📊 Capacity Planning Guide

Overview

Capacity planning is the process of determining and planning the system resources required to meet future demands. Think of it like planning a city's infrastructure: you need to predict population growth, traffic patterns, and utility usage to ensure the city can handle future demands without wasting resources on overbuilding.

🔑 Key Concepts

1. Resource Metrics

• CPU Utilization
• Memory Usage
• Storage Capacity
• Network Bandwidth
• Response Times

2. Planning Horizons

• Short-term (1-3 months)
• Medium-term (3-12 months)
• Long-term (1+ years)

3. Planning Methods

• Trend Analysis
• Predictive Modeling
• Workload Analysis
• Performance Modeling

💻 Implementation

Capacity Monitoring System

Java

import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.ScheduledFuture;
import java.util.concurrent.TimeUnit;
import java.util.ArrayList;
import java.util.List;

public class CapacityMonitor {
private final List<MetricCollector> collectors;
private final ScheduledExecutorService scheduler;
private final MetricStorage storage;

    public CapacityMonitor(ScheduledExecutorService scheduler, MetricStorage storage) {
        this.collectors = new ArrayList<>();
        this.scheduler = scheduler;
        this.storage = storage;
    }
    
    public void addCollector(MetricCollector collector) {
        collectors.add(collector);
    }
    
    public void startMonitoring(long period, TimeUnit unit) {
        for (MetricCollector collector : collectors) {
            scheduler.scheduleAtFixedRate(
                () -> collectAndStore(collector),
                0,
                period,
                unit
            );
        }
    }
    
    private void collectAndStore(MetricCollector collector) {
        try {
            MetricData data = collector.collect();
            storage.store(data);
            
            if (data.getValue() > data.getThreshold()) {
                handleThresholdExceeded(data);
            }
        } catch (Exception e) {
            // Handle exception
        }
    }
    
    private void handleThresholdExceeded(MetricData data) {
        // Implement threshold handling logic
        notifyAdministrators(data);
        triggerScaling(data);
    }
    
    public CapacityForecast generateForecast(String metricType, int daysAhead) {
        List<MetricData> historicalData = storage.getHistoricalData(metricType);
        return new CapacityForecast(historicalData, daysAhead);
    }
}

interface MetricCollector {
MetricData collect();
}

class MetricData {
private String type;
private double value;
private double threshold;
private long timestamp;

    // Constructor and getters/setters
}

class CapacityForecast {
private List<MetricData> predictions;
private double confidence;

    // Constructor and methods
}

Go

package main

import (
    "sync"
    "time"
)

type MetricData struct {
    Type      string
    Value     float64
    Threshold float64
    Timestamp time.Time
}

type MetricCollector interface {
    Collect() (MetricData, error)
}

type CapacityMonitor struct {
    collectors []MetricCollector
    storage    MetricStorage
    stopChan   chan struct{}
    wg         sync.WaitGroup
}

func NewCapacityMonitor(storage MetricStorage) *CapacityMonitor {
    return &CapacityMonitor{
        collectors: make([]MetricCollector, 0),
        storage:    storage,
        stopChan:   make(chan struct{}),
    }
}

func (cm *CapacityMonitor) AddCollector(collector MetricCollector) {
    cm.collectors = append(cm.collectors, collector)
}

func (cm *CapacityMonitor) StartMonitoring(period time.Duration) {
    for _, collector := range cm.collectors {
        cm.wg.Add(1)
        go cm.monitorMetric(collector, period)
    }
}

func (cm *CapacityMonitor) monitorMetric(collector MetricCollector, period time.Duration) {
    defer cm.wg.Done()
    ticker := time.NewTicker(period)
    defer ticker.Stop()

    for {
        select {
        case <-ticker.C:
            cm.collectAndStore(collector)
        case <-cm.stopChan:
            return
        }
    }
}

