Table of Contents


  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

Parsing JSON Records on the Spark Engine

Parsing JSON Records on the Spark Engine

In the mapping run-time properties, you can configure how the Spark engine parses corrupt records and multiline records when it reads from JSON sources in a mapping.
Configure the following Hadoop run-time properties:
Specifies the parser how to handle corrupt JSON records. You can set the value to one of the following modes:
  • DROPMALFORMED. The parser ignores all corrupted records. Default mode.
  • PERMISSIVE. The parser accepts non-standard fields as nulls in corrupted records.
  • FAILFAST. The parser throws an exception when it encounters a corrupted record and the Spark application goes down.
Specifies whether the parser can read a multiline record in a JSON file. You can set the value to true or false. Default is false.
Applies only to Hadoop distributions that use Spark version 2.2.x.

Updated October 23, 2019