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This commit is contained in:
markilue 2023-05-15 13:07:13 +08:00
parent bb9062f2a5
commit 5619d60ed1
18 changed files with 657 additions and 67 deletions
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2
Big_data_example/rt-gmall-parent/gmall-logger/src/main/resources/logback.xml
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@ -26,7 +26,7 @@
<appender-ref ref="console" />
</logger>
<root level="error" additivity="false">
<root level="info" additivity="false">
<appender-ref ref="console" />
</root>
</configuration>

32
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/beans/ProvinceStats.java
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@ -1,11 +1,29 @@
package com.atguigu.rtgmall.beans;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import java.math.BigDecimal;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall.beans
*@Author: markilue
*@CreateTime: 2023-05-12 21:29
*@Description: TODO
*@Version: 1.0
*/
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall.beans
*@Author: markilue
*@CreateTime: 2023-05-12 21:29
*@Description: TODO 地区交易额统计实体类
*@Version: 1.0
*/
@AllArgsConstructor
@Data
@NoArgsConstructor
public class ProvinceStats {
private String stt;
private String edt;
private String province_id;
private String province_name;
private BigDecimal order_amount;
private String ts;
}

168
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/controller/SugarController.java
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@ -1,7 +1,10 @@
package com.atguigu.rtgmall.controller;
import com.atguigu.rtgmall.beans.ProductStats;
import com.atguigu.rtgmall.beans.ProvinceStats;
import com.atguigu.rtgmall.service.ProductStatsService;
import com.atguigu.rtgmall.service.ProvinceStatsService;
import org.apache.commons.lang3.StringUtils;
import org.apache.commons.lang3.time.DateFormatUtils;
import org.springframework.beans.factory.annotation.Autowired;
@ -24,6 +27,9 @@ public class SugarController {
@Autowired
private ProductStatsService productStatsService;
@Autowired
private ProvinceStatsService provinceStatsService;
@RequestMapping("/gmv")
public String getGMV(@RequestParam(value = "date", defaultValue = "0") Integer date) {
@ -39,6 +45,168 @@ public class SugarController {
return json;
}
@RequestMapping("/tm")
public String getProductStatsByTm(@RequestParam(value = "date", defaultValue = "0") Integer date, @RequestParam(value = "limit", defaultValue = "20") Integer limit) {
if (date == 0) {
date = now();
}
/*
{
"status": 0,
"data": {
"categories": ["苹果","三星", "华为","oppo","vivo","小米98"],
"series": [{
"name": "商品品牌",
"data": [5101,6100,6371,5682,5430,9533]
}]
}
}
*/
//调用service获取品牌交易额
List<ProductStats> productStatsList = productStatsService.getProductStatsByTm(date, limit);
//对查询结果进行遍历,获取品牌以及品牌交易额,将品牌以及品牌交易额分别封装到独立的list集合中
List<String> tmList = new ArrayList<>();
List<BigDecimal> amountList = new ArrayList<>();
for (ProductStats productStats : productStatsList) {
tmList.add(productStats.getTm_name());
amountList.add(productStats.getOrder_amount());
}
String json = "{" +
" \"status\": 0," +
" \"data\": {" +
" \"categories\": [\"" + StringUtils.join(tmList, "\",\"") + "\"]," +
" \"series\": [{" +
" \"name\": \"商品品牌\"," +
" \"data\": [" + StringUtils.join(amountList, ",") + "]" +
" }]" +
" }" +
" }";
return json;
}
// @RequestMapping("/category3")
// public String getProductStatsByCategory3(@RequestParam(value = "date", defaultValue = "0") Integer date, @RequestParam(value = "limit", defaultValue = "20") Integer limit) {
