视频地址：尚硅谷大数据项目《在线教育之实时数仓》_哔哩哔哩_bilibili
目录
第10章 数仓开发之DWS层
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第10章 数仓开发之DWS层
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第10章 数仓开发之DWS层
设计要点：
（1）DWS层的设计参考指标体系。
（2）DWS层表名的命名规范为dws_数据域_统计粒度_业务过程_统计周期（window）。
注：window 表示窗口对应的时间范围。
10.1 流量域来源关键词粒度页面浏览各窗口汇总表
10.1.1 主要任务
从 Kafka 页面浏览明细主题读取数据，过滤搜索行为，使用自定义 UDTF（一进多出）函数对搜索内容分词。统计各窗口各关键词出现频次，写入 ClickHouse。
10.1.2 思路分析
尚硅谷大数据项目之在线教育数仓\尚硅谷大数据项目之在线教育数仓-3实时\资料\13.总线矩阵及指标体系
在线教育实时指标体系.xlsx
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DwsTrafficSourceKeywordPageViewWindow
//TODO 1 创建环境设置状态后端
//TODO 2 自定义拆词函数
//TODO 3 读取kafka中的page_log数据
//TODO 4 过滤数据得到搜索的关键字
//TODO 5 使用自定义函数对关键字拆词
//TODO 6 分组开窗合并计算
//TODO 7 转换为流
//TODO 8 写出到clickHouse中
//TODO 9 运行任务
package com.atguigu.edu.realtime.util;
import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;
import java.io.IOException;
import java.io.StringReader;
import java.util.ArrayList;
/**
* @author yhm
* @create 2023-04-25 16:05
*/
public class KeyWordUtil {
public static ArrayList<String> analyze(String text) {
StringReader reader = new StringReader(text);
IKSegmenter ikSegmenter = new IKSegmenter(reader, true);
ArrayList<String> strings = new ArrayList<>();
try {
Lexeme lexeme = null;
while ((lexeme = ikSegmenter.next()) != null) {
String keyWord = lexeme.getLexemeText();
strings.add(keyWord);
}
} catch (IOException e) {
e.printStackTrace();
}
return strings;
}
public static void main(String[] args) {
String s = "Apple iPhoneXSMax (A2104) 256GB 深空灰色 移动联通电信4G手机 双卡双待";
ArrayList<String> strings = analyze(s);
System.out.println(strings);
}
}P068
User-defined Functions | Apache Flink
DwsTrafficSourceKeywordPageViewWindow
//TODO 1 创建环境设置状态后端
//TODO 2 自定义拆词函数
//TODO 3 读取kafka中的page_log数据
//TODO 4 过滤数据得到搜索的关键字
//TODO 5 使用自定义函数对关键字拆词
//TODO 6 分组开窗合并计算
//TODO 7 转换为流
//TODO 8 写出到clickHouse中
//TODO 9 运行任务
P069
package com.atguigu.edu.realtime.app.dws;
import com.atguigu.edu.realtime.app.func.KeyWordUDTF;
import com.atguigu.edu.realtime.bean.KeywordBean;
import com.atguigu.edu.realtime.common.EduConfig;
import com.atguigu.edu.realtime.common.EduConstant;
import com.atguigu.edu.realtime.util.ClickHouseUtil;
import com.atguigu.edu.realtime.util.EnvUtil;
import com.atguigu.edu.realtime.util.KafkaUtil;
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.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import java.sql.PreparedStatement;
import java.sql.SQLException;
/**
* @author yhm
* @create 2023-04-25 16:01
*/
public class DwsTrafficSourceKeywordPageViewWindow {
public static void main(String[] args) throws Exception {
//TODO 1 创建环境设置状态后端
StreamExecutionEnvironment env = EnvUtil.getExecutionEnvironment(1);
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
//TODO 2 自定义拆词函数
tableEnv.createTemporarySystemFunction("ik_analyze", new KeyWordUDTF());
//TODO 3 读取kafka中的page_log数据
String topicName = "dwd_traffic_page_log";
String groupId = "dws_traffic_source_keyword_page_view_window";
tableEnv.executeSql("create table page_log(\n" +
" common map<String,String>,\n" +
" page map<String,String>,\n" +
" ts bigint, \n" +
" row_time as to_timestamp(from_unixtime(ts/1000,'yyyy-MM-dd HH:mm:ss')), \n" +
" WATERMARK FOR row_time AS row_time - INTERVAL '3' SECOND" +
")" + KafkaUtil.getKafkaDDL(topicName, groupId));
//TODO 4 过滤数据得到搜索的关键字
//① page 字段下 item 字段不为 null；
//② page 字段下 last_page_id 为 search；
//③ page 字段下 item_type 为 keyword。
