常用命令：

创建topic
bin/kafka-topics.sh --create --zookeeper node01:2181,node02:2181,node03:2181 --replication-factor 2 --partitions 3 --topic 18BD12-1
查看topic
bin/kafka-topics.sh --list --zookeeper node01:2181,node02:2181,node03:2181
查看topic的结构
bin/kafka-topics.sh --describe --zookeeper node01:2181,node02:2181,node03:2181 --topic 18BD12-1
模拟生产者生产数据
bin/kafka-console-producer.sh --broker-list node01:9092,node02:9092,node03:9092 --topic 18BD12
修改分区数量
bin/kafka-topics.sh --zookeeper node01:2181,node02:2181,node03:2181 --alter --topic 18BD12 --partitions 4
模拟消费者消费数数据
bin/ kafka-console-consumer.sh --from-beginning --topic 18BD12 --zookeeper node01:2181,node02:2181,node03:2181
添加配置
bin/kafka-topics.sh --zookeeper node01:2181 --alter --topic test --config flush.messages=1
删除配置
bin/kafka-topics.sh --zookeeper node01:2181 --alter --topic test --delete-config flush.messages
删除topic
kafka-topics.sh --zookeeper zkhost:port --delete --topic topicName

JavaAPI使用：

生产者

package com.czxy.producer;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.util.Properties;

/**
 * 用于生产数据到kafka集群
 */
public class Producer1 {

    public static void main(String[] args) {
        // 1. 配置kafka集群环境
        Properties props = new Properties();
        // kafka 服务器地址
        props.put("bootstrap.servers", "node01:9092,node02:9092,node03:9092");
        // 消息确认机制
        props.put("acks", "all");
        //重试机制
        props.put("retries", 0);
        // 批量发送的大小
        props.put("batch.size", 16384);
        // 消息延迟
        props.put("linger.ms", 1);
        // 批量的缓冲区大小
        props.put("buffer.memory", 33554432);
        // kafka key value 缓冲区
        props.put("key.serializer",
                "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer",
                "org.apache.kafka.common.serialization.StringSerializer");
        // 自定义分区
//        props.put("partitioner.class", "Producer.ProducerPartition"); 
        // 2. 实例一个生产者对象
        KafkaProducer<String, String> kafkaProducer = new KafkaProducer<String, String>(props);
        // 3. 通过生产者将数据发送到kafka集群
        for (int i = 0; i < 10; i++) {
            //1、没有指定分区编号，没有指定key,时采用轮询方式存户数据
            ProducerRecord producerRecord = new ProducerRecord("18BD12_1", "JAV2AAPi" + i);
            //2、没有指定分区编号，指定key时，数据分发策略为对key求取hash值，这个值与分区数量取余，于数就是分区编号。
            //ProducerRecord producerRecord = new ProducerRecord("18BD12_1", "key", "JAV2AAPi" + i);
            //3、指定分区编号，所有数据输入到指定的分区内
            //ProducerRecord producerRecord = new ProducerRecord("18BD12_1", 1, "", "JAV2AAPi" + i);
            //4、定义分区策略
            //ProducerRecord producerRecord = new ProducerRecord("18BD12_1", 1, "", "JAV2AAPi" + i);
            kafkaProducer.send(producerRecord);
        }

        // 关闭生产者
        kafkaProducer.close();
    }
}

消费者

自动提交offset

package com.czxy.consumer;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;

import java.util.Arrays;
import java.util.Properties;

/**
 * 消费者
 */
public class Consumer1 {
    public static void main(String[] args) {
        // 1.添加配置文件
        Properties props = new Properties();
        // 添加kafka服务器
        props.put("bootstrap.servers", "node01:9092,node02:9092,node03:9092");
        // 指定消费组
        props.put("group.id", "test");
        //以下两行代码 ---消费者自动提交offset值
        props.put("enable.auto.commit", "true");
        //自动提交的周期
        props.put("auto.commit.interval.ms", "1000");
        // kafka key  value 反序列化
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        // 2.实例消费对象
        KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<>(props);
        // 3.设置读哪个topic
        kafkaConsumer.subscribe(Arrays.asList("18BD12_1"));
        // 循环遍历拉取数据
        while (true) {
            // 4.拉取数据并输出
            //获取到所有的数据
            ConsumerRecords<String, String> consumerRecords = kafkaConsumer.poll(1000);
            // 循环遍历数据 获取一条数据
            for (ConsumerRecord<String, String> record : consumerRecords) {
                String value = record.value();
                System.out.println(value);
            }
        }
    }
}

