[Performance]: Optimize Resource Allocation Based on Performance Metrics

[benjaminpaige]

⚡ Performance Enhancement

What performance improvement is needed?
Optimize ECS resource allocation based on actual performance metrics to improve cost efficiency and system performance across all SEA Tool CDC services.

Performance Impact
Right-sizing resources will reduce costs while maintaining or improving performance for critical Medicare and Medicaid data processing workflows.

🚀 Proposed Performance Solution

Data-Driven Resource Optimization Strategy

Implement comprehensive performance monitoring and automated resource optimization based on actual usage patterns across all CDC services (connector, debezium, ksqldb, ksqlthree).

Current Resource Allocation Analysis

Generic Configurations (Need Optimization):

• Connector: cpu: 256, memory: 2048, maxContainerMemory: 1024
• Debezium: memory: 8GB, cpu: 4096 (production)
• KsqlDB: Environment-specific but may not reflect actual usage
• KsqlThree: Enhanced memory but potentially over-provisioned

Performance Optimization Opportunity
Current resource allocations appear to be initial estimates rather than data-driven optimizations based on actual CDC workload patterns.

📊 Detailed Performance Analysis Plan

Phase 1: Performance Baseline Establishment (Week 1)

CloudWatch Metrics Collection

# Connector service performance analysis
aws cloudwatch get-metric-statistics --namespace AWS/ECS \
  --metric-name CPUUtilization --start-time $(date -d '30 days ago' -u +%Y-%m-%dT%H:%M:%S) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%S) --period 3600 --statistics Average,Maximum \
  --dimensions Name=ServiceName,Value=kafka-connect Name=ClusterName,Value=seatool-connector-${stage}-connect

aws cloudwatch get-metric-statistics --namespace AWS/ECS \
  --metric-name MemoryUtilization --start-time $(date -d '30 days ago' -u +%Y-%m-%dT%H:%M:%S) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%S) --period 3600 --statistics Average,Maximum \
  --dimensions Name=ServiceName,Value=kafka-connect Name=ClusterName,Value=seatool-connector-${stage}-connect

CDC Performance Metrics

// Debezium connector performance tuning configuration
{
  "config": {
    "max.batch.size": "2048",
    "max.queue.size": "16384", 
    "poll.interval.ms": "1000",
    "snapshot.fetch.size": "10240",
    "incremental.snapshot.chunk.size": "1024",
    "database.query.timeout.ms": "600000"
  }
}

ksqlDB Stream Processing Analysis

# ksqlDB performance optimization settings
ksql.streams.auto.offset.reset=earliest
ksql.streams.commit.interval.ms=2000
ksql.streams.cache.max.bytes.buffering=20971520  # 20MB
ksql.streams.num.stream.threads=4

# RocksDB cache optimization  
rocksdb.cache.size=134217728                     # 128MB cache
rocksdb.block.cache.size=67108864               # 64MB block cache

Phase 2: Resource Optimization Implementation (Week 2)

Environment-Specific Optimization

Production Environment Tuning:

params:
  production:
    # Connector resources (optimized based on metrics)
    taskCpu: "2048"      # Increased from 256 for production workload
    taskMemory: "4096"   # Optimized for high-volume CDC
    maxContainerCpu: "1024"    # Balanced for connector performance
    maxContainerMemory: "2048" # Right-sized for production load
    
    # Debezium resources (production-optimized)
    debeziumCpu: "4096"
    debeziumMemory: "8192"
    
    # ksqlDB resources (stream processing optimized)  
    ksqldbCpu: "4096"
    ksqldbMemory: "8192"
    ksqldbHeap: "6G"
    rocksdbCache: 134217728    # 128MB optimized cache

Development Environment Efficiency:

params:
  default:
    # Cost-optimized for development
    taskCpu: "512"       # Reduced for cost efficiency
    taskMemory: "1024"   # Minimal for development workload
    maxContainerCpu: "256"     # Cost-effective development
    maxContainerMemory: "512"  # Right-sized for testing

Phase 3: Automated Performance Monitoring (Week 3)

Performance Monitoring Dashboard

// CloudWatch dashboard for performance optimization
export class PerformanceOptimizationDashboard extends Construct {
  constructor(scope: Construct, id: string, props: DashboardProps) {
    // Resource utilization metrics
    // Cost analysis and recommendations
    // Performance trend analysis
    // Automated right-sizing recommendations
  }
}

Automated Resource Recommendations

# Automated resource optimization script
#!/bin/bash
# Resource optimization analysis and recommendations

analyze_service_performance() {
  local service=$1
  local stage=$2
  
  echo "Analyzing $service performance for stage $stage..."
  
