08-Flume案例-复制和多路复用

# Flume案例-复制和多路复用
## 1）案例需求：
使用 Flume-1 监控文件变动， Flume-1 将变动内容传递给 Flume-2， Flume-2 负责存储到 HDFS。同时 Flume-1 将变动内容传递给 Flume-3，Flume-3 负责输出到 Local FileSystem。
## 2）需求分析：
![](https://hexo-img.obs.cn-east-3.myhuaweicloud.com/img/202204140938383.jpg)
## 3）实现步骤：

### 1.准备工作
在/usr/local/flume/job 目录下创建 group1 文件夹:
```bash
nbu@ecs:/usr/local/flume/job$ mkdir group1
nbu@ecs:/usr/local/flume/job$ cd group1
```
在/usr/local/flume/datas/目录下创建 flume3 文件夹:
```bash
nbu@ecs:/usr/local/flume/job/group1$ cd /usr/local/flume
nbu@ecs:/usr/local/flume$ mkdir -p datas/flume3
```
### 2.创建 flume-file-flume.conf
配置 1 个接收日志文件的 source 和两个 channel、两个 sink，分别输送给 flume-flume-hdfs 和 flume-flume-dir。
编辑配置文件
```bash
nbu@ecs:~$ cd /usr/local/flume/job/group1/
nbu@ecs:/usr/local/flume/job/group1$ vim flume-file-flume.conf
```
添加如下内容:
```bash
# Name the components on this agent
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
# 将数据流复制给所有 channel
a1.sources.r1.selector.type = replicating
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /usr/local/hive/logs/hive.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
# sink 端的 avro 是一个数据发送者
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = ecs
a1.sinks.k1.port = 4141
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = ecs
a1.sinks.k2.port = 4142
# Describe the channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
a1.channels.c2.type = memory
a1.channels.c2.capacity = 1000
a1.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a1.sources.r1.channels = c1 c2
a1.sinks.k1.channel = c1
a1.sinks.k2.channel = c2
```
### 3.创建 flume-flume-hdfs.conf
配置上级 Flume 输出的 Source， 输出是到 HDFS 的 Sink。
编辑配置文件：
```bash
nbu@ecs:/usr/local/flume/job/group1$ vim flume-flume-hdfs.conf
```
添加如下内容：
```bash
# Name the components on this agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# Describe/configure the source
# source 端的 avro 是一个数据接收服务
a2.sources.r1.type = avro
a2.sources.r1.bind = hadoop102
a2.sources.r1.port = 4141
# Describe the sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://hadoop102:9000/flume2/%Y%m%d/%H
#上传文件的前缀
a2.sinks.k1.hdfs.filePrefix = flume2-
#是否按照时间滚动文件夹
a2.sinks.k1.hdfs.round = true
#多少时间单位创建一个新的文件夹
a2.sinks.k1.hdfs.roundValue = 1
#重新定义时间单位
a2.sinks.k1.hdfs.roundUnit = hour
#是否使用本地时间戳
a2.sinks.k1.hdfs.useLocalTimeStamp = true
#积攒多少个 Event 才 flush 到 HDFS 一次
a2.sinks.k1.hdfs.batchSize = 100
#设置文件类型，可支持压缩
a2.sinks.k1.hdfs.fileType = DataStream
#多久生成一个新的文件
a2.sinks.k1.hdfs.rollInterval = 600
#设置每个文件的滚动大小大概是 128M
a2.sinks.k1.hdfs.rollSize = 134217700
#文件的滚动与 Event 数量无关
a2.sinks.k1.hdfs.rollCount = 0
# Describe the channel
a2.channels.c1.type = memory
a2.channels.c1.capacity = 1000
a2.channels.c1.transactionCapacity = 100
# Bind the source and sink to the channel
a2.sources.r1.channels = c1
a2.sinks.k1.channel = c1
```
### 4.创建 flume-flume-dir.conf
配置上级 Flume 输出的 Source， 输出是到本地目录的 Sink。
编辑配置文件:
```bash
nbu@ecs:/usr/local/flume/job/group1$ vim  flume-flume-dir.conf
```
添加如下内容:
```bash
# Name the components on this agent
a3.sources = r1
a3.sinks = k1
a3.channels = c2
# Describe/configure the source
a3.sources.r1.type = avro
a3.sources.r1.bind = hadoop102
a3.sources.r1.port = 4142
# Describe the sink
a3.sinks.k1.type = file_roll
a3.sinks.k1.sink.directory = /opt/module/data/flume3
# Describe the channel
a3.channels.c2.type = memory
a3.channels.c2.capacity = 1000
a3.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a3.sources.r1.channels = c2
a3.sinks.k1.channel = c2
```
提示：输出的本地目录必须是已经存在的目录，如果该目录不存在，并不会创建新的目录。
### 5.执行配置文件
分别启动对应的 flume 进程： flume-flume-dir， flume-flume-hdfs， flume-file-flume。
```bash
nbu@ecs:/usr/local/flume$ bin/flume-ng agent --conf conf/ --name a3 --conf-file job/group1/flume-flume-dir.conf
nbu@ecs:/usr/local/flume$ bin/flume-ng agent --conf conf/ --name a2 --conf-file job/group1/flume-flume-hdfs.conf
nbu@ecs:/usr/local/flume$ bin/flume-ng agent --conf conf/ --name a1 --conf-file job/group1/flume-file-flume.conf
```
### 6.启动 Hadoop 和 Hive
```bash
nbu@ecs:~$ cd /usr/local/hadoop/
nbu@ecs:/usr/local/hadoop$ sbin/start-dfs.sh
nbu@ecs:/usr/local/hadoop$ sbin/start-yarn.sh
nbu@ecs:/usr/local/hadoop$ cd /usr/local/hive/
nbu@ecs:/usr/local/hive$ bin/hive
hive (default)>
```
### 7.检查 HDFS 上数据
![](https://hexo-img.obs.cn-east-3.myhuaweicloud.com/img/202210092318119.png)
### 8.检查/opt/module/datas/flume3 目录中数据
```bash
nbu@ecs:~$ ll /usr/local/flume/datas/flume3/
total 20
drwxrwxr-x 2 nbu nbu 4096 Oct  9 23:19 ./
drwxrwxr-x 3 nbu nbu 4096 Oct  9 22:53 ../
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:14 1665328442213-1
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:18 1665328442213-10
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:19 1665328442213-11
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:19 1665328442213-12
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:14 1665328442213-2
-rw-rw-r-- 1 nbu nbu 1560 Oct  9 23:15 1665328442213-3
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:15 1665328442213-4
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:16 1665328442213-5
-rw-rw-r-- 1 nbu nbu 2294 Oct  9 23:16 1665328442213-6
-rw-rw-r-- 1 nbu nbu  700 Oct  9 23:17 1665328442213-7
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:17 1665328442213-8
-rw-rw-r-- 1 nbu nbu    0 Oct  9 23:18 1665328442213-9
```

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