// if (date == 0) {
// date = now();
// }
// /*
// {"status": 0,"data": [{"name": "PC","value": 97},{"name": "iOS","value": 50,}]}
// */
// //调用service获取品牌交易额
// List<ProductStats> productStatsList = productStatsService.getProductStatsByCategory3(date, limit);
//
// //对查询结果进行遍历,获取品牌以及品牌交易额,将品牌以及品牌交易额分别封装到独立的list集合中
// StringBuilder json = new StringBuilder("{\"status\": 0,\"data\": [");
//
// for (int i = 0; i < productStatsList.size(); i++) {
// ProductStats productStats = productStatsList.get(i);
// json.append("{\"name\": \"" + productStats.getCategory3_name() + "\",\"value\": " + productStats.getOrder_amount() + "}");
// if (i < productStatsList.size() - 1) {
// json.append(",");
// }
// }
//
// json.append("]}");
//
// return json.toString();
// }
@RequestMapping("/category3")
public Object getProductStatsByCategory3(@RequestParam(value = "date", defaultValue = "0") Integer date, @RequestParam(value = "limit", defaultValue = "20") Integer limit) {
if (date == 0) {
date = now();
}
/*
{"status": 0,"data": [{"name": "PC","value": 97},{"name": "iOS","value": 50,}]}
*/
//调用service获取品牌交易额
List<ProductStats> productStatsList = productStatsService.getProductStatsByCategory3(date, limit);
HashMap resMap = new HashMap();
List dateList = new ArrayList();
for (ProductStats productStats : productStatsList) {
HashMap dateMap = new HashMap();
dateMap.put("name", productStats.getCategory3_name());
dateMap.put("value", productStats.getOrder_amount());
dateList.add(dateMap);
}
resMap.put("status", 0);
resMap.put("data", dateList);
return resMap;
}
@RequestMapping("/spu")
public String getProductStatsBySPU(@RequestParam(value = "date", defaultValue = "0") Integer date, @RequestParam(value = "limit", defaultValue = "20") Integer limit) {
if (date == 0) {
date = now();
}
//调用service获取品牌交易额
List<ProductStats> productStatsList = productStatsService.getProductStatsBySPU(date, limit);
//对查询结果进行遍历,获取品牌以及品牌交易额,将品牌以及品牌交易额分别封装到独立的list集合中
StringBuilder json = new StringBuilder("{ " +
" \"status\": 0, " +
" \"data\": { " +
" \"columns\": [ " +
" { " +
" \"name\": \"商品名称\", " +
" \"id\": \"name\" " +
" }, " +
" { " +
" \"name\": \"交易额\", " +
" \"id\": \"amount\" " +
" }, " +
" { " +
" \"name\": \"订单数\", " +
" \"id\": \"ct\" " +
" } " +
" ], " +
" \"rows\": [");
for (int i = 0; i < productStatsList.size(); i++) {
ProductStats productStats = productStatsList.get(i);
// System.out.println(productStats.getSpu_name()+":"+productStats.getOrder_amount()+":"+productStats.getOrder_ct());
json.append("{\"name\": \"" + productStats.getSpu_name() + "\",\"amount\": " + productStats.getOrder_amount() + ",\"ct\": " + productStats.getOrder_ct() + "}");
if (i < productStatsList.size() - 1) {
json.append(",");
}
}
json.append("]}}");
return json.toString();
}
@RequestMapping("province")
public String getProvinceStats(@RequestParam(value = "date", defaultValue = "0") Integer date) {
if (date == 0) {
date = now();
}
List<ProvinceStats> provinceStatsList = provinceStatsService.getProvinceStats(date);
StringBuilder json = new StringBuilder("{\"status\": 0,\"data\": {\"mapData\": [");
for (int i = 0; i < provinceStatsList.size(); i++) {
ProvinceStats provinceStats = provinceStatsList.get(i);
json.append("{\"name\": \"" + provinceStats.getProvince_name() + "\",\"value\": " + provinceStats.getOrder_amount() + "}");
if (i < provinceStatsList.size() - 1) {
json.append(",");
}
}
json.append("],\"valueName\": \"省份交易额\"}}");
return json.toString();
}
//获取当前日期
private Integer now() {
String yyyyMMdd = DateFormatUtils.format(new Date(), "yyyyMMdd");

