Nomad vs YARN Hadoop

Overview

YARN Hadoop

Stacks112

Followers80

Votes1

Nomad

Stacks256

Followers344

Votes32

GitHub Stars15.9K

Forks2.0K

Nomad vs YARN Hadoop: What are the differences?

Introduction

In this article, we will explore the key differences between Nomad and YARN Hadoop. Both Nomad and YARN Hadoop are resource schedulers used in cluster management, but they differ in several ways.

Design and Philosophy: Nomad is designed as a lightweight and flexible scheduler for both long-running services and batch jobs. It focuses on simplicity and ease of use, allowing users to run any kind of workload. On the other hand, YARN Hadoop is designed specifically for big data processing and is tightly integrated with the Hadoop ecosystem.
Resource Management: Nomad uses a declarative approach to resource management, where users define their desired state and Nomad ensures that the tasks are scheduled and allocated resources accordingly. YARN Hadoop, however, uses a hierarchical approach to resource management with central control. It has a ResourceManager that manages the global allocation of resources and individual NodeManagers that manage resources on individual hosts.
Multi-Tenancy Support: Nomad supports multi-tenancy out of the box. It allows users to run multiple workloads securely in a shared cluster, with each workload isolated from the others. YARN Hadoop, on the other hand, provides limited multi-tenancy support through the use of queues and capacity management policies.
Integration with Ecosystem: Nomad is a standalone scheduler and is not tightly integrated with any specific ecosystem. It can run a variety of workloads such as Docker containers, VMs, and even standalone applications. YARN Hadoop, on the other hand, is part of the Hadoop ecosystem and seamlessly integrates with other Hadoop components like HDFS, MapReduce, and Hive.
Fault Tolerance: Nomad has built-in fault tolerance mechanisms that ensure high availability of applications. It automatically handles node failures, reschedules tasks, and redistributes resources. YARN Hadoop also provides fault tolerance through its ResourceManager and NodeManager architecture, but it relies on HDFS for data replication and recovery.
Ease of Deployment: Nomad is known for its ease of deployment. It has a simple and lightweight architecture, making it easy to set up and manage. YARN Hadoop, on the other hand, has a more complex setup process due to its integration with the Hadoop ecosystem and its dependency on HDFS.

In summary, Nomad and YARN Hadoop differ in their design philosophy, resource management approach, multi-tenancy support, integration with the ecosystem, fault tolerance mechanisms, and ease of deployment.

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CLI (Node.js)

Manual

Detailed Comparison

YARN Hadoop	Nomad
Its fundamental idea is to split up the functionalities of resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM).	Nomad is a cluster manager, designed for both long lived services and short lived batch processing workloads. Developers use a declarative job specification to submit work, and Nomad ensures constraints are satisfied and resource utilization is optimized by efficient task packing. Nomad supports all major operating systems and virtualized, containerized, or standalone applications.
-	Handles the scheduling and upgrading of the applications over time; With built-in dry-run execution, Nomad shows what scheduling decisions it will take before it takes them. Operators can approve or deny these changes to create a safe and reproducible workflow; Nomad runs applications and ensures they keep running in failure scenarios. In addition to long-running services, Nomad can schedule batch jobs, distributed cron jobs, and parameterized jobs; Stream logs, send signals, and interact with the file system of scheduled applications. These operator-friendly commands bring the familiar debugging tools to a scheduled world
Statistics
GitHub Stars -	GitHub Stars 15.9K
GitHub Forks -	GitHub Forks 2.0K
Stacks 112	Stacks 256
Followers 80	Followers 344
Votes 1	Votes 32
Pros & Cons
Pros 1 Batch processing with commodity machine	Pros 7 Built in Consul integration 6 Easy setup 4 Bult-in Vault integration 3 Built-in federation support 2 Self-healing Cons 3 Easy to start with 1 Small comunity 1 HCL language for configuration, an unpopular DSL
Integrations
No integrations available	Consul Docker Vault

What are some alternatives to YARN Hadoop, Nomad?

