YARN Hadoop vs Yarn

Overview

YARN Hadoop

Stacks112

Followers80

Votes1

Yarn

Stacks28.2K

Followers13.5K

Votes151

GitHub Stars41.5K

Forks2.7K

YARN Hadoop vs Yarn: What are the differences?

Introduction

YARN (Yet Another Resource Negotiator) is a component of Apache Hadoop, which is an open-source framework used for distributed processing of large data sets across clusters of computers.

1. Scalability and Resource Management: YARN Hadoop is designed to provide enhanced scalability and efficient resource management in Hadoop clusters. It allows multiple applications to run simultaneously, optimizing resource utilization by allocating resources dynamically based on application needs.
2. Application Framework Integration: YARN Hadoop provides a flexible and extensible architecture that allows various application frameworks to coexist and share the same cluster resources. It supports multiple programming models such as MapReduce, Apache Pig, Apache Hive, and Spark, enabling developers to choose the best framework for their specific processing requirements.
3. Fault Tolerance: YARN Hadoop has built-in fault tolerance mechanisms to ensure the reliability of data processing. It automatically detects and recovers from failures, redistributing work to healthy nodes in the cluster, thus minimizing data loss and disruption of service.
4. Multi-tenancy Support: YARN Hadoop supports multi-tenancy, allowing different users or organizations to securely share the same cluster while isolating their computing resources. It provides resource limits and scheduling policies to ensure fair sharing and prevent one user from monopolizing the cluster resources.
5. Fine-grained Resource Allocation: YARN Hadoop allows fine-grained resource allocation, enabling better control and utilization of cluster resources. It supports containerization technology, which provides isolation and resource monitoring at the task level, allowing for more efficient resource management and optimization.
6. Enhanced Cluster Utilization: YARN Hadoop improves cluster utilization by supporting dynamic allocation of resources based on application requirements. It enables applications to request and release resources as needed, ensuring efficient resource utilization and reducing idle resource wastage.

In summary, YARN Hadoop provides enhanced scalability, application framework integration, fault tolerance, multi-tenancy support, fine-grained resource allocation, and improved cluster utilization in Apache Hadoop clusters.

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CLI (Node.js)

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Advice on YARN Hadoop, Yarn

StackShare

Apr 23, 2019

Needs adviceon

Node.js

npm

Yarn

From a StackShare Community member: “I’m a freelance web developer (I mostly use Node.js) and for future projects I’m debating between npm or Yarn as my default package manager. I’m a minimalist so I hate installing software if I don’t need to- in this case that would be Yarn. For those who made the switch from npm to Yarn, what benefits have you noticed? For those who stuck with npm, are you happy you with it?"

294k views294k

Comments

zen-li

Apr 24, 2019

Reviewon

Yarn

p.s.

I am not sure about the performance of the latest version of npm, whether it is different from my understanding of it below. Because I use npm very rarely when I had the following knowledge.

------⏬

I use Yarn because, first, yarn is the first tool to lock the version. Second, although npm also supports the lock version, when you use npm to lock the version, and then use package-lock.json on other systems, package-lock.json Will be modified. You understand what I mean, when you deploy projects based on Git...

250k views250k

Comments

Oleksandr

Senior Software Engineer at joyn

Dec 7, 2019

Decided

As we have to build the application for many different TV platforms we want to split the application logic from the device/platform specific code. Previously we had different repositories and it was very hard to keep the development process when changes were done in multiple repositories, as we had to synchronize code reviews as well as merging and then updating the dependencies of projects. This issues would be even more critical when building the project from scratch what we did at Joyn. Therefor to keep all code in one place, at the same time keeping in separated in different modules we decided to give a try to monorepo. First we tried out lerna which was fine at the beginning, but later along the way we had issues with adding new dependencies which came out of the blue and were not easy to fix. Next round of evolution was yarn workspaces, we are still using it and are pretty happy with dev experience it provides. And one more advantage we got when switched to yarn workspaces that we also switched from npm to yarn what improved the state of the lock file a lot, because with npm package-lock file was updated every time you run npm install, frequent updates of package-lock file were causing very often merge conflicts. So right now we not just having faster dependencies installation time but also no conflicts coming from lock file.

