Comments (3)
Kind of cheating but a naive solution is to use pandas json_normalized to parse the json and then convert the resulting pandas df into Spark. The logic seems a bit too simple to justify a dedicated helper function though
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@huynguyent - would be nice to create an implementation that's really performant and doesn't depend on pandas!
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It is possible only if you know the final schema. Otherwise you need to infer the schema first somehow. And even with known schema the simplest solution is still to use UDFs. My first question, do we know the schema in such a case? If not, I would suggest to start from the function like infer_json_schema(col)
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Related Issues (20)
- Add documentation content HOT 1
- Extend Quinn's Data Validation functionality HOT 2
- Remove all references to rdd in this project HOT 1
- Remove existing SparkSession logic HOT 2
- Linter working locally but failing in CI HOT 1
- 0.10.1 causing issues on Databricks Unity Catalog clusters HOT 7
- 0.10.2 has broken import statements in quinn extensions HOT 1
- 0.10.1 and 0.10.2 have changed import method of array_choice from 0.10.0 HOT 4
- quinn 1.0 release planning HOT 4
- Deprecate `print_athena_create_table` HOT 2
- Drop support for Python 3.7
- Run tests on latest Spark version for every PR HOT 1
- Create a GitHub action to auto-assign issues HOT 2
- Add Apache-2.0 LICENSE, license-header to all the files and incorporate license-header checks HOT 2
- Update surround_substring to surround all substrings HOT 2
- DOC: Structural inconsistency of quinn examples in the README documentation HOT 2
- Update search_file(s) to return counts of each keyword
- BUG: Failure in tests/extensions/test_dataframe_ext.py for Spark 2.4
- BUG: Code cleanup HOT 1
- DOC: Typo in the Contributions doc HOT 2
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