消息中间件系列教程(21) -Kafka- 集群搭建(自带Zookeeper)

本文涉及的产品
MSE Nacos 企业版免费试用,1600元额度,限量50份
服务治理 MSE Sentinel/OpenSergo,Agent数量 不受限
注册配置 MSE Nacos/ZooKeeper,118元/月
简介: 消息中间件系列教程(21) -Kafka- 集群搭建(自带Zookeeper)

在前面一篇文章中《消息中间件系列教程(20) -Kafka-集群搭建》,我们分别搭建了Zookeeper和Kafka来实现Kafka的集群。其实Kfaka里面的压缩包里面包含了Zookeeper,可以在里面配置,本文基于上一篇博客来讲解。

1.首先新建kafka的日志目录和zookeeper数据目录(3台服务器同样操作)

mkdir /usr/local/kafka/zookeeper       
mkdir /usr/local/kafka/log             
mkdir /usr/local/kafka/log/zookeeper    
mkdir /usr/local/kafka/log/kafka

2.进入config目录下,修改zookeeper.properties配置文件(3台服务器同样操作)

cd /usr/local/kafka/config/
vi zookeeper.properties

修改内容如下:

tickTime=2000
initLimit=10
syncLimit=5
dataDir=/usr/local/kafka/zookeeper
clientPort=2181
server.0=192.168.162.131:2888:3888
server.1=192.168.162.132:2888:3888
server.2=192.168.162.133:2888:3888

3.zookeeper数据目录添加myid配置(3台服务器按zookeeper.properties的标识设置对应的id,比如192.168.162.131的id为0)

cd /usr/local/kafka/zookeeper
vi myid

4. 进入config目录下,修改server.properties文件(3台服务器除了broker.id、advertised.listeners,其它的都一样)

############################# 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://192.168.162.131:9092
#listeners=PLAINTEXT://: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 seperated 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 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 exceessive 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=192.168.162.131:2181,192.168.162.132:2181,192.168.162.133:2181
# 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

5. 启动Zookeeper(kafka启动时先启动zookeeper,再启动kafka,关闭时相反,先关闭kafka,再关闭zookeeper)

cd /usr/local/kafka/bin/
./zookeeper-server-start.sh -daemon ../config/zookeeper.properties

6.查看Zookeeper是否启动成功

ps -ef|grep zookeeper

如果现实如下信息,表示启动成功

7.启动kafka

cd /usr/local/kafka/bin/
./kafka-server-start.sh -daemon ../config/server.properties

8.查看kafka是否启动成功

ps -ef|grep kafka

如果现实如下信息,表示启动成功

9.使用Zookeeper可视化工具,可以看到zookeeper新增了3个kafka broker节点:

