Table of Contents

Search

  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

Troubleshooting a Mapping in a Hadoop Environment

Troubleshooting a Mapping in a Hadoop Environment

When I run a mapping with a Hive source or a Hive target on a different cluster, the Data Integration Service fails to push the mapping to Hadoop with the following error:
Failed to execute query [exec0_query_6] with error code [10], error message [FAILED: Error in semantic analysis: Line 1:181 Table not found customer_eur], and SQL state [42000]].
When you run a mapping in a Hadoop environment, the Hive connection selected for the Hive source or Hive target, and the mapping must be on the same Hive metastore.
When I run a mapping with a Hadoop distribution on MapReduce 2, the Administrator tool shows the percentage of completed reduce tasks as 0% instead of 100%.
Verify that the Hadoop jobs have reduce tasks.
When the Hadoop distribution is on MapReduce 2 and the Hadoop jobs do not contain reducer tasks, the Administrator tool shows the percentage of completed reduce tasks as 0%.
When the Hadoop distribution is on MapReduce 2 and the Hadoop jobs contain reducer tasks, the Administrator tool shows the percentage of completed reduce tasks as 100%.
When I run mappings with SQL overrides concurrently, the mappings hang.
There are not enough available resources because the cluster is being shared across different engines.
Configure YARN to use the capacity scheduler and use different YARN scheduler queues for Blaze, Spark, and Hive.
When I configure a mapping to create a partitioned Hive table, the mapping fails with the error "Need to specify partition columns because the destination table is partitioned."
This issue happens because of internal Informatica requirements for a query that is designed to create a Hive partitioned table. For details and a workaround, see Knowledge Base article 516266.


Updated October 23, 2019