零基础学Flink：Data Source & Data Sink

在上一篇讲述CEP的文章里，直接使用了自定义Source和Sink，我翻阅了一下以前的文章，似乎没有对这部分进行一个梳理，那么今天我们来就这上次的代码，来说说 Data Source 和 Data Sink吧。

从宏观上讲，flink的编程模型就可以概况成接入data source，然后进行数据转换操作，再讲处理结果sink出来。如下图所示。

其实这可以形成一个完美的闭环，将处理结果sink到另外一个流里的时候，那么这个sink就又可以变成下一个flink job的source了。当然也可以选择sink到一个csv文件里，或是通过jdbc写到数据库里。

Data Source

我们还是以上一篇文章的空气质量例子为例，我们制造一个发生器，来向制造数据，然后将数据写入kafka。

代码语言：javascript复制

import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.serialization.SerializationSchema;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSink;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer010;
import wang.datahub.cep.event.AirQualityRecoder;
//import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer09;
import java.io.*;
import java.util.HashMap;
import java.util.Map;
public class WriteIntoKafka {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        Map prop = new HashMap();
        prop.put("bootstrap.servers", "localhost:9092");
        prop.put("topic", "test1");
        ParameterTool parameterTool = ParameterTool.fromMap(prop);
        DataStream<AirQualityRecoder> messageStream = env.addSource(new SimpleGenerator());
        DataStreamSink<AirQualityRecoder> airQualityVODataStreamSink = messageStream.addSink(new FlinkKafkaProducer010<>(parameterTool.getRequired("bootstrap.servers"),
                parameterTool.getRequired("topic"),
                new SimpleAirQualityRecoderSchema()));
        messageStream.print();
        env.execute("write to kafka !!!");
    }

    public static class SimpleGenerator implements SourceFunction<AirQualityRecoder>{
        private static final long serialVersionUID = 1L;
        boolean running = true;
        @Override
        public void run(SourceContext<AirQualityRecoder> ctx) throws Exception {
            while(running) {
                ctx.collect(AirQualityRecoder.createOne());
            }
        }

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

    }

    public static class SimpleAirQualityRecoderSchema implements DeserializationSchema<AirQualityRecoder>, SerializationSchema<AirQualityRecoder>{
        @Override
        public AirQualityRecoder deserialize(byte[] message) throws IOException {
            //System.out.println(new String(message));
            ByteArrayInputStream bi = new ByteArrayInputStream(message);
            ObjectInputStream oi = new ObjectInputStream(bi);
            AirQualityRecoder obj = null;
            try {
                obj = (AirQualityRecoder)oi.readObject();
            } catch (ClassNotFoundException e) {
                e.printStackTrace();
            }
            bi.close();
            oi.close();
            return obj;
        }

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

        @Override
        public byte[] serialize(AirQualityRecoder element) {
            byte[] bytes = null;
            try {

                ByteArrayOutputStream bo = new ByteArrayOutputStream();
                ObjectOutputStream oo = new ObjectOutputStream(bo);
                oo.writeObject(element);
                bytes = bo.toByteArray();
                bo.close();
                oo.close();
            } catch (IOException e) {
                e.printStackTrace();
            }
            return bytes;
//            return element.toCsvRec().getBytes();
        }

        @Override
        public TypeInformation<AirQualityRecoder> getProducedType() {
            return TypeInformation.of(new TypeHint<AirQualityRecoder>(){});
        }
    }

}

这里是完整代码，我们需要实现一个类，实现SourceFunction接口，重写run和cancel两个方法，run方法用于告诉上下文如何添加数据，而cancel方法则实现如何取消这个数据发生器。将这个类的实例addSource到当前环境实例上，就完成的数据的接入。这个例子，我们还使用了一个kafka connector提供的默认sink，将模拟数据写入kafka。

Data Sink

Sink部分会介绍两部分内容，1.Sink 到 JDBC 2.通过 Flink SQL Sink 到 CSV

Sink 到 JDBC

首先我们创建一个sink类，继承RichSinkFunction，需要实现其open，close，invoke三个方法，其中open和close用于初始化资源和释放资源，invoke用于实现具体的sink动作。

代码语言：javascript复制

import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
import wang.datahub.cep.event.AirQualityRecoder;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
public class MySQLSink extends RichSinkFunction<AirQualityRecoder> {
    PreparedStatement ps;
    private Connection connection;
    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        connection = getConnection();
        String sql = "insert into sinktable(id, city,airquality,emmit,et) values(?, ?, ?, ?, ?);";
        ps = this.connection.prepareStatement(sql);
    }

    @Override
    public void close() throws Exception {
        super.close();
        //关闭连接和释放资源
        if (connection != null) {
            connection.close();
        }
        if (ps != null) {
            ps.close();
        }
    }

