DC/OS vs YARN Hadoop

Overview

YARN Hadoop

Stacks112

Followers80

Votes1

DC/OS

Stacks109

Followers180

Votes12

GitHub Stars2.4K

Forks488

DC/OS vs YARN Hadoop: What are the differences?

Comparison between DC/OS and YARN Hadoop

DC/OS and YARN Hadoop are both popular distributed systems for managing and scheduling application workloads in large-scale clusters. While they share similar goals, there are key differences between them that set them apart.

Architecture: DC/OS is known for its flexible and modular architecture that allows for easy integration of various frameworks and services. On the other hand, YARN Hadoop has a more tightly coupled architecture that is specifically designed for running Hadoop MapReduce jobs.
Scheduling capabilities: DC/OS offers a highly flexible and sophisticated scheduling mechanism that supports the deployment of various types of workloads, including containerized applications and microservices, while providing resource guarantees and isolation. YARN Hadoop, on the other hand, excels in scheduling batch-oriented workloads, particularly Hadoop MapReduce jobs, with a focus on efficient resource utilization.
Framework support: DC/OS supports a broad range of frameworks and services, including Big Data technologies like Apache Spark and Apache Kafka, in addition to containers and microservices. YARN Hadoop, on the other hand, is primarily focused on supporting the Hadoop ecosystem, including Hadoop MapReduce, Apache Hive, and Apache Pig.
Ease of use and management: DC/OS is known for its ease of use and management, providing a user-friendly web interface and command-line tools for deploying and managing applications. YARN Hadoop, while powerful, can be more complex to set up and manage, requiring Hadoop-specific tools and configurations.
Scalability: Both DC/OS and YARN Hadoop are designed to scale to large clusters, but DC/OS has a more distributed architecture that allows for seamless scalability across multiple physical or virtual machines. YARN Hadoop, on the other hand, can also scale effectively but may require additional configuration and tuning for optimal performance.
Community and ecosystem: DC/OS has a vibrant and active community with a wide range of supported frameworks and services, allowing for easy integration and collaboration. YARN Hadoop also has a strong community, particularly within the Hadoop ecosystem, with a wealth of resources and expertise available.

In summary, DC/OS offers a modular and flexible architecture with advanced scheduling capabilities and support for various frameworks and services, while YARN Hadoop is more focused on the Hadoop ecosystem with a tightly coupled architecture and efficient batch job scheduling. Both systems have their strengths and are suited for different use cases and requirements.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

YARN Hadoop	DC/OS
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).	Unlike traditional operating systems, DC/OS spans multiple machines within a network, aggregating their resources to maximize utilization by distributed applications.
-	High Resource Utilization;Mixed Workload Colocation;Container Orchestration;Resource Isolation;Stateful Storage;Package Repositories;Public Cloud;Private Cloud;On-Premise;Command Line Interface;Web Interface;Elastic Scalability;High Availability;Zero Downtime Upgrades;Service Discovery;Load Balancing;Production-Ready
Statistics
GitHub Stars -	GitHub Stars 2.4K
GitHub Forks -	GitHub Forks 488
Stacks 112	Stacks 109
Followers 80	Followers 180
Votes 1	Votes 12
Pros & Cons
Pros 1 Batch processing with commodity machine	Pros 5 Easy to setup a HA cluster 3 Open source 2 Has templates to install via AWS and Azure 1 Easy Setup 1 Easy to get services running and operate them
Integrations
No integrations available	Apache Mesos

What are some alternatives to YARN Hadoop, DC/OS?

Nomad

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.

Apache Mesos

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

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.

Ecipse GlassFish

Eclipse GlassFish is a lightweight yet powerful open-source application server that fully implements the Jakarta EE platform. Designed for flexibility, scalability, and reliability, it provides a production-ready environment that adheres strictly to open standards without proprietary dependencies. GlassFish delivers comprehensive support for all required and optional Jakarta EE APIs, successfully passing all corresponding Technology Compatibility Kits (TCKs). It includes an advanced administration console, clustering capabilities, and a rich set of tools that streamline both development and deployment. Continuously maintained under the Eclipse Foundation, GlassFish remains a robust and standards-compliant choice for modern enterprise applications.

Kunobi

The Ninja Command Center for Kubernetes and GitOps. Flux with a proper UI, still CLI-fast.

Kubegrade

Kubegrade is a Kubernetes-native change safety platform that continuously detects compatibility risk, configuration drift, and breaking changes across clusters before they reach production. It integrates with GitOps workflows to propose precise, reviewable pull requests, helping platform and DevOps teams maintain cluster integrity while preserving full control over changes.

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.

Related Comparisons

DC/OS vs YARN Hadoop: What are the differences?

Comparison between DC/OS and YARN Hadoop

Architecture: DC/OS is known for its flexible and modular architecture that allows for easy integration of various frameworks and services. On the other hand, YARN Hadoop has a more tightly coupled architecture that is specifically designed for running Hadoop MapReduce jobs.
Scheduling capabilities: DC/OS offers a highly flexible and sophisticated scheduling mechanism that supports the deployment of various types of workloads, including containerized applications and microservices, while providing resource guarantees and isolation. YARN Hadoop, on the other hand, excels in scheduling batch-oriented workloads, particularly Hadoop MapReduce jobs, with a focus on efficient resource utilization.
Framework support: DC/OS supports a broad range of frameworks and services, including Big Data technologies like Apache Spark and Apache Kafka, in addition to containers and microservices. YARN Hadoop, on the other hand, is primarily focused on supporting the Hadoop ecosystem, including Hadoop MapReduce, Apache Hive, and Apache Pig.
Ease of use and management: DC/OS is known for its ease of use and management, providing a user-friendly web interface and command-line tools for deploying and managing applications. YARN Hadoop, while powerful, can be more complex to set up and manage, requiring Hadoop-specific tools and configurations.
Scalability: Both DC/OS and YARN Hadoop are designed to scale to large clusters, but DC/OS has a more distributed architecture that allows for seamless scalability across multiple physical or virtual machines. YARN Hadoop, on the other hand, can also scale effectively but may require additional configuration and tuning for optimal performance.
Community and ecosystem: DC/OS has a vibrant and active community with a wide range of supported frameworks and services, allowing for easy integration and collaboration. YARN Hadoop also has a strong community, particularly within the Hadoop ecosystem, with a wealth of resources and expertise available.

DC/OS vs YARN Hadoop

Overview

DC/OS vs YARN Hadoop: What are the differences?