twalthr commented on code in PR #25707:
URL: https://github.com/apache/flink/pull/25707#discussion_r1862101386


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flink-table/flink-table-common/src/main/java/org/apache/flink/table/functions/ProcessTableFunction.java:
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@@ -0,0 +1,239 @@
+/*
+ * 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.flink.table.functions;
+
+import org.apache.flink.annotation.PublicEvolving;
+import org.apache.flink.table.annotation.ArgumentHint;
+import org.apache.flink.table.annotation.DataTypeHint;
+import org.apache.flink.table.annotation.FunctionHint;
+import org.apache.flink.table.catalog.DataTypeFactory;
+import org.apache.flink.table.types.inference.TypeInference;
+import org.apache.flink.util.Collector;
+
+/**
+ * Base class for a user-defined process table function. A process table 
function (PTF) maps zero,
+ * one, or multiple tables to zero, one, or multiple rows (or structured 
types). Scalar arguments
+ * are also supported. If the output record consists of only one field, the 
wrapper can be omitted,
+ * and a scalar value can be emitted that will be implicitly wrapped into a 
row by the runtime.
+ *
+ * <p>PTFs are the most powerful function kind for Flink SQL and Table API. 
They enable implementing
+ * user-defined operators that can be as feature-rich as built-in operations. 
PTFs have access to
+ * Flink's managed state, event-time and timer services, underlying table 
changelogs, and can take
+ * multiple ordered and/or partitioned tables to produce a new table.
+ *
+ * <h1>Table Semantics and Virtual Processors</h1>
+ *
+ * <p>PTFs can produce a new table by consuming tables as arguments. For 
scalability, input tables
+ * are distributed into virtual processors. Each virtual processor executes a 
PTF instance and has
+ * access only to a share of the entire table. The argument declaration 
decides about the size of
+ * the share and co-location of data. Conceptually, tables can be processed 
either "as row" (i.e.
+ * with row semantics) or "as set" (i.e. with set semantics).
+ *
+ * <h2>Table Argument with Row Semantics</h2>
+ *
+ * <p>A PTF that takes a table with row semantics assumes that there is no 
correlation between rows
+ * and each row can be processed independently. The framework is free in how 
to distribute rows
+ * among virtual processors and each virtual processor has access only to the 
currently processed
+ * row.
+ *
+ * <h2>Table Argument with Set Semantics</h2>
+ *
+ * <p>A PTF that takes a table with set semantics assumes that there is a 
correlation between rows.
+ * When calling the function, the PARTITION BY clause defines the columns for 
correlation. The
+ * framework ensures that all rows belonging to same set are co-located. A PTF 
instance is able to
+ * access all rows belonging to the same set. In other words: The virtual 
processor is scoped under
+ * a key context.
+ *
+ * <h1>Basic Implementation</h1>
+ *
+ * <p>The behavior of a {@link ProcessTableFunction} can be defined by 
implementing a custom
+ * evaluation method. The evaluation method must be declared publicly, not 
static, and named <code>
+ * eval</code>. Overloading is not supported.

Review Comment:
   Overloading does not go well with named parameters. In the end we want to 
support a syntax like:
   ```
   SELECT * FROM f(name => 's', 'threshold' => 12)
   ```
   
   Supporting `name == STRING || name == INT` makes the implementation highly 
complex. We support optional arguments, which should solve most of the use 
cases. E.g. `f(numbers = [1, 2, 3])` or `f(strings = ['1', '2', '3'])`.
   



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