大数据中间件——Kafka

Kafka安装配置

首先我们把kafka的安装包上传到虚拟机中：

解压到对应的目录并修改对应的文件名：

首先我们来到kafka的config目录，我们第一个要修改的文件就是server.properties文件，修改内容如下：

# 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.
# kafka在整个集群中的身份标识，集群中的id是唯一的
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
# 存储kafka数据的位置，默认存储在临时文件夹，要修改成自己的文件夹
log.dirs=/opt/model/kafka/datas
 
# 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.
# 连接的zookeeper集群，需要将集群中部署zookeeper的所有节点写入
# 首先，在zookeeper中，数据的存储是以目录树的方式去存储的，如果后期我们的kafka的数据要修改，在不做任何的修改的情况下，默认是存储在zookeeper根目录下的，这样我们想要单独提取出zookeeper的数据就非常的麻烦
# 所以我们将kafka的数据的单独存储在一个文件分支中，这就是我们为什么要在最后写一个[/kafka]的原因。
# 前面写多个节点是为了防止单个zookeeper节点无法连接可以使用其他的zookeeper节点
zookeeper.connect=node1:2181,node2:2181,node3:2181/kafka
 
# 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

主要修改三个部分，一个是唯一标识id，kafka的文件存储路径，一个是zookeeper的节点地址。

然后我们将kafka的安装包分发到其他的节点中。

注意在分发完成之后，不要忘记修改不同节点中的唯一标识id的值。

然后我们就可以启动kafka的服务了，注意在启动kafka的服务之前，我们必须要启动zookeeper的服务。

kafka和zookeeper一样，也是要在每个节点中都分别执行启动脚本，并且kafka的启动脚本需要手动指定配置文件：

./kafka-server-start.sh -daemon ../config/server.properties

注意，我的kafka的地址和你们的可能不一样，但是只需要知道启动命令在bin目录下，配置文件在conf目录下即可，我们在三台虚拟机上分别执行脚本：

当我们看到在集群中出现kafka的进程之后，就表示我们的kafka集群启动成功了。