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
物流实时数仓：数仓搭建（DIM）
物流实时数仓：数仓搭建（DWD）一
物流实时数仓：数仓搭建（DWD）二
物流实时数仓：数仓搭建（DWS）一

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前言

这次博客，我们进行各机构分拣次数的统计。统计当日各机构的分拣次数，并补充城市、省份等维度信息，写入ClickHouse对应表。要求每十秒钟更新一次统计结果。

大体流程如图。

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一、代码编写

1.文件创建

1.主程序

2.实体类

3.自定义触发器

4.自定义聚合函数

5.在HbaseUtil中添加查询方法

6.JedisUtil工具类

7.封装DimUtil工具类，使用旁路缓存优化查询维度

8.修改实体类TmsConfigDimBean

9.传递op

10.获取线程池的工具类

11.异步关联函数DimAsyncFunction

12.ClickHouseUtil工具类

以上就是这次博客要更改或创建的java文件。

13. DimSinkFunction

当维度数据更新时，删除redis中的对应数据。

14. TransientSink注解

某些字段不需要写入ClickHouse，但对统计有帮助，我们可以通过添加自定义注解，在写出时获取字段的TransientSink注解，通过判断是否注解是否为空在写出时忽略指定字段。

2.主程序

DwsBoundOrgSortDay需要完成的任务如以下流程图。

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

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.atguigu.tms.realtime.app.func.DimAsyncFunction;
import com.atguigu.tms.realtime.app.func.MyAggregationFunction;
import com.atguigu.tms.realtime.app.func.MyTriggerFunction;
import com.atguigu.tms.realtime.beans.DwdBoundSortBean;
import com.atguigu.tms.realtime.beans.DwsBoundOrgSortDayBean;
import com.atguigu.tms.realtime.utils.*;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.streaming.api.datastream.AsyncDataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.util.concurrent.TimeUnit;

public class DwsBoundOrgSortDay {
    public static void main(String[] args) throws Exception {
        // 环境准备
        StreamExecutionEnvironment env = CreateEnvUtil.getStreamEnv(args);
        env.setParallelism(4);

        // kafka读取数据
        String topic = "tms_dwd_bound_sort";
        String groupId = "dws_tms_dwd_bound_sort";

        KafkaSource<String> kafkaSource = KafkaUtil.getKafkaSource(topic, groupId, args);
        SingleOutputStreamOperator<String> kafkaStrDS = env.fromSource(kafkaSource, WatermarkStrategy.noWatermarks(), "kafka_source")
                .uid("kafka_source");


        // 对流中的数据进行类型转换 jsonStr-> 实体类
        SingleOutputStreamOperator<DwsBoundOrgSortDayBean> dwsBoundOrgSortDayBeanSingleOutputStreamOperator = kafkaStrDS.map(
                new MapFunction<String, DwsBoundOrgSortDayBean>() {
                    @Override
                    public DwsBoundOrgSortDayBean map(String jsonStr) throws Exception {
                        DwdBoundSortBean dwdBoundSortBean = JSON.parseObject(jsonStr, DwdBoundSortBean.class);
                        return DwsBoundOrgSortDayBean.builder()
                                .orgId(dwdBoundSortBean.getOrgId())
                                .sortCountBase(1L)
                                .ts(dwdBoundSortBean.getTs() + 8 * 60 * 60 * 1000L)
                                .build();
                    }
                }
        );


        // 指定Watermark以及提取事件事件字段
        SingleOutputStreamOperator<DwsBoundOrgSortDayBean> withWatermarkDS = dwsBoundOrgSortDayBeanSingleOutputStreamOperator.assignTimestampsAndWatermarks(
                WatermarkStrategy
                        .<DwsBoundOrgSortDayBean>forMonotonousTimestamps()
                        .withTimestampAssigner(
                                new SerializableTimestampAssigner<DwsBoundOrgSortDayBean>() {
                                    @Override
                                    public long extractTimestamp(DwsBoundOrgSortDayBean boundOrgSortDayBean, long recordTimestamp) {
                                        return boundOrgSortDayBean.getTs();
                                    }
                                }
                        )

        );

//        withWatermarkDS.print("###");

        // 按照机构id进行分组
        KeyedStream<DwsBoundOrgSortDayBean, String> keyedDS = withWatermarkDS.keyBy(DwsBoundOrgSortDayBean::getOrgId);

        // 开窗
        WindowedStream<DwsBoundOrgSortDayBean, String, TimeWindow> windowDS = keyedDS.window(TumblingEventTimeWindows.of(Time.days(1L)));

        // 指定自定义触发器
        WindowedStream<DwsBoundOrgSortDayBean, String, TimeWindow> triggerDS = windowDS.trigger(new MyTriggerFunction<>());

        // 聚合
        SingleOutputStreamOperator<DwsBoundOrgSortDayBean> aggregateDS = triggerDS.aggregate(
                new MyAggregationFunction<DwsBoundOrgSortDayBean>() {
                    @Override
                    public DwsBoundOrgSortDayBean add(DwsBoundOrgSortDayBean value, DwsBoundOrgSortDayBean accumulator) {
                        if (accumulator == null) {
                            return value;
                        }
                        accumulator.setSortCountBase(accumulator.getSortCountBase() + 1);
                        return accumulator;
                    }
                },
                new ProcessWindowFunction<DwsBoundOrgSortDayBean, DwsBoundOrgSortDayBean, String, TimeWindow>() {
                    @Override
                    public void process(String s, Context context, Iterable<DwsBoundOrgSortDayBean> elements, Collector<DwsBoundOrgSortDayBean> out) throws Exception {
                        for (DwsBoundOrgSortDayBean element : elements) {
                            // 获取窗口起始时间
                            long stt = context.window().getStart();
                            // 将窗口时间左移8小时 并转换格式
                            element.setCurDate(DateFormatUtil.toDate(stt - 8 * 60 * 60 * 1000L));
                            element.setTs(System.currentTimeMillis());
                            out.collect(element);
                        }
                    }
                }
        );


