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Average Ratings 0 Ratings
Description
YARN's core concept revolves around the division of resource management and job scheduling/monitoring into distinct daemons, aiming for a centralized ResourceManager (RM) alongside individual ApplicationMasters (AM) for each application. Each application can be defined as either a standalone job or a directed acyclic graph (DAG) of jobs. Together, the ResourceManager and NodeManager create the data-computation framework, with the ResourceManager serving as the primary authority that allocates resources across all applications in the environment. Meanwhile, the NodeManager acts as the local agent on each machine, overseeing containers and tracking their resource consumption, including CPU, memory, disk, and network usage, while also relaying this information back to the ResourceManager or Scheduler. The ApplicationMaster functions as a specialized library specific to its application, responsible for negotiating resources with the ResourceManager and coordinating with the NodeManager(s) to efficiently execute and oversee the execution of tasks, ensuring optimal resource utilization and job performance throughout the process. This separation allows for more scalable and efficient management in complex computing environments.
Description
Introducing Kubestone, the operator designed for benchmarking within Kubernetes environments. Kubestone allows users to assess the performance metrics of their Kubernetes setups effectively. It offers a standardized suite of benchmarks to evaluate CPU, disk, network, and application performance. Users can exercise detailed control over Kubernetes scheduling elements, including affinity, anti-affinity, tolerations, storage classes, and node selection. It is straightforward to introduce new benchmarks by developing a fresh controller. The execution of benchmark runs is facilitated through custom resources, utilizing various Kubernetes components such as pods, jobs, deployments, and services. To get started, refer to the quickstart guide which provides instructions on deploying Kubestone and running benchmarks. You can execute benchmarks via Kubestone by creating the necessary custom resources within your cluster. Once the appropriate namespace is created, it can be utilized to submit benchmark requests, and all benchmark executions will be organized within that specific namespace. This streamlined process ensures that you can easily monitor and analyze the performance of your Kubernetes applications.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
ActiveBatch Workload Automation
Yes
Apache Knox
Yes
Apache PredictionIO
Yes
Apache Ranger
Yes
Astera Dataprep
Yes
Cloudera Data Platform
Yes
DX Unified Infrastructure Management
Yes
Hue
Yes
IronCore Labs
Yes
Kubernetes
No
Integrations
ActiveBatch Workload Automation
No
Apache Knox
No
Apache PredictionIO
No
Apache Ranger
No
Astera Dataprep
No
Cloudera Data Platform
No
DX Unified Infrastructure Management
No
Hue
No
IronCore Labs
No
Kubernetes
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Apache Software Foundation
Founded
1999
Country
Uniited States
Website
hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html
Vendor Details
Company Name
Kubestone
Website
kubestone.io/en/latest/