Job Types

The following types of jobs can be executed in the :

• Conversion jobs: Some datasources, such as binary file or JSON formats, must be converted to a format that can be easily read by the . 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 defined transformations against your source datasets 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.
  

Identify Job Failures

When a job fails to execute, a failure message appears in the Job History page:

Publish job failed

In the above example, the Transform and Profile jobs completed, but the Publish job failed. In this case, the results exist and, if the source of the problem is diagnosed, they can be published separately.

Invalid file paths

When your job uses files as inputs or outputs, you may receive invalid file path errors. Depending on the backend datastore, these can be one of the following:

• Path to the file is invalid for the current user. Path may be been created by another user that had access to the location. 
• Path contains invalid characters in it. For more information, see Supported File Formats.
• Resource was deleted.

Jobs that Hang

In some cases, a job may stay in a pending state indefinitely. Typically, these errors are related to a failure of the job tracking service. You can try to the following:

• Resubmit the job.

Try Other Job Options

You can try to re-execute the job using different options.

Tips:

• Disable flow optimizations. If your job is using data from a relational source that supports pushdowns, you can try to disable flow optimizations and then re-run the job. For more information, see Flow Optimization Settings Dialog. 

• 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. 
• 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. 

• Change the running environment. If the job failed on , try executing it on another running environment. 

  Tip: The  running environment is not suitable for jobs on large datasets. Use the default running environment to execute your job.

Contact Support

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

Report an Issue

If you believe that your job has failed due to an issue with the , select Resources menu > Report issue. Copy your session information to the clipboard and paste it into an email to .

Learn More

• https://community.trifacta.com/s/article/how-to-use-the-trifacta-job-log-files