Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. Cleanse transformation
  8. Data Masking transformation
  9. Data Services transformation
  10. Deduplicate transformation
  11. Expression transformation
  12. Filter transformation
  13. Hierarchy Builder transformation
  14. Hierarchy Parser transformation
  15. Hierarchy Processor transformation
  16. Input transformation
  17. Java transformation
  18. Java transformation API reference
  19. Joiner transformation
  20. Labeler transformation
  21. Lookup transformation
  22. Machine Learning transformation
  23. Mapplet transformation
  24. Normalizer transformation
  25. Output transformation
  26. Parse transformation
  27. Python transformation
  28. Rank transformation
  29. Router transformation
  30. Rule Specification transformation
  31. Sequence Generator transformation
  32. Sorter transformation
  33. SQL transformation
  34. Structure Parser transformation
  35. Transaction Control transformation
  36. Union transformation
  37. Velocity transformation
  38. Verifier transformation
  39. Web Services transformation

Transformations

Transformations

Running a mapping with JSON data

Running a mapping with JSON data

To run a mapping that contains a Hierarchy Processor transformation with JSON-formatted data, you need to use a mapping task.

Reading JSON input

When you read JSON data, the input files can be based on a schema with multiple lines or on a schema with a single line.
The following sample shows a JSON schema on a single line:
{"Name":"Tom","Street":"2100 Seaport Blvd","City":"Redwood City","State":"CA","Country":"USA","Zip":"94063"}
The following sample shows a JSON schema that spans across multiple lines:
{ "Name": "Tom", "Surname": "Day", "City": "Redwood City", "State": "CA", "Country": "USA", "Zip": "94063" }
By default, the Hierarchy Processor transformation reads each JSON schema as a single line. To read input that spans across multiple lines, you can configure the formatting options in the Source transformation to read multiple-line JSON files.

Writing JSON output

When you write JSON data, you can write each output record to a separate file, or you can write all output records to one file.
By default, each output record is written to a separate file. To write the output records to one JSON-formatted file, set the following Spark session property in the mapping task:
Session Property Name
Session Property Value
spark.sql.shuffle.partitions
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