SPRINGBOOT项目连接远程服务器上KAFKA遇到的坑以及完整的例子

版本

springboot	2.1.5.RELEASE
kafka	2.2

 

遇到的坑

1. 用最新的springboot就要用最新的kafka版本！
2. 当我启动云服务器上的zk后，再启动kafka后台日志也没报错，只感觉EndPoint日志信息有点奇怪，然后springboot项目连接kafka，老是有warn级别的日志："Connection to node -1 could not be established. Broker may not be available."，这是未连接上kafka
3. springboot项目控制台抛出ip地址不合法的异常。

 

telnet一下云服务器的9092端口没有响应，然后看云服务器安全组里也添加了啊，netstat也看到9092被监听，到底咋回事？

原来是kafka配置文件的问题，导致9092端口未被正确监听，ip地址的问题就是要绑定kafka服务器的ip地址。

注意下面红色三项配置很重要，解决了我所有的问题！

advertised.host.name必须写kafka服务器的ip地址！如果写localhost，并且项目运行的服务器和kafka运行的不是同一台服务器，会连接不上。

将kafka服务端的配置文件修改如下：

############################# Server Basics #############################

# The id of the broker. This must be set to a unique integer for each broker.
#broker的全局唯一编号，不能重复
broker.id=0

############################# Socket Server Settings #############################

#监听的端口
listeners=PLAINTEXT://:9092
# 客户端连接的ip地址，必须要写成服务器的ip地址！advertised.host.name
advertised.host.name = 47.XX.XX.XX 
host.name=localhost

# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL

# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3

# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8

# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400

# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400

# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600


############################# Log Basics #############################

# A comma separated list of directories under which to store log files
log.dirs=/root/mysoftware/kafka_2.12-2.2.0/logs

# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1

# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1

############################# Internal Topic Settings  #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended for to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1

############################# Log Flush Policy #############################

# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to excessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.

# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000

# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000

############################# Log Retention Policy #############################

# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.

# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168

# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824

# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824

# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000

############################# Zookeeper #############################

# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
zookeeper.connect=localhost:2181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000


############################# Group Coordinator Settings #############################

# The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0

 

代码

pom.xml

    4.0.0
    
        org.springframework.boot
        spring-boot-starter-parent
        2.1.5.RELEASE
         
    
    xy.study
    kafka-demo
    0.0.1-SNAPSHOT
    kafka-demo
    Kafka demo project for Spring Boot

    
        1.8
    

    
        
            org.springframework.boot
            spring-boot-starter
        
        
            org.springframework.kafka
            spring-kafka
        

        
            org.springframework.boot
            spring-boot-devtools
            runtime
        
        
            com.alibaba
            fastjson
            1.2.47
        

        
            org.projectlombok
            lombok
            true
        
        
            org.springframework.boot
            spring-boot-starter-test
            test
        
        
            org.springframework.kafka
            spring-kafka-test
            test
        
    

    
        
            
                org.springframework.boot
                spring-boot-maven-plugin
            
        
    

 

application.properties

#============== kafka ===================
# 指定kafka 代理地址，可以多个
spring.kafka.bootstrap-servers=47.XX.XX.XX:9092

#=============== provider  =======================

spring.kafka.producer.retries=0
# 每次批量发送消息的数量
spring.kafka.producer.batchSize=16384
spring.kafka.producer.bufferMemory=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=consumer-group-test

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

 

生产者和消费者

@Component
@Slf4j
public class KafkaProducer {

    @Autowired
    private KafkaTemplate kafkaTemplate;


    public void sendADotaHero() {
        DotaHero dotaHero = new DotaHero("虚空假面", "敏捷", "男");

        ListenableFuture> future = kafkaTemplate.send(KafkaTopic.A_DOTA_HERO, JSONObject.toJSONString(dotaHero));

        future.addCallback(new ListenableFutureCallback>() {
            @Override
            public void onFailure(Throwable throwable) {
                log.error("kafka sendMessage error, throwable = {}, topic = {}, data = {}", throwable, KafkaTopic.A_DOTA_HERO, dotaHero);
            }

            @Override
            public void onSuccess(SendResult stringDotaHeroSendResult) {
                log.info("kafka sendMessage success topic = {}, data = {}",KafkaTopic.A_DOTA_HERO, dotaHero);
            }
        });

        log.info("kafka sendMessage end");

    }

}

 

@Slf4j
@Component
public class KafkaConsumer {

    @KafkaListener(topics = KafkaTopic.A_DOTA_HERO, groupId = "${spring.kafka.consumer.group-id}")
    private void kafkaConsumer(ConsumerRecord consumerRecord) {

        log.info("kafkaConsumer: topic = {}, msg = {}", consumerRecord.topic(), consumerRecord.value());

    }
}

 

@Data
@AllArgsConstructor
@NoArgsConstructor
public class DotaHero {

    private String name;
    private String kind;
    private String sex;

    /**
     * 返回一个不同元素的数组
     * @return
     */
    public static List bulidDiffObjectList(){
        List list = new ArrayList<>();
        list.add(new DotaHero("影魔", "敏捷", "男"));
        list.add(new DotaHero("小黑", "敏捷", "女"));
        list.add(new DotaHero("马尔斯", "力量", "男"));

        return list;
    }
}

 

public class KafkaTopic {
    public static final String A_DOTA_HERO = "a_dota_hero";


    private KafkaTopic() {
    }
}

 

测试

当启动完springboot项目后，再运行test启动生产者：

@Slf4j
@RunWith(SpringRunner.class)
@SpringBootTest
public class KafkaDemoApplicationTests {

    @Autowired
    private KafkaProducer kafkaProducer;

    private Clock clock = Clock.systemDefaultZone();
    private long begin;
    private long end;

    @Before
    public void init(){


        begin = clock.millis();
    }

    @Test
    public void send(){
        kafkaProducer.sendADotaHero();
    }

    @After
    public void end(){
        end = clock.millis();
        log.info("Spend {} millis .", end-begin);
    }

}