一、前言
總體思路：source -->transform -->sink ,即從source獲取相應(yīng)的數(shù)據(jù)來(lái)源，然后進(jìn)行數(shù)據(jù)轉(zhuǎn)換，將數(shù)據(jù)從比較亂的格式，轉(zhuǎn)換成我們需要的格式，轉(zhuǎn)換處理后，然后進(jìn)行sink功能，也就是將數(shù)據(jù)寫入的相應(yīng)的數(shù)據(jù)庫(kù)DB中或者寫入Hive的HDFS文件存儲(chǔ)。
思路：

pom部分放到最后面。

二、方案及代碼實(shí)現(xiàn)

2.1 Source部分
Source部分構(gòu)建一個(gè)web對(duì)象用于保存數(shù)據(jù)等操作，代碼如下：

package com.lzl.flink;

import java.util.Date;

/**
 * @author lzl
 * @create 2024-01-18 12:19
 * @name pojo
 */
public class Web {
    private String uuid;
    private String ip;
    private String area;
    private String web;
    private String operate;
    private Date createDate;

    public String getArea() {
        return area;
    }

    public String getIp() {
        return ip;
    }

    public String getOperate() {
        return operate;
    }

    public String getUuid() {
        return uuid;
    }

    public String getWeb() {
        return web;
    }

    public Date getCreateDate() {
        return createDate;
    }

    public void setArea(String area) {
        this.area = area;
    }

    public void setIp(String ip) {
        this.ip = ip;
    }

    public void setOperate(String operate) {
        this.operate = operate;
    }

    public void setUuid(String uuid) {
        this.uuid = uuid;
    }

    public void setWeb(String web) {
        this.web = web;
    }

    public void setCreateDate(Date createDate) {
        this.createDate = createDate;
    }
}

將生成的數(shù)據(jù)轉(zhuǎn)化為JSON格式，測(cè)試如下：

public static void webDataProducer() throws Exception{
        //構(gòu)建web對(duì)象，在ip為10.117后面加兩個(gè)隨機(jī)數(shù)
        int randomInt1 = RandomUtils.nextInt(1,255);
        int randomInt2 = RandomUtils.nextInt(1,999);
        int randomInt3 = RandomUtils.nextInt(1,99999);
        List<String> areas = Arrays.asList("深圳", "廣州", "上海", "北京", "武漢", "合肥", "杭州", "南京");
        List<String> webs = Arrays.asList("www.taobao.com","www.baidu.com","www.jd.com","www.weibo.com","www.qq.com","www.weixin.com","www.360.com","www.lzl.com","www.xiaomi.com");
        List<String> operates = Arrays.asList("register","view","login","buy","click","comment","jump","care","collect");

        Web web = new Web();  //實(shí)例化一個(gè)web對(duì)象，并向?qū)ο笾蟹湃霐?shù)據(jù)
        web.setUuid("uid_" + randomInt3);
        web.setIp("10.110." + randomInt1 +"." + randomInt2);
        web.setArea(getRandomElement(areas));
        web.setWeb(getRandomElement(webs));
        web.setOperate(getRandomElement(operates));
        web.setCreateDate(new Date());

        // 轉(zhuǎn)換成JSON格式
        String webJson = JSON.toJSONString(web);
        System.out.println(webJson); //打印出來(lái)看看效果

    }
	//構(gòu)建一個(gè)從列表里面任意篩選一個(gè)元素的函數(shù)方法
    public static <T> T getRandomElement(List<T> list) {
        Collections.shuffle(list);
        return list.get(0);
    }

    public static void main(String[] args) {
        while (true) {
            try {
                // 每三秒寫一條數(shù)據(jù)
                TimeUnit.SECONDS.sleep(3);
                webDataProducer();
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }

執(zhí)行測(cè)試結(jié)果如下：

至此Source部分結(jié)束~~~~?。。。。?！

2.2 Transform_1部分

2.2.1 寫入kafka方法函數(shù)：

package com.lzl.flink;

import com.alibaba.fastjson.JSON;
import org.apache.commons.lang3.RandomUtils;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.util.*;
import java.util.concurrent.TimeUnit;

/**
 * @author lzl
 * @create 2024-01-18 12:18
 * @name KafkaWriter
 */
public class KafkaWriter {