Table searchTable = tableEnv.sqlQuery("select \n" +
" page['item'] full_word,\n" +
" row_time\n" +
"from page_log\n" +
"where page['item'] is not null \n" +
"and page['item_type'] ='keyword'\n" +
// "and page['last_page_id'] = 'search'" +
"");
tableEnv.createTemporaryView("search_table", searchTable);
//TODO 5 使用自定义函数对关键字拆词
Table splitTable = tableEnv.sqlQuery("select \n" +
" keyword,\n" +
" row_time\n" +
"from search_table ,\n" +
"lateral table (ik_analyze(full_word)) as t(keyword)");
tableEnv.createTemporaryView("split_table", splitTable);
tableEnv.executeSql("select * from split_table").print();
//TODO 6 分组开窗合并计算
//TODO 7 转换为流
//TODO 8 写出到clickHouse中
//TODO 9 运行任务
}
}P070
Window Aggregation | Apache Flink
P071
10.1.4 ClickHouse 建表语句
drop table if exists dws_traffic_source_keyword_page_view_window;
create table if not exists dws_traffic_source_keyword_page_view_window
(
stt DateTime,
edt DateTime,
source String,
keyword String,
keyword_count UInt64,
ts UInt64
) engine = ReplacingMergeTree(ts)
partition by toYYYYMMDD(stt)
order by (stt, edt, source, keyword);
package com.atguigu.edu.realtime.util;
import com.atguigu.edu.realtime.bean.KeywordBean;
import com.atguigu.edu.realtime.bean.TransientSink;
import com.atguigu.edu.realtime.common.EduConfig;
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;
/**
* @author yhm
* @create 2023-04-25 18:23
*/
public class ClickHouseUtil {
// 设计泛型 通过传入的数据类型自动补充sql 写出到clickhouse
public static <T> SinkFunction<T> getJdbcSink(String sql) {
return JdbcSink.<T>sink(sql, new JdbcStatementBuilder<T>() {
@Override
public void accept(PreparedStatement preparedStatement, T obj) throws SQLException {
// T是泛型，明文是不知道什么类型的，需要使用反射获取
Field[] declaredFields = obj.getClass().getDeclaredFields();
int skip = 0;
for (int i = 0; i < declaredFields.length; i++) {
Field field = declaredFields[i];
field.setAccessible(true);
// 获取属性的注解
TransientSink annotation = field.getAnnotation(TransientSink.class);
if (annotation != null) {
skip++;
continue;
}
// 使用类模板的属性名 get对象 获取值
try {
Object o = field.get(obj);
preparedStatement.setObject(i + 1 - skip, o);
} catch (IllegalAccessException e) {
e.printStackTrace();
}
}
}
}, JdbcExecutionOptions.builder()
.withBatchIntervalMs(5000L)
.withBatchSize(5)
.build(),
new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
.withUrl(EduConfig.CLICKHOUSE_URL)
.withDriverName(EduConfig.CLICKHOUSE_DRIVER)
.build());
}
}[atguigu@node001 ~]$ sudo systemctl start clickhouse-server
[atguigu@node001 ~]$ clickhouse-client -mnode001 :) SHOW DATABASES;
node001 :) CREATE DATABASE edu_realtime;
node001 :) SHOW DATABASES;node001 :) USE edu_realtime;
node001 :) SHOW TABLES FROM edu_realtime;node001 :) SELECT * FROM dws_traffic_source_keyword_page_view_window;
[atguigu@node001 ~]$ sudo systemctl start clickhouse-server
[atguigu@node001 ~]$ clickhouse-client -m
ClickHouse client version 20.4.5.36 (official build).
Connecting to localhost:9000 as user default.
Connected to ClickHouse server version 20.4.5 revision 54434.
node001 :) CREATE DATABASE edu_realtime;
CREATE DATABASE edu_realtime
Ok.
0 rows in set. Elapsed: 0.044 sec.
node001 :) SHOW DATABASES;
SHOW DATABASES
┌─name───────────────────────────┐
│ _temporary_and_external_tables │
│ default │
│ edu_realtime │
│ system │
└────────────────────────────────┘
4 rows in set. Elapsed: 0.031 sec.
node001 :) SHOW TABLES FROM edu_realtime;
SHOW TABLES FROM edu_realtime
Ok.
