topic Re: Can schemaHints dynamically handle nested json structures? in Data Engineering

Can schemaHints dynamically handle nested json structures?

ChristianRRL — Tue, 12 Aug 2025 17:38:56 GMT

Hi there, as I'm learning more about schemaHints, it seems like an incredibly useful way to unpack some of my json data. However, I've hit what is either a limitation of schemaHints or of my understanding of how to use it properly.

Below I have an example of a somewhat standard read_files query. However, rather than applying schemaHints to a single nested structure, I would like to apply a schemaHints pattern to multiple nested fields inside *all* structs inside a given sub_data field (e.g. everything inside elementData.element.data.*).

However, when I try using the `*` wildcard, I quickly get an error:

The only way I've seen so far to get close to (but not quite) where I'm wanting to get, is I can apply a double map<string,string> on the data field, like so:

But the challenge is I want to map two separate sub-fields (dataPoint & values).

If I try to triple map, I lose either the dataPoint field or the values field (depending on how I map it):

e.g. Mapping dataPoint properly (but values is lost)

e.g. Mapping values properly (but dataPoint is lost)

Any feedback/input on this would be much appreciated!

+Side-note: Props to @genevive_mdonça for your post Handling Complex Nested JSON in Databricks Using s... - Databricks Community - 116210. This was a great springboard!

Re: Can schemaHints dynamically handle nested json structures?

Brahmareddy — Wed, 13 Aug 2025 04:38:47 GMT

Hi ChristianRRL,

How are you doing today? as per my understanding, you're absolutely right that schemaHints is powerful for handling semi-structured JSON, especially with deeply nested fields. But it does have some limitations—one being that it doesn’t currently support applying complex hints like multiple field patterns (*) across multiple nested structures within arrays or structs. When you try using wildcards too broadly (like elementData.element.data.*), it can’t resolve how to map both dataPoint and values at the same level—so one ends up getting lost. A practical workaround would be to define the exact path to each field you're trying to unpack instead of using wildcards. If the nested schema is known and consistent, consider writing a more explicit schema or use a combination of from_json and withColumn to parse it manually after ingesting the raw data. It’s a bit more work upfront, but gives you precise control over both dataPoint and values. Hopefully, Databricks will enhance schemaHints to handle more flexible nesting in future releases. Keep experimenting—you're digging into an advanced area that many avoid, and it's a great skill to build!

Regards,

Brahma

Re: Can schemaHints dynamically handle nested json structures?

boitumelodikoko — Wed, 13 Aug 2025 04:54:21 GMT

Hi @ChristianRRL,

Would you be able to share a small sample of your JSON file (with any sensitive data removed)? That way, I can try to replicate your use case and see if we can get schemaHints working across multiple nested fields without losing dataPoint or values.

Once I can test it end-to-end, I'll share an example query or transformation that achieves what you're looking for.

Re: Can schemaHints dynamically handle nested json structures?

ChristianRRL — Thu, 14 Aug 2025 02:27:31 GMT

Ah I see, thanks for the explanation. In our case, the nested schema is neither fully known nor fully consistent (I'd say mostly consistent, but there are edge-cases). I'll need to tinker with this a bit, but I think I have a workaround in mind for this case.

This is a bit of an unfortunate limitation of schemaHints at the moment, but I agree it would be great if they enhance schemaHints to support this in the future!

Re: Can schemaHints dynamically handle nested json structures?

ChristianRRL — Thu, 14 Aug 2025 02:30:06 GMT

I'll be a bit tied up the next couple of days, but I'll try and get something pulled together to share by Friday or early next week!

Re: Can schemaHints dynamically handle nested json structures?

Brahmareddy — Thu, 14 Aug 2025 02:35:10 GMT

Absolutely ChristianRRL, when your nested schema isn't fully known or consistent, schemaHints can only take you so far before you need a workaround. A flexible pattern in these cases is to first ingest the raw JSON using permissive mode (with schema inference or minimal hints), and then flatten and transform the data using from_json or explode + selectExpr logic in a second stage, once you have more control. This two-stage approach gives you better resilience against edge cases, and also allows you to isolate logic for future schema evolution. And yes, here's hoping Databricks @Sujitha improves schemaHints to support wildcards and more advanced nested patterns—would definitely make streaming and ingestion pipelines smoother.

Re: Can schemaHints dynamically handle nested json structures?