    //kafka集群列表
    public static final String BROKER_LIST = "cdh39:9092,cdh40:9092,cdh41:9092";
    //kafka的topic
    public static final String TOPIC_WEB = "web";
    //kafka序列化的方式，采用字符串的形式
    public static final String KEY_SERIALIZER = "org.apache.kafka.common.serialization.StringSerializer";
    //value的序列化方式
    public static final String VALUE_SERIALIZER = "org.apache.kafka.common.serialization.StringSerializer";

    public static void writeToKafka() throws Exception {
        Properties props = new Properties(); //實(shí)例化一個(gè)Properties
        props.put("bootstrap.servers", BROKER_LIST);
        props.put("key.serializer", KEY_SERIALIZER);
        props.put("value.serializer", VALUE_SERIALIZER);

        // 構(gòu)建Kafka生產(chǎn)者
        KafkaProducer<String, String> producer = new KafkaProducer<>(props);

        // 將web生成的數(shù)據(jù)發(fā)送給kafka的記錄
        String webDataJson = webDataProducer();
        ProducerRecord<String,String> record = new ProducerRecord<String,String>(TOPIC_WEB,null,null,webDataJson);
        // 發(fā)送到緩存
        producer.send(record);
        System.out.println("向kafka發(fā)送數(shù)據(jù)：" + webDataJson);
        producer.flush();
    }
   public static void main(String[] args) {
        while (true) {
            try {
                // 每三秒寫一條數(shù)據(jù)
                TimeUnit.SECONDS.sleep(3);
                writeToKafka();
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }

2.2.2 建立 web的topic：

啟動(dòng)程序測(cè)試：

2.2.3 消費(fèi)kafka看看是否有數(shù)據(jù)？

[root@cdh39 kafka]# bin/kafka-console-consumer.sh --bootstrap-server cdh39:9092 --from-beginning --topic web
{"area":"合肥","createDate":1705571020461,"ip":"10.110.104.676","operate":"comment","uuid":"uid_29661","web":"www.qq.com"}
{"area":"北京","createDate":1705571024048,"ip":"10.110.49.479","operate":"jump","uuid":"uid_77119","web":"www.weibo.com"}
{"area":"合肥","createDate":1705571027106,"ip":"10.110.232.960","operate":"click","uuid":"uid_99704","web":"www.taobao.com"}
{"area":"上海","createDate":1705571030140,"ip":"10.110.12.252","operate":"buy","uuid":"uid_99850","web":"www.jd.com"}
{"area":"合肥","createDate":1705571033228,"ip":"10.110.75.328","operate":"care","uuid":"uid_33135","web":"www.qq.com"}
{"area":"上海","createDate":1705571036267,"ip":"10.110.4.862","operate":"collect","uuid":"uid_37279","web":"www.taobao.com"}
{"area":"北京","createDate":1705571039361,"ip":"10.110.139.814","operate":"register","uuid":"uid_33016","web":"www.baidu.com"}
{"area":"武漢","createDate":1705571042422,"ip":"10.110.159.143","operate":"collect","uuid":"uid_26315","web":"www.lzl.com"}
{"area":"南京","createDate":1705571045495,"ip":"10.110.81.685","operate":"login","uuid":"uid_38712","web":"www.baidu.com"}
{"area":"南京","createDate":1705571048545,"ip":"10.110.228.267","operate":"comment","uuid":"uid_23297","web":"www.weibo.com"}
{"area":"武漢","createDate":1705571051623,"ip":"10.110.102.247","operate":"collect","uuid":"uid_77340","web":"www.lzl.com"}
{"area":"武漢","createDate":1705571054687,"ip":"10.110.184.832","operate":"comment","uuid":"uid_35230","web":"www.360.com"}
{"area":"武漢","createDate":1705571057760,"ip":"10.110.90.361","operate":"buy","uuid":"uid_52082","web":"www.lzl.com"}
{"area":"北京","createDate":1705571060825,"ip":"10.110.37.707","operate":"buy","uuid":"uid_45343","web":"www.weixin.com"}
{"area":"上海","createDate":1705571063909,"ip":"10.110.178.901","operate":"care","uuid":"uid_51015","web":"www.baidu.com"}
{"area":"杭州","createDate":1705571066945,"ip":"10.110.153.758","operate":"collect","uuid":"uid_46772","web":"www.xiaomi.com"}
{"area":"合肥","createDate":1705571069980,"ip":"10.110.177.755","operate":"comment","uuid":"uid_78442","web":"www.taobao.com"}
{"area":"廣州","createDate":1705571073020,"ip":"10.110.151.427","operate":"register","uuid":"uid_92174","web":"www.weixin.com"}
{"area":"上海","createDate":1705571076072,"ip":"10.110.217.622","operate":"jump","uuid":"uid_86059","web":"www.xiaomi.com"}

