Flume入門
1Flume概述
1.1 定義
Flume是Cloudera提供的一個高可用的，高可靠的，分布式的海量日志采集、聚合和傳輸?shù)南到y(tǒng)；
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Flume基于流式架構(gòu)，靈活簡單。
1.2 特點
可以和任意存儲進(jìn)程集成
輸入的的數(shù)據(jù)速率大于寫入目的存儲的速率，Flume會進(jìn)行緩沖，減小HDFS的壓力
Flume中的事務(wù)基于Channel，使用了兩個事務(wù)模型（sender+ receiver），確保消息被可靠發(fā)送
Flume使用兩個獨立的事務(wù)分別負(fù)責(zé)從Soucrce到Channel，以及從Channel到Sink 的事件傳遞。一旦事務(wù)中所有的數(shù)據(jù)全部成功提交到Channel，那么Source才認(rèn)為該數(shù)據(jù)讀取完成，同理，只有成功被Sink寫出去的數(shù)據(jù)，才會從Channel中移除
1.3 組成架構(gòu)
1.3.1Agent
Agent是一個JVM進(jìn)程，它以事件的形式將數(shù)據(jù)從源頭傳遞到目的地
Agent主要由Source、Channel、Sink組成
1.3.2Source
Source是負(fù)責(zé)接收數(shù)據(jù)到Agent的組件，可以處理各種類型，包括avro、thrift、exec、jms、spooling directory、netcat、sequence generator、syslog、http、legacy
1.3.3Channel
Channel是位于Source和Sink之間的緩沖區(qū)，因此，Channel允許Source和Sink運作在不同的速率上，Channel是線程安全的，可以同時處理幾個Source的寫入操作和幾個Sink的讀取操作。
Flume自帶兩種Channel：
Memory Channel：內(nèi)存中的隊列速度快，適合在不需要關(guān)系數(shù)據(jù)丟失的情境下使用
File Channel：將所有事件寫入磁盤，因此在程序關(guān)閉或機(jī)器宕機(jī)的情況下不會丟失數(shù)據(jù)
1.3.4Sink
Sink不斷地輪詢Channel中的事件且批量地移除它們，并將這些事件批量寫入到存儲或索引系統(tǒng)、或者被發(fā)送到另一個Flume Agent。
Sink是完全事務(wù)性的，在從Channel批量刪除數(shù)據(jù)之前，每個Sink用Channel啟動一個事務(wù)，批量事件一旦成功寫出到存儲系統(tǒng)或下一個Flume Agent，Sink就利用Channel提交事務(wù)，事務(wù)一旦被提交，該Channel從自己的內(nèi)部緩沖區(qū)刪除事件。
Sink組件目的地包括hdfs、logger、avro、thrift、ipc、file、null、HBase、solr、自定義。
1.3.5Event
傳輸單元，Flume數(shù)據(jù)傳輸?shù)幕締卧?，以事件的形式將?shù)據(jù)從源頭送至目的地。
Event由可選的header和載有數(shù)據(jù)的一個byte array構(gòu)成，Header是容納了key-value字符串對的HashMap。
通常一條數(shù)據(jù)就是一個 Event，每2048個字節(jié)劃分一個Event。
1.4 拓?fù)浣Y(jié)構(gòu)
這種模式是將多個Flume給順序連接起來了，從最初的Source開始到最終Sink傳送的目的存儲系統(tǒng)，此模式不建議橋接過多的Flume數(shù)量， Flume數(shù)量過多不僅會影響傳輸速率，而且一旦傳輸過程中某個節(jié)點Flume宕機(jī)，會影響整個傳輸系統(tǒng)。
Flum支持將事件流向一個或者多個目的地，這種模式將數(shù)據(jù)源復(fù)制到多個Channel中，每個Channel都有相同的數(shù)據(jù)，Sink可以選擇傳送的不同的目的地。
Flume支持使用將多個Sink邏輯上分到一個Sink組，Flume將數(shù)據(jù)發(fā)送到不同的Sink，主要解決負(fù)載均衡和故障轉(zhuǎn)移問題。
這種模式是我們最常見的，也非常實用，日常web應(yīng)用通常分布在上百個服務(wù)器，大者甚至上千個、上萬個服務(wù)器，產(chǎn)生的日志，處理起來也非常麻煩，用Flume的這種組合方式能很好的解決這一問題，每臺服務(wù)器部署一個Flume采集日志，傳送到一個集中收集日志的Flume，再由此Flume上傳到 hdfs、hive、hbase、jms等進(jìn)行日志分析。
1.5Agent原理
2Flume部署
1、解壓apache-flume-1.7.0-bin.tar.gz到/opt/module目錄下
2、修改apache-flume-1.7.0-bi的名稱為flume
3、將flume/conf下的flume-env.sh.template文件修改為flume-env.sh，并配置flume-env.sh中的JAVA_HOME
3 企業(yè)開發(fā)案例
3.1 監(jiān)控端口數(shù)據(jù)
需求分析：
服務(wù)端監(jiān)聽本機(jī)44444端口
服務(wù)端使用netcat工具向44444端口發(fā)送消息
最后將數(shù)據(jù)展示在控制臺上
實現(xiàn)步驟：
1、在job文件夾下創(chuàng)建Agent配置文件flume-netcat-logger.conf
[djm@hadoop102 job]$ vim flume-netcat-logger.conf2、添加如下內(nèi)容：
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
# Describe the sink
a1.sinks.k1.type = logger
# 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 = c13、啟動任務(wù)
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a1 –f job/flume-netcat-logger.conf -Dflume.root.logger==INFO,console參數(shù)說明：
--conf conf/表示配置文件存儲在conf/目錄
--name a1表示給 Agent 起名為a1
--conf-file job/flume-netcat.conf Flume本次啟動讀取的配置文件是在job文件夾下的 flume-telnet.conf文件
-Dflume.root.logger==INFO,console -D表示Flume運行時動態(tài)修改flume.root.logger參數(shù)屬性值，并將控制臺日志打印級別設(shè)置為INFO級別