0 rows in set. Elapsed: 0.028 sec.
node001 :) use edu_realtime;
USE edu_realtime
Ok.
0 rows in set. Elapsed: 0.002 sec.
node001 :) create table if not exists dws_traffic_source_keyword_page_view_window
:-] (
:-] stt DateTime,
:-] edt DateTime,
:-] source String,
:-] keyword String,
:-] keyword_count UInt64,
:-] ts UInt64
:-] ) engine = ReplacingMergeTree(ts)
:-] partition by toYYYYMMDD(stt)
:-] order by (stt, edt, source, keyword);
CREATE TABLE IF NOT EXISTS dws_traffic_source_keyword_page_view_window
(
`stt` DateTime,
`edt` DateTime,
`source` String,
`keyword` String,
`keyword_count` UInt64,
`ts` UInt64
)
ENGINE = ReplacingMergeTree(ts)
PARTITION BY toYYYYMMDD(stt)
ORDER BY (stt, edt, source, keyword)
Ok.
0 rows in set. Elapsed: 0.016 sec.
node001 :) use edu_realtime;
USE edu_realtime
Ok.
0 rows in set. Elapsed: 0.002 sec.
node001 :) SHOW TABLES FROM edu_realtime;
SHOW TABLES FROM edu_realtime
┌─name────────────────────────────────────────┐
│ dws_traffic_source_keyword_page_view_window │
└─────────────────────────────────────────────┘
1 rows in set. Elapsed: 0.007 sec.
node001 :) SELECT * FROM dws_traffic_source_keyword_page_view_window;
SELECT *
FROM dws_traffic_source_keyword_page_view_window
┌─────────────────stt─┬─────────────────edt─┬─source─┬─keyword─┬─keyword_count─┬────────────ts─┐
│ 2022-02-21 23:58:30 │ 2022-02-21 23:58:40 │ SEARCH │ java │ 19 │ 1699513951000 │
│ 2022-02-21 23:58:30 │ 2022-02-21 23:58:40 │ SEARCH │ 前端 │ 27 │ 1699513951000 │
│ 2022-02-21 23:58:50 │ 2022-02-21 23:59:00 │ SEARCH │ 前端 │ 14 │ 1699513951000 │
│ 2022-02-21 23:59:00 │ 2022-02-21 23:59:10 │ SEARCH │ 大 │ 19 │ 1699513951000 │
│ 2022-02-21 23:59:00 │ 2022-02-21 23:59:10 │ SEARCH │ 数据库 │ 4 │ 1699513951000 │
└─────────────────────┴─────────────────────┴────────┴─────────┴───────────────┴───────────────┘
┌─────────────────stt─┬─────────────────edt─┬─source─┬─keyword─┬─keyword_count─┬────────────ts─┐
│ 2022-02-21 23:59:50 │ 2022-02-22 00:00:00 │ SEARCH │ hadoop │ 19 │ 1699513951000 │
│ 2022-02-21 23:59:50 │ 2022-02-22 00:00:00 │ SEARCH │ python │ 19 │ 1699513951000 │
│ 2022-02-21 23:59:50 │ 2022-02-22 00:00:00 │ SEARCH │ 前端 │ 39 │ 1699513951000 │
│ 2022-02-21 23:59:50 │ 2022-02-22 00:00:00 │ SEARCH │ 数据库 │ 19 │ 1699513951000 │
└─────────────────────┴─────────────────────┴────────┴─────────┴───────────────┴───────────────┘
┌─────────────────stt─┬─────────────────edt─┬─source─┬─keyword─┬─keyword_count─┬────────────ts─┐
│ 2022-02-21 23:59:00 │ 2022-02-21 23:59:10 │ SEARCH │ 数据 │ 19 │ 1699513951000 │
│ 2022-02-21 23:59:20 │ 2022-02-21 23:59:30 │ SEARCH │ java │ 33 │ 1699513951000 │
│ 2022-02-21 23:59:30 │ 2022-02-21 23:59:40 │ SEARCH │ flink │ 20 │ 1699513951000 │
│ 2022-02-21 23:59:30 │ 2022-02-21 23:59:40 │ SEARCH │ java │ 20 │ 1699513951000 │
│ 2022-02-21 23:59:30 │ 2022-02-21 23:59:40 │ SEARCH │ 前端 │ 19 │ 1699513951000 │
└─────────────────────┴─────────────────────┴────────┴─────────┴───────────────┴───────────────┘
┌─────────────────stt─┬─────────────────edt─┬─source─┬─keyword─┬─keyword_count─┬────────────ts─┐
│ 2022-02-22 00:00:10 │ 2022-02-22 00:00:20 │ SEARCH │ 多线程 │ 20 │ 1699513951000 │
└─────────────────────┴─────────────────────┴────────┴─────────┴───────────────┴───────────────┘
