kafka搭建与使用 - SpringBoot整合kafka连接远程kafka服务 (很适合小白参考使用)
1.官网下载zookeeper、kakfa
2.在linux机器上安装zookeeper、kakfa，我使用的CentOS
安装比较简单，在自己用户目录下解压：tar -zxvf 压缩文件
3.kafka自带有zookeeper，这里使用自己安装的
修改zookeeper配置
复制cp zoo_sample.cfg zoo.cfg，修改为以下点
zoo.cfg配置
# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just
# example sakes.
dataDir=/tmp/zookeeper
# the port at which the clients will connect
clientPort=2181
# the maximum number of client connections.
# increase this if you need to handle more clients
#maxClientCnxns=60
#
# Be sure to read the maintenance section of the
# administrator guide before turning on autopurge.
#
# http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1
## Metrics Providers
#
# https://prometheus.io Metrics Exporter
#metricsProvider.className=org.apache.zookeeper.metrics.prometheus.PrometheusMetricsProvider
#metricsProvider.httpPort=7000
#metricsProvider.exportJvmInfo=true
#
#日志路径
dataLogDir=/tmp/zookeeper-log
#在阿里云的服务器上保证外网可以访问到
quorumListenOnAllIPs=true
server.properties配置
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# see kafka.server.KafkaConfig for additional details and defaults
############################# Server Basics #############################
# The id of the broker. This must be set to a unique integer for each broker.
broker.id=0
############################# Socket Server Settings #############################
# The address the socket server listens on. It will get the value returned from
# java.net.InetAddress.getCanonicalHostName() if not configured.
# FORMAT:
# listeners = listener_name://host_name:port
# EXAMPLE:
# listeners = PLAINTEXT://your.host.name:9092
listeners=PLAINTEXT://192.168.88.200:9092
# Hostname and port the broker will advertise to producers and consumers. If not set,
# it uses the value for "listeners" if configured. Otherwise, it will use the value
# returned from java.net.InetAddress.getCanonicalHostName().
#advertised.listeners=PLAINTEXT://your.host.name:9092
# 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=/tmp/kafka-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 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=192.168.88.200:2181
# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=18000
############################# 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
producer.properties，consumer.properties配置
4.启动zookeeper、kakfa
进入zookeeper的bin目录执行启动脚本:
./zkServer.sh start
进入kakfa目录执行启动脚本已后台模式启动
bin/kafka-server-start.sh -daemon config/server.properties
5.输入jps查看是否启动正常
6.启动成功后，接下来可以创建2个springBoot项目，模拟生产者和消费者
为了简单可以直接选
生产者和消费者的配置文件信息
spring:
kafka:
bootstrap-servers: 192.168.88.200:9092
producer:
#发生错误后，消息重发的次数。
retries: 0
#当有多个消息需要被发送到同一个分区时，生产者会把它们放在同一个批次里。该参数指定了一个批次可以使用的内存大小，按照字节数计算。
batch-size: 16384
# 设置生产者内存缓冲区的大小。
buffer-memory: 33554432
# 键的序列化方式
key-serializer: org.apache.kafka.common.serialization.StringSerializer
# 值的序列化方式
value-serializer: org.apache.kafka.common.serialization.StringSerializer
# acks=0 ： 生产者在成功写入消息之前不会等待任何来自服务器的响应。
# acks=1 ： 只要集群的首领节点收到消息，生产者就会收到一个来自服务器成功响应。
# acks=all ：只有当所有参与复制的节点全部收到消息时，生产者才会收到一个来自服务器的成功响应。
acks: 1
consumer:
# 自动提交的时间间隔 在spring boot 2.X 版本中这里采用的是值的类型为Duration 需要符合特定的格式，如1S,1M,2H,5D
auto-commit-interval: 1S
# 该属性指定了消费者在读取一个没有偏移量的分区或者偏移量无效的情况下该作何处理：
# latest（默认值）在偏移量无效的情况下，消费者将从最新的记录开始读取数据（在消费者启动之后生成的记录）
# earliest ：在偏移量无效的情况下，消费者将从起始位置读取分区的记录
auto-offset-reset: earliest
# 是否自动提交偏移量，默认值是true,为了避免出现重复数据和数据丢失，可以把它设置为false,然后手动提交偏移量
enable-auto-commit: false
# 键的反序列化方式
key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
# 值的反序列化方式
value-deserializer: org.apache.kafka.common.serialization.StringDeserializer
