Kafka API实战

眸晓

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2022-11-11


1 环境准备

1）在eclipse中创建一个java工程

2）在工程的根目录创建一个lib文件夹

3）解压kafka安装包，将安装包libs目录下的jar包拷贝到工程的lib目录下，并build path。

4）启动zk和kafka集群，在kafka集群中打开一个消费者

[atguigu@hadoop102 kafka]$ bin/kafka-console-consumer.sh --zookeeper hadoop102:2181 --topic first

2 Kafka生产者Java API

2.1 创建生产者（过时的API）

package com.atguigu.kafka;
import java.util.Properties;
importkafka.javaapi.producer.Producer;
importkafka.producer.KeyedMessage;
importkafka.producer.ProducerConfig;
 
publicclass OldProducer {
 
       @SuppressWarnings("deprecation")
       publicstaticvoid main(String[] args) {
             
              Properties properties = new Properties();
              properties.put("metadata.broker.list", "hadoop102:9092");
              properties.put("request.required.acks", "1");
              properties.put("serializer.class", "kafka.serializer.StringEncoder");
             
              Producer<Integer, String> producer = new Producer<Integer,String>(new ProducerConfig(properties));
             
              KeyedMessage<Integer, String> message = new KeyedMessage<Integer, String>("first", "hello world");
              producer.send(message );
       }
}

2.2 创建生产者（新API）

package com.atguigu.kafka;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
 
publicclass NewProducer {
 
       publicstaticvoid main(String[] args) {
             
              Properties props = new Properties();
              // Kafka服务端的主机名和端口号
              props.put("bootstrap.servers", "hadoop103:9092");
              // 等待所有副本节点的应答
              props.put("acks", "all");
              // 消息发送最大尝试次数
              props.put("retries", 0);
              // 一批消息处理大小
              props.put("batch.size", 16384);
              // 请求延时
              props.put("linger.ms", 1);
              // 发送缓存区内存大小
              props.put("buffer.memory", 33554432);
              // key序列化
              props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
              // value序列化
              props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
 
              KafkaProducer<String, String> producer = new KafkaProducer<>(props);
              for (int i = 0; i < 50; i++) {
                     producer.send(new ProducerRecord<String, String>("first", Integer.toString(i), "hello world-" + i));
              }
 
              producer.close();
       }
}

 

2.3 创建生产者带回调函数（新API）

package com.atguigu.kafka;
import java.util.Properties;
import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
 
publicclass CallBackProducer {
 
       publicstaticvoid main(String[] args) {
 
Properties props = new Properties();
              // Kafka服务端的主机名和端口号
              props.put("bootstrap.servers", "hadoop103:9092");
              // 等待所有副本节点的应答
              props.put("acks", "all");
              // 消息发送最大尝试次数
              props.put("retries", 0);
              // 一批消息处理大小
              props.put("batch.size", 16384);
              // 增加服务端请求延时
              props.put("linger.ms", 1);
// 发送缓存区内存大小
              props.put("buffer.memory", 33554432);
              // key序列化
              props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
              // value序列化
              props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
 
              KafkaProducer<String, String> kafkaProducer = new KafkaProducer<>(props);
 
              for (int i = 0; i < 50; i++) {
 
                     kafkaProducer.send(new ProducerRecord<String, String>("first", "hello" + i), new Callback() {
 
                            @Override
                            publicvoid onCompletion(RecordMetadata metadata, Exception exception) {
 
                                   if (metadata != null) {
 
                                          System.out.println(metadata.partition() + "---" + metadata.offset());
                                   }
                            }
                     });
              }
 
              kafkaProducer.close();
       }
}

 

2.4 自定义分区生产者

0）需求：将所有数据存储到topic的第0号分区上

1）定义一个类实现Partitioner接口，重写里面的方法（过时API）

package com.atguigu.kafka;
importjava.util.Map;
importkafka.producer.Partitioner;
 
publicclass CustomPartitioner implementsPartitioner {
 
       public CustomPartitioner() {
              super();
       }
 
       @Override
       publicint partition(Object key, int numPartitions) {
              // 控制分区
              return 0;
       }
}

2）自定义分区（新API）

package com.atguigu.kafka;
import java.util.Map;
import org.apache.kafka.clients.producer.Partitioner;
import org.apache.kafka.common.Cluster;
 
publicclass CustomPartitioner implements Partitioner {
 
       @Override
       publicvoid configure(Map<String, ?> configs) {
             
       }
 
       @Override
       publicint partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
        // 控制分区
       return 0;
       }
 
