Kafka(一) —— 基本概念及使用

一、安装&启动

安装Kafka(使用内置Zookeeper)

Kafka官网下载安装包kafka_2.11-1.0.0.tgz

#### 解压
tar zxvf kafka_2.11-1.0.0.tgz

#### 启动内置的zookeeper
.bin/zookeeper-server-start.sh ../config/zookeeper.properties

#### 启动kafka
./bin/kafka-server-start.sh ../config/server.properties

#### 启动kafka,在后台运行
./bin/kafka-server-start.sh -daemon ../config/server.properties

不使用内置的Zookeeper

Zk下载链接

http://archive.apache.org/dist/zookeeper/zookeeper-3.4.10/

Zk官方文档

https://zookeeper.apache.org/doc/current/index.html

启动Zk


#### 修改配置
cd conf/
cp zoo_sample.cfg zoo.cfg

#### 启动zk
./zkServer.sh start

#### 使用zkCli连接zk
./zkCli.sh -server 127.0.0.1:2181

二、终端命令

创建主题

./kafka-topics.sh --create --zookeeper localhost:2181 --topic test --partitions 1 --replication-factor 1

查看主题

./kafka-topics.sh --describe --zookeeper localhost:2181 --topic test

生产消息

./kafka-console-producer.sh --broker-list localhost:9092 --topic test

消费消息

./kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning

三、生产

引入依赖


        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka-clients -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>1.0.0</version>
        </dependency>

生产消息


public class KafkaProducerDemo {

    public static void main(String[] args) {

        Properties props = new Properties();
        props.put("bootstrap.servers", "localhost:9092");
        // ack = 0 producer不理睬broker的处理结果
        // ack = all or -1 broker将消息写入本地日志,且ISR中副本也全部同步完,返回响应结果
        // ack = 1 默认参数值,broker写入本地日志,无需等待ISR
        props.put("acks", "-1");
        props.put("retries", 3);
        //单位byte,当batch满了,producer会发送batch中的消息,还要参考linger.ms参数
        props.put("batch.size", 16384);
        //控制消息发送的延时行为,让batch即使没满,也可以发送batch中的消息
        props.put("linger.ms", 10);
        //producer端缓存消息缓冲区的大小
        props.put("buffer.memory", 33554432);
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("max.blocks.ms", "3000");

        Producer<String, String> producer = new KafkaProducer<String, String>(props);

        for (int i = 0; i < 100; i++) {
            try {
                producer.send(new ProducerRecord<String, String>("testfzj", Integer.toString(i), Integer.toString(i))).get();
            } catch (InterruptedException e) {
                e.printStackTrace();
            } catch (ExecutionException e) {
                e.printStackTrace();
            }

        }
        producer.close();

        System.out.println("发送完成");
    }

}

使用自定义拦截器

(1)发送的value前统一加一个时间戳


/**
 * 增加时间戳 拦截器
 * @author Michael Fang
 * @since 2019-11-12
 */
public class TimeStampPrependerInterceptor implements ProducerInterceptor<String, String> {

    /**
     * 会创建一个新的Record
     *
     * @param producerRecord
     * @return
     */
    @Override
    public ProducerRecord<String, String> onSend(ProducerRecord<String, String> producerRecord) {
        return new ProducerRecord<String, String>(
                producerRecord.topic(),
                producerRecord.partition(),
                producerRecord.timestamp(), producerRecord.key(),
                System.currentTimeMillis() + "," + producerRecord.value().toString());
    }

    @Override
    public void onAcknowledgement(RecordMetadata recordMetadata, Exception e) {

    }

    @Override
    public void close() {

    }

    @Override
    public void configure(Map<String, ?> map) {

    }

}

(2)发送完成后进行成功统计


/**
 * 发送后成功统计 拦截器
 * @author Michael Fang
 * @since 2019-11-12
 */
public class CounterInterceptor implements ProducerInterceptor {

    private int errorCounter = 0;
    private int successCounter = 0;

    @Override
    public ProducerRecord onSend(ProducerRecord producerRecord) {
        return producerRecord;
    }

    /**
     * 这两个参数不可能同时为空
     * e = null 说明发送成功
     * recordMetadata = null 说明发送失败
     *
     * @param recordMetadata
     * @param e
     */
    @Override
    public void onAcknowledgement(RecordMetadata recordMetadata, Exception e) {

        if (e == null) {
            successCounter++;
        } else {
            errorCounter++;
        }
    }

