Capacity Planning
Forecasting Methodologies
Historical data analysis identifying growth patterns over time. Linear, exponential, or polynomial trend lines. Use moving averages to smooth data. Account for outliers and anomalies. Extrapolate to predict future capacity needs. Works best with stable, predictable growth. Tools: Excel, Python pandas, time-series databases.
Mathematical models predicting resource consumption. Consider business drivers (new customers, features, markets). Multiple growth scenarios (best, expected, worst case). Factor in marketing campaigns and product launches. Update models quarterly based on actuals. Common models: compound annual growth rate (CAGR), S-curve adoption.
Recurring patterns at specific intervals (daily, weekly, annually). E-commerce peak during holidays, B2B low on weekends. Decompose time series into trend, seasonal, residual components. Prepare capacity for known peaks. Historical seasonal data guides provisioning. Use seasonal decomposition of time series (STL) algorithms.
Machine learning models for sophisticated predictions. ARIMA, Prophet, LSTM neural networks for time series. Handles non-linear patterns and multiple variables. Automatic seasonality detection. Confidence intervals for predictions. Requires historical data and tuning. AWS Forecast, Azure ML, GCP AI Platform for automated ML forecasting.
Collaborate with business stakeholders for capacity planning. Product roadmap impacts (new features, markets). Marketing campaign schedules and expected lift. Sales pipeline and customer onboarding plans. Merger/acquisition impacts. Combine bottom-up (technical) with top-down (business) forecasts. Regular alignment meetings essential.
Scalability Testing
Test system under expected load conditions. Simulate target number of concurrent users or transactions. Measure response times, throughput, resource utilization. Identify bottlenecks before reaching limits. Gradual ramp-up to target load. Tools: JMeter, Gatling, k6, Locust, LoadRunner. Run regularly with production-like data.
Push system beyond normal capacity to find breaking point. Increase load until system fails or degrades. Identify maximum capacity and failure modes. Test recovery after stress. Understand graceful degradation vs catastrophic failure. Stress test databases, APIs, message queues. Essential for capacity limits documentation.
Run at sustained load for extended period (hours to days). Detect memory leaks, resource exhaustion, degradation over time. Verify system stability under normal sustained load. Monitor resource trends. Test garbage collection, connection pooling, cache behavior. Catch issues only visible in long-running scenarios.
Sudden dramatic load increase then return to normal. Test autoscaling response time. Verify system handles traffic spikes without crashes. Common in viral content, flash sales, DDoS scenarios. Measure recovery time after spike. Test rate limiting and queue overflow behavior. Ensure graceful handling of burst traffic.
Establish baseline performance metrics. Measure latency (P50, P95, P99), throughput, error rates. Compare before/after changes. Industry benchmarks for expectations. Track performance over time for regression detection. Use consistent test scenarios for comparisons. Document baseline for capacity planning.
Capacity Metrics & Thresholds
Monitor CPU usage with alerts at thresholds. Typical targets: 70% sustained triggers investigation, 80% triggers scaling. Understand CPU credits for burstable instances (T3, T4g). Track CPU steal in virtualized environments. Different thresholds for batch vs real-time workloads. Monitor at host and container level.
Monitor memory usage, page faults, swap usage. Java heap usage for JVM applications. Container memory limits and OOMKiller events. Memory leaks detection via trending. Different patterns: cache warming vs memory leak. Set alerts before reaching limits. Consider memory reservations vs limits in containers.
Monitor network throughput (inbound/outbound). Account for burst capacity and sustained bandwidth. Inter-AZ and inter-region data transfer costs. Network saturation causing packet loss or retransmissions. Enhanced networking (SR-IOV) for higher bandwidth. Network interface limits per instance type. Monitor at host, load balancer, and application level.
Disk I/O operations per second and throughput (MB/s). EBS volume types have different IOPS limits (gp3, io2). Monitor queue depth and latency. Separate OS, application, and database volumes. Provision IOPS for consistent performance. SSD vs HDD characteristics. Storage bottlenecks often cause application slowness.
