首页 > 其他分享 >实时数仓之Flink消费kafka消息队列数据入hbase

实时数仓之Flink消费kafka消息队列数据入hbase

时间：2024-03-26 15:13:23浏览次数：23  
标签：数仓 flink String Flink kafka org apache import com

一、流程架构图

 二、开源框架及本版选择

     

 本次项目中用到的相关服务有：hadoop、zookeeper、kafka、maxwell、hbase、phoenix、flink

    

三、服务部署完成后，开发Flink主程序

    3.1 结构图如下：

    

 

    3.2 代码详细内容

    3.2.1 pom文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <parent>
        <artifactId>gamll-realtime-2024</artifactId>
        <groupId>org.example</groupId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.dianyanyuan</groupId>
    <artifactId>gmall-realtime</artifactId>

    <properties>
        <java.version>1.8</java.version>
        <maven.compiler.source>${java.version}</maven.compiler.source>
        <maven.compiler.target>${java.version}</maven.compiler.target>
        <flink.version>1.13.0</flink.version>
        <scala.version>2.12</scala.version>
        <hadoop.version>3.1.3</hadoop.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-json</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.68</version>
        </dependency>

        <!--如果保存检查点到hdfs上，需要引入此依赖-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.20</version>
        </dependency>

        <!--Flink默认使用的是slf4j记录日志，相当于一个日志的接口,我们这里使用log4j作为具体的日志实现-->
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>1.7.25</version>
        </dependency>

        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.25</version>
        </dependency>

        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-to-slf4j</artifactId>
            <version>2.14.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-jdbc_${scala.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>com.ververica</groupId>
            <artifactId>flink-connector-mysql-cdc</artifactId>
            <version>2.1.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.phoenix</groupId>
            <artifactId>phoenix-spark</artifactId>
            <version>5.0.0-HBase-2.0</version>
            <exclusions>
                <exclusion>
                    <groupId>org.glassfish</groupId>
                    <artifactId>javax.el</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <!-- 如果不引入 flink-table 相关依赖，则会报错：
    Caused by: java.lang.ClassNotFoundException:
    org.apache.flink.connector.base.source.reader.RecordEmitter
    引入以下依赖可以解决这个问题（引入某些其它的 flink-table相关依赖也可）
    -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.12</artifactId>
            <version>1.13.0</version>
        </dependency>

        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>druid</artifactId>
            <version>1.1.16</version>
        </dependency>

    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.1.1</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <artifactSet>
                                <excludes>
                                    <exclude>com.google.code.findbugs:jsr305</exclude>
                                    <exclude>org.slf4j:*</exclude>
                                    <exclude>log4j:*</exclude>
                                    <exclude>org.apache.hadoop:*</exclude>
                                </excludes>
                            </artifactSet>
                            <filters>
                                <filter>
                                    <!-- Do not copy the signatures in the META-INF folder.Otherwise, this might cause SecurityExceptions when using the JAR. -->
                                    <!-- 打包时不复制META-INF下的签名文件，避免报非法签名文件的SecurityExceptions异常-->
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>

                            <transformers combine.children="append">
                                <!-- The service transformer is needed to merge META-INF/services files -->
                                <!-- connector和format依赖的工厂类打包时会相互覆盖，需要使用ServicesResourceTransformer解决-->
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

　　

     3.2.2 log4.properties文件

log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.target=System.out
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %10p (%c:%M) - %m%n

log4j.rootLogger=error,stdout

　　3.2.3 hbase配置文件 （这个可以直接复制服务器上hbase服务conf文件夹中的hbase-site.xml文件）

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
/**
 *
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
-->
<configuration>
    <property>
        <name>hbase.rootdir</name>
        <value>hdfs://hadoop101:8020/hbase</value>
    </property>

    <property>
        <name>hbase.cluster.distributed</name>
        <value>true</value>
    </property>

    <property>
        <name>hbase.zookeeper.quorum</name>
        <value>hadoop101,hadoop102,hadoop103</value>
    </property>
    <property>
        <name>phoenix.schema.isNamespaceMappingEnabled</name>
        <value>true</value>
    </property>

    <property>
        <name>phoenix.schema.mapSystemTablesToNamespace</name>
        <value>true</value>
    </property>
</configuration>

