YARN Resource Management
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 | Except where otherwise noted, this document is licensed under Creative Commons Attribution ShareAlike 3.0 License |
Hortonworks Data Platform (HDP) and any of its components are not anticipated to be combined with any hardware, software or data, except as expressly recommended in this documentation.
2015-12-21
Abstract
The Hortonworks Data Platform, powered by Apache Hadoop, is a massively scalable and 100% open source platform for storing, processing and analyzing large volumes of data. It is designed to deal with data from many sources and formats in a very quick, easy and cost-effective manner. The Hortonworks Data Platform consists of the essential set of Apache Hadoop projects including MapReduce, Hadoop Distributed File System (HDFS), HCatalog, Pig, Hive, HBase, ZooKeeper and Ambari. Hortonworks is the major contributor of code and patches to many of these projects. These projects have been integrated and tested as part of the Hortonworks Data Platform release process and installation and configuration tools have also been included.
Unlike other providers of platforms built using Apache Hadoop, Hortonworks contributes 100% of our code back to the Apache Software Foundation. The Hortonworks Data Platform is Apache-licensed and completely open source. We sell only expert technical support, training and partner-enablement services. All of our technology is, and will remain, free and open source.
For more information on Hortonworks technology, visit the Hortonworks Data Platform page. For more information on Hortonworks services, visit either the Support or Training page. Feel free to contact us directly to discuss your specific needs.
Contents
- 1. Capacity Scheduler
- 1. Enabling Capacity Scheduler
- 2. Setting up Queues
- 3. Controlling Access to Queues with ACLs
- 4. Default Queue Mapping based on User or Group
- 5. Managing Cluster Capacity with Queues
- 6. Resource Distribution Workflow
- 7. Resource Distribution Workflow Example
- 8. Setting User Limits
- 9. Application Reservations
- 10. Using Flexible Scheduling Policies
- 11. Starting and Stopping Queues
- 12. Setting Application Limits
- 13. Preemption
- 14. Scheduler User Interface
- 2. CGroups
- 3. CPU Scheduling
- 4. Log Aggregation for Long-running Applications
- 5. Node Labels
- 6. Running Applications on YARN Using Slider
- 1. System Requirements
- 2. Operating System Requirements
- 3. Installing Apache Slider
- 4. Running Applications on Slider
- 5. Running HBase on YARN via Slider
- 6. Running Accumulo on YARN via Slider
- 6.1. Downloading and Installing the Accumulo Application Package
- 6.2. Configuring Accumulo on YARN
- 6.3. Configuring Accumulo on YARN on Secure Clusters
- 6.4. Launching an Accumulo Application Instance
- 6.5. Client Connections to Accumulo and Retrieving Effective
accumulo-site.xml - 6.6. Deployment Considerations
- 7. Running Multiple MapReduce Versions Using the YARN Distributed Cache
- 8. Timeline Server
- 1. Timeline Server 1.5 Overview
- 2. Upgrading Timeline Server 1.0 to 1.5
- 3. Configuring the Timeline Server
- 4. Enabling Generic Data Collection
- 5. Configuring Per-Framework Data Collection
- 6. Configuring the Timeline Server Store
- 7. Configuring Timeline Server Security
- 8. Running the Timeline Server
- 9. Accessing Generic Data from the Command- Line
- 10. Publishing Per-Framework Data in Applications
- 9. Using the YARN REST APIs to Manage Applications
- 10. Work-Preserving Restart
