为什么将CSV的数据发到kafka
如何将CSV的数据发送到kafka
前面的图可以看出，读取CSV再发送消息到kafka的操作是Java应用所为，因此今天的主要工作就是开发这个Java应用，并验证；
版本信息
关于数据集
| 列名称 | 说明 |
|---|---|
| 用户ID | 整数类型，序列化后的用户ID |
| 商品ID | 整数类型，序列化后的商品ID |
| 商品类目ID | 整数类型，序列化后的商品所属类目ID |
| 行为类型 | 字符串，枚举类型，包括('pv', 'buy', 'cart', 'fav') |
| 时间戳 | 行为发生的时间戳 |
| 时间字符串 | 根据时间戳字段生成的时间字符串 |
Java应用简介
编码前，先把具体内容列出来，然后再挨个实现：
上述五个类即可完成Java应用的工作，接下来开始编码吧；
直接下载源码
如果您不想写代码，您可以直接从GitHub下载这个工程的源码，地址和链接信息如下表所示：
| 名称 | 链接 | 备注 |
|---|---|---|
| 项目主页 | https://github.com/zq2599/blog_demos | 该项目在GitHub上的主页 |
| git仓库地址(https) | https://github.com/zq2599/blog_demos.git | 该项目源码的仓库地址，https协议 |
| git仓库地址(ssh) | git@github.com:zq2599/blog_demos.git | 该项目源码的仓库地址，ssh协议 |
这个git项目中有多个文件夹，本章源码在flinksql这个文件夹下，如下图红框所示：
编码
创建maven工程，pom.xml如下，比较重要的jackson和javacsv的依赖：
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.bolingcavalry</groupId>
<artifactId>flinksql</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<flink.version>1.10.0</flink.version>
<kafka.version>2.2.0</kafka.version>
<java.version>1.8</java.version>
<scala.binary.version>2.11</scala.binary.version>
<maven.compiler.source>${java.version}</maven.compiler.source>
<maven.compiler.target>${java.version}</maven.compiler.target>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>${kafka.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.9.10.1</version>
</dependency>
<!-- Logging dependencies -->
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.7</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.17</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>net.sourceforge.javacsv</groupId>
<artifactId>javacsv</artifactId>
<version>2.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<!-- Java Compiler -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>${java.version}</source>
<target>${java.version}</target>
</configuration>
</plugin>
<!-- Shade plugin to include all dependencies -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.0.0</version>
<executions>
<!-- Run shade goal on package phase -->
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<artifactSet>
<excludes>
</excludes>
</artifactSet>
<filters>
<filter>
<!-- Do not copy the signatures in the META-INF folder.
Otherwise, this might cause SecurityExceptions when using the JAR. -->
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
从CSV读取记录的工具类：UserBehaviorCsvFileReader，后面在主程序中会用到java8的Steam API来处理集合，所以UserBehaviorCsvFileReader实现了Supplier接口：
public class UserBehaviorCsvFileReader implements Supplier<UserBehavior> {
private final String filePath;
private CsvReader csvReader;
public UserBehaviorCsvFileReader(String filePath) throws IOException {
this.filePath = filePath;
try {
csvReader = new CsvReader(filePath);
csvReader.readHeaders();
} catch (IOException e) {
throw new IOException("Error reading TaxiRecords from file: " + filePath, e);
}
}
@Override
public UserBehavior get() {
UserBehavior userBehavior = null;
try{
if(csvReader.readRecord()) {
csvReader.getRawRecord();
userBehavior = new UserBehavior(
Long.valueOf(csvReader.get(0)),
Long.valueOf(csvReader.get(1)),
Long.valueOf(csvReader.get(2)),
csvReader.get(3),
new Date(Long.valueOf(csvReader.get(4))*1000L));
}
} catch (IOException e) {
throw new NoSuchElementException("IOException from " + filePath);
}
if (null==userBehavior) {