83
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/controller/format.json
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@ -0,0 +1,83 @@
//横向柱状图
{
"status": 0,
"data": {
"categories": [
"苹果",
"小米",
"华为",
"Redmi",
"索芙特"
],
"series": [
{
"name": "商品品牌",
"data": [
258910.00,
153974.00,
127240.00,
24681.00,
3439.00
]
}
]
}
}
//饼状图
{
"status": 0,
"msg": "",
"data": [
{
"name": "PC",
"value": 97
},
{
"name": "iOS",
"value": 50
}
]
}
//轮播表格
{
"status": 0,
"data": {
"columns": [
{
"name": "商品名称",
"id": "name"
},
{
"name": "交易额",
"id": "amount"
},
{
"name": "订单数",
"id": "ct"
}
],
"rows": [
{
"name": "北京总部",
"amount": 1,
"cr": 1
},
{
"name": "北京总部",
"amount": 1,
"cr": 1
}
]
}
}
//色彩地图
{"status": 0,"data": {"mapData": [
{"name": "北京","value": 8985,},
{
"name": "天津",
"value": 8616,
},
],"valueName": "省份交易额",}}

14
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/mapper/ProductStatsMapper.java
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@ -26,4 +26,18 @@ public interface ProductStatsMapper {
List<ProductStats> selectProductStatsByTm(@Param("date") Integer date, @Param("limit") Integer limit);
@Select("select category3_id,category3_name,sum(order_amount) order_amount from product_stats " +
"where toYYYYMMDD(stt)=#{date} " +
"group by category3_id,category3_name " +
"having order_amount>0 " +
"order by order_amount desc limit #{limit} ")
List<ProductStats> selectProductStatsByCategory3(@Param("date") Integer date, @Param("limit") Integer limit);
@Select("select spu_id, spu_name, sum(order_amount) order_amount, sum(order_ct) order_ct " +
"from product_stats " +
"where toYYYYMMDD(stt) = #{date} " +
"group by spu_id, spu_name " +
"having order_amount > 0 " +
"order by order_amount desc, order_ct desc limit #{limit} ")
List<ProductStats> selectProductStatsBySPU(@Param("date") Integer date, @Param("limit") Integer limit);
}

31
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/mapper/ProvinceStatsMapper.java
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@ -1,11 +1,26 @@
package com.atguigu.rtgmall.mapper;
import com.atguigu.rtgmall.beans.ProvinceStats;
import org.apache.ibatis.annotations.Select;
import java.util.List;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall.mapper
*@Author: markilue
*@CreateTime: 2023-05-12 21:29
*@Description: TODO
*@Version: 1.0
*/
public class ProvinceStatsMapper {
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall.mapper
*@Author: markilue
*@CreateTime: 2023-05-12 21:29
*@Description: TODO 地区统计Mapper
*@Version: 1.0
*/
public interface ProvinceStatsMapper {
@Select("select province_id,province_name,sum(order_amount) order_amount " +
"from province_stats " +
"where toYYYYMMDD(stt) = #{date} " +
"group by province_id, province_name")
List<ProvinceStats> selectProvinceStats(Integer date);
}