        // 关联维度（城市、省份）
        // 关联机构维度 获取机构名称
        // 异步I/O
        SingleOutputStreamOperator<DwsBoundOrgSortDayBean> withOrgNameDS = AsyncDataStream.unorderedWait(
                aggregateDS,
                new DimAsyncFunction<DwsBoundOrgSortDayBean>("dim_base_organ") {
                    @Override
                    public void join(DwsBoundOrgSortDayBean sortDayBean, JSONObject dimInfoJsonObj) {
                        sortDayBean.setOrgName(dimInfoJsonObj.getString("org_name"));
                        String orgParentId = dimInfoJsonObj.getString("org_parent_id");
                        sortDayBean.setJoinOrgId(orgParentId != null?orgParentId:sortDayBean.getOrgId());
                    }

                    @Override
                    public Tuple2<String, String> getCondition(DwsBoundOrgSortDayBean sortDayBean) {
                        return Tuple2.of("id", sortDayBean.getOrgId());
                    }
                },
                60,
                TimeUnit.SECONDS
        );


		// 补充城市ID
        SingleOutputStreamOperator<DwsBoundOrgSortDayBean> withCityIdDS = AsyncDataStream.unorderedWait(
                withOrgNameDS,
                new DimAsyncFunction<DwsBoundOrgSortDayBean>("dim_base_organ") {
                    @Override
                    public void join(DwsBoundOrgSortDayBean sortDayBean, JSONObject dimInfoJsonObj) {
                        sortDayBean.setCityId(dimInfoJsonObj.getString("region_id"));
                    }

                    @Override
                    public Tuple2<String, String> getCondition(DwsBoundOrgSortDayBean sortDayBean) {
                        return Tuple2.of("id", sortDayBean.getJoinOrgId());

                    }
                },
                60,
                TimeUnit.SECONDS
        );



        // 关联地区维度表 根据城市的id获取城市名称以及当前城市所属的省份id
        SingleOutputStreamOperator<DwsBoundOrgSortDayBean> withCityNameAndProvinceIdDS = AsyncDataStream.unorderedWait(
                withCityIdDS,
                new DimAsyncFunction<DwsBoundOrgSortDayBean>("dim_base_region_info") {
                    @Override
                    public void join(DwsBoundOrgSortDayBean sortDayBean, JSONObject dimInfoJsonObj) {
                        sortDayBean.setCityName(dimInfoJsonObj.getString("name"));
                        sortDayBean.setProvinceId(dimInfoJsonObj.getString("parent_id"));
                    }

                    @Override
                    public Tuple2<String, String> getCondition(DwsBoundOrgSortDayBean sortDayBean) {
                        return Tuple2.of("id", sortDayBean.getCityId());

                    }
                },
                60, TimeUnit.SECONDS
        );


        // 关联地区维度表 根据省份的id获取省份的名称
        SingleOutputStreamOperator<DwsBoundOrgSortDayBean> withProvinceDS = AsyncDataStream.unorderedWait(
                withCityNameAndProvinceIdDS,
                new DimAsyncFunction<DwsBoundOrgSortDayBean>("dim_base_region_info") {
                    @Override
                    public void join(DwsBoundOrgSortDayBean sortDayBean, JSONObject dimInfoJsonObj) {
                        sortDayBean.setProvinceName(dimInfoJsonObj.getString("name"));
                    }

                    @Override
                    public Tuple2<String, String> getCondition(DwsBoundOrgSortDayBean sortDayBean) {
                        return Tuple2.of("id", sortDayBean.getProvinceId());
                    }
                },
                60, TimeUnit.SECONDS

        );

        withProvinceDS.print(">>>>");


        // 将关联的结果写入ck中
        withProvinceDS.addSink(
                ClickHouseUtil.getJdbcSink("insert into dws_bound_org_sort_day_base values(?,?,?,?,?,?,?,?,?)")
        );

        env.execute();
    }
}

现在我们就按照主程序的调用来完成其他文件的编写。

3.开窗聚合

开窗之前没有用到新的函数，所以不说了。

1.MyTriggerFunction

自定义触发器

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

import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.streaming.api.windowing.triggers.Trigger;
import org.apache.flink.streaming.api.windowing.triggers.TriggerResult;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

 //自定义触发器 每10s触发一次窗口计算
public class MyTriggerFunction<T>  extends Trigger<T, TimeWindow> {

    @Override
    public TriggerResult onElement(T element, long timestamp, TimeWindow window, TriggerContext ctx) throws Exception {
        ValueStateDescriptor<Boolean> valueStateDescriptor
                = new ValueStateDescriptor<Boolean>("isFirstState",Boolean.class);
        ValueState<Boolean> isFirstState = ctx.getPartitionedState(valueStateDescriptor);
        Boolean isFirst = isFirstState.value();
        if(isFirst == null){
            //如果是窗口中的第一个元素
            //将状态中的值进行更新
            isFirstState.update(true);
            //注册定时器  当前事件时间向下取整后 + 10s后执行
            ctx.registerEventTimeTimer(timestamp -timestamp%10000L  + 2000L);
        }else if(isFirst){
            isFirstState.update(false);
        }
        return TriggerResult.CONTINUE;
    }

    @Override
    public TriggerResult onProcessingTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
        return TriggerResult.CONTINUE;
    }

    //time 表示事件时间触发器 触发时间
    @Override
    public TriggerResult onEventTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
        long end = window.getEnd();
        if(time < end){
            if(time + 2000L < end){
                ctx.registerEventTimeTimer(time + 2000L);
            }
            return TriggerResult.FIRE;
        }
        return TriggerResult.CONTINUE;
    }

    @Override
    public void clear(TimeWindow window, TriggerContext ctx) throws Exception {
        ctx.deleteEventTimeTimer(window.maxTimestamp());
    }
}

2.MyAggregationFunction

自定义聚合函数

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

import org.apache.flink.api.common.functions.AggregateFunction;

public abstract class MyAggregationFunction<T> implements AggregateFunction<T,T,T> {
    @Override
    public T createAccumulator() {
        return null;
    }

