Table of Contents


  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings
  4. Sources
  5. Targets
  6. Transformations
  7. Data Preview
  8. Cluster Workflows
  9. Profiles
  10. Monitoring
  11. Hierarchical Data Processing
  12. Hierarchical Data Processing Configuration
  13. Hierarchical Data Processing with Schema Changes
  14. Intelligent Structure Models
  15. Stateful Computing
  16. Connections
  17. Data Type Reference
  18. Function Reference

Viewing Hive Tasks

Viewing Hive Tasks

When you have a Hive source with a transactional table, you can view the Hive task associated with the Spark job.
When you run a mapping on Spark that launches Hive tasks, you can view the Hive query statistics in the session log and in the Administrator tool for monitoring along with the Spark application. For example, you can monitor information related to the Update Strategy transformation and SQL authorization associated to the mapping on Spark.
You can view the Summary Statistics for a Hive task in the Administrator tool. The Spark statistics continue to appear. When the Spark engine launches a Hive task, you can see Source Load Summary and Target Load summary including Spark data frame with Hive task statistics. Otherwise, when you have only a Spark task, the Source Load Summary and Target Load Summary do not appear in the session log.
Under Target Load Summary, all Hive instances will be prefixed with ‘Hive_Target_’. You can see same instance name in the Administrator tool.
You can view the Tez job statistics in the Administrator tool when reading and writing to Hive tables that the Spark engine launches in any of the following scenarios:
  • You have resources present in the Amazon buckets.
  • You have transactional Hive tables.
  • You have table columns secured with fine grained authorization.
Incorrect statistics appears for all the Hive sources and targets indicating zero rows for average rows for each second, bytes, average bytes for each second, and rejected rows. You can see that only processed rows contain correct values, and the remaining columns will contain either 0 or N/A.


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