42
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/mapper/selectSQL.sql
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@ -1,9 +1,37 @@
-- 品牌销售类
select tm_id,tm_name,sum(order_amount) order_amount from product_stats
where toYYYYMMDD(stt)=20230511
group by tm_id,tm_name
having order_amount>0
order by order_amount desc limit 5;
select tm_id, tm_name, sum(order_amount) order_amount
from product_stats
where toYYYYMMDD(stt) = 20230511
group by tm_id, tm_name
having order_amount > 0
order by order_amount desc limit 5;
-- 品类销售额占比
select category3_id, category3_name, sum(order_amount) order_amount
from product_stats
where toYYYYMMDD(stt) = 20230511
group by category3_id, category3_name
having order_amount > 0
order by order_amount desc limit 5;
-- spu销售额
select spu_id, spu_name, sum(order_amount) order_amount, sum(order_ct) order_ct
from product_stats
where toYYYYMMDD(stt) = 20230511
group by spu_id, spu_name
having order_amount > 0
order by order_amount desc, order_ct desc limit 5;
select spu_id, spu_name, sum(order_amount) order_amount, sum(order_ct) order_ct
from product_stats
where toYYYYMMDD(stt) = 20230511
group by spu_id, spu_name
having order_amount > 0
order by order_amount desc, order_ct desc limit 5
-- 省份交易额
select province_id,province_name,sum(order_amount) order_amount
from province_stats
where toYYYYMMDD(stt) = 20230511
group by province_id, province_name

8
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/service/ProductStatsService.java
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@ -18,5 +18,13 @@ public interface ProductStatsService {
//获取品牌成交额
List<ProductStats> getProductStatsByTm(Integer date, Integer limit);
List<ProductStats> getProductStatsByCategory3(Integer date, Integer limit);
//获取spu成交额
List<ProductStats> getProductStatsBySPU(Integer date, Integer limit);
}

7
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/service/ProvinceStatsService.java
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@ -1,5 +1,9 @@
package com.atguigu.rtgmall.service;
import com.atguigu.rtgmall.beans.ProvinceStats;
import java.util.List;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall.service
@ -9,4 +13,7 @@ package com.atguigu.rtgmall.service;
*@Version: 1.0
*/
public interface ProvinceStatsService {
//地区交易额
List<ProvinceStats> getProvinceStats(Integer date);
}

10
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/service/impl/ProductStatsServiceImpl.java
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@ -30,5 +30,15 @@ public class ProductStatsServiceImpl implements ProductStatsService {
return productStatsMapper.selectProductStatsByTm(date, limit);
}
@Override
public List<ProductStats> getProductStatsByCategory3(Integer date, Integer limit) {
return productStatsMapper.selectProductStatsByCategory3(date,limit);
}
@Override
public List<ProductStats> getProductStatsBySPU(Integer date, Integer limit) {
return productStatsMapper.selectProductStatsBySPU(date, limit);
}
}

36
Big_data_example/rt-gmall-parent/gmall-publisher/src/main/java/com/atguigu/rtgmall/service/impl/ProvinceStatsServiceImpl.java
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@ -1,11 +1,31 @@
package com.atguigu.rtgmall.service.impl;
import com.atguigu.rtgmall.beans.ProvinceStats;
import com.atguigu.rtgmall.mapper.ProvinceStatsMapper;
import com.atguigu.rtgmall.service.ProvinceStatsService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import java.util.List;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall.service.impl
*@Author: markilue
*@CreateTime: 2023-05-12 21:32
*@Description: TODO
*@Version: 1.0
*/
public class ProvinceStatsServiceImpl {
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall.service.impl
*@Author: markilue
*@CreateTime: 2023-05-12 21:32
*@Description: TODO 地区统计接口实现类
*@Version: 1.0
*/
@Service
public class ProvinceStatsServiceImpl implements ProvinceStatsService {
@Autowired
private ProvinceStatsMapper provinceStatsMapper;
@Override
public List<ProvinceStats> getProvinceStats(Integer date) {
return provinceStatsMapper.selectProvinceStats(date);
}
}

27
Big_data_example/rt-gmall-parent/gmall-publisher/src/test/java/com/atguigu/rtgmall/test1.java
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@ -1,11 +1,24 @@
package com.atguigu.rtgmall;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall
*@Author: markilue
*@CreateTime: 2023-05-12 21:07
*@Description: TODO
*@Version: 1.0
*/
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.rtgmall
*@Author: markilue
*@CreateTime: 2023-05-12 21:07
*@Description: TODO
*@Version: 1.0
*/
public class test1 {
public static void main(String[] args) {
String date ="20230511";
String limit ="5";
String sql = "select spu_id,spu_name,sum(order_amount) order_amount,sum(order_ct) order_ct from product_stats " +
"where toYYYYMMDD(stt)="+date+" " +
"group by spu_id,spu_name " +
"having order_amount > 0 " +
"order by order_amount desc,order_ct desc limit "+limit+" ";
System.out.println(sql);
}
}

