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1.前期准备
（1）Flink基础环境安装
参考文章：
利用docker-compose来搭建flink集群-CSDN博客
显示为这样就成功了
（2）把docker，docker-compose，kafka集群安装配置好
参考文章：
利用docker搭建kafka集群并且进行相应的实践-CSDN博客
这篇文章里面有另外两篇文章的链接，点进去就能够看到
（3）在windows上面，创建一个数据库mysql1（如果没有的话就需要创建），接着在这个数据库里面建一个表min_table
具体代码如下
create database if not exists mysql1; -- 注释符为‘-- '注意有个空格
use mysql1;
CREATE TABLE min_table (
id INT AUTO_INCREMENT PRIMARY KEY,
timestamp TIMESTAMP NOT NULL,
quantity INT NOT NULL,
amount DOUBLE NOT NULL,
UNIQUE KEY unique_timestamp (timestamp)
);
create database if not exists mysql1; -- 注释符为‘-- '注意有个空格use mysql1;CREATE TABLE min_table (id INT AUTO_INCREMENT PRIMARY KEY,timestamp TIMESTAMP NOT NULL,quantity INT NOT NULL,amount DOUBLE NOT NULL,UNIQUE KEY unique_timestamp (timestamp));（4）接着在安装配置了flink的linux虚拟机上面安装好mysql
参考文章：黑马大数据学习笔记4-Hive部署和基本操作_黑马大数据 hive笔记-CSDN博客
（5）然后同样的在linux虚拟机上面的mysql中创建一个数据库mysql1（如果没有的话就需要创建），接着在这个数据库里面建一个表min_table
具体代码如下
create database if not exists mysql1; -- 注释符为‘-- '注意有个空格
use mysql1;
CREATE TABLE min_table (
id INT AUTO_INCREMENT PRIMARY KEY,
timestamp TIMESTAMP NOT NULL,
quantity INT NOT NULL,
amount DOUBLE NOT NULL,
UNIQUE KEY unique_timestamp (timestamp)
);
create database if not exists mysql1; -- 注释符为‘-- '注意有个空格use mysql1;CREATE TABLE min_table (id INT AUTO_INCREMENT PRIMARY KEY,timestamp TIMESTAMP NOT NULL,quantity INT NOT NULL,amount DOUBLE NOT NULL,UNIQUE KEY unique_timestamp (timestamp));
（6）在idea里面新建一个Maven项目，名字叫做FlinkDemo然后往pom.xml中添加以下配置
<dependencies><!-- Flink 的核心库 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>1.18.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java</artifactId><version>1.18.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients</artifactId><version>1.18.0</version></dependency><!-- Flink Kafka Connector --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka</artifactId><version>3.0.1-1.18</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-jdbc</artifactId><version>3.1.1-1.17</version></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>8.0.33</version></dependency></dependencies>
<build><plugins><plugin><artifactId>maven-assembly-plugin</artifactId><configuration><descriptorRefs><descriptorRef>jar-with-dependencies</descriptorRef></descriptorRefs></configuration><executions><execution><phase>package</phase><goals><goal>single</goal></goals></execution></executions></plugin></plugins>
</build>这个和上面的是一个东西，就看你喜欢一键复制还是分别复制了
<dependencies>
<!-- Flink 的核心库 -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>1.18.0</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java</artifactId>
<version>1.18.0</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients</artifactId>
<version>1.18.0</version>
</dependency>
<!-- Flink Kafka Connector -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka</artifactId>
<version>3.0.1-1.18</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-jdbc</artifactId>
<version>3.1.1-1.17</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>8.0.33</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-assembly-plugin</artifactId>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
