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

Monitoring with Tez Hive Engine

Monitoring with Tez Hive Engine

You can monitor Tez Hive engine.
Tez uses YARN timeline as its application history store. Tez stores most of its lifecycle information into the history store, such as all the DAG information. You can monitor the Tez engine information, such as DAG % complete.
Tez relies on the application time line server as a backing store for the application data generated during the lifetime of a YARN application. Tez interfaces with the application timeline server and displays both a live and historical view of the Tez application inside a Tez web application.
The following image shows the Tez 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 Tez 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 application_. Both Map % Complete and Reduce % Complete shows as N/A. DAG % Complete appears as 100%.
You can monitor and view Hive tasks that use Tez to run Spark jobs. Or, you can monitor Tez engines for Hive mappings.
The following image shows a Hive task that uses Tez 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 Tez 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 application_. Both Map % Complete and Reduce % Complete shows as N/A. DAG % Complete appears as 100%.
You can view the following information under the MR Job details for Tez:
Property
Applicable Values
Description
Job ID
Application_<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 application_ in the MR Job Details pane, the naming convention indicates that the Tez engine is in use.
You can click the link under Job ID to view the application cluster. If you click the Tracking URL for the Tez job, you get redirected to the Hadoop Resource Manager. If you then click History, you can view the Tez view, which is provided by the Hadoop distribution in Ambari.
For each application ID, there are multiple DAGs information.
Map % Complete
N/A
Map % is not applicable for Tez.
Reduce % Complete
N/A
Reduce % is not applicable for Tez.
DAG % Complete
0 - 100
You can specify a value from 0 through 100 for Tez.
When you specify a query in Hive, the script launches a Hadoop job, such as INSERT or DELETE query. Or, the script launches a Hive query. If the script launches no Hadoop jobs, it appears blank for the following fields, such as Job ID, reduce % complete, and DAG % complete.
If the active Resource Manager goes down during a mapping run on the Tez engine, the Tez monitoring statistics might become unavailable for Hive jobs or Spark jobs that use HiveServer 2 tasks.


Updated October 23, 2019