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This module contains the Table/SQL API for writing table programs within the table ecosystem using the Java programming language.

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/*
 * 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.api;

import org.apache.flink.annotation.PublicEvolving;
import org.apache.flink.table.expressions.Expression;

/**
 * A table that has been windowed for {@link GroupWindow}s.
 */
@PublicEvolving
public interface GroupWindowedTable {

	/**
	 * Groups the elements by a mandatory window and one or more optional grouping attributes.
	 * The window is specified by referring to its alias.
	 *
	 * If no additional grouping attribute is specified and if the input is a streaming table,
	 * the aggregation will be performed by a single task, i.e., with parallelism 1.
	 *
	 * 
Aggregations are performed per group and defined by a subsequent {@code select(...)}
	 * clause similar to SQL SELECT-GROUP-BY query.
	 *
	 * 
Example:
	 *
	 * 
	 * {code
	 *   tab.window([groupWindow].as("w")).groupBy("w, key").select("key, value.avg")
	 * }
	 * 
	 */
	WindowGroupedTable groupBy(String fields);

	/**
	 * Groups the elements by a mandatory window and one or more optional grouping attributes.
	 * The window is specified by referring to its alias.
	 *
	 * If no additional grouping attribute is specified and if the input is a streaming table,
	 * the aggregation will be performed by a single task, i.e., with parallelism 1.
	 *
	 * 
Aggregations are performed per group and defined by a subsequent {@code select(...)}
	 * clause similar to SQL SELECT-GROUP-BY query.
	 *
	 * 
Scala Example:
	 *
	 * 
	 * {code
	 *   tab.window([groupWindow] as 'w)).groupBy('w, 'key).select('key, 'value.avg)
	 * }
	 * 
	 */
	WindowGroupedTable groupBy(Expression... fields);
}