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Java將CSV的數(shù)據(jù)發(fā)送到kafka的示例

 更新時(shí)間:2020年11月16日 09:54:10   作者:zq2599  
這篇文章主要介紹了Java將CSV的數(shù)據(jù)發(fā)送到kafka得示例,幫助大家更好得理解和使用Java,感興趣的朋友可以了解下

為什么將CSV的數(shù)據(jù)發(fā)到kafka

  • flink做流式計(jì)算時(shí),選用kafka消息作為數(shù)據(jù)源是常用手段,因此在學(xué)習(xí)和開發(fā)flink過程中,也會(huì)將數(shù)據(jù)集文件中的記錄發(fā)送到kafka,來模擬不間斷數(shù)據(jù);
  • 整個(gè)流程如下:

  • 您可能會(huì)覺得這樣做多此一舉:flink直接讀取CSV不就行了嗎?這樣做的原因如下:
  • 首先,這是學(xué)習(xí)和開發(fā)時(shí)的做法,數(shù)據(jù)集是CSV文件,而生產(chǎn)環(huán)境的實(shí)時(shí)數(shù)據(jù)卻是kafka數(shù)據(jù)源;
  • 其次,Java應(yīng)用中可以加入一些特殊邏輯,例如數(shù)據(jù)處理,匯總統(tǒng)計(jì)(用來和flink結(jié)果對(duì)比驗(yàn)證);
  • 另外,如果兩條記錄實(shí)際的間隔時(shí)間如果是1分鐘,那么Java應(yīng)用在發(fā)送消息時(shí)也可以間隔一分鐘再發(fā)送,這個(gè)邏輯在flink社區(qū)的demo中有具體的實(shí)現(xiàn),此demo也是將數(shù)據(jù)集發(fā)送到kafka,再由flink消費(fèi)kafka,地址是:https://github.com/ververica/sql-training

如何將CSV的數(shù)據(jù)發(fā)送到kafka

前面的圖可以看出,讀取CSV再發(fā)送消息到kafka的操作是Java應(yīng)用所為,因此今天的主要工作就是開發(fā)這個(gè)Java應(yīng)用,并驗(yàn)證;

版本信息

  • JDK:1.8.0_181
  • 開發(fā)工具:IntelliJ IDEA 2019.2.1 (Ultimate Edition)
  • 開發(fā)環(huán)境:Win10
  • Zookeeper:3.4.13
  • Kafka:2.4.0(scala:2.12)

關(guān)于數(shù)據(jù)集

  1. 本次實(shí)戰(zhàn)用到的數(shù)據(jù)集是CSV文件,里面是一百零四萬條淘寶用戶行為數(shù)據(jù),該數(shù)據(jù)來源是阿里云天池公開數(shù)據(jù)集,我對(duì)此數(shù)據(jù)做了少量調(diào)整;
  2. 此CSV文件可以在CSDN下載,地址:https://download.csdn.net/download/boling_cavalry/12381698
  3. 也可以在我的Github下載,地址:https://raw.githubusercontent.com/zq2599/blog_demos/master/files/UserBehavior.7z
  4. 該CSV文件的內(nèi)容,一共有六列,每列的含義如下表:

列名稱 說明
用戶ID 整數(shù)類型,序列化后的用戶ID
商品ID 整數(shù)類型,序列化后的商品ID
商品類目ID 整數(shù)類型,序列化后的商品所屬類目ID
行為類型 字符串,枚舉類型,包括('pv', 'buy', 'cart', 'fav')
時(shí)間戳 行為發(fā)生的時(shí)間戳
時(shí)間字符串 根據(jù)時(shí)間戳字段生成的時(shí)間字符串

Java應(yīng)用簡(jiǎn)介

編碼前,先把具體內(nèi)容列出來,然后再挨個(gè)實(shí)現(xiàn):

  1. 從CSV讀取記錄的工具類:UserBehaviorCsvFileReader
  2. 每條記錄對(duì)應(yīng)的Bean類:UserBehavior
  3. Java對(duì)象序列化成JSON的序列化類:JsonSerializer
  4. 向kafka發(fā)送消息的工具類:KafkaProducer
  5. 應(yīng)用類,程序入口:SendMessageApplication

上述五個(gè)類即可完成Java應(yīng)用的工作,接下來開始編碼吧;

直接下載源碼

如果您不想寫代碼,您可以直接從GitHub下載這個(gè)工程的源碼,地址和鏈接信息如下表所示:

名稱 鏈接 備注
項(xiàng)目主頁 https://github.com/zq2599/blog_demos 該項(xiàng)目在GitHub上的主頁
git倉(cāng)庫(kù)地址(https) https://github.com/zq2599/blog_demos.git 該項(xiàng)目源碼的倉(cāng)庫(kù)地址,https協(xié)議
git倉(cāng)庫(kù)地址(ssh) git@github.com:zq2599/blog_demos.git 該項(xiàng)目源碼的倉(cāng)庫(kù)地址,ssh協(xié)議

這個(gè)git項(xiàng)目中有多個(gè)文件夾,本章源碼在flinksql這個(gè)文件夾下,如下圖紅框所示:

編碼

創(chuàng)建maven工程,pom.xml如下,比較重要的jackson和javacsv的依賴:

<?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>

從CSV讀取記錄的工具類:UserBehaviorCsvFileReader,后面在主程序中會(huì)用到j(luò)ava8的Steam API來處理集合,所以UserBehaviorCsvFileReader實(shí)現(xiàn)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;
 }
}

每條記錄對(duì)應(yīng)的Bean類:UserBehavior,和CSV記錄格式保持一致即可,表示時(shí)間的ts字段,使用了JsonFormat注解,在序列化的時(shí)候以此來控制格式:

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對(duì)象序列化成JSON的序列化類: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發(fā)送消息的工具類: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) {
  // 將對(duì)象序列化成byte數(shù)組
  byte[] data = serializer.toJSONBytes(record);
  // 封裝
  ProducerRecord<byte[], byte[]> kafkaRecord = new ProducerRecord<>(topic, data);
  // 發(fā)送
  producer.send(kafkaRecord);

  // 通過sleep控制消息的速度,請(qǐng)依據(jù)自身kafka配置以及flink服務(wù)器配置來調(diào)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;
 }
}

最后是應(yīng)用類SendMessageApplication,CSV文件路徑、kafka的topic和borker地址都在此設(shè)置,另外借助java8的Stream API,只需少量代碼即可完成所有工作:

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));
 }
}

驗(yàn)證

  1. 請(qǐng)確保kafka已經(jīng)就緒,并且名為user_behavior的topic已經(jīng)創(chuàng)建;
  2. 請(qǐng)將CSV文件準(zhǔn)備好;
  3. 確認(rèn)SendMessageApplication.java中的文件地址、kafka topic、kafka broker三個(gè)參數(shù)準(zhǔn)確無誤;
  4. 運(yùn)行SendMessageApplication.java;
  5. 開啟一個(gè) 控制臺(tái)消息kafka消息,參考命令如下:
./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
  • 正常情況下可以立即見到消息,如下圖:

至此,通過Java應(yīng)用模擬用戶行為消息流的操作就完成了,接下來的flink實(shí)戰(zhàn)就用這個(gè)作為數(shù)據(jù)源;

以上就是Java將CSV的數(shù)據(jù)發(fā)送到kafka得示例的詳細(xì)內(nèi)容,更多關(guān)于Java CSV的數(shù)據(jù)發(fā)送到kafka的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章!

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