6
Big_data_example/rt-gmall-parent/gmall-realtime/src/main/java/com/atguigu/gmall/realtime/app/dwm/OrderWideApp.java
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@ -211,9 +211,9 @@ public class OrderWideApp {
new DimAsyncFunction<OrderWide>("DIM_BASE_PROVINCE") {
@Override
public void join(OrderWide orderWide, JSONObject dimJsonObj) throws Exception {
orderWide.setProvince_3166_2_code(dimJsonObj.getString("NAME"));
orderWide.setProvince_area_code(dimJsonObj.getString("ISO_3166_2"));
orderWide.setProvince_name(dimJsonObj.getString("AREA_CODE"));
orderWide.setProvince_3166_2_code(dimJsonObj.getString("ISO_3166_2"));
orderWide.setProvince_area_code(dimJsonObj.getString("AREA_CODE"));
orderWide.setProvince_name(dimJsonObj.getString("NAME"));
orderWide.setProvince_iso_code(dimJsonObj.getString("ISO_CODE"));
}

106
Big_data_example/rt-gmall-parent/gmall-realtime/src/main/java/com/atguigu/gmall/realtime/app/dws/KeywordStatsApp.java
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@ -1,11 +1,103 @@
package com.atguigu.gmall.realtime.app.dws;
import com.atguigu.gmall.realtime.app.func.KeywordUDTF;
import com.atguigu.gmall.realtime.beans.GmallConstant;
import com.atguigu.gmall.realtime.beans.KeywordStats;
import com.atguigu.gmall.realtime.utils.ClickhouseUtils;
import com.atguigu.gmall.realtime.utils.MyKafkaUtils;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableResult;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.gmall.realtime.app.dws
*@Author: markilue
*@CreateTime: 2023-05-12 14:27
*@Description: TODO
*@Version: 1.0
*/
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.gmall.realtime.app.dws
*@Author: markilue
*@CreateTime: 2023-05-12 14:27
*@Description: TODO 关键词统计DWS
*@Version: 1.0
*/
public class KeywordStatsApp {
public static void main(String[] args) throws Exception {
//TODO 1.基本环境准备
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
EnvironmentSettings settings = EnvironmentSettings.newInstance().inStreamingMode().build();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, settings);
env.setParallelism(4);
//TODO 2.检查点设置
env.enableCheckpointing(5000L);
env.getCheckpointConfig().setCheckpointTimeout(5000L);
env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, 3000L));
env.setStateBackend(new FsStateBackend("hdfs://Ding202:8020/rt_gmall/gmall"));
System.setProperty("HADOOP_USER_NAME", "dingjiawen");
//TODO 2.k注册自定义的UDTF函数
tableEnv.createTemporarySystemFunction("ik_analyze", KeywordUDTF.class);
//TODO 3.从kafka中读取数据
String topic = "dwd_page_log";
String groupId = "keyword_stats_app_group";
String createSQl = "create table page_view" +
"(" +
" common MAP<STRING, STRING>," +
" page MAP<STRING, STRING>," +
" ts BIGINT," +
" rowtime AS TO_TIMESTAMP(FROM_UNIXTIME(ts/1000,'yyyy-MM-dd HH:mm:ss'))," +
" WATERMARK FOR rowtime as rowtime - INTERVAL '3' SECOND" +
")" +
" WITH (" + MyKafkaUtils.getKafkaDDL(topic, groupId) + ")";
tableEnv.executeSql(createSQl);
//TODO 4.将动态表中表示搜索行为的记录过滤出来
String filterSQL = "select " +
" page['item'] fullword,rowtime " +
"from" +
" page_view " +
"where " +
" page['page_id'] = 'good_list' and page['item'] is not null ";
// System.out.println(filterSQL);
Table fullwordTable = tableEnv.sqlQuery(filterSQL);
//TODO 5.使用自定义的UDTF函数 对关键词进行拆分
//相当于将fullwordTable表进行了注册
Table keywordTable = tableEnv.sqlQuery("select rowtime ,keyword from " + fullwordTable + ", LATERAL TABLE(ik_analyze(fullword)) AS T(keyword)");
//TODO 6.分组开窗 聚合计算
String selectSQL ="select DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as stt," +
" DATE_FORMAT(TUMBLE_END(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as edt," +
" keyword," +
" count(*) ct," +
" '" + GmallConstant.KEYWORD_SEARCH + "' source," +
" UNIX_TIMESTAMP() * 1000 ts " +
"from " + keywordTable + " " +
"group by " +
" TUMBLE(rowtime, INTERVAL '10' SECOND)," +
" keyword";
System.out.println(selectSQL);
Table resTable = tableEnv.sqlQuery(selectSQL);
//TODO 7.将表转换为流
DataStream<KeywordStats> keywordStatsDS = tableEnv.toAppendStream(resTable, KeywordStats.class);
keywordStatsDS.print(">>>>");
//TODO 8.将流中数据写入CK
keywordStatsDS.addSink(
ClickhouseUtils.getJdbcSink("insert into keyword_stats(keyword,ct,source,stt,edt,ts) values(?,?,?,?,?,?)")
);
env.execute();
}
}

