Kafka学习笔记(三)Kafka API

2020-07-14  本文已影响0人  做个合格的大厂程序员

1. Producer API

消息发送流程

Kafka的Producer发送消息采用的是异步发送的方式。在消息发送的过程中,涉及到了两个线程——main线程和Sender线程,以及一个线程共享变量——RecordAccumulator。main线程将消息发送给RecordAccumulator,Sender线程不断从RecordAccumulator中拉取消息发送到Kafka broker。

Xnip2020-07-12_20-40-10

相关参数:
batch.size:只有数据积累到batch.size之后,sender才会发送数据。

linger.ms:如果数据迟迟未达到batch.size,sender等待linger.time之后就会发送数据。

创建工程导入:

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>0.11.0.0</version>
</dependency>

编写代码

需要用到的类:

KafkaProducer:需要创建一个生产者对象,用来发送数据

ProducerConfig:获取所需的一系列配置参数

ProducerRecord:每条数据都要封装成一个ProducerRecord对象

不带回调函数的API

package com.atguigu.kafka;

import org.apache.kafka.clients.producer.*;

import java.util.Properties;
import java.util.concurrent.ExecutionException;

public class CustomProducer {

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        Properties props = new Properties();
        props.put("bootstrap.servers", "hadoop102:9092");//kafka集群,broker-list
        props.put("acks", "all");
        props.put("retries", 1);//重试次数
        props.put("batch.size", 16384);//批次大小
        props.put("linger.ms", 1);//等待时间
        props.put("buffer.memory", 33554432);//RecordAccumulator缓冲区大小
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<>(props);
        for (int i = 0; i < 100; i++) {
            producer.send(new ProducerRecord<String, String>("first", Integer.toString(i), Integer.toString(i)));
        }
        producer.close();
    }
}

带回调函数的API

回调函数会在producer收到ack时调用,为异步调用,该方法有两个参数,分别是RecordMetadata和Exception,如果Exception为null,说明消息发送成功,如果Exception不为null,说明消息发送失败。

注意:消息发送失败会自动重试,不需要我们在回调函数中手动重试。

package com.atguigu.kafka;

import org.apache.kafka.clients.producer.*;

import java.util.Properties;

import java.util.concurrent.ExecutionException;

public class CustomProducer {

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        Properties props = new Properties();
        props.put("bootstrap.servers", "hadoop102:9092");//kafka集群,broker-list
        props.put("acks", "all");
        props.put("retries", 1);//重试次数
        props.put("batch.size", 16384);//批次大小
        props.put("linger.ms", 1);//等待时间
        props.put("buffer.memory", 33554432);//RecordAccumulator缓冲区大小
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<>(props);
        for (int i = 0; i < 100; i++) {
            producer.send(new ProducerRecord<String, String>("first", Integer.toString(i), Integer.toString(i)), new Callback() {

                //回调函数,该方法会在Producer收到ack时调用,为异步调用
                @Override
                public void onCompletion(RecordMetadata metadata, Exception exception) {
                    if (exception == null) {
                        System.out.println("success->" + metadata.offset());
                    } else {
                        exception.printStackTrace();
                    }
                }
            });
        }
        producer.close();
    }
}

同步发送API

同步发送的意思就是,一条消息发送之后,会阻塞当前线程,直至返回ack。

由于send方法返回的是一个Future对象,根据Futrue对象的特点,我们也可以实现同步发送的效果,只需在调用Future对象的get方发即可。

package com.atguigu.kafka;

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

import java.util.Properties;

import java.util.concurrent.ExecutionException;

public class CustomProducer {

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        Properties props = new Properties();
        props.put("bootstrap.servers", "hadoop102:9092");//kafka集群,broker-list
        props.put("acks", "all");
        props.put("retries", 1);//重试次数
        props.put("batch.size", 16384);//批次大小
        props.put("linger.ms", 1);//等待时间
        props.put("buffer.memory", 33554432);//RecordAccumulator缓冲区大小
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<>(props);
        for (int i = 0; i < 100; i++) {
            producer.send(new ProducerRecord<String, String>("first", Integer.toString(i), Integer.toString(i))).get();
        }
        producer.close();
    }
}

2. Consumer API

Consumer消费数据时的可靠性是很容易保证的,因为数据在Kafka中是持久化的,故不用担心数据丢失问题。

由于consumer在消费过程中可能会出现断电宕机等故障,consumer恢复后,需要从故障前的位置的继续消费,所以consumer需要实时记录自己消费到了哪个offset,以便故障恢复后继续消费。

