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5.1 Kafka 消费方式

5.2 Kafka 消费者工作流程

5.2.1 消费者总体工作流程

5.2.2 消费者组原理

5.2.3 消费者重要参数

5.3 消费者 API

5.3.1 独立消费者案例（订阅主题）

package com.atguigu.kafka.consumer;import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;import java.time.Duration;
import java.util.ArrayList;
import java.util.Properties;public class CustomConsumer {public static void main(String[] args) {// 1.创建消费者的配置对象Properties properties = new Properties();// 2.给消费者配置对象添加参数properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// 配置序列化 必须properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());// 配置消费者组（组名任意起名） 必须properties.put(ConsumerConfig.GROUP_ID_CONFIG, "test");// 创建消费者对象KafkaConsumer<String, String> kafkaConsumer = newKafkaConsumer<String, String>(properties);// 注册要消费的主题（可以消费多个主题）ArrayList<String> topics = new ArrayList<>();topics.add("first");kafkaConsumer.subscribe(topics);// 拉取数据打印while (true) {// 设置 1s 中消费一批数据ConsumerRecords<String, String> consumerRecords =kafkaConsumer.poll(Duration.ofSeconds(1));// 打印消费到的数据for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord);}}}
}

[atguigu@hadoop102 kafka]$ bin/kafka-console-producer.sh

--

bootstrap-server hadoop102:9092 --topic first

>hello

（3）在 IDEA 控制台观察接收到的数据。

ConsumerRecord(topic = first, partition = 1, leaderEpoch = 3,

offset = 0, CreateTime = 1629160841112, serialized key size = -1,

serialized value size = 5, headers = RecordHeaders(headers = [],

isReadOnly = false), key = null, value = hello)

5.3.2 独立消费者案例（订阅分区）

2 ）实现步骤

（ 1 ）代码编写。

package com.atguigu.kafka.consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
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.time.Duration;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Properties;
public class CustomConsumerPartition {public static void main(String[] args) {Properties properties = new Properties();properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// 配置序列化 必须properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());// 配置消费者组（必须），名字可以任意起properties.put(ConsumerConfig.GROUP_ID_CONFIG,"test");KafkaConsumer<String, String> kafkaConsumer = newKafkaConsumer<>(properties);// 消费某个主题的某个分区数据ArrayList<TopicPartition> topicPartitions = newArrayList<>();topicPartitions.add(new TopicPartition("first", 0));kafkaConsumer.assign(topicPartitions);while (true){ConsumerRecords<String, String> consumerRecords =kafkaConsumer.poll(Duration.ofSeconds(1));for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord);}}} }

3 ）测试

（ 1 ）在 IDEA 中执行消费者程序。

（2）在 IDEA 中执行生产者程序 CustomProducerCallback() 在控制台观察生成几个 0 号

分区的数据。

first 0 381

first 0 382

first 2 168

first 1 165

first 1 166

（3）在 IDEA 控制台，观察接收到的数据，只能消费到 0 号分区数据表示正确。

ConsumerRecord(topic = first, partition = 0 , leaderEpoch = 14,

offset = 381, CreateTime = 1636791331386, serialized key size = -

1, serialized value size = 9, headers = RecordHeaders(headers =

[], isReadOnly = false), key = null, value = atguigu 0)

ConsumerRecord(topic = first, partition = 0 , leaderEpoch = 14,

offset = 382, CreateTime = 1636791331397, serialized key size = -

1, serialized value size = 9, headers = RecordHeaders(headers =

[], isReadOnly = false), key = null, value = atguigu 1)

5.3.3 消费者组案例

2 ）案例实操

（ 1 ）复制一份基础消费者的代码，在 IDEA 中同时启动，即可启动同一个消费者组中

的两个消费者。

package com.atguigu.kafka.consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import java.time.Duration;
import java.util.ArrayList;
import java.util.Properties;
public class CustomConsumer1 {public static void main(String[] args) {// 1.创建消费者的配置对象Properties properties = new Properties();// 2.给消费者配置对象添加参数properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// 配置序列化 必须properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());// 配置消费者组 必须properties.put(ConsumerConfig.GROUP_ID_CONFIG, "test");// 创建消费者对象KafkaConsumer<String, String> kafkaConsumer = newKafkaConsumer<String, String>(properties);// 注册主题ArrayList<String> topics = new ArrayList<>();topics.add("first");kafkaConsumer.subscribe(topics);// 拉取数据打印while (true) {// 设置 1s 中消费一批数据ConsumerRecords<String, String> consumerRecords =kafkaConsumer.poll(Duration.ofSeconds(1));// 打印消费到的数据for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord);}}} }