310k views310k

Comments

Detailed Comparison

YARN Hadoop	Yarn
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).	Yarn caches every package it downloads so it never needs to again. It also parallelizes operations to maximize resource utilization so install times are faster than ever.
Statistics
GitHub Stars -	GitHub Stars 41.5K
GitHub Forks -	GitHub Forks 2.7K
Stacks 112	Stacks 28.2K
Followers 80	Followers 13.5K
Votes 1	Votes 151
Pros & Cons
Pros 1 Batch processing with commodity machine	Pros 85 Incredibly fast 22 Easy to use 13 Open Source 11 Can install any npm package 8 Works where npm fails Cons 16 Facebook 7 Sends data to facebook 4 Should be installed separately 3 Cannot publish to registry other than npm
Integrations
No integrations available	JavaScript npm

What are some alternatives to YARN Hadoop, Yarn?

npm

npm is the command-line interface to the npm ecosystem. It is battle-tested, surprisingly flexible, and used by hundreds of thousands of JavaScript developers every day.

RequireJS

RequireJS loads plain JavaScript files as well as more defined modules. It is optimized for in-browser use, including in a Web Worker, but it can be used in other JavaScript environments, like Rhino and Node. It implements the Asynchronous Module API. Using a modular script loader like RequireJS will improve the speed and quality of your code.

Browserify

Browserify lets you require('modules') in the browser by bundling up all of your dependencies.

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.

Component

Component's philosophy is the UNIX philosophy of the web - to create a platform for small, reusable components that consist of JS, CSS, HTML, images, fonts, etc. With its well-defined specs, using Component means not worrying about most frontend problems such as package management, publishing components to a registry, or creating a custom build process for every single app.

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.

Verdaccio

A simple, zero-config-required local private npm registry. Comes out of the box with its own tiny database, and the ability to proxy other registries (eg. npmjs.org), caching the downloaded modules along the way.

pip

It is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes.

Related Comparisons

YARN Hadoop vs Yarn: What are the differences?

Introduction

YARN (Yet Another Resource Negotiator) is a component of Apache Hadoop, which is an open-source framework used for distributed processing of large data sets across clusters of computers.

1. Scalability and Resource Management: YARN Hadoop is designed to provide enhanced scalability and efficient resource management in Hadoop clusters. It allows multiple applications to run simultaneously, optimizing resource utilization by allocating resources dynamically based on application needs.
2. Application Framework Integration: YARN Hadoop provides a flexible and extensible architecture that allows various application frameworks to coexist and share the same cluster resources. It supports multiple programming models such as MapReduce, Apache Pig, Apache Hive, and Spark, enabling developers to choose the best framework for their specific processing requirements.
3. Fault Tolerance: YARN Hadoop has built-in fault tolerance mechanisms to ensure the reliability of data processing. It automatically detects and recovers from failures, redistributing work to healthy nodes in the cluster, thus minimizing data loss and disruption of service.
4. Multi-tenancy Support: YARN Hadoop supports multi-tenancy, allowing different users or organizations to securely share the same cluster while isolating their computing resources. It provides resource limits and scheduling policies to ensure fair sharing and prevent one user from monopolizing the cluster resources.
5. Fine-grained Resource Allocation: YARN Hadoop allows fine-grained resource allocation, enabling better control and utilization of cluster resources. It supports containerization technology, which provides isolation and resource monitoring at the task level, allowing for more efficient resource management and optimization.
6. Enhanced Cluster Utilization: YARN Hadoop improves cluster utilization by supporting dynamic allocation of resources based on application requirements. It enables applications to request and release resources as needed, ensuring efficient resource utilization and reducing idle resource wastage.

YARN Hadoop vs Yarn

Overview

YARN Hadoop vs Yarn: What are the differences?