所以offset的维护是Consumer消费数据是必须考虑的问题。

手动提交offset

导入依赖

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>0.11.0.0</version>
</dependency>

需要用到的类:

KafkaConsumer:需要创建一个消费者对象,用来消费数据

ConsumerConfig:获取所需的一系列配置参数

ConsuemrRecord:每条数据都要封装成一个ConsumerRecord对象

package com.atguigu.kafka;

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 CustomConsumer {

    public static void main(String[] args) {
        Properties props = new Properties();
        props.put("bootstrap.servers", "hadoop102:9092");
        props.put("group.id", "test");//消费者组,只要group.id相同,就属于同一个消费者组
        props.put("enable.auto.commit", "false");//自动提交offset
       
        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("first"));
        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());
            }
            consumer.commitSync();
        }
    }
}

代码分析:

手动提交offset的方法有两种:分别是commitSync(同步提交)和commitAsync(异步提交)。两者的相同点是,都会将本次poll的一批数据最高的偏移量提交;不同点是,commitSync会失败重试,一直到提交成功(如果由于不可恢复原因导致,也会提交失败);而commitAsync则没有失败重试机制,故有可能提交失败。

数据重复消费问题

Xnip2020-07-12_21-30-08

自动提交offset

为了使我们能够专注于自己的业务逻辑,Kafka提供了自动提交offset的功能。

自动提交offset的相关参数:

enable.auto.commit:是否开启自动提交offset功能

auto.commit.interval.ms:自动提交offset的时间间隔

以下为自动提交offset的代码:

package com.atguigu.kafka;

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 CustomConsumer {

    public static void main(String[] args) {
        Properties props = new Properties();
        props.put("bootstrap.servers", "hadoop102:9092");
        props.put("group.id", "test");
        props.put("enable.auto.commit", "true");
        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("first"));
        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());
        }
    }
}

3. 自定义Interceptor

拦截器原理

Producer拦截器(interceptor)是在Kafka 0.10版本被引入的,主要用于实现clients端的定制化控制逻辑。

对于producer而言,interceptor使得用户在消息发送前以及producer回调逻辑前有机会对消息做一些定制化需求,比如修改消息等。同时,producer允许用户指定多个interceptor按序作用于同一条消息从而形成一个拦截链(interceptor chain)。Intercetpor的实现接口org.apache.kafka.clients.producer.ProducerInterceptor,其定义的方法包括:

(1)configure(configs)

获取配置信息和初始化数据时调用。

(2)onSend(ProducerRecord):

该方法封装进KafkaProducer.send方法中,即它运行在用户主线程中。Producer确保在消息被序列化以及计算分区前调用该方法。用户可以在该方法中对消息做任何操作,但最好保证不要修改消息所属的topic和分区,否则会影响目标分区的计算。

(3)onAcknowledgement(RecordMetadata, Exception):

该方法会在消息从RecordAccumulator成功发送到Kafka Broker之后,或者在发送过程中失败时调用。并且通常都是在producer回调逻辑触发之前。onAcknowledgement运行在producer的IO线程中,因此不要在该方法中放入很重的逻辑,否则会拖慢producer的消息发送效率。

(4)close:

关闭interceptor,主要用于执行一些资源清理工作如前所述,interceptor可能被运行在多个线程中,因此在具体实现时用户需要自行确保线程安全。另外倘若指定了多个interceptor,则producer将按照指定顺序调用它们,并仅仅是捕获每个interceptor可能抛出的异常记录到错误日志中而非在向上传递。这在使用过程中要特别留意。

拦截器案例

需求:

实现一个简单的双interceptor组成的拦截链。第一个interceptor会在消息发送前将时间戳信息加到消息value的最前部;第二个interceptor会在消息发送后更新成功发送消息数或失败发送消息数。


Xnip2020-07-12_21-39-37

案例实操

(1)增加时间戳拦截器

package com.atguigu.kafka.interceptor;
import java.util.Map;
import org.apache.kafka.clients.producer.ProducerInterceptor;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;

public class TimeInterceptor implements ProducerInterceptor<String, String> {

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

    }

    @Override
    public ProducerRecord<String, String> onSend(ProducerRecord<String, String> record) {
        // 创建一个新的record,把时间戳写入消息体的最前部
        return new ProducerRecord(record.topic(), record.partition(), record.timestamp(), record.key(),
                System.currentTimeMillis() + "," + record.value().toString());
    }
    
    @Override
    public void onAcknowledgement(RecordMetadata metadata, Exception exception) {