ChristianRRL — Fri, 29 Aug 2025 18:31:51 GMT

Hey @boitumelodikoko , got busier than expected, but here's the requested sample json. I'm curious if you'd know of a better/easier way to set both dataPoint and values as string across all "NESTED_DATA_NAME_X"?

json_text = '{"packageHeader":{"transactionId":"5c3661ac-abdd-480c-88b6-c3c128bce7bd","rootName":"rootName","endpointName":"report","elementType":"Generator","elementName":"rootName","intervalSize":5,"environment":"production","startDate":"2025-07-10T04:00:00","endDate":"2025-07-10T04:10:00"},"elementData":[{"elementName":"elementName1","elementIdentifier":"ae5f1a94-33b4-4001-b926-499dc0425bf1","elementDefinitionIdentifier":2,"metaData":{},"data":{}},{"elementName":"elementName2","elementIdentifier":"c647ffb1-b8ba-4b34-8b45-8590fed273ef","elementDefinitionIdentifier":2,"metaData":{},"data":{"NESTED_DATA_NAME_1":{"dataPoint":{"name":"NESTED_DATA_NAME_1","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.975200119018556},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.21553290049235}]},"NESTED_DATA_NAME_2":{"dataPoint":{"name":"NESTED_DATA_NAME_2","friendlyName":"friendlyName","keyName":"keyName","dataType":"String","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":"ON"},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":"ON"}]},"NESTED_DATA_NAME_3":{"dataPoint":{"name":"NESTED_DATA_NAME_3","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.0},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.0}]},"NESTED_DATA_NAME_5":{"dataPoint":{"name":"NESTED_DATA_NAME_5","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.0},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.0}]},"NESTED_DATA_NAME_6":{"dataPoint":{"name":"NESTED_DATA_NAME_6","friendlyName":"friendlyName","keyName":"keyName","dataType":"String","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":"Off"},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":"Off"}]},"NESTED_DATA_NAME_7":{"dataPoint":{"name":"NESTED_DATA_NAME_7","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.00011502826237119734},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.00011502826237119734}]}}},{"elementName":"elementName3","elementIdentifier":"3e4bd6b5-389d-4297-a65e-9f717a0eebda","elementDefinitionIdentifier":2,"metaData":{},"data":{"NESTED_DATA_NAME_1":{"dataPoint":{"name":"NESTED_DATA_NAME_1","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.975200119018556},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.21553290049235}]},"NESTED_DATA_NAME_2":{"dataPoint":{"name":"NESTED_DATA_NAME_2","friendlyName":"friendlyName","keyName":"keyName","dataType":"String","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":"ON"},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":"ON"}]},"NESTED_DATA_NAME_3":{"dataPoint":{"name":"NESTED_DATA_NAME_3","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.0},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.0}]},"NESTED_DATA_NAME_5":{"dataPoint":{"name":"NESTED_DATA_NAME_5","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.0},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.0}]},"NESTED_DATA_NAME_6":{"dataPoint":{"name":"NESTED_DATA_NAME_6","friendlyName":"friendlyName","keyName":"keyName","dataType":"String","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":"Off"},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":"Off"}]},"NESTED_DATA_NAME_7":{"dataPoint":{"name":"NESTED_DATA_NAME_7","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.0},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.0}]}}},{"elementName":"elementName4","elementIdentifier":"1a167479-9314-4031-9155-31a0c36af870","elementDefinitionIdentifier":2,"metaData":{},"data":{"NESTED_DATA_NAME_1":{"dataPoint":{"name":"NESTED_DATA_NAME_1","friendlyName":"friendlyName","keyName":"keyName","dataType":"Decimal","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":0.966800231933593},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":0.1588000869751}]},"NESTED_DATA_NAME_2":{"dataPoint":{"name":"NESTED_DATA_NAME_2","friendlyName":"friendlyName","keyName":"keyName","dataType":"String","sequence":null},"values":[{"intervalLocal":"2025-07-10T04:05:00-05:00","value":"ON"},{"intervalLocal":"2025-07-10T04:10:00-05:00","value":"ON"}]},"NESTED_DATA_NAME_3":{"dataPoint":{"name":"NESTED_DATA_NAME_3","friendlyName":"friendlyName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rdd = spark.sparkContext.parallelize(json_text.split(', ')) df = spark.read.json(rdd) df.printSchema()