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  • Data Engineering Streaming 10.2.1
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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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Rules and Guidelines for Mappings in a Hadoop Environment

Rules and Guidelines for Mappings in a Hadoop Environment

You can run mappings in a Hadoop environment. When you run mappings in a Hadoop environment, some differences in processing and configuration apply.
The following processing differences apply to mappings in a Hadoop environment:
  • A mapping is run in high precision mode in a Hadoop environment for Hive 0.11 and above.
  • In a Hadoop environment, sources that have data errors in a column result in a null value for the column. In the native environment, the Data Integration Service does not process the rows that have data errors in a column.
  • When you cancel a mapping that reads from a flat file source, the file copy process that copies flat file data to HDFS may continue to run. The Data Integration Service logs the command to kill this process in the Hive session log, and cleans up any data copied to HDFS. Optionally, you can run the command to kill the file copy process.
  • When you set a limit on the number of rows read from the source for a Blaze mapping, the Data Integration Service runs the mapping with the Hive engine instead of the Blaze engine.
The following configuration differences apply to mappings in a Hadoop environment:
  • Set the optimizer level to none or minimal if a mapping validates but fails to run. If you set the optimizer level to use cost-based or semi-join optimization methods, the Data Integration Service ignores this at run-time and uses the default.
  • The Spark engine does not honor the early projection optimization method in all cases. If the Data Integration Service removes the links between unused ports, the Spark engine might reconnect the ports.
When the Spark engine runs a mapping, it processes jobs on the cluster using HiveServer2 in the following cases:
  • The mapping writes to a target that is a Hive table bucketed on fields of type char or varchar.
  • The mapping reads from or writes to Hive transaction-enabled tables.
  • The mapping reads from or writes to Hive tables where column-level security is enabled.
  • The mapping writes to a Hive target and is configured to create or replace the table at run time.
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