在异步交互模式中，我们经常会谈到消费者与生产者的模式，在这中间会使用到主流的MQ的中间件，主要为Kafka和RabbitMQ的中间件。当然也可以说是消息队列，由于在同步交互的模式中存在延迟的缺陷，那么也就说是在高并发的应用场景下，使用同步交互的模式显然是不合理的，就需要使用异步的消息队列来解决这个过程中消息的堵塞和积压。比如大量的请求对底层的DB进行请求，请求过多导致DB层面的连接数占用资源得不到释放，从而导致Too Many Connections等其他的异常信息。当然基于这样的场景很多的，因此就需要一个缓冲机制来解决这类的问题，而消息队列可以很好的解决这类堵塞以及积压的问题，准确的说消息队列通过异步处理请求来缓解系统的压力。消息队列拥有先进先出的特性，主要应用于不同进程或线程之间的通信机制，来处理输入的请求。在异步通信的机制中，客户端与服务端不需要知道对方的存在，更多关注的是MQ的消息，如下所示：
Kafka是一个分布式实时数据流的平台，起源于LinkedIn的公司，早期LinkedIn需要收集各个业务线的系统和应用服务的性能指标数据来进行分析，期间需要采用的数据量特别大，随着业务的扩展导致数据量的增大，内部自定义的系统无法满足诉求，于是内部开发了Kafka的系统，因此Kafka也拥有高吞吐量的特性。Kafka的项目目前是Apache项目基金会的一个顶级开源项目。Kafka提供了发布和订阅的功能，业务把数据发送到Kafka的集群(也可以是单机模式),也可以从Kafka集群读取数据，因此Kafka的工作机制主要也是基于生产者与消费者的模式，所谓生产者就是负责把数据写入到Kafka集群进行存储，而消费者模式就是负责读取数据。
Kafka的是一个分布式的系统，由Zookeeper来管理和协调它的各个代理节点。因此安装Kafka之前需要安装Zookeeper。在Apache的官方网站下载Zookeeper后，进行解压，解压后，在当前目录下新创建data的目录用来存储状态数据，完整的目录信息如下：
在conf的目录下，把zoo_sample.cfg修改为zoo.cfg，进行编辑Zookeeper的集群信息，主要内容为：
代码语言：javascript复制# The number of milliseconds of each tick
#服务器与客户端中间维持的心跳时间
tickTime=2000
# The number of ticks that the initial
# synchronization phase can take
#集群中Follwer服务器与Leader服务器之间最大的初始化连接数
initLimit=10
# The number of ticks that can pass between
# sending a request and getting an acknowledgement
#同步通信时间间隔
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just
# example sakes.
#zookeeper数据存放路径地址
dataDir=/Applications/devOps/bigData/zookeeper/data
dataLogDir=/Applications/devOps/bigData/zookeeper/log
# the port at which the clients will connect
#客户端端口号
clientPort=2181
admin.serverPort=9091
# the maximum number of client connections.
# increase this if you need to handle more clients
#处理客户端最大连接数
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
#需要保留的文件数目
autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#日志清理评率，单位是小时，如果是0，就表示不开启自动清理的机制
autopurge.purgeInterval=1
## Metrics Providers
#
# https://prometheus.io Metrics Exporter
#metricsProvider.className=org.apache.zookeeper.metrics.prometheus.PrometheusMetricsProvider
#metricsProvider.httpPort=7000
#metricsProvider.exportJvmInfo=true编辑完配置文件后，把Zookeeper加入到path的环境变量中，然后就可以进行启动，启动的命令为zkServer.sh start，执行后，就会输出如下的信息：
代码语言：javascript复制ZooKeeper JMX enabled by default
Using config: /Applications/devOps/bigData/zookeeper/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED执行命令zkServer.sh status可以查看是否启动成功，以及它的模式，执行后输出的信息为：
代码语言：javascript复制ZooKeeper JMX enabled by default
Using config: /Applications/devOps/bigData/zookeeper/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost. Client SSL: false.
Mode: standalone下面来说明Kafka的部署模式，首先也是在Apache的官方网站下载Kafka安装包，然后进行解压，和配置path的环境变量。在Kafka的解压的目录的conf下，配置server.properties，该文件的信息主要为：
代码语言：javascript复制# 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.
#设置一个broker唯一的ID
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
# 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
#设置消息日志存储路径
# log.dirs=/tmp/kafka-logs
log.dirs=/Applications/devOps/bigData/kafka/data
# 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.
#删除主题的配置信息
delete.topic.enable=true
# 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.
#指定Zookeeper的连接地址
zookeeper.connect=localhost: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主要需要指定Zookeeper的连接地址信息。配置完成后，就可以启动Kafka，启动的命令为:
代码语言：javascript复制kafka-server-start.sh ./config/server.properties执行后就会启动Kafka。
启动成功后，来模拟生产者和消费者的数据交互，执行命令：
代码语言：javascript复制kafka-console-producer.sh --broker-list localhost:9092 -topic login进入到生产者的模式，执行如下命令进入到消费者的模式：
代码语言：javascript复制kafka-console-consumer.sh --bootstrap-server localhost:9092 -topic login --from-beginning在生产者的控制台里面输入:Hello Kafka，就会显示到消费者的控制台里面，如下所示：
通过如上我们可以看到Kafka基于生产者和消费者模式的数据交互。
感谢您的阅读，后续持续更新Kafka的应用和实战。