（2）启动代码中的生产者发送消息，在 IDEA 控制台即可看到两个消费者在消费不同

分区的数据（如果只发生到一个分区，可以在发送时增加延迟代码 Thread.sleep(2); ）。

ConsumerRecord(topic = first, partition = 0 , leaderEpoch = 2,

offset = 3, CreateTime = 1629169606820, serialized key size = -1,

serialized value size = 8, headers = RecordHeaders(headers = [],

isReadOnly = false), key = null, value = hello1 )

ConsumerRecord(topic = first, partition = 1 , leaderEpoch = 3,

offset = 2, CreateTime = 1629169609524, serialized key size = -1,

serialized value size = 6, headers = RecordHeaders(headers = [],

isReadOnly = false), key = null, value = hello2 )

ConsumerRecord(topic = first, partition = 2 , leaderEpoch = 3,

offset = 21, CreateTime = 1629169611884, serialized key size = -1,

serialized value size = 6, headers = RecordHeaders(headers = [],

isReadOnly = false), key = null, value = hello3 )

（3）重新发送到一个全新的主题中，由于默认创建的主题分区数为 1 ，可以看到只能

有一个消费者消费到数据。

5.4 生产经验——分区的分配以及再平衡

5.4.1 Range 以及再平衡

（3）启动 CustomProducer 生产者，发送 500 条消息，随机发送到不同的分区。

package com.atguigu.kafka.producer;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;
public class CustomProducer {public static void main(String[] args) throwsInterruptedException {Properties properties = new Properties();properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());KafkaProducer<String, String> kafkaProducer = newKafkaProducer<>(properties);for (int i = 0; i < 7; i++) {kafkaProducer.send(new ProducerRecord<>("first", i,"test", "atguigu"));}kafkaProducer.close();} }

说明： Kafka 默认的分区分配策略就是 Range + CooperativeSticky ，所以不需要修改策

略。

（4）观看 3 个消费者分别消费哪些分区的数据。

5.4.2 RoundRobin 以及再平衡

5.4.3 Sticky 以及再平衡

5.5 offset 位移

5.5.1 offset 的默认维护位置

5.5.2 自动提交 offset

1）消费者自动提交 offset

package com.atguigu.kafka.consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
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;
public class CustomConsumerAutoOffset {public static void main(String[] args) {// 1. 创建 kafka 消费者配置类Properties properties = new Properties();// 2. 添加配置参数// 添加连接properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// 配置序列化 必须properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");// 配置消费者组properties.put(ConsumerConfig.GROUP_ID_CONFIG, "test");// 是否自动提交 offsetproperties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,true);// 提交 offset 的时间周期 1000ms，默认 5sproperties.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG,1000);//3. 创建 kafka 消费者KafkaConsumer<String, String> consumer = newKafkaConsumer<>(properties);//4. 设置消费主题 形参是列表consumer.subscribe(Arrays.asList("first"));//5. 消费数据while (true){// 读取消息ConsumerRecords<String, String> consumerRecords =consumer.poll(Duration.ofSeconds(1));// 输出消息for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord.value());}}} }

5.5.3 手动提交 offset

1 ）同步提交 offset

由于同步提交 offset 有失败重试机制，故更加可靠，但是由于一直等待提交结果，提

交的效率比较低。以下为同步提交 offset 的示例。

package com.atguigu.kafka.consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
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;
public class CustomConsumerByHandSync {public static void main(String[] args) {// 1. 创建 kafka 消费者配置类Properties properties = new Properties();// 2. 添加配置参数// 添加连接properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// 配置序列化 必须properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");// 配置消费者组properties.put(ConsumerConfig.GROUP_ID_CONFIG, "test");// 是否自动提交 offsetproperties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,false);//3. 创建 kafka 消费者KafkaConsumer<String, String> consumer = newKafkaConsumer<>(properties);//4. 设置消费主题 形参是列表consumer.subscribe(Arrays.asList("first"));//5. 消费数据while (true){// 读取消息ConsumerRecords<String, String> consumerRecords =consumer.poll(Duration.ofSeconds(1));// 输出消息for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord.value());}// 同步提交 offsetconsumer.commitSync();}} }

