标签:dfs apache bind class 默认 whereis sparksql Flume安装 最好
概述:Flume是一种分布式,可靠且可用的服务,用于有效地收集,聚合和移动大量日
志数据。它具有基于流数据流的简单灵活的架构。它具有可靠的可靠性机制和许多故障
转移和恢复机制,具有强大的容错性。它使用简单的可扩展数据模型,允许在线分析应
用程序。
1)数据采集(爬虫\日志数据\flume)
2)数据存储(hdfs/hive/hbase(nosql))
3)数据计算(mapreduce/hive/sparkSQL/sparkStreaming/flink)
4)数据可视化
1)source
数据源,用户采集数据,source产生数据流,同时会把产生的数据流传输到channel
2)channel
传输通道,用于桥接source和sink
3)sink
下沉,用于收集channel传输的数据,将数据源传递到目标源
4)agent
在flume中使用事件作为传输的基本单元
简单易用,只需要写配置文件即可
1)下载flume
2)上传到Linux
3)解压
tar -zxvf apache-flume-1.6.0-bin.tar.gz -C /root/hd
4)重命名
mv apache-flume-1.6.0-bin/ flume
cp flume-env.sh.template flume-env.sh
5)修改配置
vi flume-env.sh
export JAVA_HOME=/root/hd/jdk1.8.0_192
启动命令:
bin/flume-ng agent –conf conf/log4j.properties –name a1 –conf-file conf/flumejob_telnet.conf
我已经排坑了,这里我建议–conf 后面指定的路径建议是全路径,指定到log4j.properties或,我当时老师讲的是直接conf/,我实际操作是有问题的,不能实时的反馈
bin/flume-ng agent 使用ng启动agent
--conf conf/log4j.properties 指定配置所在的文件夹
--name a1 指定的agent别名
--conf-file conf/flumejob_telnet.conf 文件
-Dflume.root.logger=INFO,console 日志级别
flumejob_telnet.conf
#smple.conf: A single-node Flume configuration
# Name the components on this agent 定义变量方便调用 加s可以有多个此角色
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source 描述source角色 进行内容定制
# 此配置属于tcp source 必须是netcat类型
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
# Describe the sink 输出日志文件
a1.sinks.k1.type = logger
# Use a channel which buffers events in memory(file) 使用内存 总大小1000 每次传输100
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# Bind the source and sink to the channel 一个source可以绑定多个channel
# 一个sinks可以只能绑定一个channel 使用的是图二的模型
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
[root@hsiehchou121 flume]# bin/flume-ng agent \
> --conf conf/ \
> --name a1 \
> --conf-file conf/flumejob_telnet.conf \
> -Dflume.root.logger=INFO.console
yum search telnet
yum install telnet.x86_64
启动命令:
bin/flume-ng agent –conf conf/log4j.properties –name a1 –conf-file conf/flum
ejob_hdfs.conf
flumejob_hdfs.conf
# Name the components on this agent agent别名设置
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source 设置数据源监听本地文件配置
# exec 执行一个命令的方式去查看文件 tail -F 实时查看
a1.sources.r1.type = exec
# 要执行的脚本command tail -F 默认10行 man tail 查看帮助
a1.sources.r1.command = tail -F /tmp/root/hive.log
# 执行这个command使用的是哪个脚本 -c 指定使用什么命令
# whereis bash
# bash: /usr/bin/bash /usr/share/man/man1/bash.1.gz
a1.sources.r1.shell = /usr/bin/bash -c
# Describe the sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://hsiehchou121:9000/flume/%Y%m%d/%H
#上传文件的前缀
a1.sinks.k1.hdfs.filePrefix = logs-
#是否按照时间滚动文件夹
a1.sinks.k1.hdfs.round = true
#多少时间单位创建一个新的文件夹 秒 (默认30s)
a1.sinks.k1.hdfs.roundValue = 1
#重新定义时间单位(每小时滚动一个文件夹)
a1.sinks.k1.hdfs.roundUnit = minute
#是否使用本地时间戳
a1.sinks.k1.hdfs.useLocalTimeStamp = true
#积攒多少个 Event 才 flush 到 HDFS 一次
a1.sinks.k1.hdfs.batchSize = 500
#设置文件类型,可支持压缩
a1.sinks.k1.hdfs.fileType = DataStream
#多久生成一个新的文件 秒
a1.sinks.k1.hdfs.rollInterval = 30
#设置每个文件的滚动大小 字节(最好128M,合理)
a1.sinks.k1.hdfs.rollSize = 134217700
#文件的滚动与 Event 数量无关
a1.sinks.k1.hdfs.rollCount = 0
#最小冗余数(备份数 生成滚动功能则生效roll hadoop本身有此功能 无需配置) 1份 不冗余 hdfs已经备份3份
a1.sinks.k1.hdfs.minBlockReplicas = 1
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
[root@hsiehchou121 flume]# bin/flume-ng agent > --conf conf/log4j.properties > --name a1 > --conf-file conf/flumejob_hdfs.conf
flumejob_dir.conf
