Random Number

The random_number resource generates random integers within a specified range. It provides deterministic random number generation that remains consistent across multiple runs.

Use Cases

As a lab author, you can use random_number resources to:

• Port Assignment: Generate random port numbers for services to avoid conflicts in multi-instance lab environments
• Load Balancing: Create random weights or priorities for load balancing scenarios and traffic distribution
• Network Configuration: Create random VLAN IDs, network segments, or routing priorities for network labs

Random number resources provide controlled randomness for lab configuration while ensuring reproducible results.

HCL Syntax

Basic Syntax

resource "random_number" "name" {
  minimum = 1000
  maximum = 2000
}

Full Syntax

resource "random_number" "name" {
  minimum = 10000
  maximum = 65535
}

Fields

Field	Required	Type	Description
minimum	✓	int	The minimum number to generate (inclusive)
maximum	✓	int	The maximum number to generate (inclusive)

Computed Attributes

These attributes are set by the system after number generation:

Field	Type	Description
value	int	The generated random number

Validation Rules

• Minimum value must be less than or equal to maximum value
• Both minimum and maximum must be valid integers
• Generated values remain constant across multiple runs (idempotent)
• Random values are deterministically generated based on resource configuration

Examples

Random Port Number

resource "random_number" "port" {
  minimum = 10000
  maximum = 20000
}

resource "container" "web" {
  image {
    name = "nginx:alpine"
  }

  port {
    local = resource.random_number.port.value  # e.g., 15847
    host = resource.random_number.port.value   # e.g., 15847
  }
}

output "assigned_port" {
  value = resource.random_number.port.value  # e.g., 15847
}

Load Balancer Weight

resource "random_number" "weight" {
  minimum = 1
  maximum = 100
}

resource "template" "nginx_config" {
  source = <<-EOF
    upstream backend {
      server backend1:8080 weight=${resource.random_number.weight.value};        # e.g., weight=73
      server backend2:8080 weight=${100 - resource.random_number.weight.value}; # e.g., weight=27
    }
  EOF

  destination = "./nginx.conf"
}

Multiple Random Values

resource "random_number" "cpu_shares" {
  minimum = 1
  maximum = 4
}

resource "random_number" "memory_mb" {
  minimum = 512
  maximum = 2048
}

resource "container" "app" {
  image {
    name = "myapp:latest"
  }

  resources {
    cpu = resource.random_number.cpu_shares.value * 250     # e.g., 750 (if cpu_shares.value = 3)
    memory = resource.random_number.memory_mb.value         # e.g., 1536 (if memory_mb.value = 1536)
  }
}

Network Configuration

resource "random_number" "subnet_octet" {
  minimum = 1
  maximum = 254
}

resource "network" "lab_network" {
  subnet = "10.${resource.random_number.subnet_octet.value}.0.0/24"  # e.g., "10.142.0.0/24"
}

# Use random number in template for network configuration
resource "template" "network_config" {
  source = <<-EOF
    network:
      subnet: 10.${resource.random_number.subnet_octet.value}.0.0/24
      vlan_id: ${resource.random_number.subnet_octet.value + 100}
  EOF

  destination = "./network.yaml"
}

Scaling Simulation

resource "random_number" "instance_count" {
  minimum = 2
  maximum = 8
}

resource "container" "worker" {
  count = resource.random_number.instance_count.value  # e.g., 5 containers created

  image {
    name = "worker:latest"
  }

  environment = {
    WORKER_ID = tostring(count.index + 1)                                 # e.g., "1", "2", "3", "4", "5"
    TOTAL_WORKERS = tostring(resource.random_number.instance_count.value) # e.g., "5"
  }
}

output "worker_count" {
  value = resource.random_number.instance_count.value  # e.g., 5
  description = "Number of worker instances created"
}

Best Practices

1. Range Selection: Choose appropriate minimum and maximum values for your use case
2. Collision Avoidance: Use sufficiently wide ranges to minimize collision probability
3. Resource Naming: Use descriptive names to clarify the random number’s purpose
4. Validation: Ensure generated values work with target systems and configurations
5. Documentation: Document the purpose and expected range of random values
6. Determinism: Remember that values are consistent across runs for the same configuration
7. Testing: Test with various generated values to ensure system robustness