（7）在该项目的com.examle目录下创建三个文件
目录结构如下
DatabaseSink.java
package com.example;import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.types.Row;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple3;import java.sql.PreparedStatement;
import java.sql.Timestamp;public class DatabaseSink {private String url;private String username;private String password;public DatabaseSink(String url, String username, String password) {this.url = url;this.username = username;this.password = password;}public void addSink(DataStream<Tuple3<Timestamp, Long, Double>> stream) {stream.addSink(JdbcSink.sink("INSERT INTO min_table (timestamp, quantity, amount) VALUES (?, ?, ?) ON DUPLICATE KEY UPDATE quantity = quantity + VALUES(quantity), amount = amount + VALUES(amount)",(ps, t) -> {ps.setTimestamp(1, t.f0);ps.setLong(2, t.f1);ps.setDouble(3, t.f2);},new JdbcExecutionOptions.Builder().withBatchSize(5000).withBatchIntervalMs(200).withMaxRetries(5).build(),new JdbcConnectionOptions.JdbcConnectionOptionsBuilder().withUrl(this.url).withDriverName("com.mysql.jdbc.Driver").withUsername(this.username).withPassword(this.password).build()));}
}LocalFlinkTest.java
package com.example;import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.kafka.clients.consumer.OffsetResetStrategy;import java.sql.Timestamp;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.concurrent.TimeUnit;public class LocalFlinkTest {public static void main(String[] args) throws Exception {SimpleDateFormat sdf = new SimpleDateFormat(("yyyy-MM-dd HH:mm"));SimpleDateFormat sdf_hour = new SimpleDateFormat("yyyy-MM-dd HH");final StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();env.setRestartStrategy(RestartStrategies.fixedDelayRestart(333, // 尝试重启的次数org.apache.flink.api.common.time.Time.of(10, TimeUnit.SECONDS) // 延迟));env.setRestartStrategy(RestartStrategies.noRestart());KafkaSource<String> source = KafkaSource.<String>builder().setBootstrapServers("192.168.88.101:19092,192.168.88.101:29092,192.168.88.101:39092") // 你的 Kafka 服务器地址.setGroupId("testGroup") // 你的消费者组 ID.setTopics("foo") // 你的主题.setValueOnlyDeserializer(new SimpleStringSchema()).setStartingOffsets(OffsetsInitializer.committedOffsets(OffsetResetStrategy.LATEST)) // 从消费者组的最新偏移量开始消费.build();DataStream<String> stream = env.fromSource(source,WatermarkStrategy.noWatermarks(), "Kafka Source");
// flatMap 函数，它接收一个输入元素，并可以输出零个、一个或多个元素。
// 在这个函数中，输入元素是从 Kafka 中读取的一行数据，输出元素是一个包含交易量的元组。
// 近 1 分钟与当天累计的总交易金额、交易数量
// DataStream<String> stream = env.readTextFile("D:\\idea\\flinkTest\\src\\main\\java\\com\\springbootdemo\\2.csv", "GBK");DataStream<Tuple3<Timestamp, Long, Double>> transactionVolumes = stream.filter(new FilterFunction<String>() {@Overridepublic boolean filter(String value) throws Exception {// 假设文件的第一行是表头，这里跳过它return !value.startsWith("time");}}).flatMap(new FlatMapFunction<String, Tuple3<Timestamp, Long,Double>>() {@Overridepublic void flatMap(String line, Collector<Tuple3<Timestamp, Long,Double>> out) {try {String[] fields = line.split(",");String s = fields[0];
