrics to response -- Added cache statistics to response -- Real-time monitoring capability - -**API Response**: -```json -{ - "status": "ok", - "timestamp": "2025-01-21T06:37:58.109Z", - "uptime": 3.028732291, - "performance": { - "totalQueries": 0, - "totalTime": 0, - "avgTime": "0ms", - "slowQueries": 0, - "criticalQueries": 0, - "topSlowest": [] - }, - "cache": { - "total": 0, - "valid": 0, - "expired": 0, - "ttl": 300000, - "hitRate": "0%", - "awardCache": { - "size": 0, - "hits": 0, - "misses": 0 - }, - "statsCache": { - "size": 0, - "hits": 0, - "misses": 0 - } - } -} -``` - -## Overall Performance Comparison - -### Before Phase 2 (Phase 1 Only) -- Every page view: 3-12ms database query -- No caching layer -- No performance monitoring -- No health endpoint metrics - -### After Phase 2 Complete -- First page view: 3-12ms (cache miss) -- Subsequent page views: **<0.1ms** (cache hit) -- **601x faster** on cache hits -- **96% less** database load -- Complete performance monitoring -- Real-time health dashboard - -### Performance Metrics - -| Metric | Before | After | Improvement | -|--------|--------|-------|-------------| -| **Cache Hit Time** | N/A | **0.02ms** | N/A (new feature) | -| **Cache Miss Time** | 3-12ms | 3-12ms | No change | -| **Database Load** | 100% | **4%** | **96% reduction** | -| **Cache Hit Rate** | N/A | **91.67%** | N/A (new feature) | -| **Monitoring** | None | **Complete** | 100% visibility | - -## API Documentation - -### 1. Cache Service API - -```javascript -import { getCachedStats, setCachedStats, invalidateStatsCache, getCacheStats } from './cache.service.js'; - -// Get cached stats (with automatic hit/miss tracking) -const cached = getCachedStats(userId); - -// Cache stats data -setCachedStats(userId, data); - -// Invalidate cache after syncs -invalidateStatsCache(userId); - -// Get cache statistics -const stats = getCacheStats(); -console.log(stats); -``` - -### 2. Performance Monitoring API - -```javascript -import { trackQueryPerformance, getPerformanceStats, getPerformanceSummary } from './performance.service.js'; - -// Track query performance -const result = await trackQueryPerformance('myQuery', async () => { - return await someDatabaseOperation(); -}); - -// Get detailed statistics for a query -const stats = getPerformanceStats('myQuery'); -console.log(stats); - -// Get overall performance summary -const summary = getPerformanceSummary(); -console.log(summary); -``` - -### 3. Health Endpoint API - -```bash -# Get system health and metrics -curl http://localhost:3001/api/health - -# Watch performance metrics -watch -n 5 'curl -s http://localhost:3001/api/health | jq .performance' - -# Monitor cache hit rate -watch -n 10 'curl -s http://localhost:3001/api/health | jq .cache.hitRate' -``` - -## Files Modified - -1. **src/backend/services/cache.service.js** - - Added stats cache (Map storage) - - Added stats cache functions (get/set/invalidate) - - Added hit/miss tracking - - Enhanced getCacheStats() with stats metrics - -2. **src/backend/services/lotw.service.js** - - Added stats cache imports - - Modified getQSOStats() to use cache - - Added performance tracking wrapper - - Added cache invalidation after sync - -3. **src/backend/services/dcl.service.js** - - Added stats cache imports - - Added cache invalidation after sync - -4. **src/backend/services/performance.service.js** (NEW) - - Complete performance monitoring system - - Query tracking, statistics, slow detection - - Performance regression detection - - Percentile calculations (P50/P95/P99) - -5. **src/backend/index.js** - - Added performance service imports - - Added cache service imports - - Enhanced `/api/health` endpoint - -## Implementation Checklist - -### Phase 2: Stability & Monitoring -- ✅ Implement 5-minute TTL cache for QSO statistics -- ✅ Add performance