pratham76 opened a new pull request, #58246:
URL: https://github.com/apache/spark/pull/58246
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### What changes were proposed in this pull request?
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This PR adds a new CSV read option `variantRespectInferSchema` that controls
type inference behavior for variant ingestion in the CSV parser. When enabled
along with inferSchema=false, scalar CSV values are preserved as strings inside
Variants instead of being automatically inferred to numeric or boolean types.
### Why are the changes needed?
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Currently, the CSV to Variant parser (used by the singleVariantColumn option
and explicit VariantType columns) always infers scalar types (long, decimal,
date, timestamp, boolean) regardless of the inferSchema option. This means a
value like "0001" is stored as the integer 1 rather than the string "0001",
which may not be the desired behavior in all cases.
Users need a way to preserve the original string representation of CSV
values when ingesting into Variants, particularly for:
- Leading zeros in numeric-looking strings (e.g., "0001", "00123")
- Data that should remain as strings for semantic reasons (e.g., zip codes,
product codes)
- Preserving exact input format for downstream processing
### Does this PR introduce _any_ user-facing change?
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Yes. This PR introduces a new CSV read option, `variantRespectInferSchema`
(default: false)
- When false (default): Preserves existing behavior - types are always
inferred for variant values
- When true and inferSchema=false: CSV values are preserved as strings in
Variants
- When true and inferSchema=true: Types are still inferred (respects the
inferSchema setting)
### How was this patch tested?
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Added comprehensive tests in `CSVSuite`, and added tests in
`CsvFunctionsSuite` for the `from_csv` function
### Was this patch authored or co-authored using generative AI tooling?
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No
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