　　3.2.4 工具类-druid

package com.dianyan.utils;

import com.alibaba.druid.pool.DruidDataSource;
import com.dianyan.common.GmallConfig;

public class DruidDSUtil {
    private static DruidDataSource druidDataSource = null;

    public static DruidDataSource createDataSource() {
        // 创建连接池
        druidDataSource = new DruidDataSource();
        // 设置驱动全类名
        druidDataSource.setDriverClassName(GmallConfig.PHOENIX_DRIVER);
        // 设置连接 url
        druidDataSource.setUrl(GmallConfig.PHOENIX_SERVER);
        // 设置初始化连接池时池中连接的数量
        druidDataSource.setInitialSize(5);
        // 设置同时活跃的最大连接数
        druidDataSource.setMaxActive(20);
        // 设置空闲时的最小连接数，必须介于 0 和最大连接数之间，默认为 0
        druidDataSource.setMinIdle(1);
        // 设置没有空余连接时的等待时间，超时抛出异常，-1 表示一直等待
        druidDataSource.setMaxWait(-1);
        // 验证连接是否可用使用的 SQL 语句
        druidDataSource.setValidationQuery("select 1");
        // 指明连接是否被空闲连接回收器（如果有）进行检验，如果检测失败，则连接将被从池中去除
        // 注意，默认值为 true，如果没有设置 validationQuery，则报错
        // testWhileIdle is true, validationQuery not set
        druidDataSource.setTestWhileIdle(true);
        // 借出连接时，是否测试，设置为 false，不测试，否则很影响性能
        druidDataSource.setTestOnBorrow(false);
        // 归还连接时，是否测试
        druidDataSource.setTestOnReturn(false);
        // 设置空闲连接回收器每隔 30s 运行一次
        druidDataSource.setTimeBetweenEvictionRunsMillis(30 * 1000L);
        // 设置池中连接空闲 30min 被回收，默认值即为 30 min
        druidDataSource.setMinEvictableIdleTimeMillis(30 * 60 * 1000L);

        return druidDataSource;
    }
}

　　3.2.5 工具类-kafka

package com.dianyan.utils;

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;

import java.util.Properties;

public class MyKafkaUtil {
    private static final String KAFKA_SERVER = "hadoop101:9092";
    public static FlinkKafkaConsumer<String> getFlinkKafkaConsumer(String topic,String groupId){

        Properties properties = new Properties();
        properties.setProperty(ConsumerConfig.GROUP_ID_CONFIG,groupId);
        properties.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,KAFKA_SERVER);

        return new FlinkKafkaConsumer<String>(
                topic,
                new KafkaDeserializationSchema<String>() {
                    @Override
                    public boolean isEndOfStream(String s) {
                        return false;
                    }

                    @Override
                    public String deserialize(ConsumerRecord<byte[], byte[]> consumerRecord) throws Exception {
                        if(consumerRecord == null || consumerRecord.value() == null){
                            return null;
                        }else {
                            return new String(consumerRecord.value());
                        }

                    }

                    @Override
                    public TypeInformation<String> getProducedType() {
                        return BasicTypeInfo.STRING_TYPE_INFO;
                    }
                },
                properties
        );
    }
}

　　3.2.6 工具类-Phoenix

package com.dianyan.utils;

import com.alibaba.druid.pool.DruidPooledConnection;
import com.alibaba.fastjson.JSONObject;
import com.dianyan.common.GmallConfig;
import org.apache.commons.lang3.StringUtils;

import java.sql.PreparedStatement;
import java.sql.SQLException;
import java.util.Collection;
import java.util.Set;

public class PhoenixUtil {

    /**
     *
     * @param connection    Phoenix连接
     * @param sinkTable     表名
     * @param data          数据
     */
    public static void upsertValues(DruidPooledConnection connection, String sinkTable, JSONObject data) throws SQLException {
        //1.拼接SQL语句  upsert into db.table(id,name,sex) values("1001","张三","male")
        Set<String> columns = data.keySet();
        Collection<Object> values = data.values();
        String sql = "upsert into " + GmallConfig.HBASE_SCHEMA + "." + sinkTable + "(" +
                StringUtils.join(columns,",") + ") values ('" +
                StringUtils.join(values,"','") + "')";

        //2.预编译sql
        PreparedStatement preparedStatement = connection.prepareStatement(sql);

        //3.执行
        preparedStatement.execute();
        connection.commit();

        //4.释放资源
        preparedStatement.close();

