```html

Capacity planning requires analyzing three key metrics:

• Concurrent Users: Peak simultaneous active connections
• Request Rate: Transactions per second (TPS) or requests per minute (RPM)
• Resource Utilization: CPU, memory, disk I/O, and network throughput

For a typical LAMP stack application:

# Python pseudo-code for request simulation
import requests
from locust import HttpUser, task, between

class WebsiteUser(HttpUser):
    wait_time = between(1, 5)
    
    @task(3)
    def browse_posts(self):
        self.client.get("/posts")
        
    @task(1)
    def create_post(self):
        self.client.post("/create", json={"title": "Test", "content": "..."})

# Expected throughput formula:
# Max Users = (Available RAM - OS Overhead) / (Memory per User Process)
# Example: 16GB server with 2GB OS overhead, 50MB/process:
# (16-2)*1024/50 ≈ 286 concurrent users

For MySQL performance tuning:

# MySQL configuration for high concurrency
[mysqld]
innodb_buffer_pool_size = 12G  # 70-80% of total RAM
innodb_io_capacity = 2000
max_connections = 500
thread_cache_size = 100
table_open_cache = 4000

Auto-scaling configuration for AWS:

# Terraform example for auto-scaling group
resource "aws_autoscaling_group" "web" {
  desired_capacity   = 4
  max_size           = 20
  min_size           = 2
  
  launch_template {
    id      = aws_launch_template.web.id
    version = "$Latest"
  }

  target_group_arns = [aws_lb_target_group.web.arn]
  
  scaling_policy {
    adjustment_type     = "ChangeInCapacity"
    scaling_adjustment  = 2
    cooldown            = 300
    metric_aggregation_type = "Average"
  }
}

Essential Prometheus queries for capacity analysis:

# CPU saturation
100 - (avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

# Memory pressure
(node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100

# Disk I/O bottleneck
rate(node_disk_read_bytes_total[5m]) + rate(node_disk_written_bytes_total[5m])

Capacity planning involves complex tradeoffs between:

• Concurrent user load vs. system resources
• Peak traffic patterns vs. baseline requirements
• Hardware costs vs. performance SLAs

Before sizing servers, establish these baseline measurements:

// Sample Python script to collect basic metrics
import psutil
import time

def collect_metrics(interval=5):
    while True:
        cpu = psutil.cpu_percent(interval=1)
        mem = psutil.virtual_memory().percent
        disk = psutil.disk_io_counters()
        net = psutil.net_io_counters()
        
        print(f"CPU: {cpu}% | MEM: {mem}%")
        print(f"Disk R/W: {disk.read_bytes/1024}KB/{disk.write_bytes/1024}KB")
        print(f"Network TX/RX: {net.bytes_sent/1024}KB/{net.bytes_recv/1024}KB")
        
        time.sleep(interval)

Theoretical maximum concurrent users calculation:

Max Users = (RAM Size) / (Memory per User)
          × (CPU Cores) / (CPU Utilization per User)
          / (Safety Factor of 2-5)

For MySQL performance optimization:

# MySQL Configuration Adjustments
[mysqld]
innodb_buffer_pool_size = 12G  # 70-80% of available RAM
innodb_io_capacity = 2000      # For SSDs
max_connections = 500          # Adjust based on connection pooling
thread_cache_size = 100
query_cache_size = 0           # Disabled for MySQL 8+

AWS Auto Scaling template example:

{
  "AutoScalingGroupName": "web-tier-asg",
  "LaunchTemplate": {
    "LaunchTemplateName": "web-server-template",
    "Version": "$Latest"
  },
  "MinSize": 2,
  "MaxSize": 10,
  "DesiredCapacity": 4,
  "TargetTrackingScalingPolicies": [
    {
      "PredefinedMetricSpecification": {
        "PredefinedMetricType": "ASGAverageCPUUtilization"
      },
      "TargetValue": 60.0
    }
  ]
}

Prometheus configuration snippet:

# prometheus.yml
rule_files:
  - 'alert.rules'

alerting:
  alertmanagers:
  - static_configs:
    - targets:
      - 'localhost:9093'

# alert.rules
groups:
- name: capacity-alerts
  rules:
  - alert: HighCPU
    expr: 100 - (avg by(instance)(irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
    for: 10m