    @Override
    public T getResult(T accumulator) {
        return accumulator;
    }

    @Override
    public T merge(T a, T b) {
        return null;
    }
}

4.关联维度信息

1.DimAsyncFunction

异步I/O

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


import com.alibaba.fastjson.JSONObject;
import com.atguigu.tms.realtime.beans.DimJoinFunction;
import com.atguigu.tms.realtime.commom.TmsConfig;
import com.atguigu.tms.realtime.utils.DimUtil;
import com.atguigu.tms.realtime.utils.ThreadPoolUtil;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.async.ResultFuture;
import org.apache.flink.streaming.api.functions.async.RichAsyncFunction;

import java.util.Collections;
import java.util.concurrent.ExecutorService;

public abstract class DimAsyncFunction<T> extends RichAsyncFunction<T, T> implements DimJoinFunction<T> {
    private String tableName;

    public DimAsyncFunction(String tableName) {
        this.tableName = tableName;
    }


    private ExecutorService executorService;

    @Override
    public void open(Configuration parameters) throws Exception {
        executorService = ThreadPoolUtil.getInstance();
    }

    @Override
    public void asyncInvoke(T obj, ResultFuture<T> resultFuture) throws Exception {
        // 从线程池中获取线程，发送异步请求
        executorService.submit(
                new Runnable() {
                    @Override
                    public void run() {
                        // 根据流中的对象获取要作为查询条件的主键或者外键
                        Tuple2<String, String> keyNameAndValue = getCondition(obj);
                        // 根据查询条件获取维度对象
                        JSONObject dimInfoJsonObj = DimUtil.getDimInfo(TmsConfig.HBASE_NAMESPACE, tableName, keyNameAndValue);
                        // 将维度对象的属性补充到流中的对象上
                        if (dimInfoJsonObj != null) {
                            join(obj, dimInfoJsonObj);
                        }
                        // 向下游传递数据
                        resultFuture.complete(Collections.singleton(obj));

                    }
                }
        );
    }
}

2.DimJoinFunction

我们将需要DimAsyncFunction中一些需要子类实现的函数写入DimJoinFunction，当作接口接入。

package com.atguigu.tms.realtime.beans;

import com.alibaba.fastjson.JSONObject;
import org.apache.flink.api.java.tuple.Tuple2;

public interface DimJoinFunction<T> {

    void join(T obj, JSONObject dimInfoJsonObj);

    Tuple2<String, String> getCondition(T obj);
}

3.ThreadPoolUtil

异步I/O中用作创建线程池的工具类

package com.atguigu.tms.realtime.utils;

import java.util.concurrent.LinkedBlockingDeque;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;

public class ThreadPoolUtil {
    private static volatile ThreadPoolExecutor poolExecutor;

    public static synchronized ThreadPoolExecutor getInstance() {
        if (poolExecutor == null) {
            synchronized (ThreadPoolUtil.class){
                if (poolExecutor == null) {
                    poolExecutor = new ThreadPoolExecutor(
                            4,
                            20,
                            300,
                            TimeUnit.SECONDS,
                            new LinkedBlockingDeque<Runnable>(Integer.MAX_VALUE)
                    );
                }
            }
        }

        return poolExecutor;
    }
}

4.DimUtil

在维度关联时，我们需要从hbase中获取维度信息，为了为了优化查询速度，我们引入了redis，流程如图所示

package com.atguigu.tms.realtime.utils;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import org.apache.commons.lang3.StringUtils;
import org.apache.flink.api.java.tuple.Tuple2;
import redis.clients.jedis.Jedis;
import lombok.extern.slf4j.Slf4j;

@Slf4j
public class DimUtil {
    public static JSONObject getDimInfo(String namespace, String tableName, Tuple2<String, String> nameAndValue) {
        // 获取的查询条件中的字段名以及字段值
        String keyName = nameAndValue.f0;
        String keyValue = nameAndValue.f1;

        // 拼接从Redis中查询数据的Key
        String redisKey = "dim:" + tableName.toLowerCase() + ":" + keyName + "_" + keyValue;

        // 操作Redis的客户端
        Jedis jedis = null;
        // 用于存放从Redis查询的维度数据
        String dimJsonStr = null;
        // 用于封装返回结果
        JSONObject dimJsonObj = null;

        //  先从缓存中查询维度数据
        try {
            jedis = JedisUtil.getJedis();
            dimJsonStr = jedis.get(redisKey);
            if (StringUtils.isNotEmpty(dimJsonStr)) {
                // 如果在缓存中能够找到要查询的维度
                dimJsonObj = JSON.parseObject(dimJsonStr);

            } else {
                // 如果在缓存中，没有找到要查询的维度数据
                if ("id".equals(keyName)) {
                    dimJsonObj = HbaseUtil.getRowByPrimaryKey(namespace, tableName, nameAndValue);
                } else {
                    dimJsonObj = HbaseUtil.getRowByForeignKey(namespace, tableName, nameAndValue);
                }

                if (dimJsonObj != null && jedis != null) {
                    jedis.setex(redisKey, 3600 * 24, dimJsonObj.toJSONString());
                }
            }
        } catch (Exception e) {
            log.error("从Redis中查询维度数据发生了一场", e);
        } finally {
            if (jedis != null) {
                System.out.println("关闭客户端");
                jedis.close();
            }
        }

        return dimJsonObj;


    }

    // 从Redis中删除缓存的维度数据
    public static void delCached(String tableName, Tuple2<String, String> keyNameAndValue) {
        String keyName = keyNameAndValue.f0;
        String keyValue = keyNameAndValue.f1;

        String redisKey = "dim:" + tableName.toLowerCase() + ":" + keyName + "_" + keyValue;
        Jedis jedis = null;
        try {
            jedis = JedisUtil.getJedis();
            jedis.decr(redisKey);
        }catch (Exception e){
            log.error("清除Redis中缓存的维度数据时发生了异常", e);
        }finally {
            if (jedis != null) {
                jedis.close();
            }

