feat: add Spark-compatible hypot function

[KarpagamKarthikeyan]

Which issue does this PR close?

• Closes Implement Spark-compatible hypot function #23770
• Part of [EPIC] Complete datafusion-spark Spark Compatible Functions #15914

Rationale for this change

Spark provides hypot(expr1, expr2), which returns sqrt(expr1^2 + expr2^2) computed without intermediate overflow or underflow. It was not yet implemented in datafusion-spark — only an auto-generated test stub existed at spark/math/hypot.slt with its query commented out.

What changes are included in this PR?

• Add SparkHypot (implementing ScalarUDFImpl) in datafusion/spark/src/function/math/hypot.rs, backed by Rust's f64::hypot — the same overflow-safe algorithm as Java/Spark's Math.hypot.
• Register it in datafusion/spark/src/function/math/mod.rs.
• Enable the hypot.slt sqllogictest.

The signature is exact(Float64, Float64) -> Float64, following the datafusion-spark convention of only accepting types Spark supports. Computation uses the Arrow binary kernel so NULL in either argument propagates to a NULL result, matching Spark.

Are these changes tested?

Yes — datafusion/sqllogictest/test_files/spark/math/hypot.slt covers:

• scalar Pythagorean triples (hypot(3, 4) → 5, hypot(5, 12) → 13),
• double inputs,
• NULL propagation when either argument is NULL,
• the array path (including a NULL row),
• overflow-safety: hypot(3e200, 4e200) stays finite, whereas a naive sqrt(a^2 + b^2) would overflow to Infinity.

Are there any user-facing changes?

Yes — adds the Spark-compatible hypot scalar function to datafusion-spark. No breaking changes to public APIs.

[KarpagamKarthikeyan]

cc @Jefffrey @andygrove — this is my first contribution, so CI is currently awaiting a committer's approval. Would really appreciate a trigger when you have a moment.

This adds a Spark-compatible hypot function to datafusion-spark, following the existing math function conventions (exact(Float64, Float64) -> Float64, invoke rather than simplify), backed by Rust's overflow-safe f64::hypot. Covered by sqllogictest cases (triples, doubles, NULL propagation, array path, and overflow-safety). Closes #23770.

Thanks!

[Jefffrey]

make_scalar_function() handles this for us

e.g.

fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
    make_scalar_function(hypot_array, vec![])(&args.args)
}

// outside impl

fn hypot_array(args: &[ArrayRef]) -> Result<ArrayRef> {
    let [x, y] = take_function_args("hypot", args)?;
    let x = x.as_primitive::<Float64Type>();
    let y = y.as_primitive::<Float64Type>();
    return Ok(Arc::new(binary(x, y, |a, b| a.hypot(b))))
}

[Jefffrey]

could we add some more edge cases such as infinity inputs, nans, etc.

[Jefffrey]

Suggested change

	// Spark only defines hypot over doubles; `exact` makes coercion
	// guarantee both inputs are Float64 before `invoke` runs.
	// Spark only defines hypot over doubles

extra detail unnecessary

[codecov-commenter]

Codecov Report

❌ Patch coverage is 78.78788% with 7 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (main@05b7e11). Learn more about missing BASE report.
⚠️ Report is 6 commits behind head on main.

Files with missing lines	Patch %	Lines
datafusion/spark/src/function/math/hypot.rs	78.12%	3 Missing and 4 partials ⚠️

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