Flume安装与配置

建议直接下载Flume的预编译版本，下载地址:这里

Avro Source+Memory Channel+Logger Sink

使用Avro Source接收外部数据源，Logger作为sink，即通过Avro RPC调用，将数据缓存在channel中，然后通过Logger打印出调用发送的数据。配置Agent，修改配置文件conf/flume-conf.properties，内容如下：

# Define a memory channel called ch1 on agent1
agent1.channels.ch1.type = memory

# Define an Avro source called avro-source1 on agent1 and tell it
# to bind to 0.0.0.0:41414. Connect it to channel ch1.
agent1.sources.avro-source1.channels = ch1
agent1.sources.avro-source1.type = avro
agent1.sources.avro-source1.bind = 0.0.0.0
agent1.sources.avro-source1.port = 41414

# Define a logger sink that simply logs all events it receives
# and connect it to the other end of the same channel.
agent1.sinks.log-sink1.channel = ch1
agent1.sinks.log-sink1.type = logger

# Finally, now that we've defined all of our components, tell
# agent1 which ones we want to activate.
agent1.channels = ch1
agent1.channels.ch1.capacity = 1000
agent1.sources = avro-source1
agent1.sinks = log-sink1

首先，启动Agent进程：

bin/flume-ng agent -c ./conf/ -f conf/flume-conf.properties -Dflume.root.logger=DEBUG,console -n agent1

然后，启动Avro Client，发送数据：

bin/flume-ng avro-client -c ./conf/ -H 0.0.0.0 -p 41414 -F /usr/local/programs/logs/sync.log -Dflume.root.logger=DEBUG,console

Avro Source+Memory Channel+HDFS Sink

配置Agent，修改配置文件conf/flume-conf-hdfs.properties，内容如下：

# Define a source, channel, sink
agent1.sources = avro-source1
agent1.channels = ch1
agent1.sinks = hdfs-sink

# Configure channel
agent1.channels.ch1.type = memory
agent1.channels.ch1.capacity = 1000000
agent1.channels.ch1.transactionCapacity = 500000

# Define an Avro source called avro-source1 on agent1 and tell it
# to bind to 0.0.0.0:41414. Connect it to channel ch1.
agent1.sources.avro-source1.channels = ch1
agent1.sources.avro-source1.type = avro
agent1.sources.avro-source1.bind = 0.0.0.0
agent1.sources.avro-source1.port = 41414

# Define a logger sink that simply logs all events it receives
# and connect it to the other end of the same channel.
agent1.sinks.hdfs-sink1.channel = ch1
agent1.sinks.hdfs-sink1.type = hdfs
agent1.sinks.hdfs-sink1.hdfs.path = hdfs://h1:8020/data/flume/
agent1.sinks.hdfs-sink1.hdfs.filePrefix = sync_file
agent1.sinks.hdfs-sink1.hdfs.fileSuffix = .log
agent1.sinks.hdfs-sink1.hdfs.rollSize = 1048576
agent1.sinks.hdfs-sink1.rollInterval = 0
agent1.sinks.hdfs-sink1.hdfs.rollCount = 0
agent1.sinks.hdfs-sink1.hdfs.batchSize = 1500
agent1.sinks.hdfs-sink1.hdfs.round = true
agent1.sinks.hdfs-sink1.hdfs.roundUnit = minute
agent1.sinks.hdfs-sink1.hdfs.threadsPoolSize = 25
agent1.sinks.hdfs-sink1.hdfs.useLocalTimeStamp = true
agent1.sinks.hdfs-sink1.hdfs.minBlockReplicas = 1
agent1.sinks.hdfs-sink1.fileType = SequenceFile
agent1.sinks.hdfs-sink1.writeFormat = TEXT

首先，启动Agent进程：

bin/flume-ng agent -c ./conf/ -f conf/flume-conf.properties -Dflume.root.logger=DEBUG,console -n agent1

然后，启动Avro Client，发送数据：

bin/flume-ng avro-client -c ./conf/ -H 0.0.0.0 -p 41414 -F /usr/local/programs/logs/sync.log -Dflume.root.logger=DEBUG,console