        }

    }
}

5.JedisUtil

用于连接reids的jedis客户端。
先在pom中引入依赖

<dependency>
    <groupId>redis.clients</groupId>
    <artifactId>jedis</artifactId>
    <version>3.3.0</version>
</dependency>

package com.atguigu.tms.realtime.utils;

import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
import redis.clients.jedis.JedisPoolConfig;

public class JedisUtil {
    private static JedisPool jedisPool;

    static {
        JedisPoolConfig poolConfig = new JedisPoolConfig();
        poolConfig.setMaxTotal(1000);
        poolConfig.setMaxIdle(5);
        poolConfig.setMinIdle(5);
        poolConfig.setBlockWhenExhausted(true);
        poolConfig.setMaxWaitMillis(2000L);
        poolConfig.setTestOnBorrow(true);
        jedisPool=new JedisPool(poolConfig,"hadoop102",6379,10000);
    }

    public static Jedis getJedis(){
        System.out.println("创建Jedis客户端");
        Jedis jedis = jedisPool.getResource();
        return jedis;
    }

    public static void main(String[] args) {
        Jedis jedis = getJedis();
        String pong = jedis.ping();
        System.out.println(pong);
    }
}

6. HbaseUtil

之前我们在HbaseUtil完成了创建表和插入操作，现在来完成查询操作。

package com.atguigu.tms.realtime.utils;

import com.alibaba.fastjson.JSONObject;
import com.atguigu.tms.realtime.commom.TmsConfig;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.*;
import org.apache.hadoop.hbase.client.*;
import org.apache.hadoop.hbase.filter.SingleColumnValueFilter;
import org.apache.hadoop.hbase.util.Bytes;

import java.io.IOException;

public class HbaseUtil {
    private static Connection conn;

    static {
        try {
            Configuration conf = HBaseConfiguration.create();
            conf.set("hbase.zookeeper.quorum", TmsConfig.hbase_zookeeper_quorum);
            conn = ConnectionFactory.createConnection(conf);
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
    }


    // 创建表
    public static void createTable(String nameSpace, String tableName, String... families) {
        Admin admin = null;
        try {
            if (families.length < 1) {
                System.out.println("至少需要一个列族");
                return;
            }

            admin = conn.getAdmin();
            // 判断表是否存在
            if (admin.tableExists(TableName.valueOf(nameSpace, tableName))) {
                System.out.println(nameSpace + ":" + tableName + "已存在");
                return;
            }
            TableDescriptorBuilder builder = TableDescriptorBuilder.newBuilder(TableName.valueOf(nameSpace, tableName));
            // 指定列族
            for (String family : families) {
                ColumnFamilyDescriptor familyDescriptor = ColumnFamilyDescriptorBuilder
                        .newBuilder(Bytes.toBytes(family)).build();
                builder.setColumnFamily(familyDescriptor);
            }


            admin.createTable(builder.build());
        } catch (IOException e) {
            throw new RuntimeException(e);
        } finally {
            if (admin != null) {
                try {
                    admin.close();
                } catch (IOException e) {
                    throw new RuntimeException(e);
                }

            }
        }
    }

    // 向hbase插入对象
    public static void putPow(String namespace, String tableName, Put put) {
        BufferedMutator mutator = null;
        try {
            BufferedMutatorParams params = new BufferedMutatorParams(TableName.valueOf(namespace, tableName));
            params.writeBufferSize(5 * 1024 * 1024);
            params.setWriteBufferPeriodicFlushTimeoutMs(3000L);

            mutator = conn.getBufferedMutator(params);
            mutator.mutate(put);


        } catch (IOException e) {
            throw new RuntimeException(e);
        } finally {
            if (mutator != null) {
                try {
                    mutator.close();
                } catch (IOException e) {
                    throw new RuntimeException(e);
                }

            }
        }
    }

    // 根据主键从Hbase表中查询一行数据
    public static JSONObject getRowByPrimaryKey(String namespace, String tableName, Tuple2<String, String> rowKeyNameAndKey) {
        Table table = null;
        JSONObject dimJsonObj = null;
        String rowKeyName = rowKeyNameAndKey.f0;
        String rowKeyValue = rowKeyNameAndKey.f1;

        try {
            table = conn.getTable(TableName.valueOf(namespace, tableName));
            Result result = table.get(new Get(Bytes.toBytes(rowKeyValue)));
            Cell[] cells = result.rawCells();
            if (cells.length > 0) {
                dimJsonObj = new JSONObject();
                dimJsonObj.put(rowKeyName, rowKeyValue);
                for (Cell cell : cells) {
                    dimJsonObj.put(Bytes.toString(CellUtil.cloneQualifier(cell)), Bytes.toString(CellUtil.cloneValue(cell)));

                }
            } else {
                System.out.println("从Hbase表中没有找到对应的维度数据");
            }


        } catch (IOException e) {
            throw new RuntimeException(e);
        } finally {
            if (table != null) {
                try {
                    table.close();
                } catch (IOException e) {
                    throw new RuntimeException(e);
                }
            }
        }


        return dimJsonObj;

    }

    //根据外键从hbase表中查询一行数据
    public static JSONObject getRowByForeignKey(String namespace, String tableName, Tuple2<String, String> foreignKeyNameAndKey) {
        Table table = null;
        JSONObject dimJsonObj = null;
        try {
            table = conn.getTable(TableName.valueOf(namespace, tableName));
            Scan scan = new Scan();
            String foreignKeyName = foreignKeyNameAndKey.f0;
            String foreignKeyValue = foreignKeyNameAndKey.f1;


            SingleColumnValueFilter singleColumnValueFilter = new SingleColumnValueFilter(Bytes.toBytes("info"),Bytes.toBytes(foreignKeyName), CompareOperator.EQUAL,Bytes.toBytes(foreignKeyValue));
            singleColumnValueFilter.setFilterIfMissing(true);
            scan.setFilter(singleColumnValueFilter);
            ResultScanner scanner = table.getScanner(scan);
            Result result = scanner.next();
            if (result!=null){
                Cell[] cells = result.rawCells();
                if (cells.length > 0) {
                    dimJsonObj = new JSONObject();
                    dimJsonObj.put("id", Bytes.toString(result.getRow()));
                    for (Cell cell : cells) {
                        dimJsonObj.put(Bytes.toString(CellUtil.cloneQualifier(cell)), Bytes.toString(CellUtil.cloneValue(cell)));

