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Remove outdated phase markdown files and optimize.md that are no longer relevant to the active codebase.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 <noreply@anthropic.com>
This commit is contained in:
@@ -1,103 +0,0 @@
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# Phase 1.1 Complete: SQL Query Optimization
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## Summary
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Successfully optimized the `getQSOStats()` function to use SQL aggregates instead of loading all QSOs into memory.
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## Changes Made
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**File**: `src/backend/services/lotw.service.js` (lines 496-517)
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### Before (Problematic)
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```javascript
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export async function getQSOStats(userId) {
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const allQSOs = await db.select().from(qsos).where(eq(qsos.userId, userId));
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// Loads 200k+ records into memory
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const confirmed = allQSOs.filter((q) => q.lotwQslRstatus === 'Y' || q.dclQslRstatus === 'Y');
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const uniqueEntities = new Set();
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const uniqueBands = new Set();
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const uniqueModes = new Set();
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allQSOs.forEach((q) => {
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if (q.entity) uniqueEntities.add(q.entity);
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if (q.band) uniqueBands.add(q.band);
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if (q.mode) uniqueModes.add(q.mode);
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});
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return {
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total: allQSOs.length,
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confirmed: confirmed.length,
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uniqueEntities: uniqueEntities.size,
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uniqueBands: uniqueBands.size,
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uniqueModes: uniqueModes.size,
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};
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}
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```
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**Problems**:
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- Loads ALL user QSOs into memory (200k+ records)
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- Processes data in JavaScript (slow)
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- Uses 100MB+ memory per request
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- Takes 5-10 seconds for 200k QSOs
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### After (Optimized)
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```javascript
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export async function getQSOStats(userId) {
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const [basicStats, uniqueStats] = await Promise.all([
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db.select({
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total: sql<number>`COUNT(*)`,
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confirmed: sql<number>`SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END)`
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}).from(qsos).where(eq(qsos.userId, userId)),
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db.select({
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uniqueEntities: sql<number>`COUNT(DISTINCT entity)`,
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uniqueBands: sql<number>`COUNT(DISTINCT band)`,
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uniqueModes: sql<number>`COUNT(DISTINCT mode)`
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}).from(qsos).where(eq(qsos.userId, userId))
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]);
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return {
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total: basicStats[0].total,
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confirmed: basicStats[0].confirmed || 0,
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uniqueEntities: uniqueStats[0].uniqueEntities || 0,
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uniqueBands: uniqueStats[0].uniqueBands || 0,
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uniqueModes: uniqueStats[0].uniqueModes || 0,
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};
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}
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```
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**Benefits**:
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- Executes entirely in SQLite (fast)
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- Only returns 5 integers instead of 200k+ objects
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- Uses <1MB memory per request
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- Expected query time: 50-100ms for 200k QSOs
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- Parallel queries with `Promise.all()`
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## Verification
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✅ SQL syntax validated
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✅ Backend starts without errors
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✅ API response format unchanged
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✅ No breaking changes to existing code
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## Performance Improvement Estimates
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| Metric | Before | After | Improvement |
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|--------|--------|-------|-------------|
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| Query Time (200k QSOs) | 5-10 seconds | 50-100ms | **50-200x faster** |
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| Memory Usage | 100MB+ | <1MB | **100x less memory** |
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| Concurrent Users | 2-3 | 50+ | **16x more capacity** |
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## Next Steps
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**Phase 1.2**: Add critical database indexes to further improve performance
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The indexes will speed up the WHERE clause and COUNT(DISTINCT) operations, ensuring we achieve the sub-100ms target for large datasets.
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## Notes
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- The optimization maintains backward compatibility
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- API response format is identical to before
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- No frontend changes required
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- Ready for deployment (indexes recommended for optimal performance)
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@@ -1,160 +0,0 @@
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# Phase 1.2 Complete: Critical Database Indexes
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## Summary
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Successfully added 3 critical database indexes specifically optimized for QSO statistics queries, bringing the total to 10 performance indexes.
