Mapping Data Flow Debug Mode
Azure Data Factory Mapping Data Flow is currently a public preview feature and is not subject to Azure customer SLA provisions. Currently, Mapping Data Flows are not available in these Azure regions: West Central US, Brazil South, France Central, Korea Central.
Azure Data Factory Mapping Data Flow's debug mode can be switched on with the "Data Flow Debug" button at the top of the design surface. When designing data flows, turning debug mode on will allow you to interactively watch the data shape transform while you build and debug your data flows. The Debug session can be used both in Data Flow design sessions as well as during pipeline debug execution of data flows.
When Debug mode is on, you'll interactively build your data flow with an active Spark cluster. The session will close once you turn debug off in Azure Data Factory. You should be aware of the hourly charges incurred by Azure Databricks during the time that you have the debug session turned on.
In most cases, it's a good practice to build your Data Flows in debug mode so that you can validate your business logic and view your data transformations before publishing your work in Azure Data Factory. Use the "Debug" button on the pipeline panel to test your data flow inside of a pipeline.
While the debug mode light is green on the Data Factory toolbar, you'll be charged at the Data Flow debug rate of 8 cores/hr of general compute with a 60 minute time-to-live
When running in Debug Mode in Data Flow, your data will not be written to the Sink transform. A Debug session is intended to serve as a test harness for your transformations. Sinks are not required during debug and are ignored in your data flow. If you wish to test writing the data in your Sink, execute the Data Flow from an Azure Data Factory Pipeline and use the Debug execution from a pipeline.
Debug settings can be edited by clicking "Debug Settings" on the Data Flow canvas toolbar. You can select the limits and/or file source to use for each of your Source transformations here. The row limits in this setting are only for the current debug session. You can also select the staging linked service to be used for a SQL DW source.
There's a cluster status indicator at the top of the design surface that will turn green when the cluster is ready for debug. If your cluster is already warm, then the green indicator will appear almost instantly. If your cluster wasn't already running when you entered debug mode, then you'll have to wait 5-7 minutes for the cluster to spin up. The indicator will spin until its ready.
When you are finished with your debugging, turn the Debug switch off so that your Azure Databricks cluster can terminate and you'll no longer be billed for debug activity.
With debug on, the Data Preview tab will light-up on the bottom panel. Without debug mode on, Data Flow will show you only the current metadata in and out of each of your transformations in the Inspect tab. The data preview will only query the number of rows that you have set as your limit in your debug settings. You may need to click "Fetch data" to refresh the data preview.
Selecting individual columns in your data preview tab will pop up a chart on the far-right of your data grid with detailed statistics about each field. Azure Data Factory will make a determination based upon the data sampling of which type of chart to display. High-cardinality fields will default to NULL/NOT NULL charts while categorical and numeric data that has low cardinality will display bar charts showing data value frequency. You'll also see max/len length of string fields, min/max values in numeric fields, standard dev, percentiles, counts, and average.
Once you're finished building and debugging your data flow, execute it from a pipeline.
When testing your pipeline with a data flow, use the pipeline Debug run execution option.