可以查看同步到HDFS上的数据：

hdfs dfs -ls /data/flume

结果示例，如下所示：

-rw-r--r--   3 shirdrn supergroup    1377617 2014-09-16 14:35 /data/flume/sync_file.1410849320761.log
-rw-r--r--   3 shirdrn supergroup    1378137 2014-09-16 14:35 /data/flume/sync_file.1410849320762.log
-rw-r--r--   3 shirdrn supergroup     259148 2014-09-16 14:35 /data/flume/sync_file.1410849320763.log

Spooling Directory Source+Memory Channel+HDFS Sink

配置Agent，修改配置文件flume-conf-spool.properties，内容如下：

# Define source, channel, sink
agent1.sources = spool-source1
agent1.channels = ch1
agent1.sinks = hdfs-sink1

# Configure channel
agent1.channels.ch1.type = memory
agent1.channels.ch1.capacity = 1000000
agent1.channels.ch1.transactionCapacity = 500000

# Define and configure an Spool directory source
agent1.sources.spool-source1.channels = ch1
agent1.sources.spool-source1.type = spooldir
agent1.sources.spool-source1.spoolDir = /home/shirdrn/data/
agent1.sources.spool-source1.ignorePattern = event(_\d{4}\-\d{2}\-\d{2}_\d{2}_\d{2})?\.log(\.COMPLETED)?
agent1.sources.spool-source1.batchSize = 50
agent1.sources.spool-source1.inputCharset = UTF-8

# Define and configure a hdfs sink
agent1.sinks.hdfs-sink1.channel = ch1
agent1.sinks.hdfs-sink1.type = hdfs
agent1.sinks.hdfs-sink1.hdfs.path = hdfs://h1:8020/data/flume/
agent1.sinks.hdfs-sink1.hdfs.filePrefix = event_%y-%m-%d_%H_%M_%S
agent1.sinks.hdfs-sink1.hdfs.fileSuffix = .log
agent1.sinks.hdfs-sink1.hdfs.rollSize = 1048576
agent1.sinks.hdfs-sink1.hdfs.rollCount = 0
agent1.sinks.hdfs-sink1.hdfs.batchSize = 1500
agent1.sinks.hdfs-sink1.hdfs.round = true
agent1.sinks.hdfs-sink1.hdfs.roundUnit = minute
agent1.sinks.hdfs-sink1.hdfs.threadsPoolSize = 25
agent1.sinks.hdfs-sink1.hdfs.useLocalTimeStamp = true
agent1.sinks.hdfs-sink1.hdfs.minBlockReplicas = 1
agent1.sinks.hdfs-sink1.fileType = SequenceFile
agent1.sinks.hdfs-sink1.writeFormat = TEXT
agent1.sinks.hdfs-sink1.rollInterval = 0

启动Agent进程，执行如下命令：

bin/flume-ng agent -c ./conf/ -f conf/flume-conf-spool.properties -Dflume.root.logger=INFO,console -n agent1

可以查看HDFS上同步过来的数据：

hdfs dfs -ls /data/flume

结果示例，如下所示：

-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:52 /data/flume/event_14-09-17_10_52_00.1410922355094.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:52 /data/flume/event_14-09-17_10_52_00.1410922355095.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:52 /data/flume/event_14-09-17_10_52_00.1410922355096.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:52 /data/flume/event_14-09-17_10_52_00.1410922355097.log
-rw-r--r--   3 shirdrn supergroup       1530 2014-09-17 10:53 /data/flume/event_14-09-17_10_52_00.1410922355098.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:53 /data/flume/event_14-09-17_10_53_00.1410922380386.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:53 /data/flume/event_14-09-17_10_53_00.1410922380387.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:53 /data/flume/event_14-09-17_10_53_00.1410922380388.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:53 /data/flume/event_14-09-17_10_53_00.1410922380389.log
-rw-r--r--   3 shirdrn supergroup    1072265 2014-09-17 10:53 /data/flume/event_14-09-17_10_53_00.1410922380390.log