┌─────────────────stt─┬─────────────────edt─┬─source─┬─keyword─┬─keyword_count─┬────────────ts─┐
│ 2022-02-22 00:00:20 │ 2022-02-22 00:00:30 │ SEARCH │ flink │ 4 │ 1699513951000 │
│ 2022-02-22 00:00:20 │ 2022-02-22 00:00:30 │ SEARCH │ java │ 27 │ 1699513951000 │
└─────────────────────┴─────────────────────┴────────┴─────────┴───────────────┴───────────────┘
┌─────────────────stt─┬─────────────────edt─┬─source─┬─keyword─┬─keyword_count─┬────────────ts─┐
│ 2022-02-21 20:51:40 │ 2022-02-21 20:51:50 │ SEARCH │ 多线程 │ 1 │ 1699513903000 │
│ 2022-02-21 20:52:00 │ 2022-02-21 20:52:10 │ SEARCH │ hadoop │ 1 │ 1699449059000 │
│ 2022-02-21 20:53:10 │ 2022-02-21 20:53:20 │ SEARCH │ 多线程 │ 1 │ 1699447298000 │
│ 2022-02-21 20:54:20 │ 2022-02-21 20:54:30 │ SEARCH │ 大 │ 1 │ 1699447298000 │
│ 2022-02-21 20:54:20 │ 2022-02-21 20:54:30 │ SEARCH │ 数据 │ 1 │ 1699447298000 │
│ 2022-02-21 23:59:50 │ 2022-02-22 00:00:00 │ SEARCH │ 前端 │ 3 │ 1699449067000 │
│ 2022-02-21 23:59:50 │ 2022-02-22 00:00:00 │ SEARCH │ 数据库 │ 1 │ 1699449067000 │
└─────────────────────┴─────────────────────┴────────┴─────────┴───────────────┴───────────────┘
┌─────────────────stt─┬─────────────────edt─┬─source─┬─keyword─┬─keyword_count─┬────────────ts─┐
│ 2022-02-22 00:00:10 │ 2022-02-22 00:00:20 │ SEARCH │ 多线程 │ 2 │ 1699449067000 │
└─────────────────────┴─────────────────────┴────────┴─────────┴───────────────┴───────────────┘
1003 rows in set. Elapsed: 0.114 sec. Processed 1.00 thousand rows, 54.17 KB (8.79 thousand rows/s., 474.47 KB/s.)
node001 :) 
package com.atguigu.edu.realtime.app.dws;
import com.atguigu.edu.realtime.app.func.KeyWordUDTF;
import com.atguigu.edu.realtime.bean.KeywordBean;
import com.atguigu.edu.realtime.common.EduConfig;
import com.atguigu.edu.realtime.common.EduConstant;
import com.atguigu.edu.realtime.util.ClickHouseUtil;
import com.atguigu.edu.realtime.util.EnvUtil;
import com.atguigu.edu.realtime.util.KafkaUtil;
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.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import java.sql.PreparedStatement;
import java.sql.SQLException;
/**
* @author yhm
* @create 2023-04-25 16:01
*/
public class DwsTrafficSourceKeywordPageViewWindow {
public static void main(String[] args) throws Exception {
//TODO 1 创建环境设置状态后端
StreamExecutionEnvironment env = EnvUtil.getExecutionEnvironment(1);
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
//TODO 2 自定义拆词函数
tableEnv.createTemporarySystemFunction("ik_analyze", new KeyWordUDTF());
//TODO 3 读取kafka中的page_log数据
String topicName = "dwd_traffic_page_log";
String groupId = "dws_traffic_source_keyword_page_view_window";
tableEnv.executeSql("create table page_log(\n" +
" common map<String,String>,\n" +
" page map<String,String>,\n" +
" ts bigint, \n" +
" row_time as to_timestamp(from_unixtime(ts/1000,'yyyy-MM-dd HH:mm:ss')), \n" +
" WATERMARK FOR row_time AS row_time - INTERVAL '3' SECOND" +
")" + KafkaUtil.getKafkaDDL(topicName, groupId));
//TODO 4 过滤数据得到搜索的关键字
//① page 字段下 item 字段不为 null；
//② page 字段下 last_page_id 为 search；
//③ page 字段下 item_type 为 keyword。
Table searchTable = tableEnv.sqlQuery("select \n" +
