perf: Optimize scalar fast path for nanvl#20205
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kumarUjjawal wants to merge 1 commit intoapache:mainfrom
Open
perf: Optimize scalar fast path for nanvl#20205kumarUjjawal wants to merge 1 commit intoapache:mainfrom
kumarUjjawal wants to merge 1 commit intoapache:mainfrom
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Jefffrey
reviewed
Feb 8, 2026
| match (&x, &y) { | ||
| (ColumnarValue::Scalar(x), ColumnarValue::Scalar(y)) => { | ||
| // NULL propagation | ||
| if x.is_null() || y.is_null() { |
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I don't think this is quite right; from Spark:
>>> spark.sql("select nanvl(null, 1)").show()
+--------------+
|nanvl(NULL, 1)|
+--------------+
| NULL|
+--------------+
>>> spark.sql("select nanvl(1, null)").show()
+--------------+
|nanvl(1, NULL)|
+--------------+
| 1.0|
+--------------+
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Which issue does this PR close?
Rationale for this change
nanvlcurrently evaluates scalar inputs viamake_scalar_function(nanvl, vec![]), which converts scalar values intosize‑1 arrays before execution and then converts back. This adds unnecessary overhead for constant folding / scalar
evaluation
What changes are included in this PR?
ColumnarValue::Scalar + ColumnarValue::Scalarnanvl/scalar_f64nanvl/scalar_f32Benchmark | Before | After | Speedup
━━━━━━━━━━━━━━━━━━━━━━━
nanvl/scalar_f64 | ~240.1 ns | 50.104 ns ~4.79x
nanvl/scalar_f32 |~237.1 ns | 49.284 ns ~4.81x
Are these changes tested?
Yes
Are there any user-facing changes?
No