    }
}

　　3.2.7 数据库驱动

package com.dianyan.common;

public class GmallConfig {
    // Phoenix库名
    public static final String HBASE_SCHEMA = "GMALL_REALTIME";

    // Phoenix驱动
    public static final String PHOENIX_DRIVER = "org.apache.phoenix.jdbc.PhoenixDriver";

    // Phoenix连接参数
    public static final String PHOENIX_SERVER = "jdbc:phoenix:hadoop101,hadoop102,hadoop103:2181";
}

　　3.2.8 table配置

package com.dianyan.bean;

import lombok.Data;

@Data
public class TableProcess {
    //来源表
    String sourceTable;
    //输出表
    String sinkTable;
    //输出字段
    String sinkColumns;
    //主键字段
    String sinkPk;
    //建表扩展
    String sinkExtend;
}

　　3.2.9 简单过滤逻辑

package com.dianyan.app.func;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.dianyan.bean.TableProcess;
import com.dianyan.common.GmallConfig;
import org.apache.flink.api.common.state.BroadcastState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.state.ReadOnlyBroadcastState;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction;
import org.apache.flink.util.Collector;

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.SQLException;
import java.util.*;

public class TableProcessFunction extends BroadcastProcessFunction<JSONObject, String, JSONObject> {

    private Connection connection;
    private MapStateDescriptor<String, TableProcess> mapStateDescriptor;

    // 构造器
    public TableProcessFunction(MapStateDescriptor<String, TableProcess> mapStateDescriptor) {
        this.mapStateDescriptor = mapStateDescriptor;
    }

    @Override
    public void open(Configuration parameters) throws Exception {
        connection = DriverManager.getConnection(GmallConfig.PHOENIX_SERVER);
    }

    //{"before":null,"after":{"source_table":"3213","sink_table":"22","sink_columns":"33","sink_pk":"44","sink_extend":"55"},"source":{"version":"1.5.4.Final","connector":"mysql","name":
// "mysql_binlog_source","ts_ms":1710926254168,"snapshot":"false","db":"gmall-config","sequence":null,"table":"table_process","server_id":0,"gtid":null,"file":"","pos":0,"row":0,
// "thread":null,"query":null},"op":"r","ts_ms":1710926254171,"transaction":null}
    @Override
    public void processBroadcastElement(String value, Context context, Collector<JSONObject> out) throws Exception {
        // 1.获取并解析数据
        JSONObject jsonObject = JSON.parseObject(value);
        TableProcess tableProcess = JSON.parseObject(jsonObject.getString("after"), TableProcess.class);

        // 2.校验并建表
        checkTable(tableProcess.getSinkTable(),
                tableProcess.getSinkColumns(),
                tableProcess.getSinkPk(),
                tableProcess.getSinkExtend());

        // 3.写入状态，广播出去
        BroadcastState<String, TableProcess> broadcastState = context.getBroadcastState(mapStateDescriptor);
        broadcastState.put(tableProcess.getSourceTable(),tableProcess);


    }

    /**
     * 校验并建表 : create table if not exists db.table(id varchar primary key ,bb varchar ,cc varchar) xxx
     * @param sinkTable         phoenix表名
     * @param sinkColumns       phoenix表字段
     * @param sinkPk            phoenix表主键
     * @param sinkExtend        phoenix表扩展字段
     */
    private void checkTable(String sinkTable,String sinkColumns,String sinkPk,String sinkExtend){
        PreparedStatement preparedStatement = null;
        try {
            // 处理特殊字段，比如字段值为null的情况
            if(sinkPk == null || "".equals(sinkPk)){
                sinkPk = "id";
            }

            if(sinkExtend == null){
                sinkExtend = "";
            }

            //1.拼接SQL create table if not exists db.table(id varchar primary key ,bb varchar ,cc varchar) xxx
            StringBuilder createTableSql = new StringBuilder("create table if not exists ")
                    .append(GmallConfig.HBASE_SCHEMA)
                    .append(".")
                    .append(sinkTable)
                    .append("(");
            String[] columns = sinkColumns.split(",");
            for (int i = 0; i < columns.length; i++) {
                // 取出字段
                String column = columns[i];

                // 是否为主键
                if(sinkPk.equals(column)){
                    createTableSql.append(column).append(" varchar primary key");
                }else{
                    createTableSql.append(column).append(" varchar");
                }
                // 判断是否为最后一个字段
                if(i < columns.length-1){
                    createTableSql.append(",");
                }
            }

            createTableSql.append(")").append(sinkExtend);


