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Kafka对接采集日志Flum的集群搭建与部署_日志 flu

作者：凡人多烦事01 | 2024-04-28 12:24:08

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日志 flu

Kafka简介

消息队列

消息队列——用于存放消息的组件
程序员可以将消息放入到队列中，也可以从消息队列中获取消息
很多时候消息队列不是一个永久性的存储，是作为临时存储存在的（设定一个期限：设置消息在MQ中保存10天）
消息队列中间件：消息队列的组件，例如：Kafka、Active MQ、RabbitMQ、RocketMQ、ZeroMQ

Kafka的应用场景

异步处理
- 可以将一些比较耗时的操作放在其他系统中，通过消息队列将需要进行处理的消息进行存储，其他系统可以消费消息队列中的数据
- 比较常见的：发送短信验证码、发送邮件
系统解耦
- 原先一个微服务是通过接口（HTTP）调用另一个微服务，这时候耦合很严重，只要接口发生变化就会导致系统不可用
- 使用消息队列可以将系统进行解耦合，现在第一个微服务可以将消息放入到消息队列中，另一个微服务可以从消息队列中把消息取出来进行处理。进行系统解耦
流量削峰
- 因为消息队列是低延迟、高可靠、高吞吐的，可以应对大量并发
日志处理
- 可以使用消息队列作为临时存储，或者一种通信管道

消息队列的两种模型

生产者、消费者模型
- 生产者负责将消息生产到MQ中
- 消费者负责从MQ中获取消息
- 生产者和消费者是解耦的，可能是生产者一个程序、消费者是另外一个程序
消息队列的模式
- 点对点：一个消费者消费一个消息
- 发布订阅：多个消费者可以消费一个消息

Kafka中的重要概念

broker
- Kafka服务器进程，生产者、消费者都要连接broker
- 一个集群由多个broker组成，功能实现Kafka集群的负载均衡、容错
producer：生产者
consumer：消费者
topic：主题，一个Kafka集群中，可以包含多个topic。一个topic可以包含多个分区
- 是一个逻辑结构，生产、消费消息都需要指定topic
partition：Kafka集群的分布式就是由分区来实现的。一个topic中的消息可以分布在topic中的不同partition中
replica：副本，实现Kafkaf集群的容错，实现partition的容错。一个topic至少应该包含大于1个的副本
consumer group：消费者组，一个消费者组中的消费者可以共同消费topic中的分区数据。每一个消费者组都一个唯一的名字。配置group.id一样的消费者是属于同一个组中
offset：偏移量。相对消费者、partition来说，可以通过offset来拉取数据
group.id：消费者组的概念，可以在一个消费组中包含多个消费者。如果若干个消费者的group.id是一样的，表示它们就在一个组中，一个组中的消费者是共同消费Kafka中topic的数据。
Kafka是一种拉消息模式的消息队列，在消费者中会有一个offset，表示从哪条消息开始拉取数据

消费者组

一个消费者组中可以包含多个消费者，共同来消费topic中的数据
一个topic中如果只有一个分区，那么这个分区只能被某个组中的一个消费者消费
有多少个分区，那么就可以被同一个组内的多少个消费者消费

幂等性

生产者消息重复问题
- Kafka生产者生产消息到partition，如果直接发送消息，kafka会将消息保存到分区中，但Kafka会返回一个ack给生产者，表示当前操作是否成功，是否已经保存了这条消息。如果ack响应的过程失败了，此时生产者会重试，继续发送没有发送成功的消息，Kafka又会保存一条一模一样的消息
在Kafka中可以开启幂等性
- 当Kafka的生产者生产消息时，会增加一个pid（生产者的唯一编号）和sequence number（针对消息的一个递增序列）
- 发送消息，会连着pid和sequence number一块发送
- kafka接收到消息，会将消息和pid、sequence number一并保存下来
- 如果ack响应失败，生产者重试，再次发送消息时，Kafka会根据pid、sequence number是否需要再保存一条消息
- 判断条件：生产者发送过来的sequence number 是否小于等于 partition中消息对应的sequence

Kafka集群搭建

前期准备：zookeeper必须搭建完毕

kafka集群部署

上传并解压安装包

[lili@hadoop102 software]$ tar -zxvf kafka_2.11-0.11.0.0.tgz -C /opt/module/
1

修改解压后的文件名称

[lili@hadoop102 software]$ mv kafka_2.11-0.11.0.0/ kafka
1

在/opt/module/kafka目录下创建logs文件夹

作为kafka运行日志存放的文件
```
[lili@hadoop102 kafka]$ mkdir logs
1
```

修改kafka配置文件

[lili@hadoop102 kafka]$ vim config/server.properties 

# 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唯一编号，不能重复
broker.id=0

# Switch to enable topic deletion or not, default value is false
#删除topic功能
delete.topic.enable=true

############################# 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
#处理网路请求的线程数量
num.network.threads=3

