maropu commented on a change in pull request #28264:
URL: https://github.com/apache/spark/pull/28264#discussion_r411043838
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File path: docs/sql-ref-datatypes.md
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@@ -706,3 +708,28 @@ The following table shows the type names as well as
aliases used in Spark SQL pa
</table>
</div>
</div>
+
+### Floating Point Special Values
+
+Spark SQL supports several special floating point values in a case-insensitive
manner:
+
+ * Inf/+Inf/Infinity/+Infinity: positive infinity
+ * ```FloatType```: 1.0f / 0.0f
+ * ```DoubleType```: 1.0 / 0.0
+ * -Inf/-Infinity: negative infinity
+ * ```FloatType```: -1.0f / 0.0f
+ * ```DoubleType```: -1.0 / 0.0
+ * NaN: not a number
+ * ```FloatType```: 0.0f / 0.0f
+ * ```DoubleType```: 0.0d / 0.0
+
+### NaN Semantics
+
+There is special handling for not-a-number (NaN) when dealing with `float` or
`double` types that
+do not exactly match standard floating point semantics.
+Specifically:
+
+ - NaN = NaN returns true.
+ - In aggregations, all NaN values are grouped together.
+ - NaN is treated as a normal value in join keys.
+ - NaN values go last when in ascending order, larger than any other numeric
value.
Review comment:
Also, it might be worth describing the sort order of NaN, Null, and
infinity in this page.
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