2 ）异步提交 offset

虽然同步提交 offset 更可靠一些，但是由于其会阻塞当前线程，直到提交成功。因此

吞吐量会受到很大的影响。因此更多的情况下，会选用异步提交 offset 的方式。

以下为异步提交 offset 的示例：

package com.atguigu.kafka.consumer;
import org.apache.kafka.clients.consumer.*;
import org.apache.kafka.common.TopicPartition;
import java.util.Arrays;
import java.util.Map;
import java.util.Properties;
public class CustomConsumerByHandAsync {public static void main(String[] args) {// 1. 创建 kafka 消费者配置类Properties properties = new Properties();// 2. 添加配置参数// 添加连接properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// 配置序列化 必须properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer");// 配置消费者组properties.put(ConsumerConfig.GROUP_ID_CONFIG, "test");// 是否自动提交 offsetproperties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"false");//3. 创建 Kafka 消费者KafkaConsumer<String, String> consumer = newKafkaConsumer<>(properties);//4. 设置消费主题 形参是列表consumer.subscribe(Arrays.asList("first"));//5. 消费数据while (true){// 读取消息ConsumerRecords<String, String> consumerRecords =consumer.poll(Duration.ofSeconds(1));// 输出消息for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord.value());}// 异步提交 offsetconsumer.commitAsync();}} }

5.5.4 指定 Offset 消费

（4）任意指定 offset 位移开始消费

package com.atguigu.kafka.consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
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 org.apache.kafka.common.serialization.StringDeserializer;
import java.time.Duration;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.Properties;
import java.util.Set;
public class CustomConsumerSeek {public static void main(String[] args) {// 0 配置信息Properties properties = new Properties();// 连接properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// key value 反序列化properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ConsumerConfig.GROUP_ID_CONFIG, "test2");// 1 创建一个消费者KafkaConsumer<String, String> kafkaConsumer = newKafkaConsumer<>(properties);// 2 订阅一个主题ArrayList<String> topics = new ArrayList<>();topics.add("first");kafkaConsumer.subscribe(topics);Set<TopicPartition> assignment= new HashSet<>();while (assignment.size() == 0) {kafkaConsumer.poll(Duration.ofSeconds(1));// 获取消费者分区分配信息（有了分区分配信息才能开始消费）assignment = kafkaConsumer.assignment();}// 遍历所有分区，并指定 offset 从 1700 的位置开始消费for (TopicPartition tp: assignment) {kafkaConsumer.seek(tp, 1700);}// 3 消费该主题数据while (true) {ConsumerRecords<String, String> consumerRecords =kafkaConsumer.poll(Duration.ofSeconds(1));for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord);}}} }

注意：每次执行完，需要修改消费者组名；

5.5.5 指定时间消费

需求：在生产环境中，会遇到最近消费的几个小时数据异常，想重新按照时间消费。

例如要求按照时间消费前一天的数据，怎么处理？

操作步骤：

package com.atguigu.kafka.consumer;
import org.apache.kafka.clients.consumer.*;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.serialization.StringDeserializer;
import java.time.Duration;
import java.util.*;
public class CustomConsumerForTime {public static void main(String[] args) {// 0 配置信息Properties properties = new Properties();// 连接properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092");// key value 反序列化properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());properties.put(ConsumerConfig.GROUP_ID_CONFIG, "test2");// 1 创建一个消费者KafkaConsumer<String, String> kafkaConsumer = newKafkaConsumer<>(properties);// 2 订阅一个主题ArrayList<String> topics = new ArrayList<>();topics.add("first");kafkaConsumer.subscribe(topics);Set<TopicPartition> assignment = new HashSet<>();while (assignment.size() == 0) {kafkaConsumer.poll(Duration.ofSeconds(1));// 获取消费者分区分配信息（有了分区分配信息才能开始消费）assignment = kafkaConsumer.assignment();}HashMap<TopicPartition, Long> timestampToSearch = newHashMap<>();// 封装集合存储，每个分区对应一天前的数据for (TopicPartition topicPartition : assignment) {timestampToSearch.put(topicPartition,System.currentTimeMillis() - 1 * 24 * 3600 * 1000);}// 获取从 1 天前开始消费的每个分区的 offsetMap<TopicPartition, OffsetAndTimestamp> offsets =kafkaConsumer.offsetsForTimes(timestampToSearch);// 遍历每个分区，对每个分区设置消费时间。for (TopicPartition topicPartition : assignment) {OffsetAndTimestamp offsetAndTimestamp =offsets.get(topicPartition);// 根据时间指定开始消费的位置if (offsetAndTimestamp != null){kafkaConsumer.seek(topicPartition,offsetAndTimestamp.offset());}}// 3 消费该主题数据while (true) {ConsumerRecords<String, String> consumerRecords =kafkaConsumer.poll(Duration.ofSeconds(1));for (ConsumerRecord<String, String> consumerRecord :consumerRecords) {System.out.println(consumerRecord);}}} }