3.2 實時讀取本地文件到HDFS
需求分析：
實時監(jiān)控Hive日志，并上傳到HDFS中
實現(xiàn)步驟：
1、在job文件夾下創(chuàng)建Agent配置文件flume-file-hdfs.conf
[djm@hadoop102 job]$ vim flume-file-hdfs.conf2、添加如下內(nèi)容：
# Name the components on this agent
a2.sources = r2
a2.sinks = k2
a2.channels = c2
# Describe/configure the source
a2.sources.r2.type = exec
a2.sources.r2.command = tail -F /opt/module/hive/logs/hive.log
a2.sources.r2.shell = /bin/bash -c
# Describe the sink
a2.sinks.k2.type = hdfs
a2.sinks.k2.hdfs.path = hdfs://hadoop102:9000/flume/%Y%m%d/%H
#上傳文件的前綴
a2.sinks.k2.hdfs.filePrefix = logs-
#是否按照時間滾動文件夾
a2.sinks.k2.hdfs.round = true
#多少時間單位創(chuàng)建一個新的文件夾
a2.sinks.k2.hdfs.roundValue = 1
#重新定義時間單位
a2.sinks.k2.hdfs.roundUnit = hour
#是否使用本地時間戳
a2.sinks.k2.hdfs.useLocalTimeStamp = true
#積攢多少個Event才flush到HDFS一次
a2.sinks.k2.hdfs.batchSize = 1000
#設(shè)置文件類型，可支持壓縮
a2.sinks.k2.hdfs.fileType = DataStream
#多久生成一個新的文件
a2.sinks.k2.hdfs.rollInterval = 60
#設(shè)置每個文件的滾動大小
a2.sinks.k2.hdfs.rollSize = 134217700
#文件的滾動與Event數(shù)量無關(guān)
a2.sinks.k2.hdfs.rollCount = 0
# Use a channel which buffers events in memory
a2.channels.c2.type = memory
a2.channels.c2.capacity = 1000
a2.channels.c2.transactionCapacity = 100
# Bind the source and sink to the channel
a2.sources.r2.channels = c2
a2.sinks.k2.channel = c23、啟動任務(wù)
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a2 –f job/flume-file-hdfs.conf注意：
要想讀取Linux系統(tǒng)中的文件，就得按照Linux命令的規(guī)則執(zhí)行命令，由于Hive日志在Linux系統(tǒng)中所以讀取文件的類型選擇：exec即execute執(zhí)行的意思。表示執(zhí)行Linux命令來讀取文件。
3.3 實時讀取目錄文件到 HDFS
需求分析：
使用Flume監(jiān)聽整個目錄的文件
實現(xiàn)步驟：
1、在job文件夾下創(chuàng)建Agent配置文件flume-dir-hdfs.conf
[djm@hadoop102 job]$ vim flume-dir-hdfs.conf2、添加如下內(nèi)容：
a3.sources = r3
a3.sinks = k3
a3.channels = c3
# Describe/configure the source
a3.sources.r3.type = spooldir
a3.sources.r3.spoolDir = /opt/module/flume/upload
a3.sources.r3.fileSuffix = .COMPLETED
a3.sources.r3.fileHeader = true
#忽略所有以.tmp結(jié)尾的文件，不上傳
a3.sources.r3.ignorePattern = ([^ ]*\.tmp)
# Describe the sink
a3.sinks.k3.type = hdfs
a3.sinks.k3.hdfs.path = hdfs://hadoop102:9000/flume/upload/%Y%m%d/%H
#上傳文件的前綴
a3.sinks.k3.hdfs.filePrefix = upload-
#是否按照時間滾動文件夾
a3.sinks.k3.hdfs.round = true
#多少時間單位創(chuàng)建一個新的文件夾
a3.sinks.k3.hdfs.roundValue = 1
#重新定義時間單位
a3.sinks.k3.hdfs.roundUnit = hour
#是否使用本地時間戳
a3.sinks.k3.hdfs.useLocalTimeStamp = true
#積攢多少個Event才flush到HDFS一次
a3.sinks.k3.hdfs.batchSize = 100
#設(shè)置文件類型，可支持壓縮
a3.sinks.k3.hdfs.fileType = DataStream
#多久生成一個新的文件
a3.sinks.k3.hdfs.rollInterval = 60
#設(shè)置每個文件的滾動大小大概是128M
a3.sinks.k3.hdfs.rollSize = 134217700
#文件的滾動與Event數(shù)量無關(guān)
a3.sinks.k3.hdfs.rollCount = 0
# Use a channel which buffers events in memory
a3.channels.c3.type = memory
a3.channels.c3.capacity = 1000
a3.channels.c3.transactionCapacity = 100
# Bind the source and sink to the channel
a3.sources.r3.channels = c3
a3.sinks.k3.channel = c33、啟動任務(wù)
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a3 –f job/flume-dir-hdfs.conf注意：
不要在監(jiān)控目錄中創(chuàng)建并持續(xù)修改文件
3.4 單數(shù)據(jù)源多出口案例（選擇器）
需求分析：
使用Flume-1監(jiān)控文件變動，Flume-1將變動內(nèi)容傳遞給Flume-2