至此，Transform_1部分結(jié)束~~~！?。。?/p>

2.3 Sink部分
創(chuàng)建一個(gè)MySQLSink，繼承RichSinkFunction類。重載里邊的open、invoke 、close方法，在執(zhí)行數(shù)據(jù)sink之前先執(zhí)行open方法，然后開(kāi)始調(diào)用invoke方法，調(diào)用完之后最后執(zhí)行close方法關(guān)閉資源。即在open里面創(chuàng)建數(shù)據(jù)庫(kù)連接，然后調(diào)用invoke執(zhí)行具體的數(shù)據(jù)庫(kù)寫入程序，完畢之后調(diào)用close關(guān)閉和釋放資源。這里要繼承flink的RichSinkFunction接口。代碼如下：

package com.lzl.flink;

import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;

import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.SQLException;
import java.sql.Timestamp;
import java.util.logging.Logger;

/**
 * @author lzl
 * @create 2024-01-22 15:30
 * @name MySqlToPojoSink
 */
public class MySqlToPojoSink extends RichSinkFunction<Web> {

    private static final Logger log = Logger.getLogger(MySqlToPojoSink.class.getName());
    private static final long serialVersionUID = 1L;
    private Connection connection = null;
    private PreparedStatement ps = null;
    private String tableName = "web";
    
 	@Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        log.info("獲取數(shù)據(jù)庫(kù)連接");

        // 通過(guò)Druid獲取數(shù)據(jù)庫(kù)連接，準(zhǔn)備寫入數(shù)據(jù)庫(kù)
        connection = DbUtils.getConnection();

        // 插入數(shù)據(jù)庫(kù)的語(yǔ)句   因?yàn)槲覀兎庋b的pojo的類型為PojoType<com.lzl.flink.Web, fields = [area: String, createDate: Date, ip: String, operate: String, uuid: String, web: String]>
        String insertQuery = "INSERT INTO " +   tableName + "(time,ip,uid,area,web,operate) VALUES (?,?,?,?,?,?)" ;

        // 執(zhí)行插入語(yǔ)句
        ps = connection.prepareStatement(insertQuery);
    }

	 // 重新關(guān)閉方法。   關(guān)閉并釋放資源
    @Override
    public void close() throws Exception {
    super.close();
        if(connection != null) {
            connection.close();
        }
        if (ps != null ) {
            ps.close();
        }
    }

 	// 重寫invoke方法
    @Override
    public void invoke(Web value,Context context) throws Exception {
        //組裝數(shù)據(jù)，執(zhí)行插入操作
        ps.setTimestamp(1, new Timestamp(value.getCreateDate().getTime()));
        ps.setString(2,value.getIp());
        ps.setString(3, value.getUuid());
        ps.setString(4, value.getArea());
        ps.setString(5, value.getWeb());
        ps.setString(6, value.getOperate());
        ps.addBatch();

        // 一次性寫入
        int[] count = ps.executeBatch();
        System.out.println("成功寫入MySQL數(shù)量：" + count.length);
    }
}

特別說(shuō)明：從kafka讀取到的內(nèi)容是String，里面包含JSON格式。本文是先將它封裝成Pojo對(duì)象，然后在Sink這里解析它的Value。（開(kāi)始是嘗試通過(guò)apply算子將它轉(zhuǎn)換為L(zhǎng)ist，但是失敗了（時(shí)間有限，后續(xù)再弄），最后是通過(guò)map算子）

至此，Sink部分結(jié)束~！

2.4 Transform_2部分。消費(fèi)kafka 數(shù)據(jù)，添加Sink。

package com.lzl.flink;

import com.alibaba.fastjson.JSON;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import java.util.Properties;
import java.util.concurrent.TimeUnit;
/**
 * @author lzl
 * @create 2024-01-19 8:49
 * @name DataSourceFromKafka
 */
public class DataSourceFromKafka {
    public static void transformFromKafka() throws Exception {
        // 構(gòu)建流執(zhí)行環(huán)境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
		//kafka 配置
        Properties prop = new Properties();
        prop.put("bootstrap.servers", KafkaWriter.BROKER_LIST);
        prop.put("zookeeper.connect", "cdh39:2181");
        prop.put("group.id", KafkaWriter.TOPIC_WEB);
        prop.put("key.serializer", KafkaWriter.KEY_SERIALIZER);
        prop.put("value.serializer", KafkaWriter.VALUE_SERIALIZER);
        prop.put("auto.offset.reset", "earliest");

        // 建立流數(shù)據(jù)源
        DataStreamSource<String> dataStreamSource = env.addSource(new FlinkKafkaConsumer<String>(
                KafkaWriter.TOPIC_WEB,
                new SimpleStringSchema(),
                prop
        )).setParallelism(1); // 單線程打印，控制臺(tái)不亂序，不影響結(jié)果

