尚硅谷大数据技术之Kafka第4章 Kafka API实战

4.1 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

4.2 Kafka生产者Java API

4.2.1 创建生产过时的API)

package com.atguigu.kafka;

import java.util.Properties;

import kafka.javaapi.producer.Producer;

import kafka.producer.KeyedMessage;

import kafka.producer.ProducerConfig;

 

public class OldProducer {

 

@SuppressWarnings(“deprecation”)

public static void 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 );

}

}

4.2.2 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;

 

public class NewProducer {

 

public static void 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”);

 

Producer<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();

}

}

 

4.2.3 创建生产者回调函数(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;

 

public class CallBackProducer {

 

public static void 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

public void onCompletion(RecordMetadata metadata, Exception exception) {

 

if (metadata != null) {

 

System.err.println(metadata.partition() + “—” + metadata.offset());

}

}

});

}

 

kafkaProducer.close();

}

}

 

4.2.4 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;

import java.util.Map;

import kafka.producer.Partitioner;

 

public class CustomPartitioner implements Partitioner {

 

public CustomPartitioner() {

super();

}

 

@Override

public int 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;

 

public class CustomPartitioner implements Partitioner {

 

@Override

public void configure(Map<String, ?> configs) {

}

 

@Override

public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {

        // 控制分区

return 0;

}

 

@Override

public void 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;

 

public class PartitionerProducer {

 

public static void 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。

4.3 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;

import kafka.consumer.ConsumerConfig;

import kafka.consumer.ConsumerIterator;

import kafka.consumer.KafkaStream;

import kafka.javaapi.consumer.ConsumerConnector;

 

public class CustomConsumer {

 

@SuppressWarnings(“deprecation”)

public static void 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;

 

public class CustomNewConsumer {

 

public static void 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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