Table of Contents


  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. 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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