    }

    @Override
    public void close() {

    }
}

(2)统计发送消息成功和发送失败消息数,并在producer关闭时打印这两个计数器

package com.atguigu.kafka.interceptor;
import java.util.Map;
import org.apache.kafka.clients.producer.ProducerInterceptor;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;

public class CounterInterceptor implements ProducerInterceptor<String, String>{
    private int errorCounter = 0;
    private int successCounter = 0;

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

    @Override
    public ProducerRecord<String, String> onSend(ProducerRecord<String, String> record) {
         return record;
    }

    @Override
    public void onAcknowledgement(RecordMetadata metadata, Exception exception) {
        // 统计成功和失败的次数
        if (exception == null) {
            successCounter++;
        } else {
            errorCounter++;
        }
    }

    @Override
    public void close() {
        // 保存结果
        System.out.println("Successful sent: " + successCounter);
        System.out.println("Failed sent: " + errorCounter);
    }
}

(3)producer主程序

package com.atguigu.kafka.interceptor;
import java.util.ArrayList;

import java.util.List;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;

public class InterceptorProducer {

    public static void main(String[] args) throws Exception {
        // 1 设置配置信息
        Properties props = new Properties();
        props.put("bootstrap.servers", "hadoop102:9092");
        props.put("acks", "all");
        props.put("retries", 0);
        props.put("batch.size", 16384);
        props.put("linger.ms", 1);
        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");
        
        // 2 构建拦截链
        List<String> interceptors = new ArrayList<>();
        interceptors.add("com.atguigu.kafka.interceptor.TimeInterceptor");  interceptors.add("com.atguigu.kafka.interceptor.CounterInterceptor"); 
        props.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, interceptors);
         
        String topic = "first";
        Producer<String, String> producer = new KafkaProducer<>(props);
        
        // 3 发送消息
        for (int i = 0; i < 10; i++) {
            
            ProducerRecord<String, String> record = new ProducerRecord<>(topic, "message" + i);
            producer.send(record);
        }
         
        // 4 一定要关闭producer,这样才会调用interceptor的close方法
        producer.close();
    }
}

4. Flume对接Kafka

4.1. 配置flume(flume-kafka.conf)

# define
a1.sources = r1
a1.sinks = k1
a1.channels = c1

# source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F -c +0 /opt/module/datas/flume.log
a1.sources.r1.shell = /bin/bash -c

# sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sinks.k1.kafka.topic = first
a1.sinks.k1.kafka.flumeBatchSize = 20
a1.sinks.k1.kafka.producer.acks = 1
a1.sinks.k1.kafka.producer.linger.ms = 1

# channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# bind
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

4.2. 启动kafkaIDEA消费者

4.3. 进入flume根目录下,启动flume

$ bin/flume-ng agent -c conf/ -n a1 -f jobs/flume-kafka.conf

4.4. 向 /opt/module/datas/flume.log里追加数据,查看kafka消费者消费情况

$ echo hello >> /opt/module/datas/flume.log

5. Kafka监控

5.1 Kafka Monitor

1.上传jar包KafkaOffsetMonitor-assembly-0.4.6.jar到集群
2.在/opt/module/下创建kafka-offset-console文件夹
3.将上传的jar包放入刚创建的目录下
4.在/opt/module/kafka-offset-console目录下创建启动脚本start.sh,内容如下:

#!/bin/bash
java -cp KafkaOffsetMonitor-assembly-0.4.6-SNAPSHOT.jar \
com.quantifind.kafka.offsetapp.OffsetGetterWeb \
--offsetStorage kafka \
--kafkaBrokers hadoop102:9092,hadoop103:9092,hadoop104:9092 \
--kafkaSecurityProtocol PLAINTEXT \
--zk hadoop102:2181,hadoop103:2181,hadoop104:2181 \
--port 8086 \
--refresh 10.seconds \
--retain 2.days \
--dbName offsetapp_kafka &

5.在/opt/module/kafka-offset-console目录下创建mobile-logs文件夹

5.在/opt/module/kafka-offset-console目录下创建mobile-logs文件夹

6.启动KafkaMonitor

./start.sh

7.登录页面hadoop102:8086端口查看详情

5.2 Kafka Manager

1.上传压缩包kafka-manager-1.3.3.15.zip到集群
2.解压到/opt/module
3.修改配置文件conf/application.conf

kafka-manager.zkhosts="kafka-manager-zookeeper:2181"

修改为:

kafka-manager.zkhosts="hadoop102:2181,hadoop103:2181,hadoop104:2181"

4.启动kafka-manager

bin/kafka-manager

5.登录hadoop102:9000页面查看详细信息

上一篇下一篇

猜你喜欢

热点阅读