            //2.编译SQL
            System.out.println("建表语句为>>>>>" + createTableSql);
            // 预编译
            preparedStatement = connection.prepareStatement(createTableSql.toString());

            //3.执行SQL，建表
            preparedStatement.execute();


        } catch (SQLException e) {
            throw new RuntimeException("建表失败:" + sinkTable ); // 把编译时异常转换为运行时异常
        } finally {
            //4.释放资源
            if(preparedStatement != null){
                try {
                    preparedStatement.close();
                } catch (SQLException e) {
                    e.printStackTrace();
                }
            }
        }


    }

//    {"database":"gmall","table":"base_trademark","type":"bootstrap-insert","ts":1710921861,"data":{"id":2,"tm_name":"苹果","logo_url":"/static/default.jpg"}}
    @Override
    public void processElement(JSONObject value, ReadOnlyContext readOnlyContext, Collector<JSONObject> collector) throws Exception {
        //1.获取广播的配置数据
        ReadOnlyBroadcastState<String, TableProcess> broadcastState = readOnlyContext.getBroadcastState(mapStateDescriptor);
        String table = value.getString("table");
        TableProcess tableProcess = broadcastState.get(table);

        if(tableProcess != null){
            //2.过滤字段
            filterColumn(value.getJSONObject("data"),tableProcess.getSinkColumns());
            //3.补充SinkTable并写出到流中
            value.put("sinkTable",tableProcess.getSinkTable());
            collector.collect(value);
        }else{
            System.out.println("找不到对应的Key:" + table);
        }
    }

    /**
     *  过滤字段
     * @param data          {"id":2,"tm_name":"苹果","logo_url":"/static/default.jpg"}
     * @param sinkColumns   "id","tm_name"
     */
    private void filterColumn(JSONObject data, String sinkColumns) {
        // 切分
        String[] columns = sinkColumns.split(",");
        List<String> columnList = Arrays.asList(columns);

        Set<Map.Entry<String, Object>> entries = data.entrySet(); // 遍历
        Iterator<Map.Entry<String, Object>> iterator = entries.iterator(); // 迭代器
        while(iterator.hasNext()){
            Map.Entry<String, Object> next = iterator.next();
            if(!columnList.contains(next.getKey())){
                iterator.remove();
            }
        }

    }


}

　　3.2.10 sink端

package com.dianyan.app.func;

import com.alibaba.druid.pool.DruidDataSource;
import com.alibaba.druid.pool.DruidPooledConnection;
import com.alibaba.fastjson.JSONObject;
import com.dianyan.utils.DruidDSUtil;
import com.dianyan.utils.PhoenixUtil;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;

public class DimSinkFunction extends RichSinkFunction<JSONObject> {

    private DruidDataSource druidDataSource = null;

    @Override
    public void open(Configuration parameters) throws Exception {
        druidDataSource = DruidDSUtil.createDataSource();
    }

    //    {"database":"gmall","table":"base_trademark","type":"bootstrap-insert","ts":1710921861,"data":{"id":2,"tm_name":"苹果","logo_url":"/static/default.jpg"},"sinkTable":"dim_xxx"}
    @Override
    public void invoke(JSONObject value, Context context) throws Exception {
        // 获取连接
        DruidPooledConnection connection = druidDataSource.getConnection();
        // 写出数据
        String sinkTable = value.getString("sinkTable"); // 表名
        JSONObject data = value.getJSONObject("data");
        PhoenixUtil.upsertValues(connection,sinkTable,data);

        // 归还连接
        connection.close();

    }
}

　　3.2.11 主程序

package com.dianyan.app.dim;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.dianyan.app.func.DimSinkFunction;
import com.dianyan.app.func.TableProcessFunction;
import com.dianyan.bean.TableProcess;
import com.dianyan.utils.MyKafkaUtil;
import com.ververica.cdc.connectors.mysql.source.MySqlSource;
import com.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.runtime.state.hashmap.HashMapStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.BroadcastConnectedStream;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
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.api.functions.co.BroadcastProcessFunction;
import org.apache.flink.util.Collector;

public class DimApp {
    public static void main(String[] args) throws Exception {
        //TODO 1.获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1); // 并行度 生产环境根据Kafka的分区数

