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.
Cancel job: Click this button to cancel your job while it is still in progress.
NOTE: This option may not be available for all running environments.
NOTE: If you do not have permission to cancel a
job, 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 .
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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
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View BigQuery job: If the job was able to be executed in BigQuery, you can review the job in the BigQuery console.
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.listpermissions 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: When you download your JSON profile, any rules applied to the generated results are included in the profile. Search for
You can review a snapshot of the results of your job.
The output data section displays a preview of the generated output of your job.
NOTE: This section is not displayed if the job fails.
You can also perform the following:
View: If it is present, you can click the View link to view the job results in the datastore where they were written.
NOTE: The View link may not be available for all jobs.
Download : If it is present, click the Download link to download the generated job results to your local desktop.
- View details: Click View details to view the generated results in the side bar. See the Output Destinations below.
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.
- 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.
- 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.
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 ID: Unique identifier for the job
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.
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.
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.
- 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
- vCPU usage: Total vCPU hours used to run the job. For more information, see Usage Metrics.
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.
- Project owners must enable the optimization feature for the project. For more information, see Dataprep Project Settings Page.
- When the feature is enabled, optimizations must be enabled for each flow. You can also select the optimizations to apply. For more information, see Flow Optimization Settings Dialog.
When optimizations have been applied to your flow, they are listed on the Overview tab:
- Optimization: This setting is displayed if flow optimizations have been enabled for this flow.
- Columns pruned: If one or more unused columns have been pruned in the datasource via SQL, the count of columns is listed here.
- Filters pushed down: If one or more row filters has been applied in the datasource via SQL, the count of filters is listed here.
If an optimization is disabled or was not applied to the job run, it is not listed.
Output Destinations Tab
If the job has successfully completed, you can review the set of generated outputs and export results.
View details: View details about the generated output in the side bar.
- View on
: View the results within the
D s storage
. See Google Cloud Storage BrowserAccess.
D s storage
Download result: Download the generated output to your local desktop.
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.
NOTE: This option is not available for all file formats.
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.
NOTE: If a SQL script fails to execute, all downstream phases of the job fail to execute.
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.
SQL scripts tab
- Connection: Name of the connection through which the script was executed.
- SQL statement: The first part of the SQL script that was executed.
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.
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.
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: 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.
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.
- Download as PDF: Download your visual profile and results of your data quality rules on the entire dataset as a PDF file. For more information, see Overview of Data Quality.
- Download as JSON: Download your visual profile as a JSON file.
NOTE: The computational cost of generating exact visual profiling measurements on large datasets in interactive visual profiles severely impacts performance. As a result, visual profiles across an entire dataset represent statistically significant approximations.
Tip: You should review the type information for each column, which is indicated by the icon to the left of the column.
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If you have defined data quality rules for your recipe, those rules are applied to the generated results. In the Rules tab, you can review the application of the rules across your entire dataset.
The zoom control options are available at the top-right corner of the dependency graph canvas. The following are the available zoom options:
Tip: You can use the keyboard shortcuts listed in the zoom options menu to make quick adjustments to the zoom level.
- Zoom in: Zoom in 10% on the canvas to focus on greater detail.
- Zoom out: Zoom out 10% from the canvas to see more of it.
- Zoom to fit: Change the zoom level to fit all of the objects of your flow onto the screen.
- 25%, 50%, or 100%: Change the zoom level to one of the preset levels.
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When a webhook task has been triggered for this job, you can review the status of its delivery to the target system.