目录
相关文章
|
3月前
|
消息中间件 运维 Java
搭建Zookeeper、Kafka集群
本文详细介绍了Zookeeper和Kafka集群的搭建过程,涵盖系统环境配置、IP设置、主机名设定、防火墙与Selinux关闭、JDK安装等基础步骤。随后深入讲解了Zookeeper集群的安装与配置,包括数据目录创建、节点信息设置、SASL认证配置及服务启动管理。接着描述了Kafka集群的安装,涉及配置文件修改、安全认证设置、生产消费认证以及服务启停操作。最后通过创建Topic、发送与查看消息等测试验证集群功能。全网可搜《小陈运维》获取更多信息。
265 1
|
8月前
|
消息中间件 存储 监控
构建高可用性Apache Kafka集群:从理论到实践
【10月更文挑战第24天】随着大数据时代的到来,数据传输与处理的需求日益增长。Apache Kafka作为一个高性能的消息队列服务,因其出色的吞吐量、可扩展性和容错能力而受到广泛欢迎。然而,在构建大规模生产环境下的Kafka集群时,保证其高可用性是至关重要的。本文将从个人实践经验出发,详细介绍如何构建一个高可用性的Kafka集群,包括集群规划、节点配置以及故障恢复机制等方面。
243 4
|
9月前
|
消息中间件 运维 算法
Kafka 为什么要抛弃 Zookeeper?
本文探讨了Kafka为何逐步淘汰ZooKeeper。长久以来,ZooKeeper作为Kafka的核心组件,负责集群管理和协调任务。然而,随着Kafka的发展,ZooKeeper带来的复杂性增加、性能瓶颈及一致性问题日益凸显。为解决这些问题,Kafka引入了KRaft,这是一种基于Raft算法的内置元数据管理方案,不仅简化了部署流程,还提升了系统的一致性和扩展性。本文详细分析了这一转变背后的原因及其带来的优势,并展望了Kafka未来的发展方向。
554 1
|
9月前
|
消息中间件 监控 数据可视化
大数据-79 Kafka 集群模式 集群监控方案 JavaAPI获取集群指标 可视化监控集群方案: jconsole、Kafka Eagle
大数据-79 Kafka 集群模式 集群监控方案 JavaAPI获取集群指标 可视化监控集群方案: jconsole、Kafka Eagle
358 2
|
4月前
|
消息中间件 人工智能 安全
秒级灾备恢复:Kafka 2025 AI自愈集群下载及跨云Topic迁移终极教程
Apache Kafka 2025作为企业级实时数据中枢,实现五大革新:量子安全传输(CRYSTALS-Kyber抗量子加密算法)、联邦学习总线(支持TensorFlow Federated/Horizontal FL框架)、AI自愈集群(MTTR缩短至30秒内)、多模态数据处理(原生支持视频流、3D点云等)和跨云弹性扩展(AWS/GCP/Azure间自动迁移)。平台采用混合云基础设施矩阵与软件依赖拓扑设计,提供智能部署架构。安装流程涵盖抗量子安装包获取、量子密钥配置及联邦学习总线设置。
|
7月前
|
存储 SpringCloudAlibaba Java
【SpringCloud Alibaba系列】一文全面解析Zookeeper安装、常用命令、JavaAPI操作、Watch事件监听、分布式锁、集群搭建、核心理论
一文全面解析Zookeeper安装、常用命令、JavaAPI操作、Watch事件监听、分布式锁、集群搭建、核心理论。
【SpringCloud Alibaba系列】一文全面解析Zookeeper安装、常用命令、JavaAPI操作、Watch事件监听、分布式锁、集群搭建、核心理论
|
7月前
|
消息中间件 Java Kafka
【手把手教你Linux环境下快速搭建Kafka集群】内含脚本分发教程,实现一键部署多个Kafka节点
本文介绍了Kafka集群的搭建过程,涵盖从虚拟机安装到集群测试的详细步骤。首先规划了集群架构,包括三台Kafka Broker节点,并说明了分布式环境下的服务进程配置。接着,通过VMware导入模板机并克隆出三台虚拟机(kafka-broker1、kafka-broker2、kafka-broker3),分别设置IP地址和主机名。随后,依次安装JDK、ZooKeeper和Kafka,并配置相应的环境变量与启动脚本,确保各组件能正常运行。最后,通过编写启停脚本简化集群的操作流程,并对集群进行测试,验证其功能完整性。整个过程强调了自动化脚本的应用,提高了部署效率。
1660 1
【手把手教你Linux环境下快速搭建Kafka集群】内含脚本分发教程,实现一键部署多个Kafka节点
|
7月前
|
消息中间件 缓存 监控
go高并发之路——消息中间件kafka
本文介绍了高并发业务中的流量高峰应对措施,重点讲解了Kafka消息中间件的使用,包括常用的Go语言库sarama及其版本问题,以及Kafka的版本选择建议。文中还详细解释了Kafka生产者的四种分区策略:轮询、随机、按Key和指定分区,并提供了相应的代码示例。
157 1
go高并发之路——消息中间件kafka
|
7月前
|
消息中间件 存储 Kafka
2024最全Kafka集群方案汇总
Apache Kafka 是一个高吞吐量、可扩展、可靠的分布式消息系统,广泛应用于数据驱动的应用场景。Kafka 支持集群架构,具备高可用性和容错性。其核心组件包括 Broker(服务器实例)、Topic(消息分类)、Partition(有序消息序列)、Producer(消息发布者)和 Consumer(消息消费者)。每个分区有 Leader 和 Follower,确保数据冗余和高可用。Kafka 2.8+ 引入了不依赖 Zookeeper 的 KRaft 协议,进一步简化了集群管理。常见的集群部署方案包括单节点和多节点集群,后者适用于生产环境以确保高可用性。
312 0
|
8月前
|
消息中间件 存储 Prometheus
Kafka集群如何配置高可用性
Kafka集群如何配置高可用性
156 1