                    }
                } else {
                    System.out.println("从Hbase表中没有找到对应的维度数据");
                }
            }

        } catch (IOException e) {
            throw new RuntimeException(e);
        } finally {
            if (table != null) {
                try {
                    table.close();
                } catch (IOException e) {
                    throw new RuntimeException(e);
                }
            }
        }
        return dimJsonObj;

    }
}

7. DwsBoundOrgSortDayBean

自定义的工具类，其中包含我们要写入ck的字段

package com.atguigu.tms.realtime.beans;

import lombok.Builder;
import lombok.Data;

@Data
@Builder
public class DwsBoundOrgSortDayBean {
    // 统计日期
    String curDate;

    // 机构 ID
    String orgId;

    // 机构名称
    String orgName;

    // 用于关联获取省份信息的机构 ID
    @TransientSink
    String joinOrgId;

    // 城市 ID
    String cityId;

    // 城市名称
    String cityName;

    // 省份 ID
    String provinceId;

    // 省份名称
    String provinceName;

    // 分拣次数
    Long sortCountBase;

    // 时间戳
    Long ts;
}

8.补充维度字段

我代码编写我们需要维度表的外键字段，所以我们重新修改mysql维度表，添加外键字段。

DROP TABLE IF EXISTS `tms_config_dim`;
CREATE TABLE `tms_config_dim` (
  `source_table` varchar(200) NOT NULL COMMENT '数据源表',
  `sink_table` varchar(200) DEFAULT NULL COMMENT '目标表',
  `sink_family` varchar(200) DEFAULT NULL COMMENT '目标表列族',
  `sink_columns` varchar(200) DEFAULT NULL COMMENT '目标表列',
  `sink_pk` varchar(256) DEFAULT NULL COMMENT '主键字段',
  `foreign_keys` varchar(256) DEFAULT NULL COMMENT '外键查询字段',
  PRIMARY KEY (`source_table`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='物流实时配置表';

然后从新导入数据。

然后我们使用dimapp同步一下数据即可，具体方法可看Dim层搭建。

9. MyBroadcastProcessFunction

我们之前在DIM层的搭建中，在MyBroadcastProcessFunction的processElement函数中过滤掉了外键，但现在需要他，我们把它加上。
在传递前添加一段代码

// 清除Redis缓存的准备工作（传递操作类型、外键字段的k-v）
String op = jsonObj.getString("op");
if ("u".equals(op)) {
    afterJsonObj.put("op", op);

    // 从配置表中获取当前维度表关联的外键名
    String foreignKeys = tmsConfigDimBean.getForeignKeys();
    // 定义个json对象，用于存储当前维度表对应的外键名以及值
    JSONObject foreignjsonObj = new JSONObject();
    if (StringUtils.isNotEmpty(foreignKeys)) {
        String[] foreignNameArr = foreignKeys.split(",");
        for (String foreignName : foreignNameArr) {
            // 获取修改前的数据
            JSONObject before = jsonObj.getJSONObject("before");
            String foreignKeyBefore = before.getString(foreignName);
            String foreignKeyAfter = afterJsonObj.getString(foreignName);

            if (!foreignKeyBefore.equals(foreignKeyAfter)) {
                // 如果修改的是外键
                foreignjsonObj.put(foreignName, foreignKeyBefore);
            }else {
                foreignjsonObj.put(foreignName, foreignKeyBefore);

            }

        }

    }
    afterJsonObj.put("foreign_key", foreignjsonObj);
}

完成代码

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

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.atguigu.tms.realtime.beans.TmsConfigDimBean;
import com.atguigu.tms.realtime.utils.DateFormatUtil;
import org.apache.commons.codec.digest.DigestUtils;
import org.apache.commons.lang3.StringUtils;
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.api.java.utils.ParameterTool;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction;
import org.apache.flink.util.Collector;

import java.sql.*;
import java.util.*;


// 自定义类 完成主流和广播流的处理
public class MyBroadcastProcessFunction extends BroadcastProcessFunction<JSONObject, String, JSONObject> {

    private MapStateDescriptor<String, TmsConfigDimBean> mapStateDescriptor;

    private Map<String, TmsConfigDimBean> configMap = new HashMap<>();

    private String username;
    private String password;

    public MyBroadcastProcessFunction(MapStateDescriptor<String, TmsConfigDimBean> mapStateDescriptor, String[] args) {
        this.mapStateDescriptor = mapStateDescriptor;
        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        this.username = parameterTool.get("mysql-username", "root");
        this.password = parameterTool.get("mysql-password", "000000");

    }

    @Override
    public void open(Configuration parameters) throws Exception {
        // 将配置表中的数据进行预加载-JDBC

        Class.forName("com.mysql.cj.jdbc.Driver");
        String url = "jdbc:mysql://hadoop102:3306/tms_config?useSSL=false&useUnicode=true" +
                "&user=" + username + "&password=" + password +
                "&charset=utf8&TimeZone=Asia/Shanghai";
        Connection conn = DriverManager.getConnection(url);

        PreparedStatement ps = conn.prepareStatement("select * from tms_config.tms_config_dim");

        ResultSet rs = ps.executeQuery();

        ResultSetMetaData metaData = rs.getMetaData();

        while (rs.next()) {
            JSONObject jsonObj = new JSONObject();
            for (int i = 1; i <= metaData.getColumnCount(); i++) {
                String columnName = metaData.getColumnName(i);
                Object columValue = rs.getObject(i);
                jsonObj.put(columnName, columValue);
            }

            TmsConfigDimBean tmsConfigDimBean = jsonObj.toJavaObject(TmsConfigDimBean.class);
            configMap.put(tmsConfigDimBean.getSourceTable(), tmsConfigDimBean);

