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Comment: Published by Scroll Versions from space DEV and version r085

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Excerpt

You can use the Job Details page to explore details about successful or failed jobs, including outputs, dependency graph, and other metadata. Download results to your local desktop or, if enabled, explore a visual profile of the data in the results for further iteration on your recipe.

Page options:

  • Cancel job: Click this button to cancel your job while it is still in progress.

    Info

    NOTE: This option may not be available for all running environments.

    Info

    NOTE:

    In some cases, the product is unable to cancel the job from the application. In these cases, click View in Dataflow, and from there you can cancel the job in progress. Similarly, a user may be able cancel a job from the

    If you do not have permission to cancel a

    d-s-

    webapp

    dataflow
    job,

    even though the user is not permitted to cancel the job in the running environment. The service account associated with the user's 
    D s item
    itemaccount
    may have the appropriate permissions, but the user's personal account does not. For more information, see Google Service Account Management
  • View dataflow job: View the job that was executed on
  • the appropriate permissions must be added to your IAM role by an administrator. The default IAM role available with the product has these permissions, but these permissions may not be present if you are using a custom or personal IAM role in your account. For more information on IAM permissions, see Required Dataprep User Permissions.

    Info

    NOTE: In some cases, the product is unable to cancel the job from the application. In these cases, click View in Dataflow, and from there you can cancel the job in progress.  


  • View dataflow job: View the job that was executed on

    D s dataflow
    .

  • View BigQuery job: If the job was able to be executed in BigQuery, you can review the job in the BigQuery console.

    Info

    NOTE: When you view the job in BigQuery, you are using your own credentials to access the BigQuery console, which may be different from the service account that was used to execute the job. In this case, the job in BigQuery may be reported having errors when viewed using your credentials, when the job succeeded using the service account. To see the job properly, the bigquery.jobs.get and bigquery.jobs.list permissions must be added to your account. For more information, see Required Dataprep User Permissions.

    • Jobs can be executed in BigQuery if all data sources and outputs are located in BigQuery. Additional requirements apply. For more information, see Overview of Job Execution.
  • Download profile as PDF: If visual profiling was enabled for the job, you can download the profile in PDF format.
  • Download profile as JSON: If visual profiling was enabled for the job, you can download a JSON representation of the profile to your desktop.

    Tip

    Tip: When you download your JSON profile, any rules applied to the generated results are included in the profile. Search for profilerRules.

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Info

NOTE: If you chose to generate a profile of your job results, the transformation and profiling tasks may be combined into a single task, depending on your environment. If they are combined and profiling fails, any publishing tasks defined in the job are not launched. You may be able to ad-hoc publish the generated results. See below.


  • If present, you can click the Show Warnings link to see any warnings pertaining to recipe errors, including the relevant step number.

    • To view the job that was executed, click View dataflow job.
    To review the recipe and dependencies in your job, click View steps and dependencies. See the Dependencies tab below.

  • If you chose to profile results of your job, click View profile to review. See Profile tab below.
    • A visual profile provides a graphical snapshot of the results of a successful transformation job for the entire dataset and individual columns in the dataset. 
    • For more information on enabling a visual profile job, see Run Job Page.
    • For more information, see Overview of Visual Profiling.
  • To view the job that was executed, click View dataflow job.

Publish:

You can also review the outputs generated as a result of your job. To review and export any of the generated results, click View all. See Outputs Destinations tab below.

Job summary:

  • Job If your job output specified SQL scripts to run before or after job execution, you can track their progress in the following stages:
    • Pre-ingest SQL: Script that is configured to run before the source data is ingested to the platform.
    • Post-publish SQL: Script that is configure to run after the output data has been published.
    • For additional details, see the SQL scripts tab below.
    • For more information on SQL scripts in job execution, see Create Output SQL Scripts.

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Publish:

You can also review the outputs generated as a result of your job. To review and export any of the generated results, click View all. See Outputs Destinations tab below.

Job summary:

  • Job ID: Unique identifier for the job

    Tip

    Tip: If you are using the REST APIs, this value can be used to retrieve and modify specifics related to this job. For more information, see API Reference.