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## Changes Made
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**File**: `src/backend/migrations/add-performance-indexes.js`
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### New Indexes Added
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#### Index 8: Primary User Filter
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```sql
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CREATE INDEX IF NOT EXISTS idx_qsos_user_primary ON qsos(user_id);
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```
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**Purpose**: Speed up basic WHERE clause filtering
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**Impact**: 10-100x faster for user-based queries
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#### Index 9: Unique Counts
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```sql
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CREATE INDEX IF NOT EXISTS idx_qsos_user_unique_counts ON qsos(user_id, entity, band, mode);
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```
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**Purpose**: Optimize COUNT(DISTINCT) operations
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**Impact**: Critical for `getQSOStats()` unique entity/band/mode counts
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#### Index 10: Confirmation Status
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```sql
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CREATE INDEX IF NOT EXISTS idx_qsos_stats_confirmation ON qsos(user_id, lotw_qsl_rstatus, dcl_qsl_rstatus);
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```
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**Purpose**: Optimize confirmed QSO counting
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**Impact**: Fast SUM(CASE WHEN ...) confirmed counts
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### Complete Index List (10 Total)
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1. `idx_qsos_user_band` - Filter by band
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2. `idx_qsos_user_mode` - Filter by mode
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3. `idx_qsos_user_confirmation` - Filter by confirmation status
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4. `idx_qsos_duplicate_check` - Sync duplicate detection (most impactful for sync)
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5. `idx_qsos_lotw_confirmed` - LoTW confirmed QSOs (partial index)
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6. `idx_qsos_dcl_confirmed` - DCL confirmed QSOs (partial index)
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7. `idx_qsos_qso_date` - Date-based sorting
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8. **`idx_qsos_user_primary`** - Primary user filter (NEW)
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9. **`idx_qsos_user_unique_counts`** - Unique counts (NEW)
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10. **`idx_qsos_stats_confirmation`** - Confirmation counting (NEW)
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## Migration Results
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```bash
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$ bun src/backend/migrations/add-performance-indexes.js
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Starting migration: Add performance indexes...
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Creating index: idx_qsos_user_band
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Creating index: idx_qsos_user_mode
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Creating index: idx_qsos_user_confirmation
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Creating index: idx_qsos_duplicate_check
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Creating index: idx_qsos_lotw_confirmed
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Creating index: idx_qsos_dcl_confirmed
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Creating index: idx_qsos_qso_date
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Creating index: idx_qsos_user_primary
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Creating index: idx_qsos_user_unique_counts
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Creating index: idx_qsos_stats_confirmation
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Migration complete! Created 10 performance indexes.
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```
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### Verification
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```bash
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$ sqlite3 src/backend/award.db "SELECT name FROM sqlite_master WHERE type='index' AND tbl_name='qsos' ORDER BY name;"
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idx_qsos_dcl_confirmed
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idx_qsos_duplicate_check
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idx_qsos_lotw_confirmed
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idx_qsos_qso_date
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idx_qsos_stats_confirmation
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idx_qsos_user_band
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idx_qsos_user_confirmation
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idx_qsos_user_mode
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idx_qsos_user_primary
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idx_qsos_user_unique_counts
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```
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✅ All 10 indexes successfully created
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## Performance Impact
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### Query Execution Plans
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**Before (Full Table Scan)**:
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```
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SCAN TABLE qsos USING INDEX idx_qsos_user_primary
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```
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**After (Index Seek)**:
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```
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SEARCH TABLE qsos USING INDEX idx_qsos_user_primary (user_id=?)
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USE TEMP B-TREE FOR count(DISTINCT entity)
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```
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### Expected Performance Gains
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| Operation | Before | After | Improvement |
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|-----------|--------|-------|-------------|
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| WHERE user_id = ? | Full scan | Index seek | 50-100x faster |
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| COUNT(DISTINCT entity) | Scan all rows | Index scan | 10-20x faster |
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| SUM(CASE WHEN confirmed) | Scan all rows | Index scan | 20-50x faster |
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| Overall getQSOStats() | 5-10s | **<100ms** | **50-100x faster** |
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## Database Impact
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- **File Size**: No significant increase (indexes are efficient)
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- **Write Performance**: Minimal impact (indexing is fast)
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- **Disk Usage**: Slightly higher (index storage overhead)
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- **Memory Usage**: Slightly higher (index cache)
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## Combined Impact (Phase 1.1 + 1.2)
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### Before Optimization
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- Query Time: 5-10 seconds
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- Memory Usage: 100MB+
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- Concurrent Users: 2-3
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- Table Scans: Yes (slow)
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### After Optimization
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- ✅ Query Time: **<100ms** (50-100x faster)
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- ✅ Memory Usage: **<1MB** (100x less)
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- ✅ Concurrent Users: **50+** (16x more)
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- ✅ Table Scans: No (uses indexes)
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## Next Steps
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**Phase 1.3**: Testing & Validation
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We need to:
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1. Test with small dataset (1k QSOs) - target: <10ms
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2. Test with medium dataset (50k QSOs) - target: <50ms
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3. Test with large dataset (200k QSOs) - target: <100ms
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4. Verify API response format unchanged
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5. Load test with 50 concurrent users
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## Notes
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- All indexes use `IF NOT EXISTS` (safe to run multiple times)
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- Partial indexes used where appropriate (e.g., confirmed status)
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- Index names follow consistent naming convention
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- Ready for production deployment
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## Verification Checklist
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- ✅ All 10 indexes created successfully
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- ✅ Database integrity maintained
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- ✅ No schema conflicts
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- ✅ Index names are unique
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- ✅ Database accessible and functional
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- ✅ Migration script completes without errors
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---
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**Status**: Phase 1.2 Complete
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**Next**: Phase 1.3 - Testing & Validation
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@@ -1,311 +0,0 @@
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# Phase 1.3 Complete: Testing & Validation
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## Summary
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Successfully tested and validated the optimized QSO statistics query. All performance targets achieved with flying colors!
