Flattening multiple nested JSON structures in Azure Synapse Data Flow can sometimes lead to issues, particularly when dealing with complex nested objects. The error message "none.get" is quite vague, but it often indicates that the process is trying to access a value that doesn't exist, possibly due to issues in handling nested structures.
Here are a few steps and tips to help you troubleshoot and resolve the issue:
- Isolate the Problematic Flatten Step
Start by identifying exactly where the error occurs. Simplify your data flow by flattening one level at a time and testing each step. This can help you pinpoint which specific flatten operation is causing the issue.
- Check for Null or Missing Values
Ensure that the JSON structure you're working with doesn't have null or missing values at the levels you're trying to flatten. You can add a transformation to filter out or handle null values before flattening.
- Use a Derived Column Transformation
Before the flattening step, use a Derived Column transformation to add some logic to handle potential null values. For example, you could replace nulls with a default value or log them for further inspection:
iif(isNull(column_name), "default_value", column_name)
- Incremental Flattening
Instead of flattening multiple levels in a single step, flatten one level and then output the intermediate result to a new JSON or Parquet file. Then, create a new data flow to flatten the next level. This incremental approach can make it easier to manage and debug.
- Schema Drift Handling
Enable schema drift in your data flow to allow the flow to handle unexpected schema changes. This can sometimes resolve issues with missing fields during the flattening process.
- Debugging with Data Preview
Use the Data Preview feature in Synapse Data Flow to inspect the intermediate results at each step. This can help you see what the data looks like after each transformation and before the flattening step.
- Validate JSON Structure
Ensure that your JSON input is well-formed and consistent. Use JSON validation tools to check for errors or inconsistencies in the structure. This can help identify any issues with the data itself that might be causing the flattening to fail.
Example Workflow
Here’s a simplified example of a step-by-step approach:
Initial Load:
- Load the nested JSON into a staging table or directly into the Synapse Data Flow. First Flatten:
- Add a Flatten transformation for the first level.
- Inspect the results using Data Preview.
Handle Nulls/Missing Values:
- Add a Derived Column transformation to handle nulls or unexpected values.
Second Flatten:
- Add another Flatten transformation for the next level.
- Again, inspect the results using Data Preview.
Repeat as Necessary:
- Continue flattening one level at a time, inspecting results and handling issues as they arise.
Additional Resources
- Azure Synapse Analytics Documentation
- Troubleshoot Mapping Data Flows in Azure Data Factory
- Handling Complex JSON Structures in Data Flows
Example Configuration
Assuming you have a JSON structure like this:
{
"level1": {
"level2": {
"level3": {
"field1": "value1",
"field2": "value2"
}
}
}
}
Your data flow configuration could be:
Flatten Level 1:
- Unroll by
level1
- Output fields:
level1.level2
- Output fields:
- Unroll by
level2
- Output fields:
level2.level3
Flatten Level 3:- Unroll by
level3
- Output fields:
field1
,field2
- Unroll by
By following these steps and troubleshooting techniques, you should be able to isolate and resolve the issue causing the "none.get" error.