        // 1.1 开启checkpoint
//        env.enableCheckpointing(5 * 6000L, CheckpointingMode.EXACTLY_ONCE);
//        env.getCheckpointConfig().setCheckpointTimeout(10 * 6000L);
//        env.getCheckpointConfig().setMaxConcurrentCheckpoints(2); //checkpoint的并行度
//        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3,5000L)); // 如果Flink服务意外挂了，此处配置每隔5秒重新连接一次，一共尝试3次。
        // 1.2 设置状态后端
//        env.setStateBackend(new HashMapStateBackend());
//        env.getCheckpointConfig().setCheckpointStorage("hdfs://hadoop101:8020/dianyan");
//        System.setProperty("HADOOP_USER_NAME","linxueze100");


        //TODO 2.读取kafka topic=maxwell 主题数据创建主流
        String topic = "maxwell";
        String groupid = "dim_app_001";
        DataStreamSource<String> kafkaDS = env.addSource(MyKafkaUtil.getFlinkKafkaConsumer(topic, groupid));

        //TODO 3.过滤掉非JSON数据以及保留新增、变化以及初始化数据 并将数据转换为JSON格式
        SingleOutputStreamOperator<JSONObject> filterJsonObjDS = kafkaDS.flatMap(new FlatMapFunction<String, JSONObject>() {
            @Override
            public void flatMap(String value, Collector<JSONObject> collector) throws Exception {
                try {
                    // 将数据转换为JSON格式
                    JSONObject jsonObject = JSON.parseObject(value);
                    // 获取数据中的操作字段
                    String type = jsonObject.getString("type");
                    // 保留新增、变化和初始化的数据
                    if ("insert".equals(type) || "update".equals(type) || "bootstrap-insert".equals(type)) {
                        collector.collect(jsonObject);
                    }
                } catch (Exception e) {
                    System.out.println("发现脏数据：》》》》》》" + value);
                }

            }
        });

        //TODO 4.使用FlinkCDC读取Mysql配置信息表，创建配置流
        MySqlSource<String> mySqlSource = MySqlSource.<String>builder()
                .hostname("hadoop101")
                .port(3306)
                .username("root")
                .password("123456")
                .databaseList("gmall-config")
                .tableList("gmall-config.table_process")
                .startupOptions(StartupOptions.initial()) // 启动方式
                .deserializer(new JsonDebeziumDeserializationSchema()) // 反序列化 binlog二进制
                .build();
        DataStreamSource<String> mySqlSourceDS = env.fromSource(mySqlSource, WatermarkStrategy.noWatermarks(), "MysqlSource");

        //TODO 5.将配置流处理为广播流
        MapStateDescriptor<String, TableProcess> mapStateDescriptor = new MapStateDescriptor<>("map-state", String.class, TableProcess.class);
        BroadcastStream<String> broadcastStream = mySqlSourceDS.broadcast(mapStateDescriptor);

        //TODO 6.连接主流和配置流
        BroadcastConnectedStream<JSONObject, String> connectedStream = filterJsonObjDS.connect(broadcastStream);

        //TODO 7.处理连接流 根据配置信息 处理主流数据
        SingleOutputStreamOperator<JSONObject> dimDS = connectedStream.process(new TableProcessFunction(mapStateDescriptor));

        //TODO 8.将数据写入Phoenix
        dimDS.addSink(new DimSinkFunction());
        dimDS.print(">>>>>>>>>>>>>>>");

        //TODO 9.启动任务
        env.execute("DimApp");
        }
}

 

四、实现效果

    通过maxwell实时监控并抽取mysql的binlog文件，对数据的insert、update做实时采集并写入kafka对应topic；通过Flink程序消费kafka指定topic中的数据，简单清洗数据并写入hbase中。过程中zk做协同，phoenix做select等便捷查询。

五、写在最后

    此篇文章，重在记录调研实时数仓的碎片记忆。很多细节的地方，没有写出来，也是因为时间有限，比如主程序中Flink消费kafka的topic的名称，要和maxwell采集过来写入kafka的topic保持一致，还有maxwell监控mysql的binlog的配置表的一些问题。

 

标签：数仓,flink,String,Flink,kafka,org,apache,import,com
From： https://www.cnblogs.com/lxzcloud/p/18096677