" page['item'] full_word,\n" +
" row_time\n" +
"from page_log\n" +
"where page['item'] is not null \n" +
"and page['item_type'] ='keyword'\n" +
// "and page['last_page_id'] = 'search'" +
"");
tableEnv.createTemporaryView("search_table", searchTable);
//TODO 5 使用自定义函数对关键字拆词
Table splitTable = tableEnv.sqlQuery("select \n" +
" keyword,\n" +
" row_time\n" +
"from search_table ,\n" +
"lateral table (ik_analyze(full_word)) as t(keyword)");
tableEnv.createTemporaryView("split_table", splitTable);
//tableEnv.executeSql("select * from split_table").print();
//TODO 6 分组开窗合并计算
Table keywordBeanTable = tableEnv.sqlQuery("select \n" +
" date_format(TUMBLE_START(\n" +
" row_time, INTERVAL '10' second),'yyyy-MM-dd HH:mm:ss') stt,\n" +
" date_format(TUMBLE_END(\n" +
" row_time, INTERVAL '10' second),'yyyy-MM-dd HH:mm:ss') edt,\n" +
"\n" + "'" + EduConstant.KEYWORD_SEARCH + "' source," +
" 0 keywordLength,\n" +
" keyword,\n" +
" count(*) keyword_count,\n" +
" UNIX_TIMESTAMP()*1000 ts\n" +
"from split_table\n" +
"group by TUMBLE(row_time, INTERVAL '10' second),keyword");
//TODO 7 转换为流
DataStream<KeywordBean> keywordBeanDataStream = tableEnv.toDataStream(keywordBeanTable, KeywordBean.class);
keywordBeanDataStream.print();
//TODO 8 写出到clickHouse中
keywordBeanDataStream.addSink(ClickHouseUtil.<KeywordBean>getJdbcSink("insert into dws_traffic_source_keyword_page_view_window values(?,?,?,?,?,?)"));
//TODO 9 运行任务
env.execute();
}
}P072
package com.atguigu.edu.realtime.bean;
import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;
/**
* @author yhm
* @create 2023-04-25 18:37
*/
@Target(ElementType.FIELD)
@Retention(RetentionPolicy.RUNTIME)
public @interface TransientSink {
}P073
10.2 流量域版本-来源-地区-访客类别粒度页面浏览各窗口汇总表
10.2.1 主要任务
DWS 层是为 ADS 层服务的，通过对指标体系的分析，本节汇总表中需要有会话数、页面浏览数、浏览总时长、独立访客数、跳出会话数五个度量字段。我们的任务是统计这五个指标，并将数据写入 ClickHouse 汇总表。
P074
DwsTrafficVcSourceArIsNewPageViewWindow
TODO 1 ~ TODO 6
P075
DwsTrafficVcSourceArIsNewPageViewWindow
TODO 7 ~ TODO 9
P076
PhoenixUtil、public static <T> List<T> queryList(String sql, Class<T> clazz) {}
P077
DimUtil、public static JSONObject getDimInfoNoCache(String tableName, Tuple2<String, String>... columnNamesAn {}
[atguigu@node001 ~]$ start-hbase.sh
[atguigu@node001 ~]$ cd /opt/module/hbase/apache-phoenix-5.0.0-HBase-2.0-bin/
[atguigu@node001 apache-phoenix-5.0.0-HBase-2.0-bin]$ bin/sqlline.py node001:2181P078
DwsTrafficVcSourceArIsNewPageViewWindow
TODO 10
P079
10.2 流量域版本-来源-地区-访客类别粒度页面浏览各窗口汇总表
10.2.2 思路分析
4）旁路缓存优化
外部数据源的查询常常是流式计算的性能瓶颈。以本程序为例，每次查询都要连接 Hbase，数据传输需要做序列化、反序列化，还有网络传输，严重影响时效性。可以通过旁路缓存对查询进行优化。
P080
DimUtil、public static JSONObject getDimInfo(String tableName, Tuple2<String, String>... columnNamesAndValues) {}
P081
DimUtil 、public static void deleteCached(String tableName, String id) {}
[atguigu@node001 ~]$ redis-server ./my_redis.conf
[atguigu@node001 ~]$ redis-cli
127.0.0.1:6379> ping
PONG
127.0.0.1:6379> [atguigu@node001 ~]$ /opt/module/hbase/apache-phoenix-5.0.0-HBase-2.0-bin/bin/sqlline.py node001:2181P082
10.2 流量域版本-来源-地区-访客类别粒度页面浏览各窗口汇总表
10.2.2 思路分析
6）异步 IO
DwsTrafficVcSourceArIsNewPageViewWindow
//TODO 10 维度关联
[atguigu@node001 ~]$ clickhouse-client -m
ClickHouse client version 20.4.5.36 (official build).
