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YARN Hadoop

Resource management and job scheduling technology
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What is YARN Hadoop?

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 Hadoop is a tool in the Cluster Management category of a tech stack.

Who uses YARN Hadoop?

Companies
17 companies reportedly use YARN Hadoop in their tech stacks, including Mobikwik, SCMC-CMU, and BigData.

Developers
43 developers on StackShare have stated that they use YARN Hadoop.

YARN Hadoop Integrations

YARN Hadoop Alternatives & Comparisons

What are some alternatives to YARN Hadoop?
Yarn
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.
Apache Mesos
Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers.
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.
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.
See all alternatives

YARN Hadoop's Followers
37 developers follow YARN Hadoop to keep up with related blogs and decisions.
Daniel Ciruelo Sanz
Ramesh Borukati
Maxim Zverev
Mamallan Uthaman
log2ly9006
Mohsin  Banedar
Lei Lee
OthmanDZ
Miguel Alves
Saran Pal