Nomad Scheduler

Overview

Nomad is a flexible workload orchestrator for deploying containers, VMs, and standalone applications. Supports multiple task drivers, bin packing, and multi-region federation.

Capabilities

• Multi-driver task scheduling (Docker, exec, Java, QEMU)
• Job specification in HCL
• Rolling updates and canary deployments
• Multi-region federation
• Autoscaling with external metrics
• Integration with Consul and Vault

When to Use

Trigger phrases:

• "nomad scheduler"

• "Mixed workloads (containers + non-containerized)"

• "Simple alternative to Kubernetes"

• "Multi-region deployments"

• Mixed workloads (containers + non-containerized)

• Simple alternative to Kubernetes

• Multi-region deployments

• Batch job scheduling

• Integration with HashiCorp stack (Consul, Vault)

When NOT to Use

• Task is outside your authorization scope
• You need to implement controls (use implementing-* skills)
• Task is about analysis, not action (use analyzing-* skills)
• You don't have access to target systems
• Task requires compliance expertise (consult professionals)
• Task is about defense, not offense (use defensive skills)

Pseudo Code

The nomad-scheduler workflow follows a standard pipeline pattern.

Core flow:

# nomad-scheduler primary flow
input = prepare(raw_data)
result = process(input, config={allocations, drivers, federation, hashicorp, nomad})
validate(result)
deliver(result)

Error handling:

on error:
  log(error_details)
  retry_with_backoff(max=3)
  if still_failing: alert_and_escalate()

Job Specification

# web.nomad
job "web" {
  datacenters = ["dc1"]
  type        = "service"

  group "app" {
    count = 3

    network {
      port "http" { to = 8080 }
    }

    service {
      name     = "web"
      port     = "http"
      provider = "consul"

      check {
        type     = "http"
        path     = "/health"
        interval = "10s"
      }
    }

    task "server" {
      driver = "docker"

      config {
        image = "nginx:alpine"
        ports = ["http"]
      }

      resources {
        cpu    = 256
        memory = 256
      }

      template {
        data        = "{{ key \"config/nginx.conf\" }}"
        destination = "local/nginx.conf"
      }

      vault {
        policies = ["web-policy"]
      }
    }
  }

  update {
    max_parallel = 1
    canary       = 1
    auto_revert  = true
  }
}

Commands

# Run job
nomad job run web.nomad

# Status
nomad job status web
nomad alloc status <alloc-id>

# Scale
nomad job scale web app 5

# Stop
nomad job stop web

# Plan (dry run)
nomad job plan web.nomad

# Regions
nomad server members
nomad job run -region=eu web.nomad

Common Patterns

• Periodic jobs: cron schedule for batch work
• Parameterized jobs: trigger with meta payload
• Canary deploys: canary = 1 for safe rollouts
• Spread: distribute across datacenters
• Affinity/Constraints: pin to specific nodes

How to Use

1. Define infrastructure as code (Terraform, CloudFormation, Pulumi)
2. Review changes through PR process before applying
3. Configure monitoring and alerting for critical paths
4. Set up secrets management (Vault, AWS Secrets Manager, etc.)
5. Document runbooks for deployment, rollback, and incident response
6. Test disaster recovery procedures regularly

Red Flags

• Infrastructure changes without review: Unreviewed changes cause outages — use PRs for infra code
• No rollback strategy: Every deployment needs a tested rollback plan before it runs
• Secrets in configuration files: Secrets in YAML/JSON get committed to version control
• Missing monitoring and alerting: Without monitoring, outages go undetected until users report them
• No documentation for runbooks: Without runbooks, on-call engineers waste time re-discovering procedures

Verification

• Skill output matches expected behavior

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table