Flink最后一站___Flink数据写入Kafka+从Kafka存入Mysql

前言

大家好,我是ChinaManor,直译过来就是中国码农的意思,我希望自己能成为国家复兴道路的铺路人,大数据领域的耕耘者,平凡但不甘于平庸的人。

今天为大家带来Flink的一个综合应用案例:Flink数据写入Kafka+从Kafka存入Mysql

第一部分:写数据到kafka中

 public static void writeToKafka() throws Exception{Properties props = new Properties();props.put("bootstrap.servers", BROKER_LIST);props.put("key.serializer", CONST_SERIALIZER);props.put("value.serializer", CONST_SERIALIZER);KafkaProducer<String, String> producer = new KafkaProducer<>(props);//构建User对象，在name为data后边加个随机数int randomInt = RandomUtils.nextInt(1, 100000);User user = new User();user.setName("data" + randomInt);user.setId(randomInt);//转换成JSONString userJson = JSON.toJSONString(user);//包装成kafka发送的记录ProducerRecord<String, String> record = new ProducerRecord<String, String>(TOPIC_USER, partition,null, userJson);//发送到缓存producer.send(record);System.out.println("向kafka发送数据:" + userJson);//立即发送producer.flush();}

重点:

//发送到缓存producer.send(record);

为了增强代码的Robust,我们将常量单独拎出来:

   //本地的kafka机器列表public static final String BROKER_LIST = "192.168.88.161:9092";//kafka的topicpublic static final String TOPIC_USER = "USER";//kafka的partition分区public static final Integer partition = 0;//序列化的方式public static final String CONST_SERIALIZER = "org.apache.kafka.common.serialization.StringSerializer";//反序列化public static final String CONST_DESERIALIZER = "org.apache.kafka.common.serialization.StringDeserializer";

main方法如下:

public static void main(String[] args) {while(true) {try {//每三秒写一条数据TimeUnit.SECONDS.sleep(3);writeToKafka();} catch (Exception e) {e.printStackTrace();}}}

第二部分:从kafka获取数据

KafkaRickSourceFunction.java

import com.hy.flinktest.entity.User;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.TopicPartition;
import org.apache.zookeeper.WatchedEvent;
import org.apache.zookeeper.Watcher;
import org.apache.zookeeper.ZooKeeper;import java.io.IOException;
import java.time.Duration;
import java.util.Collections;
import java.util.List;
import java.util.Properties;@Slf4j
public class KafkaRickSourceFunction extends RichSourceFunction<String>{//kafkaprivate static Properties prop = new Properties();private boolean running = true;//作静态化处理,增强robustprivate static Integer partition = WritedatatoKafka.partition;static {prop.put("bootstrap.servers",WritedatatoKafka.BROKER_LIST);prop.put("zookeeper.connect","192.168.88.161:2181");prop.put("group.id",WritedatatoKafka.TOPIC_USER);prop.put("key.deserializer",WritedatatoKafka.CONST_DESERIALIZER);prop.put("value.deserializer",WritedatatoKafka.CONST_DESERIALIZER);prop.put("auto.offset.reset","latest");prop.put("max.poll.records", "500");prop.put("auto.commit.interval.ms", "1000");}@Overridepublic void run(SourceContext sourceContext) throws Exception {//创建一个消费者客户端实例KafkaConsumer<String,String> kafkaConsumer = new KafkaConsumer<String, String>(prop);//只消费TOPIC_USER 分区TopicPartition topicPartition = new TopicPartition(WritedatatoKafka.TOPIC_USER,partition);long offset =0; //这个初始值应该从zk或其他地方获取offset = placeOffsetToBestPosition(kafkaConsumer, offset, topicPartition);while (running){ConsumerRecords<String, String> records = kafkaConsumer.poll(1000);if(records.isEmpty()){continue;}for (ConsumerRecord<String, String> record : records) {//record.offset();//record.key()String value = record.value();sourceContext.collect(value);}}}

然后 返回最合适的offset

    /*** 将offset定位到最合适的位置，并返回最合适的offset。* @param kafkaConsumer consumer* @param offset offset* @param topicPartition partition* @return the best offset*/private long placeOffsetToBestPosition(KafkaConsumer<String, String> kafkaConsumer,long offset, TopicPartition topicPartition) {List<TopicPartition> partitions = Collections.singletonList(topicPartition);kafkaConsumer.assign(partitions);long bestOffset = offset;if (offset == 0) {log.info("由于offset为0，重新定位offset到kafka起始位置.");kafkaConsumer.seekToBeginning(partitions);} else if (offset > 0) {kafkaConsumer.seekToBeginning(partitions);long startPosition = kafkaConsumer.position(topicPartition);kafkaConsumer.seekToEnd(partitions);long endPosition = kafkaConsumer.position(topicPartition);if (offset < startPosition) {log.info("由于当前offset({})比kafka的最小offset({})还要小，则定位到kafka的最小offset({})处。",offset, startPosition, startPosition);kafkaConsumer.seekToBeginning(partitions);bestOffset = startPosition;} else if (offset > endPosition) {log.info("由于当前offset({})比kafka的最大offset({})还要大，则定位到kafka的最大offset({})处。",offset, endPosition, endPosition);kafkaConsumer.seekToEnd(partitions);bestOffset = endPosition;} else {kafkaConsumer.seek(topicPartition, offset);}}return bestOffset;}@Overridepublic void cancel() {running = false;}}

第三部分
主类:从kafka读取数据写入mysql

    //1.构建流执行环境 并添加数据源

		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> dataStreamSource = env.addSource(new KafkaRickSourceFunction());

    //2.从kafka里读取数据，转换成User对象

 DataStream<User> dataStream = dataStreamSource.map(lines -> JSONObject.parseObject(lines, User.class));

//3.收集5秒钟的总数

dataStream.timeWindowAll(Time.seconds(5L)).apply(new AllWindowFunction<User, List<User>, TimeWindow>() {@Overridepublic void apply(TimeWindow timeWindow, Iterable<User> iterable, Collector<List<User>> out) throws Exception {List<User> users = Lists.newArrayList(iterable);if(users.size() > 0) {System.out.println("5秒内总共收到的条数：" + users.size());out.collect(users);}}})//sink 到数据库.addSink(new MysqlRichSinkFunction());//打印到控制台//.print();

第四部分:
写入到目标数据库sink
MysqlRichSinkFunction.java

@Slf4j
public class MysqlRichSinkFunction extends RichSinkFunction<List<User>> {private Connection connection = null;private PreparedStatement ps = null;@Overridepublic void open(Configuration parameters) throws Exception {// super.open(parameters);log.info("获取数据库连接");connection = DbUtil.getConnection();String sql = "insert into user1(id,name) values (?,?)";ps = connection.prepareStatement(sql);}public void invoke(List<User> users, Context ctx) throws Exception {//获取ReadMysqlResoure发送过来的结果for(User user : users) {ps.setLong(1, user.getId());ps.setString(2, user.getName());ps.addBatch();}//一次性写入int[] count = ps.executeBatch();log.info("成功写入Mysql数量：" + count.length);}@Overridepublic void close() throws Exception {//关闭并释放资源if(connection != null) {connection.close();}if(ps != null) {ps.close();}}}

总结

以上便是Flink数据写入Kafka+从Kafka存入Mysql
如果有帮助,给manor一键三连吧~~