       @Override
       publicvoid close() {
             
       }
}

3）在代码中调用

package com.atguigu.kafka;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
 
publicclass PartitionerProducer {
 
       publicstaticvoid main(String[] args) {
             
              Properties props = new Properties();
              // Kafka服务端的主机名和端口号
              props.put("bootstrap.servers", "hadoop103:9092");
              // 等待所有副本节点的应答
              props.put("acks", "all");
              // 消息发送最大尝试次数
              props.put("retries", 0);
              // 一批消息处理大小
              props.put("batch.size", 16384);
              // 增加服务端请求延时
              props.put("linger.ms", 1);
              // 发送缓存区内存大小
              props.put("buffer.memory", 33554432);
              // key序列化
              props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
              // value序列化
              props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
// 自定义分区
              props.put("partitioner.class", "com.atguigu.kafka.CustomPartitioner");
 
              Producer<String, String> producer = new KafkaProducer<>(props);
              producer.send(new ProducerRecord<String, String>("first", "1", "atguigu"));
 
              producer.close();
       }
}

4）测试

       （1）在hadoop102上监控/opt/module/kafka/logs/目录下first主题3个分区的log日志动态变化情况

[atguigu@hadoop102 first-0]$ tail -f 00000000000000000000.log

[atguigu@hadoop102 first-1]$ tail -f 00000000000000000000.log

[atguigu@hadoop102 first-2]$ tail -f 00000000000000000000.log

       （2）发现数据都存储到指定的分区了。

3 Kafka消费者Java API

0）在控制台创建发送者

[atguigu@hadoop104 kafka]$ bin/kafka-console-producer.sh --broker-list hadoop102:9092 --topic first

>hello world

1）创建消费者（过时API）

package com.atguigu.kafka.consume;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import kafka.consumer.Consumer;
importkafka.consumer.ConsumerConfig;
importkafka.consumer.ConsumerIterator;
importkafka.consumer.KafkaStream;
importkafka.javaapi.consumer.ConsumerConnector;
 
publicclass CustomConsumer {
 
       @SuppressWarnings("deprecation")
       publicstaticvoid main(String[] args) {
              Properties properties = new Properties();
             
              properties.put("zookeeper.connect", "hadoop102:2181");
              properties.put("group.id", "g1");
              properties.put("zookeeper.session.timeout.ms", "500");
              properties.put("zookeeper.sync.time.ms", "250");
              properties.put("auto.commit.interval.ms", "1000");
             
              // 创建消费者连接器
              ConsumerConnector consumer = Consumer.createJavaConsumerConnector(new ConsumerConfig(properties));
             
              HashMap<String, Integer> topicCount = new HashMap<>();
              topicCount.put("first", 1);
             
              Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = consumer.createMessageStreams(topicCount);
             
              KafkaStream<byte[], byte[]> stream = consumerMap.get("first").get(0);
             
              ConsumerIterator<byte[], byte[]> it = stream.iterator();
             
              while (it.hasNext()) {
                     System.out.println(new String(it.next().message()));
              }
       }
}

2）官方提供案例（自动维护消费情况）（新API）

package com.atguigu.kafka.consume;
import java.util.Arrays;
import java.util.Properties;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
 
publicclass CustomNewConsumer {
 
       publicstaticvoid main(String[] args) {
 
              Properties props = new Properties();
              // 定义kakfa 服务的地址，不需要将所有broker指定上
              props.put("bootstrap.servers", "hadoop102:9092");
              // 制定consumer group
              props.put("group.id", "test");
              // 是否自动确认offset
              props.put("enable.auto.commit", "true");
              // 自动确认offset的时间间隔
              props.put("auto.commit.interval.ms", "1000");
              // key的序列化类
              props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
              // value的序列化类
              props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
              // 定义consumer
              KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
             
              // 消费者订阅的topic, 可同时订阅多个
              consumer.subscribe(Arrays.asList("first", "second","third"));
 
              while (true) {
                     // 读取数据，读取超时时间为100ms
                     ConsumerRecords<String, String> records = consumer.poll(100);
                    
                     for (ConsumerRecord<String, String> record : records)
                            System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
              }
       }
}

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