# The number of threads that the server uses for processing requests, which may include disk I/O
#用来处理磁盘IO的线程数量
num.io.threads=8

# The send buffer (SO_SNDBUF) used by the socket server
#Socket(套接字)可以看成是两个网络应用程序进行通信时，各自通信连接中的端点，这是一个逻辑上的
#概念。它是网络环境中进程间通信的API(应用程序编程接口)，也是可以被命名和寻址的通信端点，使
#用中的每一个套接字都有其类型和一个与之相连进程。通信时其中一个网络应用程序将要传输的一段信
#息写入它所在主机的 Socket中，该 Socket通过与网络接口卡(NIC)相连的传输介质将这段信息送到另
#外一台主机的 Socket中，使对方能够接收到这段信息。 Socket是由IP地址和端口结合的，提供向应
#用层进程传送数据包的机制 [2] 。
#发送套接字的缓冲区大小
socket.send.buffer.bytes=102400

# The receive buffer (SO_RCVBUF) used by the socket server
#接收套接字的缓冲区大小
socket.receive.buffer.bytes=102400

# The maximum size of a request that the socket server will accept (protection against OOM)
#请求套接字的缓冲区大小
socket.request.max.bytes=104857600


############################# Log Basics #############################

# A comma seperated list of directories under which to store log files
#kafka运行日志存放的路径
log.dirs=/opt/module/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.
#topic在当前broker上的分区个数
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.
#用来恢复和清理data下数据的线程数量
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
#segment文件保留的最长时间，超时将被删除
log.retention.hours=168

# A size-based retention policy for logs. Segments are pruned from the log as long as the remaining
# segments don't 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.
#配置连接Zookeeper集群地址
zookeeper.connect=hadoop102:2181,hadoop103:2181,hadoop104:2181

# 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

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分发kafka文件夹到三台服务器
```
[lili@hadoop102 module]$ xsync kafka/
1
```
分发之后，分别在其他服务器上修改配置/opt/module/kafka/config/server.properties中的broker.id=1、broker.id=2

配置环境变量

分别在三台服务器上配置环境变量

[lili@hadoop102 module]$ vim /etc/profile.d/env.sh 
#KAFKA_HOME
export KAFKA_HOME=/opt/module/kafka
export PATH=$PATH:$KAFKA_HOME/bin
[lili@hadoop102 module]$ source /etc/profile.d/env.sh
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启动集群

[lili@hadoop102 kafka]$ bin/kafka-server-start.sh config/server.properties &
[lili@hadoop103 kafka]$ bin/kafka-server-start.sh config/server.properties &
[lili@hadoop104 kafka]$ bin/kafka-server-start.sh config/server.properties &
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在打开一个shell窗口连接hadoop102查看系统进程

[lili@hadoop102 ~]$ xcall.sh jps
----------hadoop102----------
21025 NameNode
21460 NodeManager
21142 DataNode
21543 JobHistoryServer
25946 Kafka
20826 QuorumPeerMain
23707 Application
30063 Jps
----------hadoop103----------
16547 Kafka
13589 QuorumPeerMain
13800 ResourceManager
15736 Application
17993 Jps
13918 NodeManager
13662 DataNode
----------hadoop104----------
19041 Jps
14242 DataNode
14358 SecondaryNameNode
16840 Kafka
14447 NodeManager
14175 QuorumPeerMain
[lili@hadoop102 ~]$ 
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关闭集群

[lili@hadoop102 kafka]$ bin/kafka-server-stop.sh stop
[lili@hadoop103 kafka]$ bin/kafka-server-stop.sh stop
[lili@hadoop104 kafka]$ bin/kafka-server-stop.sh stop
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3

关闭集群后查看系统进程

[lili@hadoop102 ~]$ xcall.sh jps
----------hadoop102----------
21025 NameNode
21460 NodeManager
21142 DataNode
21543 JobHistoryServer
30135 Jps
20826 QuorumPeerMain
23707 Application
----------hadoop103----------
18050 Jps
13589 QuorumPeerMain
13800 ResourceManager
15736 Application
13918 NodeManager
13662 DataNode
----------hadoop104----------
14242 DataNode
14358 SecondaryNameNode
19102 Jps
14447 NodeManager
14175 QuorumPeerMain
[lili@hadoop102 ~]$ 
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kafka启动脚本

编写脚本

[lili@hadoop102 bin]$ vim kf.sh
#!/bin/bash
case $1 in
"start"){
        for i in hadoop102 hadoop103 hadoop104
        do
                echo " --------启动 $i Kafka-------"
                ssh $i "/opt/module/kafka/bin/kafka-server-start.sh -daemon /opt/module/kafka/config/server.properties "
#daemon进程又称为守护 进程，是在系统 启动就运行，系统关闭才停止的进程，独立于终端之外，不与客户端交
#互。一般进程在关闭终端后就停止了，而daemon进程不会停止。
        done
};;
"stop"){
        for i in hadoop102 hadoop103 hadoop104
        do
                echo " --------停止 $i Kafka-------"
                ssh $i "/opt/module/kafka/bin/kafka-server-stop.sh  stop"
        done
};;
esac
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增加脚本权限
```
[lili@hadoop102 bin]$ chmod 777 kf.sh
1
```
启动脚本
```
[lili@hadoop102 module]$ kf.sh start
1
```
关闭脚本
```
[lili@hadoop102 module]$ kf.sh stop
1
```

