andygrove commented on code in PR #6563:
URL: https://github.com/apache/datafusion-comet/pull/6563#discussion_r4194799346
##########
spark/src/main/scala/org/apache/comet/serde/arrays.scala:
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@@ -540,39 +540,73 @@ object CometSlice extends CometExpressionSerde[Slice] {
private[comet] object ArraySetSupport {
val floatingPointReason: String =
- "Floating-point elements match Spark's signed-zero and NaN semantics
natively only on " +
- "Spark 4.2.0, whose optimizer normalizes the arguments (SPARK-54918)"
-
- // The native kernels match Spark only when the plan has already normalized
the arguments, and
- // only Spark 4.2.0 does that (SPARK-54918). Earlier releases keep flat
signed zeros apart.
- // From 4.0.5, 4.1.4 and 4.2.1, SPARK-59602 normalizes during evaluation
instead, which the
- // native kernels do not match for NaN payloads or nested zeros. A top-level
- // KnownFloatingPointNormalized marker cannot replace the version check:
Spark also normalizes
- // CreateArray, If, CaseWhen, and Coalesce recursively without wrapping the
resulting array.
- def normalizesArgumentsInPlan(version: String): Boolean =
- Utils.majorMinorPatchVersion(version).contains((4, 2, 0))
+ "Floating-point elements match Spark's signed-zero semantics natively only
on Spark " +
+ "4.0.5+, 4.1.4+ and 4.2+, which treat -0.0 and 0.0 as one value in these
functions " +
+ "(SPARK-54918, SPARK-59602)"
+
+ val collationReason: String =
+ "Elements that hold both a floating-point value and a non-UTF8_BINARY
collated string fall " +
+ "back to Spark, which compares the strings under their collation, while
Comet's native " +
+ "kernels compare their raw bytes"
+
+ // Spark 4.2.0 normalizes the arguments of these functions in the plan
(SPARK-54918), and 4.0.5,
+ // 4.1.4 and 4.2.1 normalize while evaluating them (SPARK-59602). Either
way, Spark treats -0.0
+ // and 0.0, and every NaN, as one value at any depth, which the spark_
variants match. Earlier
+ // releases keep -0.0 and 0.0 apart in a flat array. The check reads the
version rather than a
+ // KnownFloatingPointNormalized marker, because SPARK-59602 adds no marker,
and SPARK-54918
+ // normalizes CreateArray, If, CaseWhen and Coalesce without wrapping the
resulting array.
+ def normalizesFloats(version: String): Boolean =
+ Utils.majorMinorPatchVersion(version).exists {
+ case (4, 0, patch) => patch >= 5
+ case (4, 1, patch) => patch >= 4
Review Comment:
Reproduced, but `main` already fails this way, so it isn't new with this PR.
On `main` at 9dc8c3ca8, `array_union(a, slice(b, 0, 1))` over a NULL
`ARRAY<INT>` fails with the `slice` error on both 4.1.3 and 4.2.0, where Spark
returns NULL. Your `ARRAY<DOUBLE>` query fails the same way on 4.2.0, which
already runs float elements natively. The ANSI form, `array_union(a,
array(CAST(s AS INT)))` with `'bad'` on a row whose `a` is NULL, fails with
`CAST_INVALID_INPUT` too. `array_intersect` and `array_except` return NULL,
because both always go through the codegen dispatcher.
It's the same eager evaluation that #6613 tracks for the lookup functions.
I'd rather fix `array_union` there, with a `CASE WHEN a IS NOT NULL` guard like
the one `element_at` uses, than add a float-only fallback here that would leave
every other element type failing.
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