相关文章

  • hive 、spark 、flink之想一想
    hive1：hive是怎么产生的？2：hive的框架是怎么样的？3：hive执行流程是什么？4：hivesql是如何把sql语句一步一步到最后执行的？5：hivesql任务常用参数调优做过什么？spark6：spark是怎么产生的？7：spark框架是怎么样的？8:spark的DAG是什么？9：spark中的app，job,stage,task是什么？有什么好处？......
  • 如何判断一个数仓模型的好坏？
     评判一个数仓模型的好处需要考虑以下几个方面：1：数据准确性：数仓模型的数据必须准确，能够真实反映业务的情况，否则整个数仓就失去了意义。评判数据准确性的方式是与原始数据进行比对和校验，确保数据的一致性和正确性。2：数据质量:数仓模型的数据质量也是评判的重要指标之一。数......
  • Kafka Connect 自定义Sink Connector实现在数据同步时增加时间戳字段和假删除功能
    1.情景展示以debezium为例，结合kafka很容易就能实现两个数据库表与表之间的数据同步问题。但是，现在甲方有这样的需求：其一，在源表数据同步至目标表时，目标表增加一个时间戳字段（就是我们通常意义上讲的last_update_time），无论是insert还是update操作，都在此字段插入系统当前时间。用......
  • 二、kafka的文件存储机制
    简图： producer将数据写入kafka后，集群需要对数据进行保存。kafka将数据保存在磁盘，kafka初始会单独开辟一块磁盘空间，顺序写入（效率比随机写入高）。 1、partition结构partition在服务器上表现形式是一个个文件夹，生产者将生产的消息不断追加到log文件的末尾，为防止log文件过大......
  • 在Flink 1.11中，assignTimestampsAndWatermarks方法已经被新的方法assignTimestamps和a
    在Flink1.11中，assignTimestampsAndWatermarks方法已经被新的方法assignTimestamps和assignWatermarks所替代。这是为了更好地将时间戳和水位线的定义分离开来以下是使用新API的示例代码：importorg.apache.flink.api.common.eventtime.WatermarkStrategy;importorg.apache.fli......
  • 数仓 - [02] 数据仓库模型设计
      一、什么是数据模型模型，指对于某个实际问题或者客观事物、规律进行抽象后的一种形式化表达方式。数据模型，多指在设计和建立数据库时，用于提供数据表示和操作手段的形式架构。企业数据模型，是企业数据特征的抽象，主要用来体现企业的业务规则以及信息。 二、数据模型的层次......
  • Flink 流数据处理 基于Flink1.12.0版本 MapFunction 只适用于一对一的转换，而 FlatMap
    Flink流数据处理基于Flink1.12.0版本MapFunction只适用于一对一的转换，而FlatMapFunction中可以一对多，或者多对一序言基于官网教程整理的一个教程。基于Flink1.12.0版本。目前该版本的Flink支持的source与sink如下所示参考资料：https://ci.apache.org/projects/flink......
  • Flink: Function And Rich Function ， 对比 Function ，Rich functions还提供了这些方法：o
    Flink:FunctionAndRichFunction，对比Function，Richfunctions还提供了这些方法：open、close、getRuntimeContext和setRuntimeContext序言    了解了Flink提供的算子,那我们就可以自定义算子了.自定义算子的目的是为了更加灵活的处理我们的业务数据,并将满足条件......
  • Flink API的四层抽象级别是什么？能用实际项目案例讲解一下吗？ Flink API的四层抽象级别
    FlinkAPI的四层抽象级别是什么？能用实际项目案例讲解一下吗？首先，FlinkAPI的四层抽象级别是指：最底层抽象：ProcessFunctionAPI、核心API：DataStreamAPI、TableAPI、SQL。这四个抽象级别可以比作烹饪中的不同方式，从简单的速食制作到复杂的大餐烹饪。1.最底层抽象：ProcessFunctionAP......
  • 【Flink】Flink如何覆盖系统类、优先加载用户类、child-first使用技巧
    1.概述一个问题，关于类加载的，就是我使用了flink-sql-connector-kafka的依赖，但是我改了这个类，和任务在一个jar包里面，flink-sql-connector-kafka.jar和Flink的lib中的jar在hdfs上，Flinkonyarn的方式提交作业，但是我改的这个类不生效（还是用的flink-sql-connector-kafka里面的......

赞助商

阅读排行

网站主页关于我们联系我们网站地图

本网站内容转载自其他媒体，侵权联系[admin##ips99.com]。

Copyright © 2020-2023 IPS99 版权所有 IPS99