# 定义别名
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = spooldir
# 监控的文件夹
a1.sources.r1.spoolDir = /root/testdir
# 上传成功后显示后缀名
a1.sources.r1.fileSuffix = .COMPLETED
# 如论如何 加绝对路径的文件名 默认false
a1.sources.r1.fileHeader = true
#忽略所有以.tmp 结尾的文件(正在被写入),不上传
# ^以任何开头 出现无限次 以.tmp结尾的
a1.sources.r1.ignorePattern = ([^ ]*\.tmp)
# Describe the sink 下沉到hdfs
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://hsiehchou121:9000/flume/testdir/%Y%m%d/%H
#上传文件的前缀
a1.sinks.k1.hdfs.filePrefix = testdir-
#是否按照时间滚动文件夹
a1.sinks.k1.hdfs.round = true
#多少时间单位创建一个新的文件夹
a1.sinks.k1.hdfs.roundValue = 1
#重新定义时间单位
a1.sinks.k1.hdfs.roundUnit = hour
#是否使用本地时间戳
a1.sinks.k1.hdfs.useLocalTimeStamp = true
#积攒多少个 Event 才 flush 到 HDFS 一次
a1.sinks.k1.hdfs.batchSize = 100
#设置文件类型,可支持压缩
a1.sinks.k1.hdfs.fileType = DataStream
#多久生成一个新的文件
a1.sinks.k1.hdfs.rollInterval = 600
#设置每个文件的滚动大小大概是 128M
a1.sinks.k1.hdfs.rollSize = 134217700
#文件的滚动与 Event 数量无关
a1.sinks.k1.hdfs.rollCount = 0
#最小副本数
a1.sinks.k1.hdfs.minBlockReplicas = 1
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
[root@hsiehchou121 conf]# bin/flume-ng agent –conf conf/log4j.properties –name a1 –conf-file conf/flumejob_dir.conf
[root@hsiehchou121 flume]# bin/flume-ng agent > --conf conf/log4j.properties > --name a1 > --conf-file conf/flumejob_dir.conf
需求:监控hive.log文件,用同时产生两个channel,一个channel对应的sink存储到hdfs中,另外一个channel对应的sink存储到本地
flumejob_1.conf
# name the components on this agent 别名设置
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
# 将数据流复制给多个 channel
a1.sources.r1.selector.type = replicating
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /tmp/root/hive.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
# 分两个端口发送数据
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hsiehchou121
a1.sinks.k1.port = 4141
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = hsiehchou121
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
[root@hsiehchou121 flume]# bin/flume-ng agent –conf conf/log4j.properties –name a1 –conf-file conf/flumejob_1.conf
flumejob_2.conf
# Name the components on this agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# Describe/configure the source
a2.sources.r1.type = avro
# 端口抓取数据
a2.sources.r1.bind = hsiehchou121
a2.sources.r1.port = 4141
# Describe the sink
a2.sinks.k1.type = hdfs
a2.sinks.k1.hdfs.path = hdfs://hsiehchou121:9000/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
#最小副本数
a2.sinks.k1.hdfs.minBlockReplicas = 1
# 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
[root@hsiehchou121 flume]# bin/flume-ng agent –conf conf/log4j.properties –name a2 –conf-file conf/flumejob_1.conf
flumejob_3.conf
# Name the components on this agent
a3.sources = r1
a3.sinks = k1
a3.channels = c1
# Describe/configure the source
a3.sources.r1.type = avro
a3.sources.r1.bind = hsiehchou121
a3.sources.r1.port = 4142
# Describe the sink
a3.sinks.k1.type = file_roll
a3.sinks.k1.sink.directory = /root/flume2
# Describe the channel
a3.channels.c1.type = memory
a3.channels.c1.capacity = 1000
a3.channels.c1.transactionCapacity = 100
# Bind the source and sink to the channel
a3.sources.r1.channels = c1
a3.sinks.k1.channel = c1
[root@hsiehchou121 flume]# bin/flume-ng agent –conf conf/log4j.properties –name a3 –conf-file conf/flumejob_1.conf
标签:dfs apache bind class 默认 whereis sparksql Flume安装 最好
原文地址:https://www.cnblogs.com/hsiehchou/p/10502457.html