throw new NoSuchElementException("All records read from " + filePath);
}
return userBehavior;
}
}
每条记录对应的Bean类：UserBehavior，和CSV记录格式保持一致即可，表示时间的ts字段，使用了JsonFormat注解，在序列化的时候以此来控制格式：
public class UserBehavior {
@JsonFormat
private long user_id;
@JsonFormat
private long item_id;
@JsonFormat
private long category_id;
@JsonFormat
private String behavior;
@JsonFormat(shape = JsonFormat.Shape.STRING, pattern = "yyyy-MM-dd'T'HH:mm:ss'Z'")
private Date ts;
public UserBehavior() {
}
public UserBehavior(long user_id, long item_id, long category_id, String behavior, Date ts) {
this.user_id = user_id;
this.item_id = item_id;
this.category_id = category_id;
this.behavior = behavior;
this.ts = ts;
}
}
Java对象序列化成JSON的序列化类：JsonSerializer
public class JsonSerializer<T> {
private final ObjectMapper jsonMapper = new ObjectMapper();
public String toJSONString(T r) {
try {
return jsonMapper.writeValueAsString(r);
} catch (JsonProcessingException e) {
throw new IllegalArgumentException("Could not serialize record: " + r, e);
}
}
public byte[] toJSONBytes(T r) {
try {
return jsonMapper.writeValueAsBytes(r);
} catch (JsonProcessingException e) {
throw new IllegalArgumentException("Could not serialize record: " + r, e);
}
}
}
向kafka发送消息的工具类：KafkaProducer：
public class KafkaProducer implements Consumer<UserBehavior> {
private final String topic;
private final org.apache.kafka.clients.producer.KafkaProducer<byte[], byte[]> producer;
private final JsonSerializer<UserBehavior> serializer;
public KafkaProducer(String kafkaTopic, String kafkaBrokers) {
this.topic = kafkaTopic;
this.producer = new org.apache.kafka.clients.producer.KafkaProducer<>(createKafkaProperties(kafkaBrokers));
this.serializer = new JsonSerializer<>();
}
@Override
public void accept(UserBehavior record) {
// 将对象序列化成byte数组
byte[] data = serializer.toJSONBytes(record);
// 封装
ProducerRecord<byte[], byte[]> kafkaRecord = new ProducerRecord<>(topic, data);
// 发送
producer.send(kafkaRecord);
// 通过sleep控制消息的速度，请依据自身kafka配置以及flink服务器配置来调整
try {
Thread.sleep(500);
}catch(InterruptedException e){
e.printStackTrace();
}
}
/**
* kafka配置
* @param brokers The brokers to connect to.
* @return A Kafka producer configuration.
*/
private static Properties createKafkaProperties(String brokers) {
Properties kafkaProps = new Properties();
kafkaProps.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers);
kafkaProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, ByteArraySerializer.class.getCanonicalName());
kafkaProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, ByteArraySerializer.class.getCanonicalName());
return kafkaProps;
}
}
最后是应用类SendMessageApplication，CSV文件路径、kafka的topic和borker地址都在此设置，另外借助java8的Stream API，只需少量代码即可完成所有工作：
public class SendMessageApplication {
public static void main(String[] args) throws Exception {
// 文件地址
String filePath = "D:\\temp\\202005\\02\\UserBehavior.csv";
// kafka topic
String topic = "user_behavior";
// kafka borker地址
String broker = "192.168.50.43:9092";
Stream.generate(new UserBehaviorCsvFileReader(filePath))
.sequential()
.forEachOrdered(new KafkaProducer(topic, broker));
}
}
验证
./kafka-console-consumer.sh \ --bootstrap-server 127.0.0.1:9092 \ --topic user_behavior \ --consumer-property group.id=old-consumer-test \ --consumer-property consumer.id=old-consumer-cl \ --from-beginning
至此，通过Java应用模拟用户行为消息流的操作就完成了，接下来的flink实战就用这个作为数据源；
