FLINK消费KAFAK数据包括自定义序列化和反序列化

/*
 * 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 com.youfan.kafka;

import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarks;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.watermark.Watermark;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer010;

import javax.annotation.Nullable;

/**
 * A simple example that shows how to read from and write to Kafka. This will read String messages
 * from the input topic, parse them into a POJO type {@link KafkaEvent}, group by some key, and finally
 * perform a rolling addition on each key for which the results are written back to another topic.
 *
 * 
This example also demonstrates using a watermark assigner to generate per-partition
 * watermarks directly in the Flink Kafka consumer. For demonstration purposes, it is assumed that
 * the String messages are of formatted as a (word,frequency,timestamp) tuple.
 *
 * 
Example usage:
 * 	--input-topic test-input --output-topic test-output --bootstrap.servers localhost:9092 --zookeeper.connect localhost:2181 --group.id myconsumer
 */
public class Kafka010Example {

	public static void main(String[] args) throws Exception {
		// parse input arguments
		args = new String[]{"--input-topic","test1","--output-topic","test2","--bootstrap.servers","192.168.80.134:9092","--zookeeper.connect","192.168.80.134:2181","--group.id","myconsumer"};
		final ParameterTool parameterTool = ParameterTool.fromArgs(args);

		if (parameterTool.getNumberOfParameters() < 5) {
			System.out.println("Missing parameters!\n" +
					"Usage: Kafka --input-topic  --output-topic  " +
					"--bootstrap.servers  " +
					"--zookeeper.connect  --group.id ");
			return;
		}

		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		env.getConfig().disableSysoutLogging();
		env.getConfig().setRestartStrategy(RestartStrategies.fixedDelayRestart(4, 10000));
		env.enableCheckpointing(5000); // create a checkpoint every 5 seconds
		env.getConfig().setGlobalJobParameters(parameterTool); // make parameters available in the web interface
		env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

		DataStream input = env
				.addSource(
					new FlinkKafkaConsumer010<>(
						parameterTool.getRequired("input-topic"),
						new KafkaEventSchema(),
						parameterTool.getProperties())
					.assignTimestampsAndWatermarks(new CustomWatermarkExtractor()))
				.keyBy("word")
				.map(new RollingAdditionMapper());

		input.addSink(
				new FlinkKafkaProducer010<>(
						parameterTool.getRequired("output-topic"),
						new KafkaEventSchema(),
						parameterTool.getProperties()));

		env.execute("Kafka 0.10 Example");
	}

	/**
	 * A {@link RichMapFunction} that continuously outputs the current total frequency count of a key.
	 * The current total count is keyed state managed by Flink.
	 */
	private static class RollingAdditionMapper extends RichMapFunction {

		private static final long serialVersionUID = 1180234853172462378L;

		private transient ValueState currentTotalCount;

		@Override
		public KafkaEvent map(KafkaEvent event) throws Exception {
			Integer totalCount = currentTotalCount.value();
			System.out.println("哈哈--");
			if (totalCount == null) {
				totalCount = 0;
			}
			totalCount += event.getFrequency();

			currentTotalCount.update(totalCount);

			return new KafkaEvent(event.getWord(), totalCount, event.getTimestamp());
		}

		@Override
		public void open(Configuration parameters) throws Exception {
			currentTotalCount = getRuntimeContext().getState(new ValueStateDescriptor<>("currentTotalCount", Integer.class));
		}
	}

	/**
	 * A custom {@link AssignerWithPeriodicWatermarks}, that simply assumes that the input stream
	 * records are strictly ascending.
	 *
	 * 
Flink also ships some built-in convenience assigners, such as the
	 * {@link BoundedOutOfOrdernessTimestampExtractor} and {@link AscendingTimestampExtractor}
	 */
	private static class CustomWatermarkExtractor implements AssignerWithPeriodicWatermarks {

		private static final long serialVersionUID = -742759155861320823L;

		private long currentTimestamp = Long.MIN_VALUE;

		@Override
		public long extractTimestamp(KafkaEvent event, long previousElementTimestamp) {
			// the inputs are assumed to be of format (message,timestamp)
			this.currentTimestamp = event.getTimestamp();
			return event.getTimestamp();
		}

		@Nullable
		@Override
		public Watermark getCurrentWatermark() {
			return new Watermark(currentTimestamp == Long.MIN_VALUE ? Long.MIN_VALUE : currentTimestamp - 1);
		}
	}
}

自定义POJO类

/*
 * 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 com.youfan.kafka;

/**
 * The event type used in the {@link Kafka010Example}.
 *
 * 
This is a Java POJO, which Flink recognizes and will allow "by-name" field referencing
 * when keying a {@link org.apache.flink.streaming.api.datastream.DataStream} of such a type.
 * For a demonstration of this, see the code in {@link Kafka010Example}.
 */
public class KafkaEvent {
    private final static String splitword = "##";
	private String word;
	private int frequency;
	private long timestamp;

	public KafkaEvent() {}

	public KafkaEvent(String word, int frequency, long timestamp) {
		this.word = word;
		this.frequency = frequency;
		this.timestamp = timestamp;
	}

	public String getWord() {
		return word;
	}

	public void setWord(String word) {
		this.word = word;
	}

	public int getFrequency() {
		return frequency;
	}

	public void setFrequency(int frequency) {
		this.frequency = frequency;
	}

	public long getTimestamp() {
		return timestamp;
	}

	public void setTimestamp(long timestamp) {
		this.timestamp = timestamp;
	}

	public static KafkaEvent fromString(String eventStr) {
		String[] split = eventStr.split(splitword);
		return new KafkaEvent(split[0], Integer.valueOf(split[1]), Long.valueOf(split[2]));
	}

	@Override
	public String toString() {
		return word +splitword + frequency + splitword + timestamp;
	}
}

 自定义序列化实现序列化接口

/*
 * 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 com.youfan.kafka;

import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.serialization.SerializationSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;

import java.io.IOException;

/**
 * The serialization schema for the {@link KafkaEvent} type. This class defines how to transform a
 * Kafka record's bytes to a {@link KafkaEvent}, and vice-versa.
 */
public class KafkaEventSchema implements DeserializationSchema, SerializationSchema {

	private static final long serialVersionUID = 6154188370181669758L;

	@Override
	public byte[] serialize(KafkaEvent event) {
		return event.toString().getBytes();
	}

	@Override
	public KafkaEvent deserialize(byte[] message) throws IOException {
		return KafkaEvent.fromString(new String(message));
	}

	@Override
	public boolean isEndOfStream(KafkaEvent nextElement) {
		return false;
	}

	@Override
	public TypeInformation getProducedType() {
		return TypeInformation.of(KafkaEvent.class);
	}
}