Kafka命令行操作

1.查看Kafka Topic列表

[lili@hadoop102 kafka]$ bin/kafka-topics.sh --zookeeper hadoop102:2181 --list
topic_event
topic_start
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3

如果没有出现这两个topic可能的原因

在kafka启动前应当先启动采集日志flume
缺乏数据源，查看/tmp/logs/目录下是否有日志数据。
如果仍然没有解决，尝试自行创建Kafka Topic

2.创建Kafka Topic

进入到/opt/module/kafka/目录下分别创建：启动日志主题、事件日志主题。

创建启动日志主题

[lili@hadoop102 kafka]$ bin/kafka-topics.sh --zookeeper hadoop102:2181,hadoop103:2181,hadoop104:2181  --create --replication-factor 1 --partitions 1 --topic topic_start
1

创建事件日志主题

[lili@hadoop102 kafka]$ bin/kafka-topics.sh --zookeeper hadoop102:2181,hadoop103:2181,hadoop104:2181  --create --replication-factor 1 --partitions 1 --topic topic_event
1

3.删除Kafka Topic

删除启动日志主题

[lili@hadoop102 kafka]$ bin/kafka-topics.sh --delete --zookeeper hadoop102:2181,hadoop103:2181,hadoop104:2181 --topic topic_start
1

删除事件日志主题

[lili@hadoop102 kafka]$ bin/kafka-topics.sh --delete --zookeeper hadoop102:2181,hadoop103:2181,hadoop104:2181 --topic topic_event
1

4.kafka消费信息

消费启动日志主题
```
[lili@hadoop102 kafka]$ bin/kafka-console-consumer.sh \
--bootstrap-server hadoop102:9092 --from-beginning --topic topic_start
1
2
```
–from-beginning：会把主题中以往所有的数据都读取出来。根据业务场景选择是否增加该配置。

消费事件日志主题

[lili@hadoop102 kafka]$ bin/kafka-console-consumer.sh \
--bootstrap-server hadoop102:9092 --from-beginning --topic topic_event
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2

5.查看kafka Topic详情

查看启动日志主题

[lili@hadoop102 kafka]$ bin/kafka-topics.sh --zookeeper hadoop102:2181 \
--describe --topic topic_start
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2

查看事件日志主题

[lili@hadoop102 kafka]$ bin/kafka-topics.sh --zookeeper hadoop102:2181 \
--describe --topic --topic topic_event
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6.kafka压力测试

利用kafka自带的官方脚本，对Kafka进行压测。

kafka-producer-perf-test.sh

kafka-consumer-perf-test.sh

Kafka Producer压力测试
```
[lili@hadoop102 kafka]$ bin/kafka-producer-perf-test.sh  --topic test --record-size 100 --num-records 100000 --throughput -1 --producer-props bootstrap.servers=hadoop102:9092,hadoop103:9092,hadoop104:9092
1
```
说明：

record-size是一条信息有多大，单位是字节。

num-records是总共发送多少条信息。

throughput 是每秒多少条信息，设成-1，表示不限流，可测出生产者最大吞吐量。

打印结果：
```
100000 records sent, 27510.316369 records/sec (2.62 MB/sec), 1303.49 ms avg latency, 
1597.00 ms max latency, 1434 ms 50th, 1569 ms 95th, 1589 ms 99th, 1595 ms 99.9th.
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2
```
参数解析：本例中一共写入10w条消息，吞吐量为2.62 MB/sec，每次写入的平均延迟为1597.00毫秒，最大的延迟为1595毫秒。（我的电脑好菜！）
Kafka Consumer压力测试
```
[lili@hadoop102 kafka]$ bin/kafka-consumer-perf-test.sh --zookeeper hadoop102:2181 --topic test --fetch-size 10000 --messages 10000000 --threads 1
1
```
说明：

–zookeeper 指定zookeeper的链接信息

–topic 指定topic的名称

–fetch-size 指定每次fetch的数据的大小

–messages 总共要消费的消息个数

注：Consumer的测试，如果这四个指标（IO，CPU，内存，网络）都不能改变，考虑增加分区数来提升性能。

打印结果：
```
start.time, end.time, data.consumed.in.MB, MB.sec, data.consumed.in.nMsg, nMsg.sec
2021-07-31 07:23:43:903, 2021-07-31 07:23:49:424, 19.0735, 3.4547, 200000, 36225.3215
1
2
```
开始测试时间，测试结束数据，共消费数据19.0735MB，吞吐量3.4547MB/s，共消费200000条，平均每秒消费36225.3215条。

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