Flume-2負(fù)責(zé)存儲到HDFS
同時Flume-1將變動內(nèi)容傳遞給Flume-3，Flume-3負(fù)責(zé)輸出到Local FileSystem
1、在group1文件夾下創(chuàng)建Agent配置文件flume-file-flume.conf
[djm@hadoop102 group1]$ vim flume-file-flume.conf2、添加如下內(nèi)容：
# Name the components on this agent
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
# 將數(shù)據(jù)流復(fù)制給所有channel
a1.sources.r1.selector.type = replicating
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/module/hive/logs/hive.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
# sink端的avro是一個數(shù)據(jù)發(fā)送者
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop102
a1.sinks.k1.port = 4141
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = hadoop102
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 = c23、在group1文件夾下創(chuàng)建Agent配置文件flume-flume-hdfs.conf
[djm@hadoop102 group1]$ vim flume-flume-hdfs.conf4、添加如下內(nèi)容：
# Name the components on this agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# Describe/configure the source
# source端的avro是一個數(shù)據(jù)接收服務(wù)
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
#上傳文件的前綴
a2.sinks.k1.hdfs.filePrefix = flume2-
#是否按照時間滾動文件夾
a2.sinks.k1.hdfs.round = true
#多少時間單位創(chuàng)建一個新的文件夾
a2.sinks.k1.hdfs.roundValue = 1
#重新定義時間單位
a2.sinks.k1.hdfs.roundUnit = hour
#是否使用本地時間戳
a2.sinks.k1.hdfs.useLocalTimeStamp = true
#積攢多少個Event才flush到HDFS一次
a2.sinks.k1.hdfs.batchSize = 100
#設(shè)置文件類型，可支持壓縮
a2.sinks.k1.hdfs.fileType = DataStream
#多久生成一個新的文件
a2.sinks.k1.hdfs.rollInterval = 600
#設(shè)置每個文件的滾動大小大概是128M
a2.sinks.k1.hdfs.rollSize = 134217700
#文件的滾動與Event數(shù)量無關(guān)
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 = c15、在group1文件夾下創(chuàng)建 Agent 配置文件flume-flume-dir.conf
[djm@hadoop102 group1]$ vim flume-flume-dir.conf6、添加如下內(nèi)容：
# 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 = c27、啟動任務(wù)
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a3 -f job/group1/flume-flume-dir.conf
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a2 -f job/group1/flume-flume-hdfs.conf
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a1 -f job/group1/flume-file-flume.conf注意：
Avro是一種語言無關(guān)的數(shù)據(jù)序列化和RPC框架
輸出的本地目錄必須是已經(jīng)存在的目錄，如果該目錄不存在，并不會創(chuàng)建新的目錄
必須先啟動Sink存在的job
3.5 單數(shù)據(jù)源多出口案例（Sink組）
需求分析：
使用Flume-1監(jiān)控端口數(shù)據(jù)，Flume-1將變動內(nèi)容傳遞給Flume-2
Flume-2負(fù)責(zé)將數(shù)據(jù)展示在控制臺上
同時Flume-1將變動內(nèi)容傳遞給Flume-3，Flume-3也負(fù)責(zé)將數(shù)據(jù)展示在控制臺上
實現(xiàn)步驟：
1、在group2文件夾下創(chuàng)建Agent配置文件flume-netcat-flume.conf
2、添加如下內(nèi)容：
# Name the components on this agent
a1.sources = r1
a1.channels = c1
a1.sinkgroups = g1
a1.sinks = k1 k2
# Describe/configure the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
a1.sinkgroups.g1.processor.type = load_balance
a1.sinkgroups.g1.processor.backoff = true
a1.sinkgroups.g1.processor.selector = round_robin
a1.sinkgroups.g1.processor.selector.maxTimeOut=10000
# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop102
a1.sinks.k1.port = 4141
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = hadoop102
a1.sinks.k2.port = 4142