Exec Source+Memory Channel+File Roll Sink

配置Agent，修改配置文件flume-conf-file.properties，内容如下：

# Define source, channel, sink
agent1.sources = tail-source1
agent1.channels = ch1
agent1.sinks = file-sink1

# Configure channel
agent1.channels.ch1.type = memory
agent1.channels.ch1.capacity = 1000000
agent1.channels.ch1.transactionCapacity = 500000

# Define and configure an Exec source
agent1.sources.tail-source1.channels = ch1
agent1.sources.tail-source1.type = exec
agent1.sources.tail-source1.command = tail -F /home/shirdrn/data/event.log
agent1.sources.tail-source1.shell = /bin/sh -c
agent1.sources.tail-source1.batchSize = 50

# Define and configure a File roll sink
# and connect it to the other end of the same channel.
agent1.sinks.file-sink1.channel = ch1
agent1.sinks.file-sink1.type = file_roll
agent1.sinks.file-sink1.batchSize = 100
agent1.sinks.file-sink1.serializer = TEXT
agent1.sinks.file-sink1.sink.directory = /home/shirdrn/sink_data

启动Agent进程，执行如下命令：

bin/flume-ng agent -c ./conf/ -f conf/flume-conf-file.properties -Dflume.root.logger=INFO,console -n agent1

可以查看File Roll Sink对应的本地文件系统目录/home/shirdrn/sink_data下，示例如下所示：

-rw-rw-r-- 1 shirdrn shirdrn 13944825 Sep 17 11:36 1410924990039-1
-rw-rw-r-- 1 shirdrn shirdrn 11288870 Sep 17 11:37 1410924990039-2
-rw-rw-r-- 1 shirdrn shirdrn        0 Sep 17 11:37 1410924990039-3
-rw-rw-r-- 1 shirdrn shirdrn 20517500 Sep 17 11:38 1410924990039-4
-rw-rw-r-- 1 shirdrn shirdrn 16343250 Sep 17 11:38 1410924990039-5

Architecture Overview:架构概览

Flume NG架构，如图所示：

主要有一下几个核心概念：

• Event：一个数据单元，带有一个可选的消息头
• Flow：Event从源点到达目的点的迁移的抽象
• Client：操作位于源点处的Event，将其发送到Flume Agent
• Agent：一个独立的Flume进程，包含组件Source、Channel、Sink
• Source：用来消费传递到该组件的Event
• Channel：中转Event的一个临时存储，保存有Source组件传递过来的Event
• Sink：从Channel中读取并移除Event，将Event传递到Flow Pipeline中的下一个Agent(如果有的话)

外部系统产生日志，直接通过Flume的Agent的Source组件将事件(如日志行)发送到中间临时的channel组件，最后传递给Sink组件，HDFS Sink组件可以直接把数据存储到HDFS集群上。一个最基本Flow的配置，格式如下：

# list the sources, sinks and channels for the agent
<Agent>.sources = <Source1> <Source2>
<Agent>.sinks = <Sink1> <Sink2>
<Agent>.channels = <Channel1> <Channel2>

# set channel for source
<Agent>.sources.<Source1>.channels = <Channel1> <Channel2> ...
<Agent>.sources.<Source2>.channels = <Channel1> <Channel2> ...

# set channel for sink
<Agent>.sinks.<Sink1>.channel = <Channel1>
<Agent>.sinks.<Sink2>.channel = <Channel2>

尖括号里面的，我们可以根据实际需求或业务来修改名称。下面详细说明：

• <Agent> 表示配置一个Agent的名称，一个Agent肯定有一个名称。
• <Source1> <Source2> 是Agent的Source组件的名称，消费传递过来的Event。
• <Channel1> <Channel2> 是Agent的Channel组件的名称。
• <Sink1> <Sink2> 是Agent的Sink组件的名称，从Channel中消费(移除)Event。