// 解析时间字符串后，将日期时间对象的秒字段设置为 0Date date = sdf.parse(s);Timestamp sqlTimestamp = new Timestamp(date.getTime());double price = Double.parseDouble(fields[3]);long quantity = Long.parseLong(fields[4]);double amount = price * quantity;out.collect(Tuple3.of(sqlTimestamp, quantity, amount));
// System.out.println(line);} catch (Exception e) {System.out.println(line); }}}); // 过滤掉解析失败的记录;// 计算每 500 毫秒的数据
// keyBy(t -> t.f0)代表以第一个字段 Timestamp 为键，确保一个窗口内的时间都是相同的DataStream<Tuple3<Timestamp,Long ,Double>> oneSecondAmounts =transactionVolumes.keyBy(t -> t.f0).windowAll(TumblingProcessingTimeWindows.of(Time.seconds(10))).reduce((Tuple3<Timestamp,Long ,Double> value1,Tuple3<Timestamp,Long ,Double> value2) -> {
// System.out.println(Tuple3.of(value1.f0,value1.f1 + value2.f1, value1.f2 + value2.f2));return Tuple3.of(value1.f0,value1.f1 + value2.f1, value1.f2 +value2.f2);});oneSecondAmounts.print();DatabaseSink dbSink = new DatabaseSink("jdbc:mysql://localhost:3306/mysql1", "root", "123456");dbSink.addSink(oneSecondAmounts);env.execute("Kafka Flink Demo");}
}
DatabaseSink dbSink = new DatabaseSink("jdbc:mysql://localhost:3306/mysql1", "root", "123456");
这里的密码应该改成你自己的。（当然博主本人的是123456）
FlinkTest.java
package com.example;import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.kafka.clients.consumer.OffsetResetStrategy;import java.sql.Timestamp;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.concurrent.TimeUnit;public class FlinkTest {public static void main(String[] args) throws Exception {SimpleDateFormat sdf = new SimpleDateFormat(("yyyy-MM-dd HH:mm"));SimpleDateFormat sdf_hour = new SimpleDateFormat("yyyy-MM-dd HH");final StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();env.setRestartStrategy(RestartStrategies.fixedDelayRestart(333, // 尝试重启的次数org.apache.flink.api.common.time.Time.of(10, TimeUnit.SECONDS) // 延迟));env.setRestartStrategy(RestartStrategies.noRestart());KafkaSource<String> source = KafkaSource.<String>builder().setBootstrapServers("192.168.88.101:19092,192.168.88.101:29092,192.168.88.101:39092") // 你的 Kafka 服务器地址.setGroupId("testGroup") // 你的消费者组 ID.setTopics("foo") // 你的主题.setValueOnlyDeserializer(new SimpleStringSchema()).setStartingOffsets(OffsetsInitializer.committedOffsets(OffsetResetStrategy.LATEST)) // 从消费者组的最新偏移量开始消费.build();DataStream<String> stream = env.fromSource(source,WatermarkStrategy.noWatermarks(), "Kafka Source");
// flatMap 函数，它接收一个输入元素，并可以输出零个、一个或多个元素。
// 在这个函数中，输入元素是从 Kafka 中读取的一行数据，输出元素是一个包含交易量的元组。
// 近 1 分钟与当天累计的总交易金额、交易数量
// DataStream<String> stream = env.readTextFile("D:\\idea\\flinkTest\\src\\main\\java\\com\\springbootdemo\\2.csv", "GBK");DataStream<Tuple3<Timestamp, Long, Double>> transactionVolumes = stream.filter(new FilterFunction<String>() {@Overridepublic boolean filter(String value) throws Exception {// 假设文件的第一行是表头，这里跳过它return !value.startsWith("time");}}).flatMap(new FlatMapFunction<String, Tuple3<Timestamp, Long,Double>>() {@Overridepublic void flatMap(String line, Collector<Tuple3<Timestamp, Long,Double>> out) {try {String[] fields = line.split(",");String s = fields[0];
