Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. B2B transformation
  7. Aggregator transformation
  8. Cleanse transformation
  9. Data Masking transformation
  10. Data Services transformation
  11. Deduplicate transformation
  12. Expression transformation
  13. Filter transformation
  14. Hierarchy Builder transformation
  15. Hierarchy Parser transformation
  16. Hierarchy Processor transformation
  17. Input transformation
  18. Java transformation
  19. Java transformation API reference
  20. Joiner transformation
  21. Labeler transformation
  22. Lookup transformation
  23. Machine Learning transformation
  24. Mapplet transformation
  25. Normalizer transformation
  26. Output transformation
  27. Parse transformation
  28. Python transformation
  29. Rank transformation
  30. Router transformation
  31. Rule Specification transformation
  32. Sequence Generator transformation
  33. Sorter transformation
  34. SQL transformation
  35. Structure Parser transformation
  36. Transaction Control transformation
  37. Union transformation
  38. Velocity transformation
  39. Verifier transformation
  40. Web Services transformation

Transformations

Transformations

Configuring bulk requests

Configuring bulk requests

Configure bulk requests to optimize performance by combining multiple requests into one request that the Machine Learning transformation sends to the machine learning model. Bulk requests can improve performance by reducing processing overhead and the amount of time that it takes to communicate with the model.
To create a bulk request, the Machine Learning transformation selects the highest-level array field in the request schema. In the JSON request body for the bulk request, the transformation combines request rows as elements of the selected array field so that one JSON request body contains data for multiple requests. You can configure bulk request options to determine how much data each bulk request contains.
For example, the request schema might have the following structure:
The request schema contains a struct called instances. The instances struct contains a nested array called data which contains another nested array called features.
The Machine Learning transformation selects the
data
array as the highest-level array field to combine requests. If you configure each bulk request to send 2 MB of data to the machine learning model, the Machine Learning transformation configures the
data
array in the JSON request body to include data for 2 MB of request rows.
The highest-level array cannot have sibling array fields. If the highest-level array has a sibling field of a primitive data type, the data in the sibling field will not be combined. Instead, one random record in the sibling field will be sent to the machine learning model.
To create a bulk response, the machine learning endpoint must combine response rows as elements of the highest-level array in the response schema. The Machine Learning transformation parses the array into output rows. Review the bulk request options to verify which field the Machine Learning transformation will parse.
To use bulk requests in the Machine Learning transformation, the machine learning endpoint must be configured to accept bulk requests and send bulk responses.

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