80
Big_data_example/rt-gmall-parent/gmall-realtime/src/main/java/com/atguigu/gmall/realtime/app/dws/tablesql.sql
View File

@ -1,3 +1,4 @@
-- 省份订单表
create table order_wide
(
province_id BIGINT,
@ -19,7 +20,7 @@ create table order_wide
'scan.startup.mode' = 'latest-offset',
'format' = 'json')
-- 开窗聚合计算
select DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as stt,
DATE_FORMAT(TUMBLE_END(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as edt,
province_id,
@ -29,11 +30,84 @@ select DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:m
province_3166_2_code iso_3166_2,
count(distinct order_id) order_count,
sum(split_total_amount) order_amount,
UNIX_TIMESTAMP() * 1000 ts
UNIX_TIMESTAMP() * 1000 ts
from order_wide
group by TUMBLE(rowtime, INTERVAL '10' SECOND),
province_id,
province_name,
province_area_code,
province_iso_code,
province_3166_2_code
province_3166_2_code
-- 关键词主题统计动态表
create table page_view
(
common MAP<STRING,
STRING>,
page MAP<STRING,
STRING>,
ts BIGINT,
rowtime AS TO_TIMESTAMP(FROM_UNIXTIME(ts/1000,'yyyy-MM-dd HH:mm:ss')),
WATERMARK FOR rowtime as rowtime - INTERVAL '3' SECOND
)
WITH (
'connector' = 'kafka',
'topic' = 'dwm_page_log',
'properties.bootstrap.servers' = 'Ding202:9092,Ding203:9092,Ding204:9092',
'properties.group.id' = 'keyword_stats_app_group',
'scan.startup.mode' = 'latest-offset',
'format' = 'json')
-- 将动态表中表示搜索行为的记录过滤出来
select page['item'] fullword,
rowtime
from page_view
where page['page_id'] = 'good_list'
and page['item'] is not null
--使用自定义UDTF函数 对搜索关键词进行拆分
/*
搜索的内容: 小米插线板自营包邮
fullword rowtime
小米插线板自营包邮 20210814
T表示一个临时表的名字
select rowtime ,keyword from fullwordTable,LATERAL TABLE(函数名(fullword)) AS T(keyword)
拆分后的效果: 小米 插线板 自营 包邮
keyword rowtime
小米 20210814
插线板 20210814
自营 20210814
包邮 20210814
*
*/
select rowtime, keyword
from fullwordTable,
LATERAL TABLE(函数名(fullword)) AS T(keyword)
-- 分组开窗聚合计算
select DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as stt,
DATE_FORMAT(TUMBLE_END(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as edt,
keyword,
count(*) ct,
'KEYWORD_SEARCH' source,
UNIX_TIMESTAMP() * 1000 ts
from order_wide
group by TUMBLE(rowtime, INTERVAL '10' SECOND),
keyword
select DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as stt,
DATE_FORMAT(TUMBLE_END(rowtime, INTERVAL '10' SECOND), 'yyyy-MM-dd HH:mm:ss') as edt,
keyword,
count(*) ct,
'SEARCH' source,
UNIX_TIMESTAMP() * 1000 ts
from UnnamedTable$1group by TUMBLE(rowtime, INTERVAL '10' SECOND), keyword