        }
        rs.close();
        ps.close();
        conn.close();

        super.open(parameters);
    }

    @Override
    public void processElement(JSONObject jsonObj, BroadcastProcessFunction<JSONObject, String, JSONObject>.ReadOnlyContext ctx, Collector<JSONObject> out) throws Exception {
        // 获取操作的业务数据库的表名
        String table = jsonObj.getString("table");
        // 获取广播状态
        ReadOnlyBroadcastState<String, TmsConfigDimBean> broadcastState = ctx.getBroadcastState(mapStateDescriptor);
        // 根据操作的业务数据库的表名 到广播状态中获取对应的配置信息
        TmsConfigDimBean tmsConfigDimBean;

        if ((tmsConfigDimBean = broadcastState.get(table)) != null || (tmsConfigDimBean = configMap.get(table)) != null) {
            // 如果对应的配置信息不为空 是维度信息

            // 获取after对象，对应的是影响的业务数据表中的一条记录
            JSONObject afterJsonObj = jsonObj.getJSONObject("after");

            // 数据脱敏
            switch (table) {
                // 员工表信息脱敏
                case "employee_info":
                    String empPassword = afterJsonObj.getString("password");
                    String empRealName = afterJsonObj.getString("real_name");
                    String idCard = afterJsonObj.getString("id_card");
                    String phone = afterJsonObj.getString("phone");

                    // 脱敏
                    empPassword = DigestUtils.md5Hex(empPassword);
                    empRealName = empRealName.charAt(0) +
                            empRealName.substring(1).replaceAll(".", "\\*");
                    //知道有这个操作  idCard是随机生成的，和标准的格式不一样 所以这里注释掉
                    // idCard = idCard.matches("(^[1-9]\\d{5}(18|19|([23]\\d))\\d{2}((0[1-9])|(10|11|12))(([0-2][1-9])|10|20|30|31)\\d{3}[0-9Xx]$)|(^[1-9]\\d{5}\\d{2}((0[1-9])|(10|11|12))(([0-2][1-9])|10|20|30|31)\\d{2}$)")
                    //     ? DigestUtils.md5Hex(idCard) : null;
                    phone = phone.matches("^(13[0-9]|14[01456879]|15[0-35-9]|16[2567]|17[0-8]|18[0-9]|19[0-35-9])\\d{8}$")
                            ? DigestUtils.md5Hex(phone) : null;

                    afterJsonObj.put("password", empPassword);
                    afterJsonObj.put("real_name", empRealName);
                    afterJsonObj.put("id_card", idCard);
                    afterJsonObj.put("phone", phone);
                    break;
                // 快递员信息脱敏
                case "express_courier":
                    String workingPhone = afterJsonObj.getString("working_phone");
                    workingPhone = workingPhone.matches("^(13[0-9]|14[01456879]|15[0-35-9]|16[2567]|17[0-8]|18[0-9]|19[0-35-9])\\d{8}$")
                            ? DigestUtils.md5Hex(workingPhone) : null;
                    afterJsonObj.put("working_phone", workingPhone);
                    break;
                // 卡车司机信息脱敏
                case "truck_driver":
                    String licenseNo = afterJsonObj.getString("license_no");
                    licenseNo = DigestUtils.md5Hex(licenseNo);
                    afterJsonObj.put("license_no", licenseNo);
                    break;
                // 卡车信息脱敏
                case "truck_info":
                    String truckNo = afterJsonObj.getString("truck_no");
                    String deviceGpsId = afterJsonObj.getString("device_gps_id");
                    String engineNo = afterJsonObj.getString("engine_no");

                    truckNo = DigestUtils.md5Hex(truckNo);
                    deviceGpsId = DigestUtils.md5Hex(deviceGpsId);
                    engineNo = DigestUtils.md5Hex(engineNo);

                    afterJsonObj.put("truck_no", truckNo);
                    afterJsonObj.put("device_gps_id", deviceGpsId);
                    afterJsonObj.put("engine_no", engineNo);
                    break;
                // 卡车型号信息脱敏
                case "truck_model":
                    String modelNo = afterJsonObj.getString("model_no");
                    modelNo = DigestUtils.md5Hex(modelNo);
                    afterJsonObj.put("model_no", modelNo);
                    break;
                // 用户地址信息脱敏
                case "user_address":
                    String addressPhone = afterJsonObj.getString("phone");
                    addressPhone = addressPhone.matches("^(13[0-9]|14[01456879]|15[0-35-9]|16[2567]|17[0-8]|18[0-9]|19[0-35-9])\\d{8}$")
                            ? DigestUtils.md5Hex(addressPhone) : null;
                    afterJsonObj.put("phone", addressPhone);
                    break;
                // 用户信息脱敏
                case "user_info":
                    String passwd = afterJsonObj.getString("passwd");
                    String realName = afterJsonObj.getString("real_name");
                    String phoneNum = afterJsonObj.getString("phone_num");
                    String email = afterJsonObj.getString("email");

                    // 脱敏
                    passwd = DigestUtils.md5Hex(passwd);
                    if (StringUtils.isNotEmpty(realName)) {
                        realName = DigestUtils.md5Hex(realName);
                        afterJsonObj.put("real_name", realName);
                    }
                    phoneNum = phoneNum.matches("^(13[0-9]|14[01456879]|15[0-35-9]|16[2567]|17[0-8]|18[0-9]|19[0-35-9])\\d{8}$")
                            ? DigestUtils.md5Hex(phoneNum) : null;
                    email = email.matches("^[a-zA-Z0-9_-]+@[a-zA-Z0-9_-]+(\\.[a-zA-Z0-9_-]+)+$")
                            ? DigestUtils.md5Hex(email) : null;

                    afterJsonObj.put("birthday", DateFormatUtil.toDate(afterJsonObj.getInteger("birthday") * 24 * 60 * 60 * 1000L));
                    afterJsonObj.put("passwd", passwd);
                    afterJsonObj.put("phone_num", phoneNum);
                    afterJsonObj.put("email", email);
                    break;
            }