    @Override
    public void invoke(AirQualityRecoder value, Context context) throws Exception {

        //组装数据，执行插入操作
        ps.setString(1, value.getId());
        ps.setString(2, value.getCity());
        ps.setInt(3, value.getAirQuality());
        ps.setDate(4,new java.sql.Date(value.getEmmit().getTime()));
        ps.setDate(5,new java.sql.Date(value.getEt()));
        ps.executeUpdate();
    }

    private static Connection getConnection() {
        Connection con = null;
        try {
            Class.forName("com.mysql.jdbc.Driver");
            con = DriverManager.getConnection("jdbc:mysql://localhost:3306/flinksink?useUnicode=true&characterEncoding=UTF-8", "dafei1288", "dafei1288");
        } catch (Exception e) {
            e.printStackTrace();
        }
        return con;
    }
}

构建一个测试类，直接读取kafka输入的数据，然后将其sink出来。

代码语言：javascript复制

import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import wang.datahub.cep.WriteIntoKafka;
import wang.datahub.cep.event.AirQualityRecoder;
import java.util.HashMap;
import java.util.Map;
public class SinkToMySQLApp {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        Map properties= new HashMap();
        properties.put("bootstrap.servers", "localhost:9092");
        properties.put("group.id", "test");
        properties.put("enable.auto.commit", "true");
        properties.put("auto.commit.interval.ms", "1000");
        properties.put("auto.offset.reset", "earliest");
        properties.put("session.timeout.ms", "30000");
//        properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
//        properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.put("topic", "test1");
        ParameterTool parameterTool = ParameterTool.fromMap(properties);
        FlinkKafkaConsumer010 consumer010 = new FlinkKafkaConsumer010(
                parameterTool.getRequired("topic"), new WriteIntoKafka.SimpleAirQualityRecoderSchema(), parameterTool.getProperties());
        DataStream<AirQualityRecoder> aqrStream = env
                .addSource(consumer010);
        MySQLSink mSink = new MySQLSink();
        aqrStream.addSink(mSink);
        env.execute("write to mysql");
    }
}

执行成功。

通过 Flink SQL Sink 到 CSV

这个sink比较特殊，是通过flink sql执行DML来，最终达到sink的目的，我们这个案例，使用了API提供的CsvTableSink。

这里我们将输入流的数据，注册成了AirQualityRecoder表，然后sink table ss以及其包含的字段名称和类型，，最后通过SQL语句

INSERT INTO ss SELECT id,city,airQuality,emmit,et FROM AirQualityRecoder

将数据写入csv文件中

代码语言：javascript复制

import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.table.api.StreamTableEnvironment;
import org.apache.flink.table.sinks.CsvTableSink;
import org.apache.flink.table.sinks.TableSink;
import wang.datahub.cep.WriteIntoKafka;
import wang.datahub.cep.event.AirQualityRecoder;
import java.util.HashMap;
import java.util.Map;
public class FlinkSQLSinkToMySQLApp {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tableEnv = StreamTableEnvironment.getTableEnvironment(env);
        Map properties= new HashMap();
        properties.put("bootstrap.servers", "localhost:9092");
        properties.put("group.id", "test");
        properties.put("enable.auto.commit", "true");
        properties.put("auto.commit.interval.ms", "1000");
        properties.put("auto.offset.reset", "earliest");
        properties.put("session.timeout.ms", "30000");
//        properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
//        properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.put("topic", "test1");
        ParameterTool parameterTool = ParameterTool.fromMap(properties);
        FlinkKafkaConsumer010 consumer010 = new FlinkKafkaConsumer010(
                parameterTool.getRequired("topic"), new WriteIntoKafka.SimpleAirQualityRecoderSchema(), parameterTool.getProperties());
        DataStream<AirQualityRecoder> aqrStream = env
                .addSource(consumer010);
        tableEnv.registerDataStreamInternal("AirQualityRecoder",aqrStream);
        TableSink csvSink = new CsvTableSink("E:\devlop\sourcespace\cept\src\test\aa",",");
        String[] fieldNames = {"id", "city","airQuality","emmit","et"};
        TypeInformation[] fieldTypes = {Types.STRING, Types.STRING,Types.INT,Types.SQL_DATE,Types.LONG};
        tableEnv.registerTableSink("ss", fieldNames, fieldTypes, csvSink);
        tableEnv.sqlUpdate(
                "INSERT INTO ss SELECT id,city,airQuality,emmit,et FROM AirQualityRecoder");
        env.execute("write to mysql");
    }
}

为了方便演示，我们将AirQualityRecoder里的emmit字段，变更为了java.sql.Date类型。下面是sink的结果。

好了，关于 Data Source 和 Data Sink 就先介绍到这里，欢迎大家和我交流。

kafka sql flink jdbc 大数据

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