        SingleOutputStreamOperator<Web> webStream = env.addSource(new FlinkKafkaConsumer<>(
                "web",
                new SimpleStringSchema(),
                prop
        )).setParallelism(1)
                .map(string-> JSON.parseObject(string,Web.class));

        webStream.addSink(new MySqlToPojoSink());
        env.execute();
    }
    
    public static void main(String[] args) throws Exception {
        while (true) {
            try {
                // 每1毫秒寫一條數(shù)據(jù)
                TimeUnit.MILLISECONDS.sleep(1);
                transformFromKafka();
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }
}

如果要設(shè)置空值報(bào)錯(cuò)異常，或者排除空值可以：

package com.lzl.flink;

import com.alibaba.fastjson.JSON;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.util.Collector;
import java.util.Properties;
import java.util.concurrent.TimeUnit;

public class DataSourceFromKafka {


    public static void transformFromKafka() throws Exception {
        // 構(gòu)建流執(zhí)行環(huán)境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //checkpoint設(shè)置
        //每隔10s進(jìn)行啟動(dòng)一個(gè)檢查點(diǎn)【設(shè)置checkpoint的周期】
        env.enableCheckpointing(10000);
        //設(shè)置模式為：exactly_one，僅一次語(yǔ)義
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        //確保檢查點(diǎn)之間有1s的時(shí)間間隔【checkpoint最小間隔】
        env.getCheckpointConfig().setMinPauseBetweenCheckpoints(1000);
        //檢查點(diǎn)必須在10s之內(nèi)完成，或者被丟棄【checkpoint超時(shí)時(shí)間】
        env.getCheckpointConfig().setCheckpointTimeout(10000);
        //同一時(shí)間只允許進(jìn)行一次檢查點(diǎn)
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
        //表示一旦Flink程序被cancel后，會(huì)保留checkpoint數(shù)據(jù)，以便根據(jù)實(shí)際需要恢復(fù)到指定的checkpoint
        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        //設(shè)置statebackend,將檢查點(diǎn)保存在hdfs上面，默認(rèn)保存在內(nèi)存中。先保存到resources目錄下
        env.setStateBackend(new FsStateBackend("D:java//Flink1.17//src//main//resources"));


//         kafka 配置
        Properties prop = new Properties();
        prop.put("bootstrap.servers", KafkaWriter.BROKER_LIST);
        prop.put("zookeeper.connect", "cdh39:2181");
        prop.put("group.id", KafkaWriter.TOPIC_WEB);
        prop.put("key.serializer", KafkaWriter.KEY_SERIALIZER);
        prop.put("value.serializer", KafkaWriter.VALUE_SERIALIZER);
        prop.put("auto.offset.reset", "earliest")

        DataStreamSource<String> webStream = env.addSource(new FlinkKafkaConsumer<>(
                "web",
                new SimpleStringSchema(),
                prop
        )).setParallelism(1);

        //使用process算子 排除空值
        DataStream<Web> processData = webStream.process(new ProcessFunction<String, Web>() {
            @Override
            public void processElement(String s, Context context, Collector<Web> collector) throws Exception {
                try {
                    Web webs = JSON.parseObject(s, Web.class);
                    if (webs != null) {
                        collector.collect(webs);
                    }
                } catch (Exception e) {
                    System.out.println("有空值數(shù)據(jù)");
                }
            }
        });

        processData.addSink(new MySqlToPojoSink());
        env.execute();
    }

    public static void main(String[] args) throws Exception {
        while (true) {
            try {
                // 每1毫秒寫一條數(shù)據(jù)
                TimeUnit.MILLISECONDS.sleep(1);
                transformFromKafka();
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }
}

至此，Transfrom結(jié)束~！

2.5 DB部分（這部分可以先做，或者放到前面，因?yàn)樾枰獪y(cè)試）
本次的DB演示采用常規(guī)的MySQL數(shù)據(jù)庫(kù)。采用Druid工具連接。
思路：創(chuàng)建一個(gè)數(shù)據(jù)庫(kù)連接的工具，用于連接數(shù)據(jù)庫(kù)。使用Druid工具，然后放入具體的Driver，Url，數(shù)據(jù)庫(kù)用戶名和密碼，初始化連接數(shù)，最大活動(dòng)連接數(shù)，最小空閑連接數(shù)也就是數(shù)據(jù)庫(kù)連接池，創(chuàng)建好之后返回需要的連接。

package com.lzl.flink;

import com.alibaba.druid.pool.DruidDataSource;
import java.sql.Connection;
/**
 * @author lzl
 * @create 2024-01-18 17:58
 * @name DbUtils
 */
public class DbUtils {
    private static DruidDataSource dataSource;