# Describe the channel
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.sinkgroups.g1.sinks = k1 k2
a1.sinks.k1.channel = c1
a1.sinks.k2.channel = c1
3、在group2文件夾下創(chuàng)建Agent配置文件flume-flume-console1.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
a2.sources.r1.bind = hadoop102
a2.sources.r1.port = 4141
# Describe the sink
a2.sinks.k1.type = logger
# 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
5、在 group2文件夾下創(chuàng)建Agent配置文件flume-flume-console2.conf
6、添加如下內(nèi)容：
# 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 = logger
# 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
7、啟動任務(wù)
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a3 -f job/group2/flume-flume-console2.conf -Dflume.root.logger=INFO,console
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a2 -f job/group2/flume-flume-console1.conf -Dflume.root.logger=INFO,console
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a1 -f job/group2/flume-netcat-flume.conf
3.6 多數(shù)據(jù)源匯總
需求分析：
hadoop103上的Flume-1監(jiān)控文件/opt/module/group.log
hadoop102上的Flume-2監(jiān)控某一個端口的數(shù)據(jù)流
Flume-1與Flume-2將數(shù)據(jù)發(fā)送給hadoop104上的Flume-3，Flume-3將最終數(shù)據(jù)打印到控制臺
實現(xiàn)步驟：
1、在group3文件夾下創(chuàng)建Agent配置文件flume1-logger-flume.conf
[djm@hadoop102 group3]$ vim flume1-logger-flume.conf
2、添加如下內(nèi)容：
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/module/group.log
a1.sources.r1.shell = /bin/bash -c
# Describe the sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop104
a1.sinks.k1.port = 4141
# Describe the channel
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
3、在group3文件夾下創(chuàng)建Agent配置文件flume2-netcat-flume.conf
[djm@hadoop102 group3]$ vim flume2-netcat-flume.conf
4、添加如下內(nèi)容：
# Name the components on this agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
# Describe/configure the source
a2.sources.r1.type = netcat
a2.sources.r1.bind = hadoop102
a2.sources.r1.port = 44444
# Describe the sink
a2.sinks.k1.type = avro
a2.sinks.k1.hostname = hadoop104
a2.sinks.k1.port = 4141
# Use a channel which buffers events in memory
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
5、在group3文件夾下創(chuàng)建Agent配置文件flume3-flume-logger.conf
[djm@hadoop102 group3]$ vim flume3-flume-logger.conf
6、添加如下內(nèi)容：
# 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 = hadoop104
a3.sources.r1.port = 4141
# Describe the sink
# Describe the sink
a3.sinks.k1.type = logger
# 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
7、分發(fā)配置文件
[djm@hadoop102 group3]$ xsync /opt/module/flume/job
8、啟動任務(wù)
[djm@hadoop104 flume]$ bin/flume-ng agent -c conf/ -n a3 -f job/group3/flume3-flume-logger.conf -Dflume.root.logger=INFO,console
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -n a2 -f job/group3/flume2-netcat-flume.conf
[djm@hadoop103 flume]$ bin/flume-ng agent -c conf/ -n a1 -f job/group3/flume1-logger-flume.conf
4Ganglia部署
1、安裝httpd服務(wù)與php
yum -y install httpd php
2、安裝其他依賴
yum -y install rrdtool perl-rrdtool rrdtool-devel
3、安裝ganglia
rpm -Uvh http://dl.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm
yum -y install ganglia-gmetad ganglia-gmond ganglia-web