func (cm *CapacityMonitor) collectAndStore(collector MetricCollector) {
    data, err := collector.Collect()
    if err != nil {
        // Handle error
        return
    }

    err = cm.storage.Store(data)
    if err != nil {
        // Handle error
        return
    }

    if data.Value > data.Threshold {
        cm.handleThresholdExceeded(data)
    }
}

func (cm *CapacityMonitor) handleThresholdExceeded(data MetricData) {
    // Implement threshold handling logic
    cm.notifyAdministrators(data)
    cm.triggerScaling(data)
}

func (cm *CapacityMonitor) GenerateForecast(metricType string, daysAhead int) (*CapacityForecast, error) {
    historicalData, err := cm.storage.GetHistoricalData(metricType)
    if err != nil {
        return nil, err
    }
    return NewCapacityForecast(historicalData, daysAhead), nil
}

type CapacityForecast struct {
    Predictions []MetricData
    Confidence  float64
}

func NewCapacityForecast(historicalData []MetricData, daysAhead int) *CapacityForecast {
    // Implement forecasting logic
    return &CapacityForecast{
        Predictions: make([]MetricData, daysAhead),
        Confidence:  calculateConfidence(historicalData),
    }
}

🤝 Related Patterns

1. Throttling Pattern

   • Controls resource consumption
   • Prevents system overload
   • Complements capacity planning

2. Queue-Based Load Leveling

   • Smooths out workload spikes
   • Helps in capacity estimation
   • Improves resource utilization

3. Circuit Breaker Pattern

   • Prevents system overload
   • Helps maintain stability
   • Supports capacity management

⚙️ Best Practices

Configuration

• Set clear monitoring intervals
• Define appropriate thresholds
• Configure alerting rules
• Implement automation

Monitoring

• Track all critical metrics
• Use historical data analysis
• Monitor trends and patterns
• Implement predictive analytics

Testing

• Load testing scenarios
• Capacity validation
• Performance benchmarking
• Stress testing

🚫 Common Pitfalls

1. Inadequate Data Collection

   • Missing critical metrics
   • Insufficient history
   • Solution: Comprehensive monitoring

2. Poor Forecasting

   • Ignoring seasonal patterns
   • Over-simplifying trends
   • Solution: Advanced analytics

3. Reactive Planning

   • Late response to needs
   • Crisis management
   • Solution: Proactive monitoring

🎯 Use Cases

1. Cloud Infrastructure

• Resource allocation
• Cost optimization
• Performance management
• Scaling decisions

2. Database Systems

• Storage planning
• Query performance
• Backup capacity
• Replication needs

3. Microservices

• Container sizing
• Service scaling
• Resource allocation
• Network capacity

🔍 Deep Dive Topics

Thread Safety

• Concurrent monitoring
• Resource locking
• Data consistency
• Race condition prevention

Distributed Systems

• Cross-node monitoring
• Aggregated metrics
• Network planning
• Data replication

Performance

• Response time analysis
• Resource utilization
• Throughput planning
• Bottleneck identification

📚 Additional Resources

Documentation

• AWS Capacity Planning
• Google Cloud Capacity Planning
• Microsoft Azure Capacity Planning

Tools

• Monitoring: Prometheus, Grafana
• Analytics: Elasticsearch, Kibana
• Testing: Apache JMeter, K6
• Forecasting: Prophet, TensorFlow

❓ FAQs

How far ahead should I plan?

• Short-term: 3-6 months
• Medium-term: 6-12 months
• Long-term: 1-3 years
• Depends on business growth

What metrics are most important?

• CPU utilization
• Memory usage
• Storage capacity
• Network bandwidth
• Response times
• Error rates

How often should I review capacity?

• Weekly for critical systems
• Monthly for normal operations
• Quarterly for long-term planning
• Immediately after incidents

How do I handle unexpected growth?

• Maintain buffer capacity
• Use auto-scaling
• Have emergency procedures
• Monitor leading indicators