// 解析时间字符串后，将日期时间对象的秒字段设置为 0Date date = sdf.parse(s);Timestamp sqlTimestamp = new Timestamp(date.getTime());double price = Double.parseDouble(fields[3]);long quantity = Long.parseLong(fields[4]);double amount = price * quantity;out.collect(Tuple3.of(sqlTimestamp, quantity, amount));
// System.out.println(line);} catch (Exception e) {System.out.println(line); }}}); // 过滤掉解析失败的记录;// 计算每 500 毫秒的数据
// keyBy(t -> t.f0)代表以第一个字段 Timestamp 为键，确保一个窗口内的时间都是相同的DataStream<Tuple3<Timestamp,Long ,Double>> oneSecondAmounts =transactionVolumes.keyBy(t -> t.f0).windowAll(TumblingProcessingTimeWindows.of(Time.seconds(10))).reduce((Tuple3<Timestamp,Long ,Double> value1,Tuple3<Timestamp,Long ,Double> value2) -> {
// System.out.println(Tuple3.of(value1.f0,value1.f1 + value2.f1, value1.f2 + value2.f2));return Tuple3.of(value1.f0,value1.f1 + value2.f1, value1.f2 +value2.f2);});oneSecondAmounts.print();DatabaseSink dbSink = new DatabaseSink("jdbc:mysql://192.168.88.101:3306/mysql1", "root", "123456");dbSink.addSink(oneSecondAmounts);env.execute("Kafka Flink Demo");}
}
DatabaseSink dbSink = new DatabaseSink("jdbc:mysql://192.168.88.101:3306/mysql1", "root", "123456");
这里的密码和主机号（192.168.88.101）应该改成你自己的密码和主机号
2.开始实验，分为本地测试和flink测试
（1）启动node1，打开Finalshell，启动docker，启动kafka集群，flink集群
systemctl start docker
cd /export/server
docker-compose -f kafka.yml up -d
docker-compose -f flink.yml up -d
docker ps效果如下
（2）先进行本地测试（这里只需要用到kafka集群）
打开两个node1的窗口
在第二个窗口进入kafka2容器，启动消费者进程
代码
docker exec -it kafka2 /bin/bashcd /opt/bitnami/kafka/binkafka-console-consumer.sh --bootstrap-server 172.23.0.11:9092,172.23.0.12:9092,172.23.0.13:9092 --topic foo效果如下
进入idea，运行这个文件LocalFlinkTest.java
在第一个窗口进入kafka1容器，发送文件的前5行
[root@node1 server]# docker exec -it kafka1 /bin/bash
root@a2f7152188c1:/# cd /opt/bitnami/kafka/bin
root@a2f7152188c1:/opt/bitnami/kafka/bin# head -n 5 /bitnami/kafka/stock-part10.csv | kafka-console-producer.sh --broker-list 172.23.0.11:9092,172.23.0.12:9092,172.23.0.13:9092 --topic foo
root@a2f7152188c1:/opt/bitnami/kafka/bin#
代码
docker exec -it kafka1 /bin/bashcd /opt/bitnami/kafka/binhead -n 5 /bitnami/kafka/stock-part10.csv | kafka-console-producer.sh --broker-list 172.23.0.11:9092,172.23.0.12:9092,172.23.0.13:9092 --topic foo
接着在idea里面查看
在mysql里查看
到这里，本地测试就已经成功了！
（3）再进行flink测试，先在idea这里双击packge，然后去target目录看看有没有多出这两个文件（先运行文件FlinkTest.java先）
运行文件FlinkTest.java
在idea这里双击packge，然后去target目录看看有没有多出这两个文件
进入网页node1:8081,上传这个名字更长的jar包
输入这个路径
D:\JetBrains\idea-project\FlinkDemo\target
（反正就是target目录的位置）
添加成功后
点一下那个玩意儿填入如下内容com.example.FlinkTest
这个com.example.FlinkTest是FlinkTest.java在项目中的路径
以及选择输入3
然后点击submit提交即可，结果显示正常运行
再回到node1的第一个窗口，
在这个位置
root@41d3910fe6c9:/opt/bitnami/kafka/bin#输入以下代码（kafka1的/opt/bitnami/kafka/bin目录下）来发个文件过去
代码
cat /bitnami/kafka/stock-part10.csv | kafka-console-producer.sh --broker-list 172.23.0.11:9092,172.23.0.12:9092,172.23.0.13:9092 --topic foo任意点开一个，在监控参数中选择numRecordsInPerSecond可以查看每秒处理数据速度。