4、修改ganglia配置文件
vim /etc/httpd/conf.d/ganglia.conf
#
# Ganglia monitoring system php web frontend
#
Alias /ganglia /usr/share/ganglia
<Location /ganglia>
# Require local
Require all granted
# Require ip 10.1.2.3
# Require host example.org
</Location>
特別注意：以下配置是不能起作用的
<Location /ganglia>
Order deny,allow
Allow from all
</Location>
5、修改gmetad配置文件
vim /etc/ganglia/gmetad.conf
data_source "hadoop102" 192.168.1.102
6、修改gmond配置文件
vim /etc/ganglia/gmond.conf
cluster {
#name = "unspecified"
name = "hadoop102"
owner = "unspecified"
latlong = "unspecified"
url = "unspecified"
}
udp_send_channel {
#bind_hostname = yes # Highly recommended, soon to be default.
# This option tells gmond to use a source address
# that resolves to the machine's hostname. Without
# this, the metrics may appear to come from any
# interface and the DNS names associated with
# those IPs will be used to create the RRDs.
#mcast_join = 239.2.11.71
host = 192.168.10.102
port = 8649
ttl = 1
}
/* You can specify as many udp_recv_channels as you like as well. */
udp_recv_channel {
#mcast_join = 239.2.11.71
port = 8649
#bind = 239.2.11.71
bind = 192.168.10.102
retry_bind = true
# Size of the UDP buffer. If you are handling lots of metrics you really
# should bump it up to e.g. 10MB or even higher.
# buffer = 10485760
}
6、查看SELinux狀態(tài)
sestatus
如果不是disabled，需修改以下配置文件：
vim /etc/selinux/config
或者臨時關(guān)閉SELinux：
setenforce 0
7、啟動ganglia
systemctl start httpd
systemctl start gmetad
systemctl start gmond
8、打開瀏覽器訪問
http://hadoop102/ganglia/
如果完成以上操作仍出現(xiàn)權(quán)限不足錯誤，可修改/var/lib/ganglia目錄的權(quán)限嘗試
chmod -R 777 /var/lib/ganglia
5 自定義Source
需求分析：
編碼實現(xiàn)：
1、引入依賴
<dependency>
<groupId>org.apache.flume</groupId>
<artifactId>flume-ng-core</artifactId>
<version>1.7.0</version>
</dependency>
2、代碼編寫
package com.djm.flume;
import org.apache.flume.Context;
import org.apache.flume.EventDeliveryException;
import org.apache.flume.PollableSource;
import org.apache.flume.conf.Configurable;
import org.apache.flume.event.SimpleEvent;
import org.apache.flume.source.AbstractSource;
import java.util.HashMap;
public class MySource extends AbstractSource implements Configurable, PollableSource {
//定義配置文件將來要讀取的字段
private Long delay;
private String field;
/**
* 接收數(shù)據(jù)，將數(shù)據(jù)封裝成一個個event，寫入channel
* @return
* @throws EventDeliveryException
*/
public Status process() throws EventDeliveryException {
HashMap<String, String> hearderMap = new HashMap<>();
SimpleEvent event = new SimpleEvent();
try {
for (int i = 0; i < 5; i++) {
event.setHeaders(hearderMap);
event.setBody((field + i).getBytes());
getChannelProcessor().processEvent(event);
Thread.sleep(delay);
}
} catch (InterruptedException e) {
e.printStackTrace();
return Status.BACKOFF;
}
return Status.READY;
}
public long getBackOffSleepIncrement() {
return 0;
}
public long getMaxBackOffSleepInterval() {
return 0;
}
/**
* 讀取配置文件
* @param context
*/
public void configure(Context context) {
delay = context.getLong("delay");
field = context.getString("field", "hello");
}
}
3、打包測試
利用Maven打包并上傳到 /opt/module/flume/lib目錄下
