zk启动
bin/zkServer.sh start
后台启动kafka
bin/kafka-server-start.sh config/server.properties 1>/dev/null 2>&1 &
测试生产者消费者
创建主题
bin/kafka-topics.sh --zookeeper localhost:2181 --create --topic calvin --partitions 2 --replication-factor 1
查看所有主题
bin/kafka-topics.sh --zookeeper localhost:2181 --list
查看对应的主题
bin/kafka-topics.sh --zookeeper localhost:2181 --describe --topic calvin
指定生产者发送消息到主题
bin/kafka-console-producer.sh --broker-list localhost:9092 --topic calvin
指定消费者订阅主题
bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic calvin
单机版注意一个坑，需要 将server.properties设置advertised.listeners=PLAINTEXT://your.host.name:9092
改为kafka所在机器的ip，即：
advertised.listeners=PLAINTEXT://192.168.0.8:9092
否则Java应用去连接kafka，发现会连接出错，报如下的错误：
org.apache.kafka.common.network.Selector - [Producer clientId=producer-1] Connection with localhost/127.0.0.1 disconnected
异步发送
producer.send(record, (metadata, e) -> {
if (e == null) {
System.out.println("topic:" + metadata.topic());
System.out.println("partition:" + metadata.partition());
System.out.println("offset:" + metadata.offset());
}
});
发送原理：
消息发送的过程中，涉及到两个线程协同工作，主线程首先将业务数据封装成ProducerRecord对象，
之后调用send()方法将消息放入RecordAccumulator(消息收集器，也可以理解为主线程与Sender线程
直接的缓冲区)中暂存，Sender线程负责将消息信息构成请求，并最终执行网络I/O的线程，它从
RecordAccumulator中取出消息并批量发送出去，需要注意的是，KafkaProducer是线程安全的，多个
线程间可以共享使用同一个KafkaProducer对象。
zookeeper集群搭建
2888端口号是zookeeper服务之间通信的端口
3888端口是zookeeper与其他应用程序通信的端口。
# 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-01/data
# 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
server.0=192.168.0.8:2888:3888
server.1=192.168.0.9:2888:3888
server.2=192.168.0.10:2888:3888
不同的节点分别修改dataDir
/tmp/zookeeper-01/data包下
touch myid
vi myid
写入0保存
其他两个节点分别为1，2
kafka集群搭建
# 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://:9092
listeners=PLAINTEXT://192.168.0.8:9092
# 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://192.168.0.8:9092
# 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-01/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
zookeeper.connect=192.168.0.8:2181,192.168.0.9:2181,192.168.0.10:2181
# 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
其他两个节点对应修改即可
创建主题验证，创建三个分区三个副本
bin/kafka-topics.sh --zookeeper 192.168.0.8:2181 --create --topic kafka-cluster --partitions 3 --replication-factor 3
查看对应的主题，验证集群搭建是否成功
bin/kafka-topics.sh --zookeeper 192.168.0.8:2181 --describe --topic kafka-cluster
可以看到如下的主题分区副本信息，说明kafka集群搭建成功。
[root@localhost kafka-01]# bin/kafka-topics.sh --zookeeper 192.168.0.8:2181 --describe --topic kafka-cluster
Topic:kafka-cluster PartitionCount:3 ReplicationFactor:3 Configs:
Topic: kafka-cluster Partition: 0 Leader: 2 Replicas: 2,0,1 Isr: 2,0,1
Topic: kafka-cluster Partition: 1 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2
Topic: kafka-cluster Partition: 2 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0
给topic增加分区，注意只能增加不能减少
bin/kafka-topics.sh --alter --zookeeper 192.168.0.8:2181 --topic kafka-cluster --partitions 4
再次查看主题信息，发现已经新增分区完成。
[root@localhost kafka-01]# bin/kafka-topics.sh --zookeeper 192.168.0.8:2181 --describe --topic kafka-cluster
Topic:kafka-cluster PartitionCount:4 ReplicationFactor:3 Configs:
Topic: kafka-cluster Partition: 0 Leader: 2 Replicas: 2,0,1 Isr: 2,0,1
Topic: kafka-cluster Partition: 1 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2
Topic: kafka-cluster Partition: 2 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0
Topic: kafka-cluster Partition: 3 Leader: 2 Replicas: 2,1,0 Isr: 2,1,0
Kafka的Message存储采用了分区(partition)，分段(LogSegment)和稀疏索引这几个手段来达到了高效性。
Kafka幂等性，默认该配置参数就是true。
Properties props = new Properties();
props.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, "true");
Kafka事务
package com.calvin.exercise.kafka;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
/**
* @Title ProducerTransactionSend
* @Description kafka事务发送
* @author calvin
* @date: 2020/3/6 4:50 PM
*/
public class ProducerTransactionSend {
// Kafka集群地址
private static final String brokerList = "192.168.0.8:9092";
// 主题名称-之前已经创建
private static final String topic = "calvin";
private static final String transactionId = "transactionId";
public static void main(String[] args) {
Properties properties = new Properties();
// 设置key序列化器
properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
// 设置值序列化器
properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
// 设置集群地址
properties.put("bootstrap.servers", brokerList);
properties.put(ProducerConfig.TRANSACTIONAL_ID_CONFIG, transactionId);
// KafkaProducer 线程安全
KafkaProducer<String, String> producer = new KafkaProducer<>(properties);
// 初始化事务
producer.initTransactions();
// 开启事务
producer.beginTransaction();
try {
ProducerRecord<String, String> record1 = new ProducerRecord<>(topic, "message-1");
producer.send(record1);
ProducerRecord<String, String> record2 = new ProducerRecord<>(topic, "message-2");
producer.send(record2);
ProducerRecord<String, String> record3 = new ProducerRecord<>(topic, "message-3");
producer.send(record3);
} catch (Exception e) {
producer.abortTransaction();
} finally {
producer.close();
}
}
}
Kafka与SpringBoot整合
application.yml
spring:
kafka:
producer:
bootstrap-servers: 192.168.0.8:9092,192.168.0.9:9092,192.168.0.10:9092
transaction-id-prefix: kafka-tx
consumer:
bootstrap-servers: 192.168.0.8:9092,192.168.0.9:9092,192.168.0.10:9092
pom.xml引入依赖
<dependency>
<groupId>org.springframework.kafka</groupId>
<artifactId>spring-kafka</artifactId>
</dependency>
发送与接收消息
package com.calvin.exercise.kafka;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.transaction.annotation.Transactional;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.PathVariable;
import org.springframework.web.bind.annotation.RestController;
/**
* @Title KafkaController
* @Description
* @author calvin
* @date: 2020/3/7 4:24 PM
*/
@RestController
public class KafkaController {
@Autowired
private KafkaTemplate kafkaTemplate;
@GetMapping("send/{message}")
public String sendMessage(@PathVariable String message) {
kafkaTemplate.send("calvin",message);
return "send success" + message;
}
@KafkaListener(id = "", topics = "calvin", groupId = "group.demo")
public void getMessage(String message) {
System.out.println("message = " + message);
}
}
来源：CSDN
作者：电商技术进阶
链接：https://blog.csdn.net/haogexiang9700/article/details/104719316