org.apache.flink.runtime.io.network.partition.consumer.InputGate Maven / Gradle / Ivy

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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.runtime.io.network.partition.consumer;

import org.apache.flink.runtime.event.TaskEvent;
import org.apache.flink.runtime.util.event.EventListener;

import java.io.IOException;

/**
 * An input gate consumes one or more partitions of a single produced intermediate result.
 *
 *  Each intermediate result is partitioned over its producing parallel subtasks; each of these
 * partitions is furthermore partitioned into one or more subpartitions.
 *
 * 
 As an example, consider a map-reduce program, where the map operator produces data and the
 * reduce operator consumes the produced data.
 *
 * 
{@code
 * +-----+              +---------------------+              +--------+
 * | Map | = produce => | Intermediate Result | <= consume = | Reduce |
 * +-----+              +---------------------+              +--------+
 * }
 *
 *  When deploying such a program in parallel, the intermediate result will be partitioned over its
 * producing parallel subtasks; each of these partitions is furthermore partitioned into one or more
 * subpartitions.
 *
 * 
{@code
 *                            Intermediate result
 *               +-----------------------------------------+
 *               |                      +----------------+ |              +-----------------------+
 * +-------+     | +-------------+  +=> | Subpartition 1 | | <=======+=== | Input Gate | Reduce 1 |
 * | Map 1 | ==> | | Partition 1 | =|   +----------------+ |         |    +-----------------------+
 * +-------+     | +-------------+  +=> | Subpartition 2 | | <==+    |
 *               |                      +----------------+ |    |    | Subpartition request
 *               |                                         |    |    |
 *               |                      +----------------+ |    |    |
 * +-------+     | +-------------+  +=> | Subpartition 1 | | <==+====+
 * | Map 2 | ==> | | Partition 2 | =|   +----------------+ |    |         +-----------------------+
 * +-------+     | +-------------+  +=> | Subpartition 2 | | <==+======== | Input Gate | Reduce 2 |
 *               |                      +----------------+ |              +-----------------------+
 *               +-----------------------------------------+
 * }
 *
 *  In the above example, two map subtasks produce the intermediate result in parallel, resulting
 * in two partitions (Partition 1 and 2). Each of these partitions is further partitioned into two
 * subpartitions -- one for each parallel reduce subtask. As shown in the Figure, each reduce task
 * will have an input gate attached to it. This will provide its input, which will consist of one
 * subpartition from each partition of the intermediate result.
 */
public interface InputGate {

	int getNumberOfInputChannels();

	boolean isFinished();

	void requestPartitions() throws IOException, InterruptedException;

	BufferOrEvent getNextBufferOrEvent() throws IOException, InterruptedException;

	void sendTaskEvent(TaskEvent event) throws IOException;

	void registerListener(EventListener listener);

	int getPageSize();
}