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. Appendix A: Connections
  17. Appendix B: Data Type Reference
  18. Appendix C: Function Reference

Parsing JSON Records on the Spark Engines

Parsing JSON Records on the Spark Engines

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 Spark 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 generates 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 non-native distributions that use Spark version 2.2.x and above.


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