BLACK_BOX_LOGS

EXECUTIVE_SUMMARY

When scaling PostgreSQL to handle 10,000+ concurrent connections, traditional approaches quickly hit bottlenecks. This document outlines the systematic approach used to achieve 99.9% uptime under extreme load.

CONNECTION_POOLING_STRATEGY

# PgBouncer Configuration
pool_mode = transaction
max_client_conn = 10000
default_pool_size = 100
server_lifetime = 3600
server_idle_timeout = 600

QUERY_OPTIMIZATION_TECHNIQUES

• Implemented prepared statements for 90% of queries
• Added composite indexes for complex WHERE clauses
• Partitioned large tables by date ranges
• Used EXPLAIN ANALYZE for all slow queries

PERFORMANCE_RESULTS

OPTIMIZATION_STRATEGY

Standard Go Docker images often exceed 1GB. Through multi-stage builds and distroless base images, we achieved production containers under 20MB while maintaining full functionality.

DOCKERFILE_IMPLEMENTATION

# Build stage
FROM golang:1.21-alpine AS builder
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-w -s" -o main .

# Production stage
FROM gcr.io/distroless/static-debian11
COPY --from=builder /app/main /
EXPOSE 8080
ENTRYPOINT ["/main"]

SECURITY_ENHANCEMENTS

• Distroless images eliminate shell access
• No package managers or unnecessary binaries
• Minimal attack surface
• Regular base image updates

SIZE_COMPARISON

CIRCUIT_BREAKER_PATTERN

Circuit breakers prevent cascading failures by monitoring service health and automatically switching to fallback mechanisms when failure thresholds are exceeded.

GO_IMPLEMENTATION

type CircuitBreaker struct {
    maxFailures int
    timeout     time.Duration
    failures    int
    lastFailure time.Time
    state       State
    mutex       sync.RWMutex
}

func (cb *CircuitBreaker) Call(fn func() error) error {
    cb.mutex.Lock()
    defer cb.mutex.Unlock()
    
    if cb.state == Open {
        if time.Since(cb.lastFailure) > cb.timeout {
            cb.state = HalfOpen
        } else {
            return ErrCircuitOpen
        }
    }
    
    err := fn()
    if err != nil {
        cb.recordFailure()
        return err
    }
    
    cb.recordSuccess()
    return nil
}

MONITORING_METRICS

• Failure rate tracking
• Response time monitoring
• Circuit state transitions
• Fallback execution counts

PRODUCTION_RESULTS

SCALING_METHODOLOGY

Effective auto-scaling requires understanding traffic patterns, resource utilization, and business requirements to create policies that maintain performance while controlling costs.

HPA_CONFIGURATION

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-gateway-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-gateway
  minReplicas: 3
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

COST_OPTIMIZATION

• Predictive scaling based on historical data
• Spot instance integration for non-critical workloads
• Resource requests tuned to actual usage
• Scheduled scaling for known traffic patterns