#yyds干货盘点#将CSV的数据发送到kafka(java版) _盘点

春衣少年当酒歌，起舞四顾以笑和。这篇文章主要讲述#yyds干货盘点#将CSV的数据发送到kafka(java版)相关的知识，希望能为你提供帮助。
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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

关于该数据集的详情，请参考《准备数据集用于flink学习》
java应用简介
编码前，先把具体内容列出来，然后再挨个实现：
从CSV读取记录的工具类：UserBehaviorCsvFileReader
每条记录对应的Bean类：UserBehavior
Java对象序列化成JSON的序列化类：JsonSerializer
向kafka发送消息的工具类：KafkaProducer
应用类，程序入口：SendMessageApplication

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：

文章图片
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;
【#yyds干货盘点#将CSV的数据发送到kafka(java版)】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-ddTHH:mm:ssZ") 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 = https://www.songbingjia.com/android/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

请确保kafka已经就绪，并且名为==user_behavior==的topic已经创建；
请将CSV文件准备好；
确认SendMessageApplication.java中的文件地址、kafka topic、kafka broker三个参数准确无误；
运行SendMessageApplication.java；
开启一个控制台消息kafka消息，参考命令如下：
./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
正常情况下可以立即见到消息，如下图：

文章图片

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；