Monitor message queue depth, pending requests, backlog size. SQS ApproximateNumberOfMessages, Kafka lag, RabbitMQ queue length. Growing queues indicate processing slower than arrival rate. Set alerts on queue growth trends. Dead letter queues for failed messages. Queue age (oldest message) more critical than count.
Track API response time percentiles (P50, P95, P99, P99.9). Set SLO targets per endpoint criticality. Response time budget breakdown (network, processing, database). Understand long tail latency. Monitor time to first byte (TTFB). Synthetic monitoring for user-facing endpoints. Differentiate fast path vs slow path operations.
Autoscaling Strategies
Maintain metric at target value (e.g., 70% CPU). AWS Auto Scaling automatically adjusts capacity. Simplest and recommended approach. Works for CPU, network, custom metrics. Continuous monitoring and gradual adjustments. Handles scaling up and down. Specify cooldown periods to prevent flapping.
Different scaling actions based on metric ranges. Example: 70-80% CPU +1 instance, 80-90% +3, >90% +5. More aggressive than target tracking. Faster response to spikes. Configure separate scale-up and scale-down policies. Add cloudwatch alarms triggering policies. More complex but granular control.
Time-based scaling for predictable patterns. Scale up before business hours, down after. Weekend scaling for B2B applications. Pre-scale for known events (sales, launches). Cron-based schedules. Lower costs for non-production environments. Combine with dynamic scaling for unexpected loads. Use for capacity reservation.
ML-based proactive scaling before demand. AWS Predictive Scaling analyzes historical patterns. Scales ahead of forecasted demand. Reduces lag between demand and capacity. Particularly useful for regular daily/weekly patterns. Learning period required for accuracy. Combination with reactive scaling for best results.
Horizontal Pod Autoscaler scales pod count based on metrics. Vertical Pod Autoscaler adjusts CPU/memory requests. Custom metrics from Prometheus or application. KEDA for event-driven scaling (queue depth, Kafka lag). Cluster Autoscaler adjusts node count. Consider pod disruption budgets and resource requests/limits.
Aurora Serverless scales database capacity automatically. Read replica autoscaling for read-heavy workloads. DynamoDB on-demand or autoscaling for predictable patterns. ElastiCache scaling for Redis/Memcached. Monitor connection pool saturation. Database scaling often requires connection pool adjustment. Consider read/write split.
Performance Tuning
Analyze slow query logs and execution plans. Add indexes for frequently queried columns. Avoid SELECT *, fetch only needed columns. Use EXPLAIN/EXPLAIN ANALYZE. Optimize JOIN operations and subqueries. Consider denormalization for read-heavy workloads. Database-specific optimizations (Postgres vs MySQL). Query result caching. Connection pooling optimization.
Multi-layer caching (CDN, reverse proxy, application, database). Cache-aside vs write-through patterns. TTL selection balancing freshness vs efficiency. Cache warming for predictable access patterns. Cache invalidation strategies. Redis/Memcached for application caching. Consider cache hit ratio and memory usage. Cache stampede prevention.
Identify performance bottlenecks in application code. CPU profiling for hot paths. Memory profiling for allocation patterns. Use language-specific profilers (Java Flight Recorder, Python cProfile, Go pprof). Flame graphs for visualization. Profile in production or production-like environment. Focus optimization on high-impact areas. Measure before and after changes.
Match instance types to workload characteristics. Compute-optimized for CPU-bound, memory-optimized for in-memory processing. Network-optimized for throughput-heavy. Graviton processors for cost-performance. Analyze CloudWatch metrics or AWS Compute Optimizer recommendations. Consider burst vs baseline performance needs. Test before production.
Offload long-running tasks to background workers. Message queues (SQS, Kafka) for decoupling. Async/await patterns in code. WebSockets or polling for results. Improves API response times. Consider idempotency for retry scenarios. Monitor queue depth and worker capacity. Dead letter queues for failures.
Anti-pattern of multiple database queries in loop. Causes severe performance issues at scale. Solution: eager loading, batch fetching, join queries. ORM query analysis (Hibernate, Django ORM). Use query logging to detect. Particularly common in GraphQL without DataLoader. Fix can improve performance 10-100x.