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  • Data Engineering Streaming 10.2.1
  • All Products

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

Big Data Management User Guide

Big Data Management User Guide

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Monitoring with MapReduce Hive Engine

Monitoring with MapReduce Hive Engine

You can monitor the MapReduce Hive engine.
You can also monitor and view Hive tasks that use MapReduce to run Spark jobs. Or, you can monitor MapReduce engines for Hive mappings.
Effective in version 10.2.1, the MapReduce mode of the Hive run-time engine is deprecated, and Informatica will drop support for it in a future release. The Tez mode remains supported.
The following image shows the MapReduce Hive Query properties on the Monitor tab in the Administrator tool:
On the Monitor tab in the Administrator tool, the contents panel contains the mapping, script, and Hive query that uses MapReduce engines. In the Details pane, you can view the MR Job details, such as the Job ID and Map % Complete. The Job_ID starts with the prefix job_. Both Map % Complete and Reduce % Complete shows as 100%. DAG % Complete appears as N/A.
The following image shows a Hive task that uses MapReduce to run Spark jobs:
On the Monitor tab in the Administrator tool, the contents panel contains the mapping, script, Hive query, and Spark application that uses MapReduce engines to run Spark jobs. In the Details pane, you can view the MR Job details, such as the Job ID and Map % Complete. The Job_ID starts with the prefix job_. Both Map % Complete and Reduce % Complete shows as 100%. DAG % Complete appears as N/A.
You can view the following information under the MR Job details for MapReduce:
Property
Applicable Values
Description
Job ID
Job_<name>
You can select the link under Job ID to view the application cluster
For example, if the Job ID property contains a value starting with the prefix job_ in the MR Job Details pane, the naming convention indicates that the MapReduce engine is in use.
Map % Complete
0 - 100
You can specify a value from 0 through 100 for MapReduce.
Reduce % Complete
0 - 100
You can specify a value from 0 through 100 for MapReduce.
DAG % Complete
N/A
DAG % is not applicable for MapReduce.
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