            // 过滤不需要的维度属性
            String sinkColumns = tmsConfigDimBean.getSinkColumns();
            filterColum(afterJsonObj, sinkColumns);

            // 补充输出目的的表名
            String sinkTable = tmsConfigDimBean.getSinkTable();
            afterJsonObj.put("sink_table", sinkTable);

            // 补充rowKey
            String sinkPk = tmsConfigDimBean.getSinkPk();
            afterJsonObj.put("sink_pk", sinkPk);

            // 清除Redis缓存的准备工作（传递操作类型、外键字段的k-v）
            String op = jsonObj.getString("op");
            if ("u".equals(op)) {
                afterJsonObj.put("op", op);

                // 从配置表中获取当前维度表关联的外键名
                String foreignKeys = tmsConfigDimBean.getForeignKeys();
                // 定义个json对象，用于存储当前维度表对应的外键名以及值
                JSONObject foreignjsonObj = new JSONObject();
                if (StringUtils.isNotEmpty(foreignKeys)) {
                    String[] foreignNameArr = foreignKeys.split(",");
                    for (String foreignName : foreignNameArr) {
                        // 获取修改前的数据
                        JSONObject before = jsonObj.getJSONObject("before");
                        String foreignKeyBefore = before.getString(foreignName);
                        String foreignKeyAfter = afterJsonObj.getString(foreignName);

                        if (!foreignKeyBefore.equals(foreignKeyAfter)) {
                            // 如果修改的是外键
                            foreignjsonObj.put(foreignName, foreignKeyBefore);
                        }else {
                            foreignjsonObj.put(foreignName, foreignKeyBefore);

                        }

                    }

                }
                afterJsonObj.put("foreign_key", foreignjsonObj);
            }

            // 将维度数据传递
            out.collect(afterJsonObj);
        }

    }

    private void filterColum(JSONObject afterJsonObj, String sinkColumns) {
        String[] fieldArr = sinkColumns.split(",");
        List<String> fieldList = Arrays.asList(fieldArr);
        Set<Map.Entry<String, Object>> entrySet = afterJsonObj.entrySet();
        entrySet.removeIf(entry -> !fieldList.contains(entry.getKey()));

    }

    @Override
    public void processBroadcastElement(String jsonStr, BroadcastProcessFunction<JSONObject, String, JSONObject>.Context ctx, Collector<JSONObject> out) throws Exception {
        JSONObject jsonObj = JSON.parseObject(jsonStr);

        // 获取广播状态
        BroadcastState<String, TmsConfigDimBean> broadcastState = ctx.getBroadcastState(mapStateDescriptor);


        // 获取对配置表的操作类型
        String op = jsonObj.getString("op");
        if ("d".equals(op)) {
            String sourceTable = jsonObj.getJSONObject("before").getString("source_table");
            broadcastState.remove(sourceTable);
            configMap.remove(sourceTable);
        } else {
            TmsConfigDimBean configDimBean = jsonObj.getObject("after", TmsConfigDimBean.class);
            String sourceTable = configDimBean.getSourceTable();
            broadcastState.put(sourceTable, configDimBean);
            configMap.put(sourceTable, configDimBean);
        }
    }
}

10. DimSinkFunction

添加清除代码

// 如果维度数据发生了变化，将Redis中缓存的维度数据清空掉
        if ("u".equals(op)) {
            // 删除当前维度数据在Redis中对应主键的缓存
            DimUtil.delCached(sinkTable, Tuple2.of("id", jsonObj.getString("id")));
            // 删除当前维度数据在Redis中对应外键的缓存
            Set<Map.Entry<String, Object>> set = foreignKeyJsonObj.entrySet();

            for (Map.Entry<String, Object> entry : set) {
                DimUtil.delCached(sinkTable, Tuple2.of(entry.getKey(), entry.getValue().toString()));
            }
        }

完整代码

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

import com.alibaba.fastjson.JSONObject;
import com.atguigu.tms.realtime.commom.TmsConfig;
import com.atguigu.tms.realtime.utils.DimUtil;
import com.atguigu.tms.realtime.utils.HbaseUtil;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;
import org.apache.hadoop.hbase.client.Put;

import java.util.Map;
import java.util.Set;

public class DimSinkFunction implements SinkFunction<JSONObject> {
    public void invoke(JSONObject jsonObj, Context context) throws Exception {
        // 获取输出目的地表名和rowKey
        String sinkTable = jsonObj.getString("sink_table");
        String sinkPk = jsonObj.getString("sink_pk");
        jsonObj.remove("sink_table");
        jsonObj.remove("sink_pk");

        String op = jsonObj.getString("op");
        jsonObj.remove("op");

        JSONObject foreignKeyJsonObj = jsonObj.getJSONObject("foreign_key");
        jsonObj.remove("foreign_key");

        // 获取json中的每一个键值对
        Set<Map.Entry<String, Object>> entrySet = jsonObj.entrySet();
        Put put = new Put(jsonObj.getString(sinkPk).getBytes());
        for (Map.Entry<String, Object> entry : entrySet) {
            if (!sinkPk.equals(entry.getKey())) {
                put.addColumn("info".getBytes(), entry.getKey().getBytes(), entry.getValue().toString().getBytes());
            }
        }
        System.out.println("向hbase表中插入数据");
        HbaseUtil.putPow(TmsConfig.HBASE_NAMESPACE, sinkTable, put);

        // 如果维度数据发生了变化，将Redis中缓存的维度数据清空掉
        if ("u".equals(op)) {
            // 删除当前维度数据在Redis中对应主键的缓存
            DimUtil.delCached(sinkTable, Tuple2.of("id", jsonObj.getString("id")));
            // 删除当前维度数据在Redis中对应外键的缓存
            Set<Map.Entry<String, Object>> set = foreignKeyJsonObj.entrySet();

            for (Map.Entry<String, Object> entry : set) {
                DimUtil.delCached(sinkTable, Tuple2.of(entry.getKey(), entry.getValue().toString()));
            }
        }
    }
}