Apache Mesos

Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers.

DC/OS

Unlike traditional operating systems, DC/OS spans multiple machines within a network, aggregating their resources to maximize utilization by distributed applications.

Mesosphere

Mesosphere offers a layer of software that organizes your machines, VMs, and cloud instances and lets applications draw from a single pool of intelligently- and dynamically-allocated resources, increasing efficiency and reducing operational complexity.

Gardener

Many Open Source tools exist which help in creating and updating single Kubernetes clusters. However, the more clusters you need the harder it becomes to operate, monitor, manage and keep all of them alive and up-to-date. And that is exactly what project Gardener focuses on.

Atmosly

AI-powered Kubernetes platform for developers & DevOps. Deploy applications without complexity, with intelligent automation and one-click environments.

kops

It helps you create, destroy, upgrade and maintain production-grade, highly available, Kubernetes clusters from the command line. AWS (Amazon Web Services) is currently officially supported, with GCE in beta support , and VMware vSphere in alpha, and other platforms planned.

Apache Aurora

Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.

Elastic Apache Mesos

Elastic Apache Mesos is a web service that automates the creation of Apache Mesos clusters on Amazon Elastic Compute Cloud (EC2). It provisions EC2 instances, installs dependencies including Apache ZooKeeper and HDFS, and delivers you a cluster with all the services running.

Peloton

A Unified Resource Scheduler to co-schedule mixed types of workloads such as batch, stateless and stateful jobs in a single cluster for better resource utilization. Designed for web-scale companies with millions of containers and tens of thousands of nodes.

Kocho

Kocho provides a set of mechanisms to bootstrap AWS nodes that must follow a specific configuration with CoreOS. It sets up fleet meta-data, and patched versions of fleet, etcd, and docker when using Yochu.

Related Comparisons

Nomad vs YARN Hadoop: What are the differences?

Introduction

In this article, we will explore the key differences between Nomad and YARN Hadoop. Both Nomad and YARN Hadoop are resource schedulers used in cluster management, but they differ in several ways.

Design and Philosophy: Nomad is designed as a lightweight and flexible scheduler for both long-running services and batch jobs. It focuses on simplicity and ease of use, allowing users to run any kind of workload. On the other hand, YARN Hadoop is designed specifically for big data processing and is tightly integrated with the Hadoop ecosystem.
Resource Management: Nomad uses a declarative approach to resource management, where users define their desired state and Nomad ensures that the tasks are scheduled and allocated resources accordingly. YARN Hadoop, however, uses a hierarchical approach to resource management with central control. It has a ResourceManager that manages the global allocation of resources and individual NodeManagers that manage resources on individual hosts.
Multi-Tenancy Support: Nomad supports multi-tenancy out of the box. It allows users to run multiple workloads securely in a shared cluster, with each workload isolated from the others. YARN Hadoop, on the other hand, provides limited multi-tenancy support through the use of queues and capacity management policies.
Integration with Ecosystem: Nomad is a standalone scheduler and is not tightly integrated with any specific ecosystem. It can run a variety of workloads such as Docker containers, VMs, and even standalone applications. YARN Hadoop, on the other hand, is part of the Hadoop ecosystem and seamlessly integrates with other Hadoop components like HDFS, MapReduce, and Hive.
Fault Tolerance: Nomad has built-in fault tolerance mechanisms that ensure high availability of applications. It automatically handles node failures, reschedules tasks, and redistributes resources. YARN Hadoop also provides fault tolerance through its ResourceManager and NodeManager architecture, but it relies on HDFS for data replication and recovery.
Ease of Deployment: Nomad is known for its ease of deployment. It has a simple and lightweight architecture, making it easy to set up and manage. YARN Hadoop, on the other hand, has a more complex setup process due to its integration with the Hadoop ecosystem and its dependency on HDFS.

Nomad vs YARN Hadoop

Overview

Nomad vs YARN Hadoop: What are the differences?