monitoring and logging -- ✅ Create cache invalidation hooks for sync operations -- ✅ Add performance metrics to health endpoint -- ✅ Test all functionality -- ✅ Document APIs and usage - -## Success Criteria - -### Phase 2.1: Caching -✅ **Cache hit time <1ms** - Achieved: 0.02ms (50x faster than target) -✅ **5-minute TTL** - Implemented: 300,000ms TTL -✅ **Automatic invalidation** - Implemented: Hooks in LoTW/DCL sync -✅ **Cache statistics** - Implemented: Hits/misses/hit rate tracking -✅ **Zero breaking changes** - Maintained: Same API, transparent caching - -### Phase 2.2: Performance Monitoring -✅ **Query performance tracking** - Implemented: Automatic tracking -✅ **Slow query detection** - Implemented: >100ms threshold -✅ **Critical query alert** - Implemented: >500ms threshold -✅ **Performance ratings** - Implemented: EXCELLENT/GOOD/SLOW/CRITICAL -✅ **Percentile calculations** - Implemented: P50/P95/P99 -✅ **Zero breaking changes** - Maintained: Works transparently - -### Phase 2.3: Cache Invalidation -✅ **Automatic invalidation** - Implemented: LoTW/DCL sync hooks -✅ **TTL expiration** - Implemented: 5-minute automatic expiration -✅ **Manual invalidation** - Implemented: invalidateStatsCache() function - -### Phase 2.4: Monitoring Dashboard -✅ **Health endpoint accessible** - Implemented: `GET /api/health` -✅ **Performance metrics included** - Implemented: Query stats, slow queries -✅ **Cache statistics included** - Implemented: Hit rate, cache size -✅ **Valid JSON response** - Implemented: Proper JSON structure -✅ **All required fields present** - Implemented: Status, timestamp, uptime, metrics - -## Monitoring Setup - -### Quick Start - -1. **Monitor System Health**: -```bash -# Check health status -curl http://localhost:3001/api/health - -# Watch health status -watch -n 10 'curl -s http://localhost:3001/api/health | jq .status' -``` - -2. **Monitor Performance**: -```bash -# Watch query performance -watch -n 5 'curl -s http://localhost:3001/api/health | jq .performance.avgTime' - -# Monitor for slow queries -watch -n 60 'curl -s http://localhost:3001/api/health | jq .performance.slowQueries' -``` - -3. **Monitor Cache Effectiveness**: -```bash -# Watch cache hit rate -watch -n 10 'curl -s http://localhost:3001/api/health | jq .cache.hitRate' - -# Monitor cache sizes -watch -n 10 'curl -s http://localhost:3001/api/health | jq .cache' -``` - -### Automated Monitoring Scripts - -**Health Check Script**: -```bash -#!/bin/bash -# health-check.sh - -response=$(curl -s http://localhost:3001/api/health) -status=$(echo $response | jq -r '.status') - -if [ "$status" != "ok" ]; then - echo "🚨 HEALTH CHECK FAILED: $status" - exit 1 -fi - -echo "✅ Health check passed" -exit 0 -``` - -**Performance Alert Script**: -```bash -#!/bin/bash -# performance-alert.sh - -response=$(curl -s http://localhost:3001/api/health) -slow=$(echo $response | jq -r '.performance.slowQueries') -critical=$(echo $response | jq -r '.performance.criticalQueries') - -if [ "$slow" -gt 0 ] || [ "$critical" -gt 0 ]; then - echo "⚠️ Slow queries detected: $slow slow, $critical critical" - exit 1 -fi - -echo "✅ No slow queries detected" -exit 0 -``` - -**Cache Alert Script**: -```bash -#!/bin/bash -# cache-alert.sh - -response=$(curl -s http://localhost:3001/api/health) -hit_rate=$(echo $response | jq -r '.cache.hitRate' | tr -d '%') - -if [ "$hit_rate" -lt 70 ]; then - echo "⚠️ Low cache hit rate: ${hit_rate}% (target: >70%)" - exit 1 -fi - -echo "✅ Cache hit rate good: ${hit_rate}%" -exit 0 -``` - -## Production Deployment - -### Pre-Deployment Checklist -- ✅ All tests passed -- ✅ Performance targets achieved -- ✅ Cache hit rate >80% (in staging) -- ✅ No slow queries in staging -- ✅ Health endpoint working -- ✅ Documentation complete - -### Post-Deployment Monitoring - -**Day 1-7**: Monitor closely -- Cache hit rate (target: >80%) -- Average query time (target: <50ms) -- Slow queries (target: 0) -- Health endpoint response time (target: <100ms) - -**Week 2-4**: Monitor trends -- Cache hit rate trend (should be stable/improving) -- Query time distribution (P50/P95/P99) -- Memory usage (cache size, performance metrics) -- Database load (should be 50-90% lower) - -**Month 1+**: Optimize -- Identify slow queries and optimize -- Adjust cache TTL if needed -- Add more caching layers if beneficial - -## Expected Production Impact - -### Performance Gains -- **User Experience**: Page loads 600x faster after first visit -- **Database Load**: 80-90% reduction (depends on traffic pattern) -- **Server Capacity**: 10-20x more concurrent users - -### Observability Gains -- **Real-time Monitoring**: Instant visibility into system health -- **Performance Detection**: Automatic slow query detection -- **Cache Analytics**: Track cache effectiveness -- **Capacity Planning**: Data-driven scaling decisions - -### Operational Gains -- **Issue Detection**: Faster identification of performance problems -- **Debugging**: Performance metrics help diagnose issues -- **Alerting**: Automated alerts for slow queries/low cache hit rate -- **Capacity Management**: Data on query patterns and load - -## Security Considerations - -### Current Status -- ⚠️ **Public health endpoint**: No authentication required -- ⚠️ **Exposes metrics**: Performance data visible to anyone -- ⚠️ **No rate limiting**: Could be abused with rapid requests - -### Recommended Production Hardening - -1. **Add Authentication**: -```javascript -// Require API key or JWT token for health endpoint -app.get('/api/health', async ({ headers }) => { - const apiKey = headers['x-api-key']; - if (!validateApiKey(apiKey)) { - return { status: 'unauthorized' }; - } - // Return health data -}); -``` - -2. **Add Rate Limiting**: -```javascript -import { rateLimit } from '@elysiajs/rate-limit'; - -app.use(rateLimit({ - max: 10, // 10 requests per minute - duration: 60000, -})); -``` - -3. **Filter Sensitive Data**: -```javascript -// Don't expose detailed performance in production -const health = { - status: 'ok', - uptime: process.uptime(), - // Omit: detailed performance, cache details -}; -``` - -## Summary - -**Phase 2 Status**: ✅ **COMPLETE** - -**Implementation**: -- ✅ Phase 2.1: Basic Caching Layer (601x faster cache hits) -- ✅ Phase 2.2: Performance Monitoring (complete visibility) -- ✅ Phase 2.3: Cache Invalidation Hooks (automatic) -- ✅ Phase 2.4: Monitoring Dashboard (health endpoint) - -**Performance Results**: -- Cache hit time: **0.02ms** (601x faster than DB) -- Database load: **96% reduction** for repeated requests -- Cache hit rate: **91.67%** (in testing) -- Average query time: **3.28ms** (EXCELLENT rating) -- Slow queries: **0** -- Critical queries: **0** - -**Production Ready**: ✅ **YES** (with security considerations noted) - -**Next**: Phase 3 - Scalability Enhancements (Month 1) - ---- - -**Last Updated**: 2025-01-21 -**Status**: Phase 2 Complete - All tasks finished -**Performance**: EXCELLENT (601x faster cache hits) -**Monitoring**: COMPLETE (performance + cache + health) diff --git a/optimize.md b/optimize.md deleted file mode 100644 index 654a63f..0000000 --- a/optimize.md +++ /dev/null @@ -1,560 +0,0 @@ -# Quickawards Performance Optimization Plan - -## Overview - -This document outlines the comprehensive optimization plan for Quickawards, focusing primarily on resolving critical performance issues in QSO statistics queries. - -## Critical Performance Issue - -### Current Problem -The `getQSOStats()` function loads ALL user QSOs into memory before calculating statistics: -- **Location**: `src/backend/services/lotw.service.js:496-517` -- **Impact**: Users with 200k QSOs experience 5-10 second page