Connecting to localhost:9000 as user default.
Connected to ClickHouse server version 20.4.5 revision 54434.
node001 :) show databases;
SHOW DATABASES
┌─name───────────────────────────┐
│ _temporary_and_external_tables │
│ default │
│ edu_realtime │
│ system │
└────────────────────────────────┘
4 rows in set. Elapsed: 0.019 sec.
node001 :) use edu_realtime;
USE edu_realtime
Ok.
0 rows in set. Elapsed: 0.007 sec.
node001 :) drop table if exists dws_traffic_vc_source_ar_is_new_page_view_window;
DROP TABLE IF EXISTS dws_traffic_vc_source_ar_is_new_page_view_window
Ok.
0 rows in set. Elapsed: 0.007 sec.
node001 :) create table dws_traffic_vc_source_ar_is_new_page_view_window(
:-] stt DateTime,
:-] edt DateTime,
:-] version_code String,
:-] source_id String,
:-] source_name String,
:-] ar String,
:-] province_name String,
:-] is_new String,
:-] uv_count UInt64,
:-] total_session_count UInt64,
:-] page_view_count UInt64,
:-] total_during_time UInt64,
:-] jump_session_count UInt64,
:-] ts UInt64
:-] ) engine = ReplacingMergeTree(ts)
:-] partition by toYYYYMMDD(stt)
:-] order by(stt, edt, version_code, source_id, source_name, ar, province_name, is_new);
CREATE TABLE dws_traffic_vc_source_ar_is_new_page_view_window
(
`stt` DateTime,
`edt` DateTime,
`version_code` String,
`source_id` String,
`source_name` String,
`ar` String,
`province_name` String,
`is_new` String,
`uv_count` UInt64,
`total_session_count` UInt64,
`page_view_count` UInt64,
`total_during_time` UInt64,
`jump_session_count` UInt64,
`ts` UInt64
)
ENGINE = ReplacingMergeTree(ts)
PARTITION BY toYYYYMMDD(stt)
ORDER BY (stt, edt, version_code, source_id, source_name, ar, province_name, is_new)
Ok.
0 rows in set. Elapsed: 0.043 sec.
node001 :) select * from edu_realtime.dws_traffic_vc_source_ar_is_new_page_view_window;
SELECT *
FROM edu_realtime.dws_traffic_vc_source_ar_is_new_page_view_window
Ok.
0 rows in set. Elapsed: 0.071 sec.
