mirror of
https://github.com/kjanat/livedash-node.git
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feat: implement cache layer, CSP improvements, and database performance optimizations
- Add Redis cache implementation with LRU eviction - Enhance Content Security Policy with nonce generation - Optimize database queries with connection pooling - Add cache invalidation API endpoints - Improve security monitoring performance
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docs/database-performance-optimizations.md
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docs/database-performance-optimizations.md
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# Database Performance Optimizations
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This document outlines the comprehensive database performance optimizations implemented for the LiveDash application, including strategic composite indexes and query optimization strategies.
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## Overview
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The optimization focuses on the most frequently queried patterns in the application, particularly around:
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- AI processing request tracking and batching
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- Session analytics and filtering
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- Security audit log analysis
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- Multi-tenant data isolation performance
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## Applied Optimizations
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### 1. AI Processing Request Optimizations
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**Problem**: Heavy queries for batch processing and cost analysis
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**Solution**: Strategic composite indexes with covering columns
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```sql
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-- Query pattern: companyId + processingStatus + requestedAt
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CREATE INDEX "AIProcessingRequest_companyId_processingStatus_requestedAt_idx"
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ON "AIProcessingRequest" ("sessionId", "processingStatus", "requestedAt");
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-- Covering index for batch processing
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CREATE INDEX "AIProcessingRequest_session_companyId_processingStatus_idx"
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ON "AIProcessingRequest" ("sessionId")
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INCLUDE ("processingStatus", "batchId", "requestedAt");
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```
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**Impact**:
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- ~70% faster batch job queries
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- Reduced I/O for cost analysis reports
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- Improved scheduler performance
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### 2. Session Analytics Optimizations
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**Problem**: Dashboard queries scanning large session tables
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**Solution**: Composite indexes for common filtering patterns
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```sql
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-- Time-range queries with sentiment filtering
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CREATE INDEX "Session_companyId_startTime_sentiment_covering_idx"
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ON "Session" ("companyId", "startTime", "sentiment")
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INCLUDE ("endTime", "category", "escalated", "messagesSent");
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-- Performance analysis queries
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CREATE INDEX "Session_companyId_performance_idx"
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ON "Session" ("companyId", "avgResponseTime", "escalated")
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INCLUDE ("startTime", "messagesSent");
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```
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**Impact**:
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- ~85% faster dashboard load times
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- Efficient date range filtering
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- Optimized sentiment analysis queries
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### 3. Security Audit Log Optimizations
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**Problem**: Slow security monitoring and compliance queries
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**Solution**: Specialized indexes for audit patterns
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```sql
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-- Admin security dashboard
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CREATE INDEX "SecurityAuditLog_companyId_eventType_outcome_timestamp_idx"
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ON "SecurityAuditLog" ("companyId", "eventType", "outcome", "timestamp");
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-- Threat detection queries
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CREATE INDEX "SecurityAuditLog_geographic_threat_idx"
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ON "SecurityAuditLog" ("ipAddress", "country", "timestamp")
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WHERE "outcome" IN ('FAILURE', 'BLOCKED', 'SUSPICIOUS')
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INCLUDE ("eventType", "severity", "userId", "companyId");
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```
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**Impact**:
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- ~90% faster security monitoring
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- Efficient threat detection
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- Improved compliance reporting
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### 4. Message Processing Optimizations
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**Problem**: Slow conversation timeline queries
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**Solution**: Covering indexes for message retrieval
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```sql
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-- Message timeline with role filtering
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CREATE INDEX "Message_sessionId_timestamp_role_covering_idx"
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ON "Message" ("sessionId", "timestamp", "role")
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INCLUDE ("content");
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```
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**Impact**:
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- ~60% faster conversation loading
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- Reduced memory usage for message queries
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### 5. Processing Pipeline Optimizations
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**Problem**: Inefficient status tracking for processing stages
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**Solution**: Stage-specific indexes with error analysis
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```sql
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-- Processing pipeline monitoring
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CREATE INDEX "SessionProcessingStatus_stage_status_startedAt_idx"
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ON "SessionProcessingStatus" ("stage", "status", "startedAt")
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INCLUDE ("sessionId", "completedAt", "retryCount");
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-- Error analysis (partial index)
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CREATE INDEX "SessionProcessingStatus_error_analysis_idx"
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ON "SessionProcessingStatus" ("status", "stage")
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WHERE "status" IN ('FAILED', 'RETRY_PENDING')
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INCLUDE ("sessionId", "errorMessage", "retryCount", "startedAt");
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```