loads -- **Memory Usage**: 100MB+ per request -- **Concurrent Users**: Limited to 2-3 due to memory pressure - -### Root Cause -```javascript -// Current implementation (PROBLEMATIC) -export async function getQSOStats(userId) { - const allQSOs = await db.select().from(qsos).where(eq(qsos.userId, userId)); - // Loads 200k+ records into memory - // ... processes with .filter() and .forEach() -} -``` - -### Target Performance -- **Query Time**: <100ms for 200k QSO users (currently 5-10 seconds) -- **Memory Usage**: <1MB per request (currently 100MB+) -- **Concurrent Users**: Support 50+ concurrent users - -## Optimization Plan - -### Phase 1: Emergency Performance Fix (Week 1) - -#### 1.1 SQL Query Optimization -**File**: `src/backend/services/lotw.service.js` - -Replace the memory-intensive `getQSOStats()` function with SQL-based aggregates: - -```javascript -// Optimized implementation -export async function getQSOStats(userId) { - const [basicStats, uniqueStats] = await Promise.all([ - // Basic statistics - db.select({ - total: sql`COUNT(*)`, - confirmed: sql`SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END)` - }).from(qsos).where(eq(qsos.userId, userId)), - - // Unique counts - db.select({ - uniqueEntities: sql`COUNT(DISTINCT entity)`, - uniqueBands: sql`COUNT(DISTINCT band)`, - uniqueModes: sql`COUNT(DISTINCT mode)` - }).from(qsos).where(eq(qsos.userId, userId)) - ]); - - return { - total: basicStats[0].total, - confirmed: basicStats[0].confirmed, - uniqueEntities: uniqueStats[0].uniqueEntities, - uniqueBands: uniqueStats[0].uniqueBands, - uniqueModes: uniqueStats[0].uniqueModes, - }; -} -``` - -**Benefits**: -- Query executes entirely in SQLite -- Only returns 5 integers instead of 200k+ objects -- Reduces memory from 100MB+ to <1MB -- Expected query time: 50-100ms for 200k QSOs - -#### 1.2 Critical Database Indexes -**File**: `src/backend/migrations/add-performance-indexes.js` (extend existing file) - -Add essential indexes for QSO statistics queries: - -```javascript -// Index for primary user queries -await db.run(sql`CREATE INDEX IF NOT EXISTS idx_qsos_user_primary ON qsos(user_id)`); - -// Index for confirmation status queries -await db.run(sql`CREATE INDEX IF NOT EXISTS idx_qsos_user_confirmed ON qsos(user_id, lotw_qsl_rstatus, dcl_qsl_rstatus)`); - -// Index for unique counts (entity, band, mode) -await db.run(sql`CREATE INDEX IF NOT EXISTS idx_qsos_user_unique_counts ON qsos(user_id, entity, band, mode)`); -``` - -**Benefits**: -- Speeds up WHERE clause filtering by 10-100x -- Optimizes COUNT(DISTINCT) operations -- Critical for sub-100ms query times - -#### 1.3 Testing & Validation - -**Test Cases**: -1. Small dataset (1k QSOs): Query time <10ms -2. Medium dataset (50k QSOs): Query time <50ms -3. Large dataset (200k QSOs): Query time <100ms - -**Validation Steps**: -1. Run test queries with logging enabled -2. Compare memory usage before/after -3. Verify frontend receives identical API response format -4. Load test with 50 concurrent users - -**Success Criteria**: -- ✅ Query time <100ms for 200k QSOs -- ✅ Memory usage <1MB per request -- ✅ API response format unchanged -- ✅ No errors in production for 1 week - -### Phase 2: Stability & Monitoring (Week 2) - -#### 2.1 Basic Caching Layer -**File**: `src/backend/services/lotw.service.js` - -Add 5-minute TTL cache for QSO statistics: - -```javascript -const statsCache = new Map(); - -export async function getQSOStats(userId) { - const cacheKey = `stats_${userId}`; - const cached = statsCache.get(cacheKey); - - if (cached && Date.now() - cached.timestamp < 300000) { // 5 minutes - return cached.data; - } - - // Run optimized SQL query (from Phase 1.1) - const stats = await calculateStatsWithSQL(userId); - - statsCache.set(cacheKey, { - data: stats, - timestamp: Date.now() - }); - - return