    public static Connection getConnection() throws Exception {
        dataSource = new DruidDataSource();
        dataSource.setDriverClassName("com.mysql.cj.jdbc.Driver");
        dataSource.setUrl("jdbc:mysql://cdh129:3306/flink?useSSL=true");
        dataSource.setUsername("root");
        dataSource.setPassword("xxb@5196");
        // 設(shè)置初始化連接數(shù)，最大連接數(shù)，最小閑置數(shù)
        dataSource.setInitialSize(10);
        dataSource.setMaxActive(50);
        dataSource.setMinIdle(5);
        // 返回連接
        return dataSource.getConnection();
    }
}

數(shù)據(jù)庫(kù)建表語(yǔ)句：

CREATE TABLE `web_traffic_analysis` (
  `time` varchar(64) DEFAULT NULL COMMENT '時(shí)間',
  `ip` varchar(32) DEFAULT NULL COMMENT 'ip地址',
  `uid` varchar(32) DEFAULT NULL COMMENT 'uuid',
  `area` varchar(32) DEFAULT NULL COMMENT '地區(qū)',
  `web` varchar(64) DEFAULT NULL COMMENT '網(wǎng)址',
  `operate` varchar(32) DEFAULT NULL COMMENT '操作'
) ENGINE=InnoDB DEFAULT CHARSET=utf8 ROW_FORMAT=DYNAMIC COMMENT='網(wǎng)頁(yè)流量分析表'

三、啟動(dòng)程序
開(kāi)始本來(lái)是想將上面所有的功能都寫成函數(shù)方法，然后單獨(dú)開(kāi)一個(gè)Main（）主函數(shù)的入口，然后在主函數(shù)下面調(diào)用那些方法（生產(chǎn)數(shù)據(jù)、消費(fèi)數(shù)據(jù)方法）。思路是借鑒python的：if name == ‘main’: 下調(diào)用很多的方法 。但實(shí)際執(zhí)行過(guò)程，是先生成數(shù)據(jù)，然后將數(shù)據(jù)寫入kafka，然后再消費(fèi)數(shù)據(jù)，過(guò)程執(zhí)行非常慢，這個(gè)方案被pass了。后來(lái)又想到多線程方案，一個(gè)線程跑生產(chǎn)數(shù)據(jù)和寫入數(shù)據(jù)，一個(gè)線程跑消費(fèi)數(shù)據(jù)和寫入下游數(shù)據(jù)庫(kù)。這個(gè)方法是測(cè)試成功了，但是跑了一會(huì)兒就出現(xiàn)數(shù)據(jù)的積壓和內(nèi)存oom了，因?yàn)槲以O(shè)定的是1毫秒生產(chǎn)一條數(shù)據(jù)，寫入kafka也需要一定的時(shí)間，加上電腦內(nèi)存不足，有點(diǎn)卡，這個(gè)方案也被pass了。最后的方案是將生產(chǎn)數(shù)據(jù)打包放到集群去跑，本地電腦開(kāi)啟消費(fèi)kafka數(shù)據(jù)寫入MySQL數(shù)據(jù)庫(kù)。結(jié)果如下：
生產(chǎn)數(shù)據(jù)：

消費(fèi)和寫入數(shù)據(jù)庫(kù)數(shù)據(jù)：

數(shù)據(jù)庫(kù)數(shù)據(jù)：

至此結(jié)束，后面有其他想法再補(bǔ)充~！

多線程部分代碼：

package com.example.study;

import com.lzl.flink.DataSourceFromKafka;
import com.lzl.flink.KafkaWriter;

public class WebApplication {
    public static void main(String[] args) throws Exception {
    // 創(chuàng)建線程1
     Thread threadOne = new Thread(new Runnable() {
        @Override
        public void run() {
            while (true){
                try{
                    KafkaWriter kafkaWriter = new KafkaWriter();
                    kafkaWriter.webDataProducer();
                    kafkaWriter.writeToKafka();
                    System.out.println("線程一在跑~！");
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }
        }
    });

  // 創(chuàng)建線程2
 Thread threadTwo = new Thread(new Runnable() {
        @Override
        public void run() {
            while (true) {
                DataSourceFromKafka dataSourceFromKafka = new DataSourceFromKafka();
                try {
                    dataSourceFromKafka.transformFromKafka();
                    System.out.println("線程二在跑~！");
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }
        }
    });

    //啟動(dòng)線程
    threadOne.start();
    threadTwo.start();
    Thread.sleep(5);

    }
}

結(jié)果：
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