cloud-fan commented on a change in pull request #24559: [SPARK-27658][SQL] Add 
FunctionCatalog API
URL: https://github.com/apache/spark/pull/24559#discussion_r282605302
 
 

 ##########
 File path: 
sql/catalyst/src/main/java/org/apache/spark/sql/catalog/v2/ScalarFunction.java
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 @@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalog.v2;
+
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.types.DataType;
+
+/**
+ * Interface for a function that produces a result value for each input row.
+ * <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()}.
+ *
+ * @param <R> the JVM type of result values
+ */
+public interface ScalarFunction<R> extends BoundFunction {
+
+  /**
+   * Applies the function to an input row to produce a value.
+   *
+   * @param input an input row
+   * @return a result value
+   */
+  R produceResult(InternalRow input);
 
 Review comment:
   Think about a UDF that adds 2 ints.
   ```
   val row = InternalRow(i, j)
   udf.call(row)
   // inside udf.call
   return row.getInt(0) + row.getInt(1)
   ```
   is much slower than
   ```
   udf.call(i, j)
   // inside udf.call
   return i + j
   ```
   
   We need to think about the tradeoffs and pick between perf and ease-of-use.

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