在job文件夾下創(chuàng)建Agent配置文件mysource.conf
[djm@hadoop102 job]$ vim mysource.conf
添加如下內(nèi)容：
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = com.djm.flume.MySource
a1.sources.r1.delay = 1000
a1.sources.r1.field = djm
# Describe the sink
a1.sinks.k1.type = logger
# 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
啟動任務(wù)
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -f job/mysource.conf -n a1 -Dflume.root.logger=INFO,console
6 自定義Sink
需求分析：
編碼實現(xiàn)：
1、引入依賴
<dependency>
<groupId>org.apache.flume</groupId>
<artifactId>flume-ng-core</artifactId>
<version>1.7.0</version>
</dependency>
2、代碼編寫
package com.djm.flume;
import org.apache.flume.*;
import org.apache.flume.conf.Configurable;
import org.apache.flume.sink.AbstractSink;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class MySink extends AbstractSink implements Configurable {
private static final Logger LOG = LoggerFactory.getLogger(AbstractSink.class);
private String prefix;
private String suffix;
@Override
public Status process() throws EventDeliveryException {
Status status = null;
Channel channel = getChannel();
Transaction transaction = channel.getTransaction();
try {
Event event;
transaction.begin();
while ((event = channel.take()) == null) {
Thread.sleep(200);
}
LOG.info(prefix + new String(event.getBody()) + suffix);
transaction.commit();
status = Status.READY;
} catch (Throwable e) {
transaction.rollback();
status = Status.BACKOFF;
if (e instanceof Error)
throw (Error) e;
} finally {
transaction.close();
}
return status;
}
@Override
public void configure(Context context) {
prefix = context.getString("prefix");
suffix = context.getString("suffix");
}
}
3、打包測試
利用Maven打包并上傳到 /opt/module/flume/lib目錄下
在job文件夾下創(chuàng)建Agent配置文件mysource.conf
[djm@hadoop102 job]$ vim mysink.conf
添加如下內(nèi)容：
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444
# Describe the sink
a1.sinks.k1.type = com.djm.flume.MySink
a1.sinks.k1.prefix = djm:
a1.sinks.k1.suffix = :end
# 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
啟動任務(wù)
[djm@hadoop102 flume]$ bin/flume-ng agent -c conf/ -f job/mysink.conf -n a1 -Dflume.root.logger=INFO,console
7Flume參數(shù)調(diào)優(yōu)
7.1Source
增加Source個數(shù)可以增大Source的讀取數(shù)據(jù)的能力，例如：當(dāng)某一個目錄產(chǎn)生的文件過多時需要將這個文件目錄拆分成多個文件目錄，同時配置好多個Source以保證Source有足夠的能力獲取到新產(chǎn)生的數(shù)據(jù)。
batchSize參數(shù)決定Source一次批量運輸?shù)?code>Channel的Event條數(shù)，適當(dāng)調(diào)大這個參數(shù)可以提高Source搬運Event到Channel時的性能。
7.2Channel
Type選擇Memory Channel時Channel的性能最好，但是如果Flume進(jìn)程意外掛掉可能會丟失數(shù)據(jù)
Type選擇File Channel時Channel的容錯性更好，但是性能上會比Memory Channel差，使用File Channel時`dataDirs 配置多個不同盤下的目錄可以提高性能。
Capacity參數(shù)決定Channel可容納最大的Event條數(shù)，TransactionCapacity 參數(shù)決定每次Source往Channel里面寫的最大Event條數(shù)和每次Sink從Channel里面讀的最大Event條數(shù)，TransactionCapacity需要大于Source和Sink的batchSize參數(shù)。
7.3Sink
增加Sink的個數(shù)可以增加Sink消費Event的能力，Sink也不是越多越好夠用就行，過多的Sink會占用系統(tǒng)資源，造成系統(tǒng)資源不必要的浪費。
batchSize參數(shù)決定Sink一次批量從Channel讀取的Event條數(shù)，適當(dāng)調(diào)大這個參數(shù)可以提高Sink從Channel搬出Event的性能。
當(dāng)前文章：Flume入門
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