Challenge

Production SQL Server databases experiencing performance degradation despite 30% YoY traffic growth. P95 query latency grew from 200ms to 1.2s. Several background jobs failing due to timeouts. Database infrastructure costs ballooning ($85K/month) as team added more compute to scale vertically.

Approach

Diagnostic Phase:

• Query execution analysis: Profiled slow query log (queries >1s) using SQL Server Extended Events
• Index analysis: Identified missing indexes and unused indexes consuming 40% of storage
• Schema review: Found N+1 query patterns and missing denormalization opportunities
• Workload characterization: Separated OLTP from analytical queries using SQL Server DMVs

Optimization Strategy:

1. Index optimization: Added 8 strategic indexes, dropped 12 unused ones (25% storage reduction)
2. Query rewrites: Refactored 47 slow queries using CTE optimization, window functions, batch operations
3. Connection pooling: Implemented SQL Server connection pooling reducing connection overhead
4. Statistics/AUTOUPDATE tuning: Optimized based on workload patterns for query optimizer
5. Materialized views: 4 heavy analytical queries converted to 2-hour refresh indexed views

Team

• Size: 4 people (1 PM, 1 senior SQL Server DBA, 1 database engineer, 1 analyst)
• Duration: 3 months (ongoing optimization)
• Stakeholder engagement: Weekly updates with engineering and finance leads

Results

Delivery Metrics

• On-time: ✓ Phase 1 (8 weeks), Phase 2 ongoing (planned)
• Budget: $320K actual vs $300K planned (+7%)
• Scope: 95% - Reserved some analyses for Phase 2

Technical Impact

• Query latency: 72% reduction (P95: 1.2s → 340ms)
• Query throughput: 3.8x improvement (1,200 queries/sec → 4,560 queries/sec)
• Database storage: 35% reduction through index cleanup
• CPU utilization: Reduced from 85% peak to 45% peak
• Background job failures: Dropped from 8/day → 0/day

Business Impact

• Infrastructure cost: $85K → $51K/month (40% savings = $408K/year)
• Eliminated need for database upgrade (saved $150K capital cost)
• Customer page load time: Improved by avg 320ms
• User experience: Reduced checkout abandonment by 1.8%

Key Decisions

1. Profiling before optimization - Data-driven approach prevented wasted effort on wrong queries using SQL Server DMVs
2. Connection pooling over scaling - Cheaper solution than vertical scaling and better long-term
3. Indexed views for analytics - Isolated analytical load from OLTP transactions
4. Scheduled index maintenance - Built sustainable optimization culture vs one-time project