stats; -} - -// Invalidate cache after QSO syncs -export async function invalidateStatsCache(userId) { - statsCache.delete(`stats_${userId}`); -} -``` - -**Benefits**: -- Cache hit: <1ms response time -- Reduces database load by 80-90% -- Automatic cache invalidation after syncs - -#### 2.2 Performance Monitoring -**File**: `src/backend/utils/logger.js` (extend existing) - -Add query performance tracking: - -```javascript -export async function trackQueryPerformance(queryName, fn) { - const start = performance.now(); - const result = await fn(); - const duration = performance.now() - start; - - logger.debug('Query Performance', { - query: queryName, - duration: `${duration.toFixed(2)}ms`, - threshold: duration > 100 ? 'SLOW' : 'OK' - }); - - if (duration > 500) { - logger.warn('Slow query detected', { query: queryName, duration: `${duration.toFixed(2)}ms` }); - } - - return result; -} - -// Usage in getQSOStats: -const stats = await trackQueryPerformance('getQSOStats', () => - calculateStatsWithSQL(userId) -); -``` - -**Benefits**: -- Detect performance regressions early -- Identify slow queries in production -- Data-driven optimization decisions - -#### 2.3 Cache Invalidation Hooks -**Files**: `src/backend/services/lotw.service.js`, `src/backend/services/dcl.service.js` - -Invalidate cache after QSO imports: - -```javascript -// lotw.service.js - after syncQSOs() -export async function syncQSOs(userId, lotwUsername, lotwPassword, sinceDate, jobId) { - // ... existing sync logic ... - await invalidateStatsCache(userId); -} - -// dcl.service.js - after syncQSOs() -export async function syncQSOs(userId, dclApiKey, sinceDate, jobId) { - // ... existing sync logic ... - await invalidateStatsCache(userId); -} -``` - -#### 2.4 Monitoring Dashboard -**File**: Create `src/backend/routes/health.js` (or extend existing health endpoint) - -Add performance metrics to health check: - -```javascript -app.get('/api/health', async (req) => { - return { - status: 'healthy', - uptime: process.uptime(), - database: await checkDatabaseHealth(), - performance: { - avgQueryTime: getAverageQueryTime(), - cacheHitRate: getCacheHitRate(), - slowQueriesCount: getSlowQueriesCount() - } - }; -}); -``` - -### Phase 3: Scalability Enhancements (Month 1) - -#### 3.1 SQLite Configuration Optimization -**File**: `src/backend/db/index.js` - -Optimize SQLite for read-heavy workloads: - -```javascript -const db = new Database('data/award.db'); - -// Enable WAL mode for better concurrency -db.pragma('journal_mode = WAL'); - -// Increase cache size (default -2000KB, set to 100MB) -db.pragma('cache_size = -100000'); - -// Optimize for SELECT queries -db.pragma('synchronous = NORMAL'); // Balance between safety and speed -db.pragma('temp_store = MEMORY'); // Keep temporary tables in RAM -db.pragma('mmap_size = 30000000000'); // Memory-map database (30GB limit) -``` - -**Benefits**: -- WAL mode allows concurrent reads -- Larger cache reduces disk I/O -- Memory-mapped I/O for faster access - -#### 3.2 Materialized Views for Large Datasets -**File**: Create `src/backend/migrations/create-materialized-views.js` - -For users with >50k QSOs, create pre-computed statistics: - -```javascript -// Create table for pre-computed stats -await db.run(sql` - CREATE TABLE IF NOT EXISTS qso_stats_cache ( - user_id INTEGER PRIMARY KEY, - total INTEGER, - confirmed INTEGER, - unique_entities INTEGER, - unique_bands INTEGER, - unique_modes INTEGER, - updated_at DATETIME DEFAULT CURRENT_TIMESTAMP - ) -`); - -// Create trigger to auto-update stats after QSO changes -await db.run(sql` - CREATE TRIGGER IF NOT EXISTS update_qso_stats - AFTER INSERT OR UPDATE OR DELETE ON qsos - BEGIN - INSERT OR REPLACE INTO qso_stats_cache (user_id, total, confirmed, unique_entities, unique_bands, unique_modes, updated_at) - SELECT - user_id, - COUNT(*) as total, - SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END) as confirmed, - COUNT(DISTINCT entity) as unique_entities, - COUNT(DISTINCT band) as unique_bands, - COUNT(DISTINCT mode) as unique_modes, - CURRENT_TIMESTAMP as updated_at - FROM qsos - WHERE user_id = NEW.user_id - GROUP BY user_id; - END; -`); -``` - -**Benefits**: -- Stats updated automatically in real-time -- Query time: <5ms for any dataset size -- No cache invalidation needed - -**Usage in getQSOStats()**: -```javascript -export async function getQSOStats(userId) { - // First check if user has pre-computed stats - const cachedStats = await db.select().from(qsoStatsCache).where(eq(qsoStatsCache.userId, userId)); - - if (cachedStats.length > 0) { - return { - total: cachedStats[0].total, - confirmed: cachedStats[0].confirmed, - uniqueEntities: cachedStats[0].uniqueEntities, - uniqueBands: cachedStats[0].uniqueBands, - uniqueModes: cachedStats[0].uniqueModes, - }; - } - - // Fall back to regular query for small users - return calculateStatsWithSQL(userId); -} -``` - -#### 3.3 Connection Pooling -**File**: `src/backend/db/index.js` - -Implement connection pooling for better concurrency: - -```javascript -import { Pool } from 'bun-sqlite3'; - -const pool = new Pool({ - filename: 'data/award.db', - max: 10, // Max connections - timeout: 30000, // 30 second timeout -}); - -export async function getDb() { - return pool.getConnection(); -} -``` - -**Note**: SQLite has limited write concurrency, but read connections can be pooled. - -#### 3.4 Advanced Caching Strategy -**File**: `src/backend/services/cache.service.js` - -Implement Redis-style caching with Bun's built-in capabilities: - -```javascript -class CacheService { - constructor() { - this.cache = new Map(); - this.stats = { hits: 0, misses: 0 }; - } - - async get(key) { - const value = this.cache.get(key); - if (value) { - this.stats.hits++; - return value.data; - } - this.stats.misses++; - return null; - } - - async set(key, data, ttl = 300000) { - this.cache.set(key, { - data, - timestamp: Date.now(), - ttl - }); - - // Auto-expire after TTL - setTimeout(() => this.delete(key), ttl); - } - - async delete(key) { - this.cache.delete(key); - } - - getStats() { - const total = this.stats.hits + this.stats.misses; - return { - hitRate: total > 0 ? (this.stats.hits / total * 100).toFixed(2) + '%' : '0%', - hits: this.stats.hits, - misses: this.stats.misses, - size: this.cache.size - }; - } -} - -export const cacheService = new CacheService(); -``` - -## Implementation Checklist - -### Phase 1: Emergency Performance Fix -- [ ] Replace `getQSOStats()` with SQL aggregates -- [ ] Add database indexes -- [ ] Run migration -- [ ] Test with 1k, 50k, 200k QSO datasets -- [ ] Verify API response format unchanged -- [ ] Deploy to production -- [ ] Monitor for 1 week - -### Phase 2: Stability & Monitoring -- [ ] Implement 5-minute TTL cache -- [ ] Add performance monitoring -- [ ] Create cache invalidation hooks -- [ ] Add performance metrics to health endpoint -- [ ] Deploy to production -- [ ] Monitor cache hit rate (target >80%) - -### Phase 3: Scalability Enhancements -- [ ] Optimize SQLite configuration (WAL mode, cache size) -- [ ] Create materialized views for large datasets -- [ ] Implement connection pooling -- [ ] Deploy advanced caching strategy -- [ ] Load test with 100+ concurrent users - -## Additional Issues Identified (Future Work) - -### High Priority - -1. **Unencrypted LoTW Password Storage** - - **Location**: `src/backend/services/auth.service.js:124` - - **Issue**: LoTW password stored in plaintext in database - - **Fix**: Encrypt with AES-256 before storing - - **Effort**: 4 hours - -2. **Weak JWT Secret Security** - - **Location**: `src/backend/config.js:27` - - **Issue**: Default JWT secret in production - - **Fix**: Use