HashiCorp Nomad
HashiCorp Nomad is an open-source workload orchestrator and scheduler designed for distributed, highly available applications. It uses a flexible scheduler to enable efficient utilization of resources across regions and clouds with support for bin packing, spreading, and reservat
orchestration
scheduling
distributed-systems
HashiCorp Nomad: Open-Source Workload Orchestration
HashiCorp Nomad is an open-source workload orchestrator and scheduler designed for distributed, highly available applications. It uses a flexible scheduler to enable efficient utilization of resources across regions and clouds with support for bin packing, spreading, and reservations.
What is HashiCorp Nomad?
HashiCorp Nomad is an open-source workload orchestrator and scheduler designed for deploying and managing containers and non-containerized applications across on-prem and public cloud infrastructure. Key features include:
- Flexible scheduling algorithms for bin packing, spreading, reservations, and affinity-based scheduling to optimize for resource utilization, high availability, and low latency.
- Supports Docker, RKT, Java, Python, and executable applications.
- Dynamic job groups for grouping tasks together for deployment.
- Built-in service discovery and Consul and Vault integrations.
- Cross datacenter and region federation to bridge infrastructure across ACLs and networks.
- Scalable horizontal autoscaling based on CPU, memory, and custom metrics.
- Easy to operate, secure, and integrate simplified workflows.
Nomad is well-suited for modern infrastructure needs requiring efficient scheduling of microservices, batch jobs, and globally distributed applications. It complements container orchestrators like Kubernetes by handling non-containerized workloads and bridging on-prem and cloud infrastructure.
HashiCorp Nomad Features
Features
- Job Scheduling - Schedule batch, service and system jobs on a cluster
- Service Discovery - Automatic service registration and DNS for services
- Flexible Workloads - Support for Docker, executables, and custom workloads
- Multi-Region Awareness - Spread jobs across regions and datacenters
- Auto Scaling - Scale jobs up and down based on utilization
- Failure Tolerance - Reschedule failed jobs and replace failed nodes
- Resource Bin Packing - Optimize cluster resource utilization
Pricing
- Open Source
- Enterprise Subscription
Pros
Easy cluster management and operation
Flexible workloads beyond just containers
Built-in service discovery and load balancing
Spread jobs across regions and clouds
Handle failures and optimize resource usage
Cons
Less mature and adopted than Kubernetes
Steep learning curve compared to traditional schedulers
Not as feature rich as Kubernetes for container workloads
No native support for orchestrating stateful workloads
Official Links
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