  # CPU utilization analysis
  aws cloudwatch get-metric-statistics --namespace AWS/ECS \
    --metric-name CPUUtilization --period 3600 --statistics Average,Maximum \
    --start-time $(date -d '7 days ago' -u +%Y-%m-%dT%H:%M:%S) \
    --end-time $(date -u +%Y-%m-%dT%H:%M:%S) \
    --dimensions Name=ServiceName,Value=$service Name=ClusterName,Value=seatool-$service-$stage-connect
  
  # Memory utilization analysis
  aws cloudwatch get-metric-statistics --namespace AWS/ECS \
    --metric-name MemoryUtilization --period 3600 --statistics Average,Maximum \
    --start-time $(date -d '7 days ago' -u +%Y-%m-%dT%H:%M:%S) \
    --end-time $(date -u +%Y-%m-%dT%H:%M:%S) \
    --dimensions Name=ServiceName,Value=$service Name=ClusterName,Value=seatool-$service-$stage-connect
}

# Generate optimization recommendations
generate_recommendations() {
  echo "Generating resource optimization recommendations..."
  # Analyze metrics and provide actionable recommendations
  # Cost impact analysis
  # Performance improvement projections
}

📈 Performance Optimization Areas

CDC Performance Tuning

Debezium Connector Optimization:

• Batch size optimization based on transaction log volume
• Queue size tuning for high-throughput periods
• Polling interval optimization for latency vs. throughput balance
• Snapshot configuration for initial data loading efficiency

Resource Allocation Optimization:

• Container CPU allocation based on actual utilization patterns
• Memory allocation optimization for CDC processing requirements
• Network performance tuning for database connectivity
• Storage optimization for transaction log processing

Stream Processing Optimization

ksqlDB Performance Enhancement:

• Stream thread optimization based on processing requirements
• Cache configuration tuning for query performance
• RocksDB optimization for state store efficiency
• Memory allocation optimization for complex queries

KsqlThree Processing Optimization:

• Enhanced memory allocation for OneMac data processing
• CPU optimization for dual-service architecture
• Topic consumption optimization for Debezium integration
• Query performance optimization for real-time analytics

✅ Acceptance Criteria

Performance Baseline and Analysis:

• 30-day performance baseline established for all CDC services
• CloudWatch metrics collection automated for resource utilization
• Cost analysis completed for current vs. optimized configurations
• Performance bottleneck identification and documentation

Resource Optimization Implementation:

• Environment-specific resource configurations optimized based on actual metrics
• Production resources tuned for high-volume CDC workloads
• Development resources optimized for cost efficiency
• Validation environment balanced for testing requirements

CDC Performance Enhancement:

• Debezium connector performance tuned for SQL Server CDC
• Kafka Connect batch and queue sizes optimized
• Database polling intervals optimized for latency and throughput
• Transaction log processing efficiency improved

Stream Processing Optimization:

• ksqlDB stream processing performance enhanced
• RocksDB cache and memory allocation optimized
• KsqlThree OneMac processing performance improved
• Query execution performance validated and optimized

Monitoring and Automation:

• Performance monitoring dashboard implemented
• Automated resource recommendation system operational
• Performance regression detection and alerting
• Cost optimization tracking and reporting

📎 Additional Context

SEA Tool CDC Context
This optimization directly impacts critical CMS operations:

• Medicare and Medicaid state plan processing efficiency
• Real-time data streaming performance to BigMac
• Change data capture latency for compliance reporting
• Stream processing analytics for operational insights

Cost Optimization Impact
Based on current infrastructure costs (~/month per environment), optimization could provide:

• 20-30% cost reduction through right-sizing
• Improved performance-to-cost ratio
• Enhanced resource utilization efficiency
• Better scaling characteristics for varying workloads

Migration Integration
This optimization should coordinate with ongoing Serverless-to-CDK migration:

• Apply optimizations during CDK stack creation
• Validate optimization in parallel deployment testing
• Ensure optimized configurations are preserved in CDK migration
• Use optimization data to improve CDK resource definitions

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📋 Issue Creator Checklist

• I have identified specific performance optimization opportunities based on current resource analysis
• I have provided detailed performance monitoring and optimization implementation plan
• I have included CDC-specific performance tuning requirements
• I have considered integration with ongoing CDK migration efforts