5.写入CK

1. ClickHouseUtil

先导入需要的依赖。

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-jdbc</artifactId>
    <version>3.1.0-1.17</version>
</dependency>

<dependency>
    <groupId>ru.yandex.clickhouse</groupId>
    <artifactId>clickhouse-jdbc</artifactId>
    <version>0.3.2</version>
    <exclusions>
        <exclusion>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
        </exclusion>
        <exclusion>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-core</artifactId>
        </exclusion>
    </exclusions>
</dependency>

ClickHouseUtil

package com.atguigu.tms.realtime.utils;

import com.atguigu.tms.realtime.beans.TransientSink;
import com.atguigu.tms.realtime.commom.TmsConfig;
import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.connector.jdbc.JdbcStatementBuilder;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;

import java.lang.reflect.Field;
import java.sql.PreparedStatement;
import java.sql.SQLException;

public class ClickHouseUtil {
    // 获取SinkFunction
    public static <T> SinkFunction<T> getJdbcSink(String sql) {
        SinkFunction<T> sinkFunction = JdbcSink.<T>sink(
                sql,
                new JdbcStatementBuilder<T>() {
                    @Override
                    public void accept(PreparedStatement ps, T obj) throws SQLException {
                        // 将流中对象的属性给问号占位符赋值
                        // 获取单签流中对象岁数的类型 以及类中的属性
                        Field[] fieldsArr = obj.getClass().getDeclaredFields();
                        // 遍历所有属性
                        int skipNum = 0;
                        for (int i = 0; i < fieldsArr.length; i++) {
                            Field field = fieldsArr[i];
                            // 判断当前属性是否需要向流中保存
                            TransientSink transientSink = field.getAnnotation(TransientSink.class);
                            if (transientSink != null) {
                                skipNum++;
                                continue;
                            }
                            // 设置私有属性的访问权限
                            field.setAccessible(true);
                            try {
                                Object fieldValue = field.get(obj);
                                ps.setObject(i + 1 - skipNum, fieldValue);
                            } catch (IllegalAccessException e) {
                                throw new RuntimeException(e);
                            }
                        }

                    }
                },
                new JdbcExecutionOptions.Builder()
                        .withBatchSize(5000)
                        .withBatchIntervalMs(3000L)
                        .build()
                ,
                new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
                        .withDriverName(TmsConfig.CLICKHOUSE_DRIVER)
                        .withUrl(TmsConfig.CLICKHOUSE_URL)
                        .build()
        );
        return sinkFunction;
    }
}

2.TransientSink

package com.atguigu.tms.realtime.beans;

// 自定义主键 用于标记不需要向ck中保存的属性

import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

@Target(ElementType.FIELD)
@Retention(RetentionPolicy.RUNTIME)
public @interface TransientSink {
}

二、代码测试

1.程序启动

根据代码逻辑，我们需要启动以下程序。
hdfs、zk、kafka、hbase、redise、ck、OdsApp、DwdBoundRelevantApp、DimApp和DwsBoundOrgSortDay。其中DimApp只需启动一次完成维度数据更新即可。

2.修改kafka分区

再从kafka读取数据时，应该保证kafka有4个分区，不然聚合无法成功。

kafka-topics.sh --bootstrap-server hadoop102:9092 --alter --topic tms_dwd_bound_sort --partitions 4

3.ck建表

1.建库

DROP DATABASE IF EXISTS tms_realtime;
CREATE DATABASE IF NOT EXISTS tms_realtime;
USE tms_realtime;

2.建表

DROP TABLE IF EXISTS dws_bound_org_sort_day_base;
CREATE TABLE IF NOT EXISTS dws_bound_org_sort_day_base
(
    `cur_date` Date COMMENT '统计日期',
    `org_id` String COMMENT '机构ID',
    `org_name` String COMMENT '机构名称',
    `city_id` String COMMENT '城市ID',
    `city_name` String COMMENT '城市名称',
    `province_id` String COMMENT '省份ID',
    `province_name` String COMMENT '省份名称',
    `sort_count_base` UInt64 COMMENT '分拣次数',
    `ts` UInt64 COMMENT '时间戳'
)
ENGINE = MergeTree
ORDER BY (cur_date, org_id, org_name, city_id, city_name, province_id, province_name);

3.物化视图

DROP VIEW IF EXISTS dws_bound_org_sort_day;
CREATE MATERIALIZED VIEW IF NOT EXISTS dws_bound_org_sort_day
(
    `cur_date` Date, 
    `org_id` String, 
    `org_name` String, 
    `city_id` String, 
    `city_name` String, 
    `province_id` String, 
    `province_name` String, 
    `sort_count` AggregateFunction(argMax, UInt64, UInt64)
)
ENGINE = AggregatingMergeTree()
ORDER BY (cur_date, org_id, org_name, city_id, city_name, province_id, province_name)
SETTINGS index_granularity = 8192 AS
SELECT 
    cur_date, 
    org_id, 
    org_name, 
    city_id, 
    city_name, 
    province_id, 
    province_name, 
    argMaxState(sort_count_base, ts) AS sort_count
FROM dws_bound_org_sort_day_base
GROUP BY 
    cur_date, 
    org_id, 
    org_name, 
    city_id, 
    city_name, 
    province_id, 
    province_name;

4.查看结果

当运行程序后，开始生成数据，等待执行完成之后，可以在ck中使用如下代码查看。

clickhouse-client -m

-m 参数代表可以使用回车。

SELECT
    cur_date,
    org_id,
    org_name,
    city_id,
    city_name,
    province_id,
    province_name,
    argMaxMerge(sort_count) AS sort_count
FROM dws_bound_org_sort_day
GROUP BY
    cur_date,
    org_id,
    org_name,
    city_id,
    city_name,
    province_id,
    province_name
LIMIT 10;

---

总结

至此，Dws的部分搭建就结束了，为了方便进行文件管理，我把项目开源到了github上。
项目地址：https://github.com/lcc-666/tms-parent