Flink 自定义source 写入 Kafka

• 添加依赖
• 基于 Flink 服务提交任务并执行时需要的依赖包
• 启动前注意
• 构建KafkaSource参数实例
• 构建自定义KafkaMQSource

	org.apache.flink
	flink-connector-kafka_2.12
	1.13.2
	provided

基于 flink 服务器提交任务前，先上传依赖包到 flink 的 lib 目录下；然后重启 flink 服务，使 jar 进行加载；否则会出现 ClassNoFoundException 的异常。

• flink-connector-kafka_2.12-1.13.2.jar
• kafka-clients-2.4.1.jar

确保 topic 在 kafka 中是真实存在的，否则将会产生如下的执行异常：

• 运行逻辑：先获取kafka中全部的topic list，再进行正则匹配，得到指定的topic list 调试发现，获取kafka全部topic list返回null。然后产生下述异常，此时创建对应的 topic，等待下次任务重启后将可正常运行。

java.lang.RuntimeException: Unable to retrieve any partitions with KafkaTopicsDescriptor: Topic Regex Pattern (WYSXT_47_(.+)_47_other_47_property_47_post)
	at org.apache.flink.streaming.connectors.kafka.internals.AbstractPartitionDiscoverer.discoverPartitions(AbstractPartitionDiscoverer.java:156)
	at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerbase.open(FlinkKafkaConsumerbase.java:577)
	at org.apache.flink.api.common.functions.util.FunctionUtils.openFunction(FunctionUtils.java:34)
	at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.open(AbstractUdfStreamOperator.java:102)
	at org.apache.flink.streaming.runtime.tasks.OperatorChain.initializeStateAndOpenOperators(OperatorChain.java:442)
	at org.apache.flink.streaming.runtime.tasks.StreamTask.restoreGates(StreamTask.java:582)
	at org.apache.flink.streaming.runtime.tasks.StreamTaskActionExecutor$SynchronizedStreamTaskActionExecutor.call(StreamTaskActionExecutor.java:100)
	at org.apache.flink.streaming.runtime.tasks.StreamTask.executeRestore(StreamTask.java:562)
	at org.apache.flink.streaming.runtime.tasks.StreamTask.runWithCleanUpOnFail(StreamTask.java:647)
	at org.apache.flink.streaming.runtime.tasks.StreamTask.restore(StreamTask.java:537)
	at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:759)
	at org.apache.flink.runtime.taskmanager.Task.run(Task.java:566)
	at java.lang.Thread.run(Thread.java:748)

public class KafkaSource implements Serializable {

	private static final long serialVersionUID = 6060562931782343343L;

	private String bootStrapServers;
	
	private String groupId;
	
	private String topic;
	
	public String getBootStrapServers() {
		return bootStrapServers;
	}

	public String getGroupId() {
		return groupId;
	}
	
	public String getTopic() {
		return topic;
	}

	public KafkaSource(Object obj) {
		final JSONObject json = JSONObject.parseObject(obj.toString());
		this.bootStrapServers = json.getString("bootStrapServers");
		this.groupId = json.getString("groupId");
		this.topic = json.getString("topic");
	}
	
}

基于FlinkKafkaConsumer< T > 类实现KafkaSource，其中KafkaDeserializationSchema< T >类型是用于数据反序列化的，可以将数据组装成你想要的方式然后传递出去。

import java.io.Serializable;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.ConcurrentHashMap;
import java.util.regex.Pattern;

import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema;
import org.apache.kafka.clients.CommonClientConfigs;
import org.apache.kafka.clients.consumer.ConsumerRecord;


public class KafkaMessageSource implements Serializable {
	
	private static final long serialVersionUID = -1128615689349479275L;
	
	private FlinkKafkaConsumer> consumer;
	
	public KafkaMessageSource(final String bootStrapServers, final String groupId, final String topic){
    	Properties properties = new Properties();
    	properties.setProperty(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, bootStrapServers);
    	// Flink Kafka Consumer 支持发现动态创建的 Kafka 分区，并使用精准一次的语义保证去消耗它们
    	properties.setProperty("flink.partition-discovery.interval-millis", "10000");//
    	properties.setProperty("group.id", groupId);
    	
    	
		//TODO： 自定义反序列化
		final KafkaDeserializationSchema> deserializer = new KafkaDeserializationSchema>(){
			
			private static final long serialVersionUID = 1574406844851249992L;
			
			private String encoding = "UTF-8";
    		
			@Override
			public TypeInformation> getProducedType() {
				return TypeInformation.of(new TypeHint>(){});
			}

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

			@Override
			public Map deserialize(ConsumerRecord record) throws Exception {
				final Map result = new ConcurrentHashMap<>();
				result.put("topic", record.topic());
				result.put("value", new String(record.value(), encoding));
				return result;
			}
    	};
    			
    	// 构建source
    	Pattern pattern = Pattern.compile(topic);
    	consumer = new FlinkKafkaConsumer<>(pattern, deserializer, properties);
	}

	public FlinkKafkaConsumer> getConsumer() {
		return consumer;
	}
}