node001 :) package com.atguigu.edu.realtime.app.dws;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.atguigu.edu.realtime.app.func.DimAsyncFunction;
import com.atguigu.edu.realtime.bean.DwsTrafficForSourcePvBean;
import com.atguigu.edu.realtime.util.*;
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.common.functions.ReduceFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.streaming.api.datastream.*;
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.time.Duration;
import java.util.concurrent.TimeUnit;
/**
* @author yhm
* @create 2023-04-26 16:08
*/
public class DwsTrafficVcSourceArIsNewPageViewWindow {
public static void main(String[] args) throws Exception {
//TODO 1 创建环境设置状态后端
StreamExecutionEnvironment env = EnvUtil.getExecutionEnvironment(1);
//TODO 2 读取pageLog主题数据
String pageTopic = "dwd_traffic_page_log";
String groupId = "dws_traffic_vc_source_ar_is_new_page_view_window";
KafkaSource<String> pageSource = KafkaUtil.getKafkaConsumer(pageTopic, groupId);
DataStreamSource<String> pageStream = env.fromSource(pageSource, WatermarkStrategy.noWatermarks(), "page_log");
//TODO 3 读取独立访客数据
String uvTopic = "dwd_traffic_unique_visitor_detail";
KafkaSource<String> uvSource = KafkaUtil.getKafkaConsumer(uvTopic, groupId);
DataStreamSource<String> uvStream = env.fromSource(uvSource, WatermarkStrategy.noWatermarks(), "uv_detail");
//TODO 4 读取跳出用户数据
String jumpTopic = "dwd_traffic_user_jump_detail";
KafkaSource<String> jumpSource = KafkaUtil.getKafkaConsumer(jumpTopic, groupId);
DataStreamSource<String> jumpStream = env.fromSource(jumpSource, WatermarkStrategy.noWatermarks(), "jump_detail");
//TODO 5 转换数据结构
SingleOutputStreamOperator<DwsTrafficForSourcePvBean> pageBeanStream = pageStream.map(new MapFunction<String, DwsTrafficForSourcePvBean>() {
@Override
public DwsTrafficForSourcePvBean map(String value) throws Exception {
// 将page_log的一条日志转换为一个对应的javaBean
JSONObject jsonObject = JSON.parseObject(value);
JSONObject common = jsonObject.getJSONObject("common");
JSONObject page = jsonObject.getJSONObject("page");
Long ts = jsonObject.getLong("ts");
return DwsTrafficForSourcePvBean.builder()
.versionCode(common.getString("vc"))
.sourceId(common.getString("sc"))
.ar(common.getString("ar"))
.isNew(common.getString("is_new"))
.uvCount(0L)
.totalSessionCount(page.getString("last_page_id") == null ? 1L : 0L)
.pageViewCount(1L)
.totalDuringTime(page.getLong("during_time"))
.jumpSessionCount(0L)
.ts(ts)
.build();
}
});
SingleOutputStreamOperator<DwsTrafficForSourcePvBean> uvBeanStream = uvStream.map(new MapFunction<String, DwsTrafficForSourcePvBean>() {
@Override
public DwsTrafficForSourcePvBean map(String value) throws Exception {
// 将page_log的一条日志转换为一个对应的javaBean
JSONObject jsonObject = JSON.parseObject(value);
JSONObject common = jsonObject.getJSONObject("common");
Long ts = jsonObject.getLong("ts");
return DwsTrafficForSourcePvBean.builder()
.versionCode(common.getString("vc"))
.sourceId(common.getString("sc"))
.ar(common.getString("ar"))
.isNew(common.getString("is_new"))
.uvCount(1L)
.totalSessionCount(0L)
.pageViewCount(0L)
.totalDuringTime(0L)
.jumpSessionCount(0L)
.ts(ts)
.build();
}
});
SingleOutputStreamOperator<DwsTrafficForSourcePvBean> jumpBeanStream = jumpStream.map(new MapFunction<String, DwsTrafficForSourcePvBean>() {
@Override
public DwsTrafficForSourcePvBean map(String value) throws Exception {
// 将page_log的一条日志转换为一个对应的javaBean
JSONObject jsonObject = JSON.parseObject(value);
JSONObject common = jsonObject.getJSONObject("common");
Long ts = jsonObject.getLong("ts");
return DwsTrafficForSourcePvBean.builder()
.versionCode(common.getString("vc"))
.sourceId(common.getString("sc"))
.ar(common.getString("ar"))
.isNew(common.getString("is_new"))
.uvCount(0L)
.totalSessionCount(0L)
.pageViewCount(0L)
.totalDuringTime(0L)
.jumpSessionCount(1L)
.ts(ts)
.build();
}
});
//TODO 6 合并3条数据流
DataStream<DwsTrafficForSourcePvBean> unionStream = pageBeanStream.union(uvBeanStream).union(jumpBeanStream);
//TODO 7 添加水位线
