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What is Swarm?

Recent versions of Docker introduce swarm mode, which facilitates the native administration of a cluster referred to as a swarm, comprising multiple Docker Engines. By utilizing the Docker CLI, users can effortlessly establish a swarm, launch various application services within it, and monitor the swarm's operational activities. The integration of cluster management into the Docker Engine allows for the creation of a swarm of Docker Engines to deploy services without relying on any external orchestration tools. Its decentralized design enables the Docker Engine to effectively manage node roles during runtime instead of at deployment, thus allowing both manager and worker nodes to be deployed simultaneously from a single disk image. Additionally, the Docker Engine embraces a declarative service model, enabling users to thoroughly define the desired state of their application’s service stack. This efficient methodology not only simplifies the deployment procedure but also significantly improves the management of intricate applications by providing a clear framework. As a result, developers can focus more on building features and less on deployment logistics, ultimately driving innovation forward.

What is Apache Hadoop YARN?

The fundamental principle of YARN centers on distributing resource management and job scheduling/monitoring through the use of separate daemons for each task. It features a centralized ResourceManager (RM) paired with unique ApplicationMasters (AM) for every application, which can either be a single job or a Directed Acyclic Graph (DAG) of jobs. In tandem, the ResourceManager and NodeManager establish the computational infrastructure required for data processing. The ResourceManager acts as the primary authority, overseeing resource allocation for all applications within the framework. In contrast, the NodeManager serves as a local agent on each machine, managing containers, monitoring their resource consumption—including CPU, memory, disk, and network usage—and communicating this data back to the ResourceManager/Scheduler. Furthermore, the ApplicationMaster operates as a dedicated library for each application, tasked with negotiating resource distribution with the ResourceManager while coordinating with the NodeManagers to efficiently execute and monitor tasks. This clear division of roles significantly boosts the efficiency and scalability of the resource management system, ultimately facilitating better performance in large-scale computing environments. Such an architecture allows for more dynamic resource allocation and the ability to handle diverse workloads effectively.

Media

Media

Integrations Supported

AWS Marketplace
HollaEx
Knox
Lightrun
Macaw
Mirantis Container Runtime
Pandora FMS
zenon

Integrations Supported

ActiveBatch Workload Automation
Apache PredictionIO
Apache Ranger
Astera Dataprep
Cloudera Data Platform
IronCore Labs
RunCode
Sematext Cloud
WINDEV

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Docker

Date Founded

2013

Company Location

United States

Company Website

docs.docker.com/engine/swarm/

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

Uniited States

Company Website

hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html

Categories and Features

Cluster Management

Not specified

Container Management

Not specified

Categories and Features

Job Scheduler

Not specified

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