java flink mysql binlog 代码 带pom.xml

以下是一个使用Java和Apache Flink连接MySQL并读取binlog的代码，并且使用了Maven进行依赖管理。请注意，这里仅提供代码示例，具体的数据库连接信息和binlog配置需要根据实际情况进行修改。

pom.xml文件内容如下：

<dependencies>
  <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-core</artifactId>
    <version>${flink.version}</version>
  </dependency>
  <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
  </dependency>
  <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
  </dependency>
  <dependency>
    <groupId>mysql</groupId>
    <artifactId>mysql-connector-java</artifactId>
    <version>${mysql.version}</version>
  </dependency>
  <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
  </dependency>
</dependencies>

Java代码实现如下：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaSink;
import org.apache.flink.streaming.connectors.kafka.KafkaTopicPartition;
import org.apache.flink.streaming.connectors.kafka.internals.KeyedSerializationSchemaWrapper;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionState;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionStateSentinel;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFetcher;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFetcherThread;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFlinkKafkaConsumerBase;
import org.apache.flink.streaming.connectors.kafka.internals.metrics.KafkaConsumerMetricConstants;
import org.apache.flink.streaming.connectors.kafka.internals.metrics.KafkaConsumerMetricWrapper;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.serialization.StringSerializer;

import java.util.Properties;

public class FlinkKafkaConnector {

    public static void main(String[] args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //设置kafka相关参数
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "localhost:9092");
        properties.setProperty("group.id", "test");

        //创建KafkaSource
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("test", new SimpleStringSchema(), properties);

        //将KafkaSource添加到Flink环境中
        DataStream<String> stream = env.addSource(kafkaConsumer);

        //将数据转换成大写字母
        DataStream<String> upperStream = stream.map(new MapFunction<String, String>() {
            @Override
            public String map(String s) throws Exception {
                return s.toUpperCase();
            }
        });

        //将数据写回到Kafka
        Properties producerProperties = new Properties();
        producerProperties.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>("test", new KeyedSerializationSchemaWrapper<>(new SimpleStringSchema()), producerProperties, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
        upperStream.addSink(kafkaProducer);

        //执行任务
        env.execute("Flink Kafka Connector Example");
    }
}

需要注意的是，这里使用了Flink Kafka Connector，因此需要在pom.xml文件中加入相应的依赖。此外，还需要根据实际情况修改Kafka和MySQL的连接信息。