Batch Operation of a Categorization Project

Batch Operation

1. Truncate Dataset: POST /dataset/datasets/{name}/truncate
   Truncate the records of the dataset {name}. This removes all existing records from the dataset.
2. Update Dataset: POST /dataset/datasets/{name}/update
   Update the records of the dataset {name} using the command CREATE. Because of Step 1. all records in this step are effectively inserted (i.e. no updates occur).
3. Update Unified Dataset: POST /recipe/recipes/{recipeId}/run/records
   Update the unified dataset using its {recipeId} and the operation keyword records. Additionally, capture the uuid of the submitted task from the response.
4. Wait For Task: GET /dataset/tasks/uuid/{uuid}
   Poll the storage status of the task submitted in Step 3 using the captured uuid until storageStatus=COMMITTED received.

If the machine learning (ML) configuration has been edited (Toggle Inclusion in Machine Learning), then proceed to Step 5 else skip to Step 6.
5. Update Recipe: POST /recipe/recipes/{id}/populate
6. Update Categorizations: POST /recipe/recipes/{recipeId}/run/categorizations
Update the categorizations using its {recipeId} and the operation keyword categorizations. Additionally, capture the uuid of the submitted task from the response.
7. Wait For Task: GET /dataset/tasks/uuid/{uuid}
Poll the storage status of the task submitted in Step 6 using the captured uuid until storageStatus=COMMITTED received.
8. Materialize Categorized Dataset: POST /export
Materialize the categorized dataset {datasetId} to a specified export configuration and data format and capture the id of the submitted job from the response.
9. Fetch UUID: GET /job/jobs/{id}
Using the captured id in Step 8, fetch the job details and capture the uuid.
10. Wait For Task: GET /dataset/tasks/uuid/{uuid}
Using the captured uuid from Step 9, poll the storage status of the task until storageStatus=COMMITTED received.
11. Export Dataset: GET /export/read/dataset/{datasetId}
Export the materialized categorized dataset {datasetId}.