43
Big_data_example/rt-gmall-parent/gmall-realtime/src/main/java/com/atguigu/gmall/realtime/app/func/KeywordUDTF.java
View File

@ -1,11 +1,38 @@
package com.atguigu.gmall.realtime.app.func;
import com.atguigu.gmall.realtime.utils.KeywordUtils;
import org.apache.flink.table.annotation.DataTypeHint;
import org.apache.flink.table.annotation.FunctionHint;
import org.apache.flink.table.functions.TableFunction;
import org.apache.flink.types.Row;
import java.util.List;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.gmall.realtime.app.func
*@Author: markilue
*@CreateTime: 2023-05-12 14:19
*@Description: TODO
*@Version: 1.0
*/
public class KeywordUDTF {
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.gmall.realtime.app.func
*@Author: markilue
*@CreateTime: 2023-05-12 14:19
*@Description: TODO 自定义flinkUDTF函数 把keyword拆分成多个冠检测
*@Version: 1.0
*/
@FunctionHint(output = @DataTypeHint("ROW<word STRING>")) //返回值有几列,列的类型是什么;Row表示返回值的结果
public class KeywordUDTF extends TableFunction<Row> {
//从参数的个数和类型,判断调用哪一个eval函数
public void eval(String text) {
List<String> keywordList = KeywordUtils.analyze(text);
for (String keyword : keywordList) {
collect(Row.of(keyword));
}
}
// public void eval(String str,int a) {
// for (String s : str.split(" ")) {
// // use collect(...) to emit a row
// collect(Row.of(s, s.length()));
// }
// }
}

28
Big_data_example/rt-gmall-parent/gmall-realtime/src/main/java/com/atguigu/gmall/realtime/beans/KeywordStats.java
View File

@ -1,11 +1,25 @@
package com.atguigu.gmall.realtime.beans;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
/**
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.gmall.realtime.beans
*@Author: markilue
*@CreateTime: 2023-05-12 15:24
*@Description: TODO
*@Version: 1.0
*/
*@BelongsProject: rt-gmall-parent
*@BelongsPackage: com.atguigu.gmall.realtime.beans
*@Author: markilue
*@CreateTime: 2023-05-12 15:24
*@Description: TODO 关键词实体类
*@Version: 1.0
*/
@Data
@AllArgsConstructor
@NoArgsConstructor
public class KeywordStats {
private String keyword;
private Long ct;
private String source;
private String stt;
private String edt;
private Long ts;
}

1
Big_data_example/rt-gmall-parent/gmall-realtime/src/main/java/com/atguigu/gmall/realtime/utils/KeywordUtils.java
View File

@ -44,6 +44,5 @@ public class KeywordUtils {
public static void main(String[] args) {
String text = "Apple iPhoneXSMax (A2104) 256GB 深空灰色 移动联通电信4G手机 双卡双待";
System.out.println(KeywordUtils.analyze(text));
}
}