flink mysql binlog

您的问题不太明确，请问您需要了解关于Flink和MySQL Binlog的整合吗？如果是的话，我可以为您提供一些简单的介绍。

Flink是一个分布式流处理框架，而MySQL Binlog是MySQL数据库的一个功能，可以将数据库操作以二进制日志的形式记录下来。通过整合Flink和MySQL Binlog，我们可以实现实时的数据同步和处理。

具体来说，Flink提供了一个名为“Debezium”的插件，可以将MySQL Binlog中的数据实时地读取到Flink的流处理程序中进行处理。通过这种方式，我们可以实现实时的数据同步、数据清洗、数据分析等业务需求。

需要注意的是，整合Flink和MySQL Binlog需要对MySQL数据库进行一些配置，例如开启Binlog功能、设置Binlog格式等。同时，也需要对Flink进行一些配置，例如配置Debezium插件、设置数据源等。

希望以上信息能够对您有所帮助。如果您有更具体的问题或需求，也欢迎随时向我提问。

相关问题

java flink mysql binlog 代码

好的，下面是一个使用Java编写的Flink和MySQL的Binlog示例代码：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.typeutils.RowTypeInfo;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaTopicPartition;
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.KafkaTopicPartitionStateUpdater;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionTestUtils;
import org.apache.flink.streaming.connectors.kafka.testutils.KafkaTestEnvironment;
import org.apache.flink.streaming.connectors.kafka.testutils.KafkaTestUtils;
import org.apache.flink.types.Row;
import org.apache.flink.util.TestLogger;
import org.junit.BeforeClass;
import org.junit.ClassRule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;

public class FlinkMySQLBinlogTest extends TestLogger {

    @ClassRule
    public static final TemporaryFolder TEMPORARY_FOLDER = new TemporaryFolder();

    private static final String TOPIC = "test-topic";

    private static final String BOOTSTRAP_SERVERS = "localhost:9092";

    private static final String GROUP_ID = "test-group";

    private static KafkaTestEnvironment kafkaTestEnvironment;

    @BeforeClass
    public static void setup() throws IOException {
        final File tmpFolder = TEMPORARY_FOLDER.newFolder();
        final Properties kafkaProps = KafkaTestUtils.getStandardProperties();
        kafkaProps.setProperty("auto.create.topics.enable", "false");
        kafkaProps.setProperty("log.dirs", tmpFolder.getAbsolutePath());
        kafkaTestEnvironment = new KafkaTestEnvironment(kafkaProps);
        kafkaTestEnvironment.prepare(1, 1);
        KafkaTestUtils.createTopic(TOPIC, 1, 1, kafkaTestEnvironment.getKafkaServer().get().config());
    }

    @Test
    public void testFlinkMySQLBinlog() throws Exception {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.enableCheckpointing(1000L);
        env.setParallelism(1);

        final String databaseName = "test";
        final String tableName = "user";

        final SourceFunction<String> kafkaSource = new SourceFunction<String>() {
            private volatile boolean running = true;

            @Override
            public void run(SourceContext<String> ctx) throws Exception {
                while (running) {
                    ctx.collect("1\t'John'\t25");
                    Thread.sleep(1000L);
                }
            }

            @Override
            public void cancel() {
                running = false;
            }
        };

        final FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>(
                TOPIC,
                new SimpleStringSchema(),
                new Properties(),
                KafkaSerializationSchema.UseTruncate.UPDATE_ON_CHANGE
        );

        final FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>(
                TOPIC,
                new SimpleStringSchema(),
                new Properties()
        );

        final MapFunction<String, Row> rowMapFunction = new MapFunction<String, Row>() {
            @Override
            public Row map(String value) throws Exception {
                final String[] fields = value.split("\t");
                final Row row = new Row(3);
                row.setField(0, Integer.parseInt(fields[0]));
                row.setField(1, fields[1].replace("'", ""));
                row.setField(2, Integer.parseInt(fields[2]));
                return row;
            }
        };

        final RowTypeInfo rowTypeInfo = new RowTypeInfo(
                new TypeInformation[]{Types.INT, Types.STRING, Types.INT},
                new String[]{"id", "name", "age"}
        );

        final JdbcSource<Row> jdbcSource = JdbcSource.<Row>builder()
                .setDrivername("com.mysql.jdbc.Driver")
                .setDBUrl("jdbc:mysql://localhost:3306/" + databaseName)
                .setUsername("root")
                .setPassword("root")
                .setQuery("SELECT * FROM " + tableName)
                .setRowTypeInfo(rowTypeInfo)
                .build();

        final BinlogSource<Row> binlogSource = MySQLSource.<Row>builder()
                .hostname("localhost")
                .port(3306)
                .databaseList(databaseName)
                .tableList(tableName)
                .username("root")
                .password("root")
                .serverId(1001L)
                .binlogFilename("mysql-bin.000001")
                .binlogPosition(4L)
                .build();

        final DataStream<Row> jdbcStream = env.addSource(jdbcSource).map(rowMapFunction);
        final DataStream<Row> binlogStream = env.addSource(binlogSource).map(rowMapFunction);

        jdbcStream.addSink(kafkaProducer);

        binlogStream.addSink(new FlinkKafkaProducer<>(
                TOPIC,
                new KafkaSerializationSchema<Row>() {
                    @Override
                    public void open(SerializationSchema.InitializationContext context) throws Exception {
                    }

                    @Override
                    public ProducerRecord<byte[], byte[]> serialize(Row element, Long timestamp) {
                        final Map<String, Object> data = new HashMap<>();
                        data.put("id", element.getField(0));
                        data.put("name", element.getField(1));
                        data.put("age", element.getField(2));
                        return new ProducerRecord<>(TOPIC, data.toString().getBytes());
                    }
                },
                new Properties(),
                KafkaSerializationSchema.UseTruncate.UPDATE_ON_CHANGE
        ));

        final DataStream<String> kafkaStream = env.addSource(kafkaConsumer);
        kafkaStream.print();

        env.execute();
    }
}

这个示例代码包括了Flink和MySQL的Binlog的使用，以及Kafka的读写操作，希望能够帮助到您。

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。