Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter Reference

Scheduling, Queuing, and Node Labeling

Scheduling, Queuing, and Node Labeling

You can use YARN schedulers, YARN queues, and node labels to optimize performance when you run a mapping in the Hadoop environment.
A YARN scheduler assigns resources to YARN applications on the Hadoop cluster while honoring organizational policies on sharing resources. You can configure YARN to use a fair scheduler or a capacity scheduler. A fair scheduler shares resources evenly among all jobs running on the cluster over time. A capacity scheduler allows multiple organizations to share a large cluster and distributes resources based on capacity allocations. The capacity scheduler guarantees each organization a certain capacity and distributes any excess capacity that is underutilized.
YARN queues are organizing structures for YARN schedulers and allow multiple tenants to share a cluster. The capacity of each queue specifies the percentage of cluster resources that are available for applications submitted to the queue. You can redirect Blaze, Spark, Hive, and Sqoop jobs to specific YARN queues.
Node labels allow YARN queues to run on specific nodes in a cluster. You can use node labels to partition a cluster into sub-clusters such that jobs run on nodes with specific characteristics. For example, you might label nodes that process data faster compared to other nodes. Nodes that are not labeled belong to the default partition. You can associate the node labels with capacity scheduler queues.
You can also use the node labels to configure the Blaze engine. When you use node labels to configure the Blaze engine, you can specify the nodes on the Hadoop cluster where you want the Blaze engine to run.
You must install and configure Big Data Management for every node on the cluster, even if the cluster is not part of the queue you are using.


Updated October 23, 2019