listener:
# 在侦听器容器中运行的线程数。
concurrency: 5
#listner负责ack，每调用一次，就立即commit
ack-mode: manual_immediate
missing-topics-fatal: false
生产者客户端
package com.kafkaproducer.demo.service;
import com.alibaba.fastjson.JSON;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.kafka.support.SendResult;
import org.springframework.stereotype.Component;
import org.springframework.util.concurrent.ListenableFuture;
import org.springframework.util.concurrent.ListenableFutureCallback;
@Component
public class MyKafkaProducer {
private static final Logger log = LoggerFactory.getLogger(MyKafkaProducer.class);
@Autowired
private KafkaTemplate<String, Object> kafkaTemplate;
//自定义topic
public static final String TOPIC_TEST = "InternetJava";
public static final String TOPIC_TEST1 = "InternetJava1";
public void send(Object obj) {
String obj2String = JSON.toJSONString(obj);
log.info("准备发送消息为：{}", obj2String);
//发送消息
ListenableFuture<SendResult<String, Object>> future = kafkaTemplate.send(TOPIC_TEST, obj);
ListenableFuture<SendResult<String, Object>> future1 = kafkaTemplate.send(TOPIC_TEST1, obj);
future.addCallback(new ListenableFutureCallback<SendResult<String, Object>>() {
@Override
public void onFailure(Throwable throwable) {
//发送失败的处理
log.error(TOPIC_TEST + " - 生产者 发送消息失败：" + throwable.getMessage());
}
@Override
public void onSuccess(SendResult<String, Object> stringObjectSendResult) {
//成功的处理
log.info(TOPIC_TEST + " - 生产者 发送消息成功：" + stringObjectSendResult.toString());
}
});
}
}
生产者前端控制器
package com.kafkaproducer.demo.controller;
import com.kafkaproducer.demo.service.MyKafkaProducer;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
@RestController
@RequestMapping("/")
public class KafkaProducerController {
@Autowired
private MyKafkaProducer myKafkaProducer;
@RequestMapping("message")
public String sendMessage(String str) {
myKafkaProducer.send(str);
return "OK";
}
}
消费者客户端
package com.kafkaconsumer.demo.service;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.kafka.support.Acknowledgment;
import org.springframework.kafka.support.KafkaHeaders;
import org.springframework.messaging.handler.annotation.Header;
import org.springframework.stereotype.Component;
import java.util.Optional;
@Component
public class MyKafkaConsumer {
private static final Logger log = LoggerFactory.getLogger(MyKafkaConsumer.class);
@KafkaListener(topics = {"InternetJava"}, groupId = "test-consumer-group")
public void topic_test(ConsumerRecord<?, ?> record, Acknowledgment ack, @Header(KafkaHeaders.RECEIVED_TOPIC) String topic) {
Optional message = Optional.ofNullable(record.value());
if (message.isPresent()) {
Object msg = message.get();
log.info("topic_test 消费了： Topic:" + topic + ",Message:" + msg);
ack.acknowledge();
}
}
@KafkaListener(topics = {"InternetJava1"}, groupId = "test-consumer-group")
public void topic_test1(ConsumerRecord<?, ?> record, Acknowledgment ack, @Header(KafkaHeaders.RECEIVED_TOPIC) String topic) {
Optional message = Optional.ofNullable(record.value());
if (message.isPresent()) {
Object msg = message.get();
log.info("topic_test1 消费了： Topic:" + topic + ",Message:" + msg);
ack.acknowledge();
}
}
}
启动生产者和消费者
浏览器中输入：
生产者打印的部分日志信息
消费者打印日志信息
7.自此项目整合结束，这里参考了网上很多资料后自行调试总结的，kafka有很多配置项（linux中安装的kafka，具体配置可以参考官网），spring整合的kafka（spring-kafka）配置可以参考spring.io官网配置，这里需要指出spring-kafka依赖的kafka需要与linux安装的kafka兼容。
附录：
直接在linux上创建topic
bin/kafka-topics.sh --create --topic InternetJava --zookeeper 192.168.88.200:2181 --partitions 1 --replication-factor 1
查询当前kafka下的所有topic
bin/kafka-topics.sh --zookeeper 192.168.88.200:2181 --list
