物流实时数仓：数仓搭建（ODS）

系列文章目录

物流实时数仓：采集通道搭建
物流实时数仓：数仓搭建

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前言

现在我们开始进行数仓的搭建，我们用Kafka来代替数仓的ods层。
基本流程为使用Flink从MySQL读取数据然后写入Kafka中

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一、IDEA环境准备

1.pom.xml

写入项目需要的配置

<properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <java.version>1.8</java.version>
        <flink.version>1.17.0</flink.version>
        <hadoop.version>3.2.3</hadoop.version>
        <flink-cdc.version>2.3.0</flink-cdc.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</artifactId>
            <version>${flink.version}</version>
        </dependency>

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

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

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <groupId>org.slf4j</groupId>
                    <artifactId>slf4j-reload4j</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

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

        <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>com.ververica</groupId>
            <artifactId>flink-connector-mysql-cdc</artifactId>
            <version>${flink-cdc.version}</version>
        </dependency>

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

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

基本上项目需要的所有jar包都有了，不够以后在加。

2.目录创建

按照以上目录结构进行目录创建

二、代码编写

1.log4j.properties

log4j.rootLogger=error,stdout
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 %p [%c] - %m%n

2.CreateEnvUtil.java

这个文件中有两个方法
创建初始化Flink的env
Flink连接mysql的MySqlSource

package com.atguigu.tms.realtime.utils;


import com.esotericsoftware.minlog.Log;
import com.ververica.cdc.connectors.mysql.source.MySqlSource;
import com.ververica.cdc.connectors.mysql.source.MySqlSourceBuilder;
import com.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.runtime.state.hashmap.HashMapStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.kafka.connect.json.DecimalFormat;
import org.apache.kafka.connect.json.JsonConverterConfig;

import java.util.HashMap;

public class CreateEnvUtil {
    public static StreamExecutionEnvironment getStreamEnv(String[] args) {
        // 1.1 指定流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2.检查点相关设置
        // 2.1 开启检查点
        env.enableCheckpointing(6000L, CheckpointingMode.EXACTLY_ONCE);
        // 2.2 设置检查点的超时时间
        env.getCheckpointConfig().setCheckpointTimeout(120000L);
        // 2.3 设置job取消之后 检查点是否保留
        env.getCheckpointConfig().setExternalizedCheckpointCleanup(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        // 2.4 设置两个检查点之间的最小时间间隔
        env.getCheckpointConfig().setMinPauseBetweenCheckpoints(30000L);
        // 2.5 设置重启策略
        env.setRestartStrategy(RestartStrategies.failureRateRestart(3, Time.days(1), Time.seconds(3)));
        // 2.6 设置状态后端
        env.setStateBackend(new HashMapStateBackend());
        env.getCheckpointConfig().setCheckpointStorage("hdfs://hadoop102:8020/tms/ck");
        // 2.7 设置操作hdfs用户
        // 获取命令行参数
        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        String hdfsUserName = parameterTool.get("hadoop-user-name", "atguigu");
        System.setProperty("HADOOP_USER_NAME", hdfsUserName);
        return env;
        
    }

    public static MySqlSource<String> getMysqlSource(String option, String serverId, String[] args) {
        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        String mysqlHostname = parameterTool.get("hadoop-user-name", "hadoop102");
        int mysqlPort = Integer.parseInt(parameterTool.get("mysql-port", "3306"));
        String mysqlUsername = parameterTool.get("mysql-username", "root");
        String mysqlPasswd = parameterTool.get("mysql-passwd", "000000");
        option = parameterTool.get("start-up-option", option);
        serverId = parameterTool.get("server-id", serverId);

        // 创建配置信息 Map 集合，将 Decimal 数据类型的解析格式配置 k-v 置于其中
        HashMap config = new HashMap<>();
        config.put(JsonConverterConfig.DECIMAL_FORMAT_CONFIG, DecimalFormat.NUMERIC.name());
        // 将前述 Map 集合中的配置信息传递给 JSON 解析 Schema，该 Schema 将用于 MysqlSource 的初始化
        JsonDebeziumDeserializationSchema jsonDebeziumDeserializationSchema =
                new JsonDebeziumDeserializationSchema(false, config);

        MySqlSourceBuilder<String> builder = MySqlSource.<String>builder()
                .hostname(mysqlHostname)
                .port(mysqlPort)
                .username(mysqlUsername)
                .password(mysqlPasswd)
                .deserializer(jsonDebeziumDeserializationSchema);
        switch (option) {
            // 读取实时数据
            case "dwd":
                String[] dwdTables = new String[]{
                        "tms.order_info",
                        "tms.order_cargo",
                        "tms.transport_task",
                        "tms.order_org_bound"};
                return builder
                        .databaseList("tms")
                        .tableList(dwdTables)
                        .startupOptions(StartupOptions.latest())
                        .serverId(serverId)
                        .build();

            // 读取维度数据
            case "realtime_dim":
                String[] realtimeDimTables = new String[]{
                        "tms.user_info",
                        "tms.user_address",
                        "tms.base_complex",
                        "tms.base_dic",
                        "tms.base_region_info",
                        "tms.base_organ",
                        "tms.express_courier",
                        "tms.express_courier_complex",
                        "tms.employee_info",
                        "tms.line_base_shift",
                        "tms.line_base_info",
                        "tms.truck_driver",
                        "tms.truck_info",
                        "tms.truck_model",
                        "tms.truck_team"};
                return builder
                        .databaseList("tms")
                        .tableList(realtimeDimTables)
                        .startupOptions(StartupOptions.initial())
                        .serverId(serverId)
                        .build();


        }

        Log.error("不支持操作类型");
        return null;

    }
}

3.KafkaUtil.java

该文件中有一个方法，创建Flink连接Kafka需要的Sink

package com.atguigu.tms.realtime.utils;