environment variable with strong secret - - **Effort**: 1 hour - -3. **ADIF Parser Logic Error** - - **Location**: `src/backend/utils/adif-parser.js:17-18` - - **Issue**: Potential data corruption from incorrect parsing - - **Fix**: Use case-insensitive regex for `` tags - - **Effort**: 2 hours - -### Medium Priority - -4. **Missing Database Transactions** - - **Location**: Sync operations in `lotw.service.js`, `dcl.service.js` - - **Issue**: No transaction support for multi-record operations - - **Fix**: Wrap syncs in transactions - - **Effort**: 6 hours - -5. **Memory Leak Potential in Job Queue** - - **Location**: `src/backend/services/job-queue.service.js` - - **Issue**: Jobs never removed from memory - - **Fix**: Implement cleanup mechanism - - **Effort**: 4 hours - -### Low Priority - -6. **Database Path Exposure** - - **Location**: Error messages reveal database path - - **Issue**: Predictable database location - - **Fix**: Sanitize error messages - - **Effort**: 2 hours - -## Monitoring & Metrics - -### Key Performance Indicators (KPIs) - -1. **QSO Statistics Query Time** - - Target: <100ms for 200k QSOs - - Current: 5-10 seconds - - Tool: Application performance monitoring - -2. **Memory Usage per Request** - - Target: <1MB per request - - Current: 100MB+ - - Tool: Node.js memory profiler - -3. **Concurrent Users** - - Target: 50+ concurrent users - - Current: 2-3 users - - Tool: Load testing with Apache Bench - -4. **Cache Hit Rate** - - Target: >80% after Phase 2 - - Current: 0% (no cache) - - Tool: Custom metrics in cache service - -5. **Database Response Time** - - Target: <50ms for all queries - - Current: Variable (some queries slow) - - Tool: SQLite query logging - -### Alerting Thresholds - -- **Critical**: Query time >500ms -- **Warning**: Query time >200ms -- **Info**: Cache hit rate <70% - -## Rollback Plan - -If issues arise after deployment: - -1. **Phase 1 Rollback** (if SQL query fails): - - Revert `getQSOStats()` to original implementation - - Keep database indexes (they help performance) - - Estimated rollback time: 5 minutes - -2. **Phase 2 Rollback** (if cache causes issues): - - Disable cache by bypassing cache checks - - Keep monitoring (helps diagnose issues) - - Estimated rollback time: 2 minutes - -3. **Phase 3 Rollback** (if SQLite config causes issues): - - Revert SQLite configuration changes - - Drop materialized views if needed - - Estimated rollback time: 10 minutes - -## Success Criteria - -### Phase 1 Success -- ✅ Query time <100ms for 200k QSOs -- ✅ Memory usage <1MB per request -- ✅ Zero bugs in production for 1 week -- ✅ User feedback: "Page loads instantly now" - -### Phase 2 Success -- ✅ Cache hit rate >80% -- ✅ Database load reduced by 80% -- ✅ Zero cache-related bugs for 1 week - -### Phase 3 Success -- ✅ Support 50+ concurrent users -- ✅ Query time <5ms for materialized views -- ✅ Zero performance complaints for 1 month - -## Timeline - -- **Week 1**: Phase 1 - Emergency Performance Fix -- **Week 2**: Phase 2 - Stability & Monitoring -- **Month 1**: Phase 3 - Scalability Enhancements -- **Month 2-3**: Address additional high-priority security issues -- **Ongoing**: Monitor, iterate, optimize - -## Resources - -### Documentation -- SQLite Performance: https://www.sqlite.org/optoverview.html -- Drizzle ORM: https://orm.drizzle.team/ -- Bun Runtime: https://bun.sh/docs - -### Tools -- Query Performance: SQLite EXPLAIN QUERY PLAN -- Load Testing: Apache Bench (`ab -n 1000 -c 50 http://localhost:3001/api/qsos/stats`) -- Memory Profiling: Node.js `--inspect` flag with Chrome DevTools -- Database Analysis: `sqlite3 data/award.db "PRAGMA index_info(idx_qsos_user_primary);"` - ---- - -**Last Updated**: 2025-01-21 -**Author**: Quickawards Optimization Team -**Status**: Planning Phase - Ready to Start Phase 1 Implementation