sunchao commented on a change in pull request #32082:
URL: https://github.com/apache/spark/pull/32082#discussion_r622367803
##########
File path:
sql/catalyst/src/main/java/org/apache/spark/sql/connector/catalog/functions/ScalarFunction.java
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@@ -23,17 +23,67 @@
/**
* Interface for a function that produces a result value for each input row.
* <p>
- * For each input row, Spark will call a produceResult method that corresponds
to the
- * {@link #inputTypes() input data types}. The expected JVM argument types
must be the types used by
- * Spark's InternalRow API. If no direct method is found or when not using
codegen, Spark will call
- * {@link #produceResult(InternalRow)}.
+ * To evaluate each input row, Spark will first try to lookup and use a "magic
method" (described
+ * below) through Java reflection. If the method is not found, Spark will call
+ * {@link #produceResult(InternalRow)} as a fallback approach.
* <p>
* The JVM type of result values produced by this function must be the type
used by Spark's
* InternalRow API for the {@link DataType SQL data type} returned by {@link
#resultType()}.
+ * <p>
+ * <b>IMPORTANT</b>: the default implementation of {@link #produceResult}
throws
+ * {@link UnsupportedOperationException}. Users can choose to override this
method, or implement
+ * a "magic method" with name {@link #MAGIC_METHOD_NAME} which takes
individual parameters
+ * instead of a {@link InternalRow}. The magic method will be loaded by Spark
through Java
+ * reflection and will also provide better performance in general, due to
optimizations such as
+ * codegen, removal of Java boxing, etc.
+ *
+ * For example, a scalar UDF for adding two integers can be defined as follow
with the magic
+ * method approach:
+ *
+ * <pre>
+ * public class IntegerAdd implements{@code ScalarFunction<Integer>} {
+ * public int invoke(int left, int right) {
Review comment:
Sure will do. It should similar to the current `invoke` and we can
leverage `StaticInvoke` for the purpose. Do you think we can do this in a
separate PR?
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