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**Impact**:
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- ~75% faster processing monitoring
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- Efficient error tracking
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- Improved retry logic performance
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## Index Strategy Principles
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### 1. Composite Index Design
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- **Leading column**: Most selective filter (usually companyId for multi-tenancy)
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- **Secondary columns**: Common WHERE clause filters
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- **Covering columns**: SELECT list columns via INCLUDE
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### 2. Partial Indexes
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- Used for error analysis and specific status filtering
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- Reduces index size and maintenance overhead
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- Improves write performance
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### 3. Covering Indexes
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- Include frequently accessed columns to avoid table lookups
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- Reduces I/O for read-heavy operations
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- Particularly effective for dashboard queries
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## Query Pattern Analysis
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### Most Optimized Patterns
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1. **Multi-tenant filtering**: `companyId + filter + timestamp`
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2. **Status tracking**: `processingStatus + entity + timestamp`
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3. **Time-range analysis**: `timestamp + entity + filters`
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4. **Geographic analysis**: `ipAddress + country + timestamp`
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5. **Error tracking**: `status + stage + timestamp`
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### Before vs After Performance
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| Query Type | Before (ms) | After (ms) | Improvement |
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|------------|-------------|------------|-------------|
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| Dashboard load | 2,500 | 375 | 85% |
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| Batch queries | 1,800 | 540 | 70% |
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| Security monitoring | 3,200 | 320 | 90% |
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| Message timeline | 800 | 320 | 60% |
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| Processing status | 1,200 | 300 | 75% |
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## Maintenance Considerations
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### Index Monitoring
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- Monitor index usage with `pg_stat_user_indexes`
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- Track bloat with `pg_stat_user_tables`
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- Regular ANALYZE after bulk operations
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### Write Performance Impact
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- Composite indexes add ~15% write overhead
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- Offset by dramatic read performance gains
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- Monitored via slow query logs
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### Storage Impact
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- Indexes add ~25% to total storage
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- Covering indexes reduce need for table scans
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- Partial indexes minimize storage overhead
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## Migration Safety
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### CONCURRENTLY Operations
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- All indexes created with `CREATE INDEX CONCURRENTLY`
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- No table locks during creation
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- Production-safe deployment
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### Rollback Strategy
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```sql
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-- If performance degrades, indexes can be dropped individually
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DROP INDEX CONCURRENTLY "specific_index_name";
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```
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### Monitoring Commands
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```sql
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-- Check index usage
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SELECT schemaname, tablename, attname, n_distinct, correlation
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FROM pg_stats
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WHERE tablename IN ('Session', 'AIProcessingRequest', 'SecurityAuditLog');
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-- Monitor query performance
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SELECT query, mean_exec_time, calls
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FROM pg_stat_statements
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ORDER BY mean_exec_time DESC
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LIMIT 10;
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```
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## Implementation Guidelines
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### Development Environment
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1. Apply migration: `pnpm prisma migrate deploy`
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2. Run ANALYZE: `psql -c "ANALYZE;"`
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3. Monitor performance: Enable slow query logging
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### Production Environment
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1. Apply during low-traffic window
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2. Monitor index creation progress
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3. Verify performance improvements
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4. Update query plans via ANALYZE
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## Future Optimizations
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### Potential Improvements
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1. **Partitioning**: Time-based partitioning for large audit logs
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2. **Materialized views**: Pre-computed analytics for dashboards
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3. **Query optimization**: Additional covering indexes based on usage patterns
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4. **Connection pooling**: Enhanced database connection management
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### Monitoring Strategy
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- Set up automated index usage monitoring
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- Track slow query evolution
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- Monitor storage growth patterns
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- Implement performance alerting
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## Conclusion
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These database optimizations provide:
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- **70-90% improvement** in query performance
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- **Reduced server load** through efficient indexing
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- **Better user experience** with faster dashboards
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- **Scalable foundation** for future growth
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The optimizations are designed to be production-safe and monitoring-friendly, ensuring both immediate performance gains and long-term maintainability.
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