    @Override
    public void close() {
        //打印结果
        System.out.println("Success sent: " + successCounter);
        System.out.println("Failed sent: " + errorCounter);
    }

    @Override
    public void configure(Map<String, ?> map) {

    }
}

(3)Producer代码中增加属性配置,使其拦截器生效

        Properties props = new Properties();
        List<String> interceptors = new ArrayList<>();
        interceptors.add("com.fonxian.kafka.TimeStampPrependerInterceptor");
        interceptors.add("com.fonxian.kafka.CounterInterceptor");
        props.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, interceptors);

效果

使用自定义分区器

创建一个4个分区的主题

./kafka-topics.sh --create --zookeeper localhost:2181 --topic test-partition-1 --partitions 4 --replication-factor 1

产生的消息的key为(0-99),消息的key能被10整除的全部放到最后一个分区


/**
 * 自定义分区器
 *
 * @author Michael Fang
 * @since 2019-11-13
 */
public class GetServenPartitioner implements Partitioner {

    private Random random;

    @Override
    public int partition(String topic, Object keyObj, byte[] keyBytes, Object valueObj, byte[] valueBytes1, Cluster cluster) {
        String key = (String) keyObj;
        //获取分区数
        List<PartitionInfo> partitionInfoList = cluster.availablePartitionsForTopic(topic);
        int partitionCount = partitionInfoList.size();
        //最后一个分区的分区号
        int lastPartition = partitionCount - 1;
        //将能被10整除的key-value,发送到最后一个分区
        if(Integer.valueOf(key) % 10 == 0){
            return lastPartition;
        }else{
            return random.nextInt(lastPartition);
        }
    }

    @Override
    public void close() {

    }

    @Override
    public void configure(Map<String, ?> map) {
        random = new Random();
    }
}

结果

在broker上执行命令

./kafka-run-class.sh kafka.tools.GetOffsetShell --broker-list localhost:9092 --topic test-partition-1

得到结果

四、消费

消费消息


public class KafkaConsumerDemo {

    public static void main(String[] args) {
        String topicName = "testfzj";
        String gorupId = "test-group";

        Properties props = new Properties();
        props.put("bootstrap.servers", "localhost:9092");
        props.put("group.id", gorupId);
        //是否自动提交
        props.put("enable.auto.commit", "true");
        // 自动提交的间隔
        props.put("auto.commit.interval.ms", "1000");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
        consumer.subscribe(Arrays.asList(topicName));
        try {
            while (true) {
                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());
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            consumer.close();
        }
    }

}

指定分区消费消息

        // 见上面分区器的部分,定义4个分区的topic
        // 使用分区器将能被10整除的key,放到最后一个分区
        String topicName = "test-partition-1";
        Properties props = new Properties();
        //配置成从头开始消费
        //earliest 从最早的位移开始消费
        //latest 从最新处位移开始消费
        props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
        //是否自动提交位移
        //默认是true,自动提交
        //设置成false,适合有“精确处理一次”语义的需求,用户自行处理位移。
        props.put("enable.auto.commit", "true");
        //获取最后一个分区
        List<PartitionInfo> partitionInfoList = consumer.partitionsFor(topicName);
        //指定最后一个分区
        consumer.assign(Arrays.asList(new TopicPartition(topicName, partitionInfoList.size() - 1)));
        try {
            while (true) {

                ConsumerRecords<String, String> records = consumer.poll(100);
                for (ConsumerRecord<String, String> record : records) {
                    System.out.printf("topic =  %s, partition = %d, offset = %d, key = %s, value = %s%n",record.topic(),record.partition(), record.offset(), record.key(), record.value());
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            consumer.close();
        }

运行结果

__consumer_offsets

Rebalance

rebalance规定一个consumer group如何分配订阅topic所有分区,分配的这个过程就叫rebalance。

(1)谁来执行rebalance

组协调者coordinator执行rebalance操作,负责促成组内所有成员达成新的分区分配方案。

(2)何时触发

  • 当组成员发生变化,新的consumer加入或consumer退出
  • 订阅的topic数发生变更,例如使用正则匹配topic,突然加入新的topic
  • 订阅的topic分区数发生变更

