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Excerpt

Use these guidelines and features to begin the process of diagnosing jobs that have failed.

Job Types

The following types of jobs can be executed in

D s product
rtrue
:

  • Convert jobs: Some datasources, such as binary file or JSON formats, must be converted to a format that can be easily read by the 
    D s webapp
    . During data ingestion, the datasource is converted to a natively supported file format and stored on backend storage. 
  • Transform job: This type of job executes the steps in your recipe against the dataset to generate results in the specified format. When you configure your job, any set of selected output formats causes a transform job to execute according to the job settings.
  • Profile job: This type of job builds a visual profile of the generated results. When you configure your job, select Profile Results to generate a profile job.
  • Publish job: This job publishes results generated by the platform to a different location or datastore. 
  • Ingest job: This job manages the import of data from a JDBC source into the default datastore for purposes of running a transform or sampling job.

For more information, see Run Job Page.

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When a job fails to execute, a failure message appears in following locations:

The following is an example from the Jobs page:

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  • Look to cut data volume. Some job failures occur due to high data volumes. For jobs that execute across a large dataset, you can re-examine your data to remove unneeded rows and columns of data. Use the Deduplicate transformation to remove duplicate rows. See Remove Data.
  • Gather a new sample. In some cases, jobs can fail when run at scale because the sample displayed in the Transformer page did not include problematic data. If you have modified the number of rows or columns in your dataset, you can generate a new sample, which might illuminate the problematic data. However, gathering a new sample may fail as well, which can indicate a broader problem. See Samples Panel.
  • Change the running environment. If the job failed on

    D s photon
    , try executing it on another running environment. 

    Tip

    Tip: The 

    D s photon
    running environment is not suitable for jobs on large datasets. You should accept the running environment recommended in the Run Job page.


Contact Support

If you are unable to diagnose your job failure, please contact 

D s support

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D s also
inCQLtrue
label((label = "job") OR (label = "projectjob_management_tasks"))