手动提交offset

package com.czxy.consumer;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;

import java.awt.event.KeyAdapter;
import java.util.Arrays;
import java.util.Properties;

/**
 * 手动提交offset
 */
public class Consumer2 {
    public static void main(String[] args) {
        // 1.添加配置文件
        Properties props = new Properties();
        // 添加kafka服务器
        props.put("bootstrap.servers", "node01:9092,node02:9092,node03:9092");
        // 指定消费组
        props.put("group.id", "test1");
        //以下两行代码 ---消费者自动提交offset值
        props.put("enable.auto.commit", "false");
        //自动提交的周期
//        props.put("auto.commit.interval.ms", "1000");
        // kafka key  value 反序列化
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        //2.实例消费者对象
        KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<>(props);
        //3.指定topic
        kafkaConsumer.subscribe(Arrays.asList("18BD12_1"));
        // 循环遍历
        while (true){
            //4.循环拉取数据并输出
            ConsumerRecords<String, String> consumerRecords = kafkaConsumer.poll(1000);
            for (ConsumerRecord<String, String> record : consumerRecords) {
                System.out.println(record.value());
            }
            // 手动提交offset
            kafkaConsumer.commitAsync();
        }
    }
}

以分区为单位进行读取

package com.czxy.consumer;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.TopicPartition;

import java.util.Arrays;
import java.util.List;
import java.util.Properties;
import java.util.Set;

/*
以分区为单位进行消费
 */
public class ConsumerPartition {
    public static void main(String[] args) {
        // 1.添加配置文件
        Properties props = new Properties();
        // 添加kafka服务器
        props.put("bootstrap.servers", "node01:9092,node02:9092,node03:9092");
        // 指定消费组
        props.put("group.id", "test1");
        //以下两行代码 ---消费者自动提交offset值
        props.put("enable.auto.commit", "false");
        //自动提交的周期
//        props.put("auto.commit.interval.ms", "1000");
        // kafka key  value 反序列化
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        // 2.实例消费对象
        KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<>(props);
        // 3.添加topic
        kafkaConsumer.subscribe(Arrays.asList("18BD12_1"));
        while (true) {
            // 4.循环获取数据
            // 根据分区读取数据
            ConsumerRecords<String, String> consumerRecords = kafkaConsumer.poll(1000);
            // 获取所有的分区
            Set<TopicPartition> partitions = consumerRecords.partitions();
            // 遍历分区
            for (TopicPartition partition : partitions) {
                // 获取每个分区的数据
                List<ConsumerRecord<String, String>> records = consumerRecords.records(partition);
                // 遍历分区所有数据 获取每一条数据
                for (ConsumerRecord<String, String> record : records) {
                    System.out.println(record.value());
                }
                // 5.手动提交offset(一个分区提交一次)
                kafkaConsumer.commitAsync();
            }

        }
    }
}

消费制定的分区

package Consumer;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.TopicPartition;

import java.util.Arrays;
import java.util.List;
import java.util.Properties;
import java.util.Set;

/*
消费指定分区的数据
 */
public class ConsumerMyPartition {
    public static void main(String[] args){

        //1配置文件
        Properties props = new Properties();
        //指定kafka服务器
        props.put("bootstrap.servers", "node01:9092,node02:9092,node03:9092");
        //消费组
        props.put("group.id", "test1");
        //以下两行代码 ---消费者自动提交offset值
        props.put("enable.auto.commit", "false");
        //自动提交的周期
        //props.put("auto.commit.interval.ms",  "1000");
        //kafka   key 和value的反序列化
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        //2消费者
        KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<String, String>(props);
        //3、设置topic
        // 添加消费配置
        TopicPartition topicPartition0 = new TopicPartition("18BD12",0);
        TopicPartition topicPartition2 = new TopicPartition("18BD12",1);
        kafkaConsumer.assign(Arrays.asList(topicPartition0,topicPartition2));
        while (true){

            //4、拉取数据，并输出
            ConsumerRecords<String, String> consumerRecords = kafkaConsumer.poll(1000);
            //通过数据获取到多有的分区  0   2
            Set<TopicPartition> partitions = consumerRecords.partitions();
            //遍历所有分区，或得到一个分区
            for (TopicPartition partition : partitions) {
                //获取每个分区的数据,多条数据
                List<ConsumerRecord<String, String>> records = consumerRecords.records(partition);
                //遍历分区内的所有数据，或得到一条
                for (ConsumerRecord<String, String> record : records) {
                    System.out.println(record.value()+"      "+record.partition());
                }


                //手动提交offset
                kafkaConsumer.commitSync();
            }




        }

    } 
}

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