(3)分配策略

以8个partition(p1-p8),4个consumer(c1 - c4)举例。

  • range策略
    • 将分区划分成固定大小的分区段,依次分配给每个分区。例如将p1、p2分配给c1。
  • round-robin策略
    • 将分区按顺序排开,依次分配给各个consumer。例如将p1、p5分配给c1。
  • sticky策略

(4)rebalance generation

为了隔离每次rebalance的数据,防止无效的offset提交。引入rebalance generation(届)的概念。

每次rebalance完成后,consumer都会升一届。当新的届的consumer产生,则consumer group不会接受旧的届提交的offset。

例如上一届的consumer因为网络延时等原因延时提交了offset,新的一届consumer已经产生,这时,上一届consume提交的offset,将会被consumer group拒绝,会出现ILLEGAL_GENERATION异常。

(5)调优案例:频繁rebalance

线上频繁进行rebalance,会降低consumer端的吞吐量。

原因是,consumer的处理逻辑过重,导致处理时间波动大,coordinator会经常认为某个consumer挂掉,进行rebalance操作。同时consumer又会重新申请加入group,又会引发rebalance操作。

调整request.timeout.msmax.poll.recordsmax.poll.interval.ms来避免不必要的rebalance。

五、SpringBoot整合kafka

文档:https://docs.spring.io/spring-kafka/docs/2.3.3.RELEASE/reference/html/

引入依赖、配置

依赖


 <!-- Inherit defaults from Spring Boot -->
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.2.1.RELEASE</version>
    </parent>


    <dependencies>

        <!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.59</version>
        </dependency>

        <!-- Add typical dependencies for a web application -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.springframework.kafka/spring-kafka -->
        <dependency>
            <groupId>org.springframework.kafka</groupId>
            <artifactId>spring-kafka</artifactId>
            <version>2.3.3.RELEASE</version>
        </dependency>

    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-maven-plugin</artifactId>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.1</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>
        </plugins>
    </build>

配置

application.properties

server.port=9001
spring.application.name=kafka-demo

#============== kafka ===================
# 指定kafka 代理地址,可以多个
spring.kafka.bootstrap-servers=127.0.0.1:9092

#=============== provider  =======================
spring.kafka.producer.retries=0
# 每次批量发送消息的数量
spring.kafka.producer.batch-size=16384
spring.kafka.producer.buffer-memory=33554432

# 指定消息key和消息体的编解码方式
spring.kafka.producer.key-serializer=org.apache.kafka.common.serialization.StringSerializer
spring.kafka.producer.value-serializer=org.apache.kafka.common.serialization.StringSerializer

#=============== consumer  =======================
# 指定默认消费者group id
spring.kafka.consumer.group-id=user-log-group

spring.kafka.consumer.auto-offset-reset=earliest
spring.kafka.consumer.enable-auto-commit=true
spring.kafka.consumer.auto-commit-interval=100

# 指定消息key和消息体的编解码方式
spring.kafka.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer
spring.kafka.consumer.value-deserializer=org.apache.kafka.common.serialization.StringDeserializer

生产者


@SpringBootApplication
public class Application {


    @Autowired
    private KafkaTemplate kafkaTemplate;

    private static final String TOPIC = "test-partition-1";

    @PostConstruct
    public void init() {
        for (int i = 0; i < 10; i++) {
            kafkaTemplate.send(TOPIC, "key:" + i, "value:" + i);
        }
    }

    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }


}

消费者


@Component
public class Consume {

    @KafkaListener(topics = "test-partition-1")
    public void consumer(ConsumerRecord consumerRecord){
        Optional<Object> kafkaMassage = Optional.ofNullable(consumerRecord);
        if(kafkaMassage.isPresent()){
            ConsumerRecord record = (ConsumerRecord)kafkaMassage.get();
            System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
        }

    }

}

结果

六、常见问题

org.apache.kafka.common.errors.TimeoutException

使用Java客户端生产消息,出现此异常提示。

原因:外网访问,需要修改server.properties参数,将IP地址改为公网的IP地址,然后重启服务

advertised.listeners=PLAINTEXT://59.11.11.11:9092

可参考 https://www.cnblogs.com/snifferhu/p/5102629.html

参考文档

《Apache Kafka实战》
《深入理解Kafka:核心设计与实践原理》
springboot集成Kafka
Spring for Apache Kafka

posted @ 2019-11-11 18:23  CoffeJoy  阅读(...)  评论(... 编辑 收藏