sunchao commented on a change in pull request #32082:
URL: https://github.com/apache/spark/pull/32082#discussion_r620710738
##########
File path:
sql/catalyst/src/main/java/org/apache/spark/sql/connector/catalog/functions/ScalarFunction.java
##########
@@ -23,17 +23,68 @@
/**
* 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) {
+ * return left + right;
+ * }
+ * }
+ * </pre>
+ * In this case, since {@link #MAGIC_METHOD_NAME} is defined, Spark will use
it over
+ * {@link #produceResult} to evalaute the inputs. In general Spark looks up
the magic method by
+ * first converting the actual input SQL data types to their corresponding
Java types following
+ * the mapping defined below, and then checking if there is a matching method
from all the
+ * declared methods in the UDF class, using method name (i.e., {@link
#MAGIC_METHOD_NAME}) and
+ * the Java types. If no magic method is found, Spark will falls back to use
{@link #produceResult}.
+ * <p>
+ * The following are the mapping from {@link DataType SQL data type} to Java
type through
+ * the magic method approach:
+ * <ul>
+ * <li>{@link org.apache.spark.sql.types.BooleanType}: {@code boolean}</li>
+ * <li>{@link org.apache.spark.sql.types.ByteType}: {@code byte}</li>
+ * <li>{@link org.apache.spark.sql.types.ShortType}: {@code short}</li>
+ * <li>{@link org.apache.spark.sql.types.IntegerType}: {@code int}</li>
+ * <li>{@link org.apache.spark.sql.types.LongType}: {@code long}</li>
+ * <li>{@link org.apache.spark.sql.types.FloatType}: {@code float}</li>
+ * <li>{@link org.apache.spark.sql.types.DoubleType}: {@code double}</li>
+ * <li>{@link org.apache.spark.sql.types.StringType}:
+ * {@link org.apache.spark.unsafe.types.UTF8String}</li>
+ * <li>{@link org.apache.spark.sql.types.DateType}: {@code int}</li>
+ * <li>{@link org.apache.spark.sql.types.TimestampType}: {@code long}</li>
+ * <li>{@link org.apache.spark.sql.types.BinaryType}: {@code byte[]}</li>
+ * <li>{@link org.apache.spark.sql.types.DayTimeIntervalType}: {@code
long}</li>
+ * <li>{@link org.apache.spark.sql.types.YearMonthIntervalType}: {@code
int}</li>
+ * <li>{@link org.apache.spark.sql.types.DecimalType}:
+ * {@link org.apache.spark.sql.types.Decimal}</li>
+ * <li>{@link org.apache.spark.sql.types.StructType}: {@link
InternalRow}</li>
+ * <li>{@link org.apache.spark.sql.types.ArrayType}:
+ * {@link org.apache.spark.sql.catalyst.util.ArrayData}</li>
+ * <li>{@link org.apache.spark.sql.types.MapType}:
+ * {@link org.apache.spark.sql.catalyst.util.MapData}</li>
+ * <li>any other type: {@code Object}</li>
Review comment:
we don't need this - will remove.
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