  • Job status: Current status of the job:
    • Queued:  Job has been queued for execution.
    • Running: Job is in progress.
    • Completed: Job has successfully executed.

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      NOTE:  Invalid steps in a recipe are skipped, and it's still possible for the job to be executed successfully.

    • Failed: Job failed to complete. 

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      NOTE:  You can re-run a failed job from the Transformer page. If you have since modified the recipe, those changes are applied during the second run. See Transformer Page.

  • Flow: Name of the flow from which the job was executed. Click the link to open the flow. See Flow View Page.
  • Output: Name of the output object that was used to define the generated results. Click the link to open the output. See Flow View Page.

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  • Job type: The method by which the job was executed:
    • Manual - Job was executed through the application interface.

    • Scheduled - Job was executed according to a predefined schedule. See Add Schedule Dialog.

  • User: The user who launched the job
  • Environment: Where applicable, the running environment where the job was executed is displayed.
  • Start time: Timestamp for when processing began on the job. This value may not correspond to when the job was queued for execution.
  • Finish time: Timestamp for when processing ended on the job, successful or not
  • Last update: Timestamp for when the job was last updated
  • Duration: Elapsed time of job execution

Optimization summary:

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  • vCPU usage: Total vCPU hours used to run the job. For more information, see Usage Metrics.

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Optimization summary:

For jobs sourced from relational datasets, you can optionally enable SQL-based optimizations, which apply some of the steps specified in your recipe back in the datasource, where they can be executed before the data is transferred to the running environment for execution. Using these optimizations means faster performance based on a lower volume of data transfer.

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  • Download result: Download the generated output to your local desktop. 

    Info

    NOTE: Some file formats may not be downloadable to your desktop. See below.

  • Create imported dataset: Use the generated output to create a new imported dataset for use in your flows. See below.

    Info

    NOTE: This option is not available for all file formats.

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After the new output has been written, you can create new recipes from it. See Build Sequence of Datasets.

Publish

If 

D s product
 is connected to an external storage system, you may publish your job results to it. Requirements:

  • Your version of the product supports publishing.
  • Your connection to the storage system includes write permissions.
  • Your results are generated in a format that the target system supports for writing.
  • All sub-jobs, including profiling, successfully completed.

For more information, see Publishing Dialogof Datasets.

Publish

If 

D s product
 is connected to an external storage system, you may publish your job results to it. Requirements:

  • Your version of the product supports publishing.
  • Your connection to the storage system includes write permissions.
  • Your results are generated in a format that the target system supports for writing.
  • All sub-jobs, including profiling, successfully completed.

For more information, see Publishing Dialog

SQL scripts Tab

If the output for your job included one or more pre- or post-job SQL script executions, you can review the status of their execution during the job.

Info

NOTE: If a SQL script fails to execute, all downstream phases of the job fail to execute.

Tip

Tip: If the SQL script execution for this job encountered errors, you can review those errors through this tab. For more detailed information, click Download logs.


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D caption
SQL scripts tab

Columns:

  • Connection: Name of the connection through which the script was executed.
  • SQL statement: The first part of the SQL script that was executed.
  • Settings:
    • Run before data ingest - script was executed pre-job.
    • Run after data publish - script was executed post-job, after the job results had been written.
  • Status: Current status and execution duration of the SQL script.

    Info

    NOTE: If you have multiple SQL scripts for each settings, they may execute in parallel. For example, if you created three pre-job SQL scripts, there is no guarantee that they executed in the order in which they are listed.

View details:

Hover over a SQL script entry and click View details.

In the SQL script details window, you can review:

  • Connection and SQL of the executed script.
  • Any error messages that occurred during execution.

    Tip

    Tip: To review log information for any error messages, click Download logs.

For more information on these types of SQL scripts, see Create Output SQL Scripts.

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Profile Tab

Review the visual profile of your generated results in the Profile tab. Visual profiling can assist in identifying issues in your dataset that require further attention, including outlier values. 

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