Flink SQL/Table API 消费Kafka的json格式数据存到MySQL--存入MySQL通过继承RichSinkFunction来实现

[另一篇通过JDBCAppendTableSink方式实现存入MySQL:https://blog.youkuaiyun.com/qq_39799876/article/details/91884031 ]

完整代码

附有Kafka生产json格式数据的代码

package cn.flink;

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.table.descriptors.Json;
import org.apache.flink.table.descriptors.Kafka;
import org.apache.flink.table.descriptors.Schema;
import org.apache.flink.types.Row;


public class MainDemo {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.enableCheckpointing(5000);
        StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);

        Kafka kafka = new Kafka()
                .version("0.10")
                .topic("kafka")
                .property("bootstrap.servers", "localhost:9092")
                .property("zookeeper.connect", "localhost:2181");
        tableEnv.connect(kafka)
                .withFormat(
                        new Json().failOnMissingField(true).deriveSchema()
                )
                .withSchema(
                        new Schema()
                                .field("id", Types.INT)
                                .field("name", Types.STRING)
                                .field("sex", Types.STRING)
                                .field("score", Types.FLOAT)
                )
                .inAppendMode()
                .registerTableSource("tmp_table");

        String sql = "select * from tmp_table";
        Table table = tableEnv.sqlQuery(sql);

        tableEnv.toAppendStream(table, Info.class).addSink(new MySQLWriter());

        env.execute();
    }
}
public class Info {
    public int id;
    public String name;
    public String sex;
    public float score;

    public Info(){}   //要带有这个无参构造
    public Info(int id,String name,String sex,float score){
        this.id= id;
        this.name = name;
        this.sex = sex;
        this.score = score;
    }

    @Override
    public String toString() {
        return id+":"+name+":"+sex+":"+score;
    }
}
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;


public class MySQLWriter
        extends RichSinkFunction<Info>
{

    private Connection connection;
    private PreparedStatement preparedStatement;

    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        String className = "com.mysql.jdbc.Driver";
        Class.forName(className);
        String url = "jdbc:mysql://localhost:3306/flink";
        String user = "root";
        String password = "123456";
        connection = DriverManager.getConnection(url, user, password);
        String sql = "replace into flinkjson(id,name,sex,score) values(?,?,?,?)";
        preparedStatement = connection.prepareStatement(sql);
        super.open(parameters);
    }

    @Override
    public void close() throws Exception {
        super.close();
        if (preparedStatement != null) {
            preparedStatement.close();
        }
        if (connection != null) {
            connection.close();
        }
        super.close();
    }

    @Override
    public void invoke(Info value, Context context) throws Exception {
        int id = value.id;
        String name = value.name;
        String sex = value.sex;
        Float score = value.score;
        preparedStatement.setInt(1, id);
        preparedStatement.setString(2, name);
        preparedStatement.setString(3,sex);
        preparedStatement.setFloat(4,score);
        int i = preparedStatement.executeUpdate();
        if (i > 0) {
            System.out.println("value=" + value);
        }else{
            System.out.println("error");
        }
    }
}

Kafka生产json格式数据的代码

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.codehaus.jettison.json.JSONObject;

import java.util.Properties;

public class KafkaProducerTest {

    public static void main(String[] args) throws Exception {
        Properties props = new Properties();
        props.put("bootstrap.servers", "localhost:9092");
        props.put("acks", "all");
        props.put("retries", 0);
        props.put("batch.size", 16384);
        props.put("linger.ms", 1);
        props.put("buffer.memory", 33554432);
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<String, String>(props);
        for (int i = 0; i < 100; i++) {
            JSONObject event = new JSONObject();
            event.put("id", (int) (Math.random() * 100 + 1))
                    .put("name", "mingzi" + i)
                    .put("sex", i)
                    .put("score", i * 1.0);
            producer.send(new ProducerRecord<String, String>("nima", Integer.toString(i), event.toString()));
            System.out.println(i);
            Thread.sleep(5000);
        }
        producer.close();
    }
}

pom.xml

有些依赖可能没有用到

  <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-core</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.7.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>


        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-json</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka-0.10_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
        
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>2.8.2</version>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>1.2.17</version>
        </dependency>

        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
            <version>2.9.8</version>
        </dependency>

        <dependency>
            <groupId>joda-time</groupId>
            <artifactId>joda-time</artifactId>
            <version>2.9.9</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.45</version>
        </dependency>

        <dependency>
            <groupId>org.codehaus.jettison</groupId>
            <artifactId>jettison</artifactId>
            <version>1.3.7</version>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>1.2.11</version>
        </dependency>
Apache Flink 1.15.4版本中使用DataStream API数据库交互通常需要使用Flink SQL或表API。以下是使用Table APIMySQL连接并读取数据的一个简单示例,同时通过Kafka作为事件源更新内存中的数据: ```java import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; import org.apache.flink.table.api.Table; import org.apache.flink.table.api.TableConfig; import org.apache.flink.table.api.TableSchema; import org.apache.flink.table.api.Types; import org.apache.flink.table.api.ValidationException; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer; public class FlinkMySQLKafkaExample { public static void main(String[] args) throws Exception { // 初始化流处理环境 final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); // 创建TableEnvironment final TableConfig tableConfig = new TableConfig(); tableConfig.set("format", "json"); // 如果MySQL数据JSON格式 tableConfig.addConnectionProvider("jdbc", new JdbcConnectionProvider()); StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, tableConfig); // 定义表连接MySQL String mysqlUrl = "jdbc:mysql://localhost:3306/mydatabase"; String tableName = "my_table"; TableSchema schema = TableSchema.builder() .field("id", Types.BIGINT) .field("name", Types.STRING) .build(); try { Table mysqlTable = tEnv.fromDatabase("default", tableName, schema, mysqlUrl); } catch (ValidationException e) { System.err.println("Error connecting to MySQL: " + e.getMessage()); return; } // 从Kafka消费更新数据 Properties kafkaProps = new Properties(); kafkaProps.setProperty("bootstrap.servers", "localhost:9092"); kafkaProps.setProperty("group.id", "test-group"); FlinkKafkaConsumer<String> kafkaSource = new FlinkKafkaConsumer<>("my-topic", new SimpleStringMapper(), kafkaProps); DataStream<String> kafkaStream = env.addSource(kafkaSource); // 将Kafka的消息映射到表结构并更新内存 DataStream<Row> updatedTable = kafkaStream.map(new MapFunction<String, Row>() { @Override public Row map(String value) throws Exception { // 解析Kafka消息并转换成Row对象 Map<String, Object> rowMap = JSON.parseToObject(value, new TypeReference<Map<String, Object>>() {}); return Row.of(rowMap.get("id"), rowMap.get("name")); } }).toTable(tEnv, schema); // 更新MySQLTable updatedMySqlTable = updatedTable.update(mysqlTable); // 运行流处理任务 updatedMySqlTable.executeInsert("default").await(); // 关闭资源 env.execute("Flink MySQL Kafka Example"); } private static class SimpleStringMapper implements MapFunction<String, String> { @Override public String map(String value) { // 实现字符串解析逻辑 return value; } } } // 相关问题:
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