SingleOutputStreamOperator<DwsTrafficForSourcePvBean> withWaterMarkStream = unionStream.assignTimestampsAndWatermarks(WatermarkStrategy.<DwsTrafficForSourcePvBean>forBoundedOutOfOrderness(Duration.ofSeconds(15L)).withTimestampAssigner(new SerializableTimestampAssigner<DwsTrafficForSourcePvBean>() {
@Override
public long extractTimestamp(DwsTrafficForSourcePvBean element, long recordTimestamp) {
return element.getTs();
}
}));
//TODO 8 分组开窗
WindowedStream<DwsTrafficForSourcePvBean, String, TimeWindow> windowStream = withWaterMarkStream.keyBy(new KeySelector<DwsTrafficForSourcePvBean, String>() {
@Override
public String getKey(DwsTrafficForSourcePvBean value) throws Exception {
return value.getVersionCode()
+ value.getSourceId()
+ value.getAr()
+ value.getIsNew();
}
}).window(TumblingEventTimeWindows.of(Time.seconds(10L)));
//TODO 9 聚合统计
SingleOutputStreamOperator<DwsTrafficForSourcePvBean> reduceStream = windowStream.reduce(new ReduceFunction<DwsTrafficForSourcePvBean>() {
@Override
public DwsTrafficForSourcePvBean reduce(DwsTrafficForSourcePvBean value1, DwsTrafficForSourcePvBean value2) throws Exception {
// 合并相同common信息的数据
value1.setTotalSessionCount(value1.getTotalSessionCount() + value2.getTotalSessionCount());
value1.setUvCount(value1.getUvCount() + value2.getUvCount());
value1.setTotalDuringTime(value1.getTotalDuringTime() + value2.getTotalDuringTime());
value1.setJumpSessionCount(value1.getJumpSessionCount() + value2.getJumpSessionCount());
value1.setPageViewCount(value1.getPageViewCount() + value2.getPageViewCount());
return value1;
}
}, new ProcessWindowFunction<DwsTrafficForSourcePvBean, DwsTrafficForSourcePvBean, String, TimeWindow>() {
@Override
public void process(String s, Context context, Iterable<DwsTrafficForSourcePvBean> elements, Collector<DwsTrafficForSourcePvBean> out) throws Exception {
TimeWindow timeWindow = context.window();
String start = DateFormatUtil.toYmdHms(timeWindow.getStart());
String end = DateFormatUtil.toYmdHms(timeWindow.getEnd());
for (DwsTrafficForSourcePvBean element : elements) {
element.setStt(start);
element.setEdt(end);
// 修正时间戳
element.setTs(System.currentTimeMillis());
out.collect(element);
}
}
});
reduceStream.print();
//TODO 10 维度关联
reduceStream.map(new MapFunction<DwsTrafficForSourcePvBean, DwsTrafficForSourcePvBean>() {
@Override
public DwsTrafficForSourcePvBean map(DwsTrafficForSourcePvBean value) throws Exception {
// 关联来源名称
String sourceId = value.getSourceId();
String provinceId = value.getAr();
JSONObject dimBaseSource = DimUtil.getDimInfo("DIM_BASE_SOURCE", sourceId);
String sourceName = dimBaseSource.getString("SOURCE_SITE");
value.setSourceName(sourceName);
JSONObject dimBaseProvince = DimUtil.getDimInfo("DIM_BASE_PROVINCE", provinceId);
String provinceName = dimBaseProvince.getString("NAME");
value.setProvinceName(provinceName);
return value;
}
}).print();
// 异步操作
// 关联来源表
SingleOutputStreamOperator<DwsTrafficForSourcePvBean> sourceBeanStream = AsyncDataStream.unorderedWait(reduceStream, new DimAsyncFunction<DwsTrafficForSourcePvBean>("DIM_BASE_SOURCE") {
@Override
public void join(DwsTrafficForSourcePvBean obj, JSONObject jsonObject) throws Exception {
String sourceName = jsonObject.getString("SOURCE_SITE");
obj.setSourceName(sourceName);
}
@Override
public String getKey(DwsTrafficForSourcePvBean obj) {
return obj.getSourceId();
}
}, 1, TimeUnit.MINUTES);
// 关联省份
SingleOutputStreamOperator<DwsTrafficForSourcePvBean> dimBeanStream = AsyncDataStream.unorderedWait(sourceBeanStream, new DimAsyncFunction<DwsTrafficForSourcePvBean>("DIM_BASE_PROVINCE") {
@Override
public void join(DwsTrafficForSourcePvBean obj, JSONObject jsonObject) throws Exception {
String provinceName = jsonObject.getString("NAME");
obj.setProvinceName(provinceName);
}
@Override
public String getKey(DwsTrafficForSourcePvBean obj) {
return obj.getAr();
}
}, 1, TimeUnit.MINUTES);
//TODO 11 写出到clickHouse
dimBeanStream.addSink(ClickHouseUtil.getJdbcSink(" " +
"insert into dws_traffic_vc_source_ar_is_new_page_view_window values" +
"(?,?,?,?,?,?,?,?,?,?,?,?,?,?)"));
// TODO 12 执行任务
env.execute();
}
}
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