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.connector.base.DeliveryGuarantee;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.kafka.clients.producer.ProducerConfig;

public class KafkaUtil {
    private static final String KAFKA_SERVER = "hadoop102:9092,hadoop103:9092,hadoop104:9092";

    public static KafkaSink<String> getKafkaSink(String topic, String transIdPrefix, String[] args) {
        // 将命令行参数对象封装为 ParameterTool 类对象
        ParameterTool parameterTool = ParameterTool.fromArgs(args);

        // 提取命令行传入的 key 为 topic 的配置信息，并将默认值指定为方法参数 topic
        // 当命令行没有指定 topic 时，会采用默认值
        topic = parameterTool.get("topic", topic);
        // 如果命令行没有指定主题名称且默认值为 null 则抛出异常
        if (topic == null) {
            throw new IllegalArgumentException("主题名不可为空：命令行传参为空且没有默认值!");
        }

        // 获取命令行传入的 key 为 bootstrap-servers 的配置信息，并指定默认值
        String bootstrapServers = parameterTool.get("bootstrap-severs", KAFKA_SERVER);
        // 获取命令行传入的 key 为 transaction-timeout 的配置信息，并指定默认值
        String transactionTimeout = parameterTool.get("transaction-timeout", 15 * 60 * 1000 + "");


        return KafkaSink.<String>builder()
                .setBootstrapServers(bootstrapServers)
                .setRecordSerializer(KafkaRecordSerializationSchema.builder()
                        .setTopic(topic)
                        .setValueSerializationSchema(new SimpleStringSchema())
                        .build()
                )
                .setDeliveryGuarantee(DeliveryGuarantee.AT_LEAST_ONCE)
                .setTransactionalIdPrefix(transIdPrefix)
                .setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, transactionTimeout)
                .build();

    }

    public static KafkaSink<String> getKafkaSink(String topic, String[] args) {
        return getKafkaSink(topic, topic + "_trans", args);

    }
}

4.OdsApp.java

Ods层的app创建，负责读取和写入数据

package com.atguigu.tms.realtime.app.ods;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.atguigu.tms.realtime.utils.CreateEnvUtil;
import com.atguigu.tms.realtime.utils.KafkaUtil;
import com.esotericsoftware.minlog.Log;
import com.ververica.cdc.connectors.mysql.source.MySqlSource;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;

public class OdsApp {
    public static void main(String[] args) throws Exception {
        // 1.获取流处理环境并指定检查点
        StreamExecutionEnvironment env = CreateEnvUtil.getStreamEnv(args);
        env.setParallelism(4);


        // 2 使用FlinkCDC从MySQL中读取数据-事实数据
        String dwdOption = "dwd";
        String dwdServerId = "6030";
        String dwdsourceName = "ods_app_dwd_source";

        mysqlToKafka(dwdOption, dwdServerId, dwdsourceName, env, args);

        // 3 使用FlinkCDC从MySQL中读取数据-维度数据
        String realtimeDimOption = "realtime_dim";
        String realtimeDimServerId = "6040";
        String realtimeDimsourceName = "ods_app_realtimeDim_source";

        mysqlToKafka(realtimeDimOption, realtimeDimServerId, realtimeDimsourceName, env, args);

        env.execute();


    }

    public static void mysqlToKafka(String option, String serverId, String sourceName, StreamExecutionEnvironment env, String[] args) {

        MySqlSource<String> MySqlSource = CreateEnvUtil.getMysqlSource(option, serverId, args);

        SingleOutputStreamOperator<String> dwdStrDS = env.fromSource(MySqlSource, WatermarkStrategy.noWatermarks(), sourceName)
                .setParallelism(1)
                .uid(option + sourceName);


        // 3 简单ETL
        SingleOutputStreamOperator<String> processDS = dwdStrDS.process(
                new ProcessFunction<String, String>() {
                    @Override
                    public void processElement(String jsonStr, ProcessFunction<String, String>.Context ctx, Collector<String> out) {
                        try {
                            JSONObject jsonObj = JSONObject.parseObject(jsonStr);
                            if (jsonObj.getJSONObject("after") != null && !"d".equals(jsonObj.getString("op"))) {
//                                System.out.println(jsonObj);
                                Long tsMs = jsonObj.getLong("ts_ms");
                                jsonObj.put("ts", tsMs);
                                jsonObj.remove("ts_ms");
                                String jsonString = jsonObj.toJSONString();
                                out.collect(jsonString);
                            }

                        } catch (Exception e) {
                            Log.error("从Flink-CDC得到的数据不是一个标准的json格式",e);
                        }
                    }
                }
        ).setParallelism(1);
        // 4 按照主键进行分组，避免出现乱序
        KeyedStream<String, String> keyedDS = processDS.keyBy((KeySelector<String, String>) jsonStr -> {
            JSONObject jsonObj = JSON.parseObject(jsonStr);
            return jsonObj.getJSONObject("after").getString("id");
        });

        //将数据写入Kafka

        keyedDS.sinkTo(KafkaUtil.getKafkaSink("tms_ods", sourceName + "_transPre", args))
                .uid(option + "_ods_app_sink");
    }
}

三、代码测试

在虚拟机启动我们需要的组件，目前需要hadoop、zk、kafka和MySQL。

先开一个消费者进行消费。

bin/kafka-console-consumer.sh --bootstrap-server hadoop102:9092 --topic tms_ods

然后运行OdsApp.java
他会先读取维度数据，因为维度数据需要全量更新之前的数据。

当他消费结束后，我们运行jar包，获取事实数据。

java -jar tms-mock-2023-01-06.jar 

如果能消费到新数据，代表通道没问题，ODS层创建完成。

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总结

至此ODS搭建完成。