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## Test Results
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### Test Environment
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- **Database**: SQLite3 (src/backend/award.db)
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- **Dataset Size**: 8,339 QSOs
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- **User ID**: 1 (random test user)
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- **Indexes**: 10 performance indexes active
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### Performance Results
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#### Query Execution Time
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```
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⏱️ Query time: 3.17ms
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```
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**Performance Rating**: ✅ EXCELLENT
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**Comparison**:
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- Target: <100ms
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- Achieved: 3.17ms
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- **Performance margin: 31x faster than target!**
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#### Scale Projections
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| Dataset Size | Estimated Query Time | Rating |
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|--------------|---------------------|--------|
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| 1,000 QSOs | ~1ms | Excellent |
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| 10,000 QSOs | ~5ms | Excellent |
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| 50,000 QSOs | ~20ms | Excellent |
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| 100,000 QSOs | ~40ms | Excellent |
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| 200,000 QSOs | ~80ms | **Excellent** ✅ |
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**Note**: Even with 200k QSOs, we're well under the 100ms target!
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### Test Results Breakdown
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#### ✅ Test 1: Query Execution
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- Status: PASSED
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- Query completed successfully
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- No errors or exceptions
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- Returns valid results
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#### ✅ Test 2: Performance Evaluation
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- Status: EXCELLENT
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- Query time: 3.17ms (target: <100ms)
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- Performance margin: 31x faster than target
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- Rating: EXCELLENT
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#### ✅ Test 3: Response Format
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- Status: PASSED
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- All required fields present:
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- `total`: 8,339
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- `confirmed`: 8,339
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- `uniqueEntities`: 194
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- `uniqueBands`: 15
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- `uniqueModes`: 10
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#### ✅ Test 4: Data Integrity
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- Status: PASSED
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- All values are non-negative integers
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- Confirmed QSOs (8,339) <= Total QSOs (8,339) ✓
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- Logical consistency verified
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#### ✅ Test 5: Index Utilization
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- Status: PASSED (with note)
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- 10 performance indexes on qsos table
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- All critical indexes present and active
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## Performance Comparison
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### Before Optimization (Memory-Intensive)
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```javascript
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// Load ALL QSOs into memory
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const allQSOs = await db.select().from(qsos).where(eq(qsos.userId, userId));
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// Process in JavaScript (slow)
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const confirmed = allQSOs.filter((q) => q.lotwQslRstatus === 'Y' || q.dclQslRstatus === 'Y');
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// Count unique values in Sets
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const uniqueEntities = new Set();
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allQSOs.forEach((q) => {
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if (q.entity) uniqueEntities.add(q.entity);
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// ...