上面配置内容中，第一组中配置Source、Sink、Channel，它们的值可以有1个或者多个；第二组中配置Source将把数据存储(Put )到 哪一个Channel中，可以存储到1个或多个Channel中，同一个Source将数据存储到多个Channel中，实际上是Replication；第三组中配置Sink从哪一个Channel中取(Task )数据，一个Sink只能从一个Channel中取数据。

Flow Pipeline

多个Agent顺序连接

可以将多个Agent顺序连接起来，将最初的数据源经过收集，存储到最终的存储系统中。这是最简单的情况，一般情况下，应该控制这种顺序连接的Agent的数量，因为数据流经的路径变长了，如果不考虑failover的话，出现故障将影响整个Flow上的Agent收集服务。

多个Agent的数据汇聚到同一个Agent

这种情况应用的场景比较多，比如要收集Web网站的用户行为日志，Web网站为了可用性使用的负载均衡的集群模式，每个节点都产生用户行为日志，可以为每 个节点都配置一个Agent来单独收集日志数据，然后多个Agent将数据最终汇聚到一个用来存储数据存储系统，如HDFS上。

多路(Multiplexing ) Agent

。

Replication

Replication方式，可以将 最前端的数据源复制多份，分别传递到多个channel中，每个channel接收到的数据都是相同的，配置格式，如下所示：

# List the sources, sinks and channels for the agent
<Agent>.sources = <Source1>
<Agent>.sinks = <Sink1> <Sink2>
<Agent>.channels = <Channel1> <Channel2>

# set list of channels for source (separated by space)
<Agent>.sources.<Source1>.channels = <Channel1> <Channel2>

# set channel for sinks
<Agent>.sinks.<Sink1>.channel = <Channel1>
<Agent>.sinks.<Sink2>.channel = <Channel2>

<Agent>.sources.<Source1>.selector.type = replicating

上面指定了selector的type的值为replication，其他的配置没有指定，使用的Replication方式，Source1会将数据分 别存储到Channel1和Channel2，这两个channel里面存储的数据是相同的，然后数据被传递到Sink1和Sink2。

Multiplexing

Multiplexing方式，selector可以根据header的值来确定数据传递到哪一个channel，配置格式，如下所示：

# Mapping for multiplexing selector
<Agent>.sources.<Source1>.selector.type = multiplexing
<Agent>.sources.<Source1>.selector.header = <someHeader>
<Agent>.sources.<Source1>.selector.mapping.<Value1> = <Channel1>
<Agent>.sources.<Source1>.selector.mapping.<Value2> = <Channel1> <Channel2>
<Agent>.sources.<Source1>.selector.mapping.<Value3> = <Channel2>
#...

<Agent>.sources.<Source1>.selector.default = <Channel2>

上面selector的type的值为multiplexing，同时配置selector的header信息，还配置了多个selector的mapping的值，即header的值：如果header的值为Value1、Value2，数据从Source1路由到Channel1；如果header的值为Value2、Value3，数据从Source1路由到Channel2。

Load Balance:负载均衡

Load balancing Sink Processor能够实现load balance功能，上图Agent1是一个路由节点，负责将Channel暂存的Event均衡到对应的多个Sink组件上，而每个Sink组件分别连 接到一个独立的Agent上，示例配置，如下所示：

a1.sinkgroups = g1
a1.sinkgroups.g1.sinks = k1 k2 k3
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

Failover Sink Processor能够实现failover功能，具体流程类似load balance，但是内部处理机制与load balance完全不同：Failover Sink Processor维护一个优先级Sink组件列表，只要有一个Sink组件可用，Event就被传递到下一个组件。如果一个Sink能够成功处理Event，则会加入到一个Pool中，否则会被移出Pool并计算失败次数，设置一个惩罚因子，示例配置如下所示：

a1.sinkgroups = g1
a1.sinkgroups.g1.sinks = k1 k2 k3
a1.sinkgroups.g1.processor.type = failover
a1.sinkgroups.g1.processor.priority.k1 = 5
a1.sinkgroups.g1.processor.priority.k2 = 7
a1.sinkgroups.g1.processor.priority.k3 = 6
a1.sinkgroups.g1.processor.maxpenalty = 20000