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});
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```
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**Performance Metrics (Estimated for 8,339 QSOs)**:
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- Query Time: ~100-200ms (loads all rows)
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- Memory Usage: ~10-20MB (all QSOs in RAM)
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- Processing Time: ~50-100ms (JavaScript iteration)
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- **Total Time**: ~150-300ms
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### After Optimization (SQL-Based)
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```javascript
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// SQL aggregates execute in database
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const [basicStats, uniqueStats] = await Promise.all([
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db.select({
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total: sql`CAST(COUNT(*) AS INTEGER)`,
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confirmed: sql`CAST(SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END) AS INTEGER)`
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}).from(qsos).where(eq(qsos.userId, userId)),
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db.select({
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uniqueEntities: sql`CAST(COUNT(DISTINCT entity) AS INTEGER)`,
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uniqueBands: sql`CAST(COUNT(DISTINCT band) AS INTEGER)`,
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uniqueModes: sql`CAST(COUNT(DISTINCT mode) AS INTEGER)`
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}).from(qsos).where(eq(qsos.userId, userId))
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]);
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```
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**Performance Metrics (Actual: 8,339 QSOs)**:
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- Query Time: **3.17ms** ✅
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- Memory Usage: **<1MB** (only 5 integers returned) ✅
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- Processing Time: **0ms** (SQL handles everything)
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- **Total Time**: **3.17ms** ✅
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### Performance Improvement
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| Metric | Before | After | Improvement |
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|--------|--------|-------|-------------|
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| Query Time (8.3k QSOs) | 150-300ms | 3.17ms | **47-95x faster** |
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| Query Time (200k QSOs est.) | 5-10s | ~80ms | **62-125x faster** |
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| Memory Usage | 10-20MB | <1MB | **10-20x less** |
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| Processing Time | 50-100ms | 0ms | **Infinite** (removed) |
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## Scalability Analysis
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### Linear Performance Scaling
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The optimized query scales linearly with dataset size, but the SQL engine is highly efficient:
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**Formula**: `Query Time ≈ (QSO Count / 8,339) × 3.17ms`
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**Predictions**:
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- 10k QSOs: ~4ms
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- 50k QSOs: ~19ms
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- 100k QSOs: ~38ms
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- 200k QSOs: ~76ms
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- 500k QSOs: ~190ms
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**Conclusion**: Even with 500k QSOs, query time remains under 200ms!
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### Concurrent User Capacity
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**Before Optimization**:
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- Memory per request: ~10-20MB
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- Query time: 150-300ms
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- Max concurrent users: 2-3 (memory limited)
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**After Optimization**:
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- Memory per request: <1MB
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- Query time: 3.17ms
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- Max concurrent users: 50+ (CPU limited)
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**Capacity Improvement**: 16-25x more concurrent users!
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## Database Query Plans
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### Optimized Query Execution
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```sql
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-- Basic stats query
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SELECT
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CAST(COUNT(*) AS INTEGER) as total,
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CAST(SUM(CASE WHEN lotw_qsl_rstatus = 'Y' OR dcl_qsl_rstatus = 'Y' THEN 1 ELSE 0 END) AS INTEGER) as confirmed
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FROM qsos
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WHERE user_id = ?
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-- Uses index: idx_qsos_user_primary
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-- Operation: Index seek (fast!)
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```
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```sql
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-- Unique counts query
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SELECT
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CAST(COUNT(DISTINCT entity) AS INTEGER) as uniqueEntities,
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CAST(COUNT(DISTINCT band) AS INTEGER) as uniqueBands,
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CAST(COUNT(DISTINCT mode) AS INTEGER) as uniqueModes
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FROM qsos
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WHERE user_id = ?
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|
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-- Uses index: idx_qsos_user_unique_counts
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-- Operation: Index scan (efficient!)
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```
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### Index Utilization
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- `idx_qsos_user_primary`: Used for WHERE clause filtering
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- `idx_qsos_user_unique_counts`: Used for COUNT(DISTINCT) operations
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- `idx_qsos_stats_confirmation`: Used for confirmed QSO counting
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## Validation Checklist
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- ✅ Query executes without errors
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- ✅ Query time <100ms (achieved: 3.17ms)
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- ✅ Memory usage <1MB (achieved: <1MB)
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- ✅ All required fields present
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- ✅ Data integrity validated (non-negative, logical consistency)
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- ✅ API response format unchanged
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- ✅ Performance indexes active (10 indexes)
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- ✅ Supports 50+ concurrent users
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- ✅ Scales to 200k+ QSOs
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## Test Dataset Analysis
|
||||
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### QSO Statistics
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- **Total QSOs**: 8,339
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- **Confirmed QSOs**: 8,339 (100% confirmation rate)
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- **Unique Entities**: 194 (countries worked)
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- **Unique Bands**: 15 (different HF/VHF bands)
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- **Unique Modes**: 10 (CW, SSB, FT8, etc.)
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||||
|
||||
### 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
|
||||
@@ -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)
|
||||
@@ -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)
|
||||
@@ -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)
|
||||
@@ -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
|
||||
@@ -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)
|
||||
560
optimize.md
560
optimize.md
@@ -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<number>`COUNT(*)`,
|
||||
confirmed: sql<number>`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<number>`COUNT(DISTINCT entity)`,
|
||||
uniqueBands: sql<number>`COUNT(DISTINCT band)`,
|
||||
uniqueModes: sql<number>`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 `<EOR>` 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
|
||||
Reference in New Issue
Block a user