flume的配置详解

Flume是一种分布式的、可靠的、可用的服务，可以有效地收集、聚合和移动大量的日志数据。
它有一个基于流数据的简单而灵活的体系结构。
它具有健壮性和容错能力，具有可调的可靠性机制和许多故障转移和恢复机制。
它使用一个简单的可扩展数据模型，允许在线分析应用程序。

source：源    
    对channel而言，相当于生产者，通过接收各种格式数据发送给channel进行传输

channel：通道
    相当于数据缓冲区，接收source数据发送给sink

sink：沉槽
    对channel而言，相当于消费者，通过接收channel数据通过指定数据类型发送到指定位置

Event：
flume传输基本单位：
head + body

flume使用：

//flume可以将配置文件写在zk上
agent:    a1
source:    s1
channel:c1
sink:    n1

使用方法：
    1、编写配置文件r_nc.conf
        # 将agent组件起名
        a1.sources = r1
        a1.sinks = k1
        a1.channels = c1

        # 配置source
        a1.sources.r1.type = netcat
        a1.sources.r1.bind = localhost
        a1.sources.r1.port = 8888

        # 配置sink
        a1.sinks.k1.type = logger

        # 配置channel
        a1.channels.c1.type = memory
        a1.channels.c1.capacity = 1000
        a1.channels.c1.transactionCapacity = 100

        # 绑定channel-source, channel-sink
        a1.sources.r1.channels = c1
        a1.sinks.k1.channel = c1

    2、启动flume，指定配置文件
        flume-ng agent -n a1 -f r_nc.conf

    3、启动另一个会话，进行测试
        nc localhost 8888

//用户手册
http://flume.apache.org/FlumeUserGuide.html

后台运行程序：

ctrl + z :将程序放在后台运行 =====> [1]+  Stopped                 flume-ng agent -n a1 -f r_nc.conf

通过 bg %1 的方式将程序后台运行

通过jobs查看后台任务

通过  fg %1 的方式将程序放在前台运行

flume:
海量日志数据的收集、聚合和移动

flume-ng agent -n a1 -f xxx.conf
source
    相对于channel是生产者    //netcat
channel
    类似于缓冲区        //memory
sink
    相对于channel是消费者    //logger

Event:
header + body
k v data

source:

1、序列(seq)源：多用作测试
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = seq
    # 总共发送的事件个数
    a1.sources.r1.totalEvents = 1000    

    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

2、压力(stress)源:多用作负载测试
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = org.apache.flume.source.StressSource
    # 单个事件大小,单位：byte
    a1.sources.r1.size = 10240
    # 事件总数
    a1.sources.r1.maxTotalEvents = 1000000

    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

3、滚动目录(Spooldir)源：监听指定目录新文件产生，并将新文件数据作为event发送
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = spooldir
    # 设置监听目录
    a1.sources.r1.spoolDir = /home/centos/spooldir

    # 通过以下配置指定消费完成后文件后缀
    #a1.sources.r1.fileSuffix = .COMPLETED 

    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1


4、exec源    //通过执行linux命令产生新数据
        //典型应用 tail -F (监听一个文件,文件增长的时候，输出追加数据)
        //不能保证数据完整性，很可能丢失数据

    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = exec
    # 配置linux命令
    a1.sources.r1.command = tail -F /home/centos/readme.txt

    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

5、Taildir源        //监控目录下文件
            //文件类型可通过正则指定
            //有容灾机制

    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = TAILDIR
    # 设置source组 可设置多个
    a1.sources.r1.filegroups = f1
    # 设置组员的监控目录和监控文件类型,使用正则表示,只能监控文件
    a1.sources.r1.filegroups.f1 = /home/centos/taildir/.*

    # 设置定位文件的位置
    # a1.sources.r1.positionFile     ~/.flume/taildir_position.json

    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

sink：

1、fileSink    //多用作数据收集
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888

    # 配置sink
    a1.sinks.k1.type = file_roll
    # 配置目标文件夹
    a1.sinks.k1.sink.directory = /home/centos/file
    # 设置滚动间隔，默认30s，设为0则不滚动，成为单个文件
    a1.sinks.k1.sink.rollInterval = 0

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

2、hdfsSink        //默认以seqFile格式写入
            //k：LongWritable
            //v: BytesWritable
            //
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888
    
    # 配置sink
    a1.sinks.k1.type = hdfs
    # 配置目标文件夹
    a1.sinks.k1.hdfs.path = /flume/events/%y-%m-%d/
    # 配置文件前缀
    a1.sinks.k1.hdfs.filePrefix = events-
    # 滚动间隔，秒
    a1.sinks.k1.hdfs.rollInterval = 0
    # 触发滚动文件大小，byte
    a1.sinks.k1.hdfs.rollSize = 1024
    # 配置使用本地时间戳
    a1.sinks.k1.hdfs.useLocalTimeStamp = true
    # 配置输出文件类型，默认SequenceFile
    # DataStream文本格式,不能设置压缩编解码器
    # CompressedStream压缩文本格式，需要设置编解码器
    a1.sinks.k1.hdfs.fileType = DataStream


    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

3、hiveSink：        //hiveserver帮助：hive --service help
            //1、hive --service metastore 启动hive的metastore服务，metastore地址：thrift://localhost:9083
            //2、将hcatalog的依赖放在/hive/lib下，cp hive-hcatalog* /soft/hive/lib    (位置/soft/hive/hcatalog/share/hcatalog)
            //3、创建hive事务表
            //SET hive.support.concurrency=true;                                  
              SET hive.enforce.bucketing=true;                                    
              SET hive.exec.dynamic.partition.mode=nonstrict;                     
              SET hive.txn.manager=org.apache.hadoop.hive.ql.lockmgr.DbTxnManager;
              SET hive.compactor.initiator.on=true;                               
              SET hive.compactor.worker.threads=1;
              
            //create table myhive.weblogs(id int, name string, age int)
              clustered by(id) into 2 buckets                                         
              row format delimited                                                          
              fields terminated by '\t'                                                     
              stored as orc                                                                 
              tblproperties('transactional'='true');                                        


    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888

    # 配置sink
    a1.sinks.k1.type = hive
    a1.sinks.k1.hive.metastore = thrift://127.0.0.1:9083
    a1.sinks.k1.hive.database = myhive
    a1.sinks.k1.hive.table = weblogs
    a1.sinks.k1.useLocalTimeStamp = true
    #输入格式，DELIMITED和json
    #DELIMITED    普通文本
    #json        json文件
    a1.sinks.k1.serializer = DELIMITED
    #输入字段分隔符,双引号
    a1.sinks.k1.serializer.delimiter = ","
    #输出字段分隔符,单引号
    a1.sinks.k1.serializer.serdeSeparator = '\t'
    #字段名称，","分隔，不能有空格
    a1.sinks.k1.serializer.fieldnames =id,name,age

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

4、hbaseSink            //SimpleHbaseEventSerializer将rowKey和col设置了默认值，不能自定义
                //RegexHbaseEventSerializer可以手动指定rowKey和col字段名称

    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888
    
    # 配置sink
    a1.sinks.k1.type = hbase
    a1.sinks.k1.table = flume_hbase
    a1.sinks.k1.columnFamily = f1
    a1.sinks.k1.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer

    
    # 配置col正则手动指定
    # rowKeyIndex手动指定rowKey，索引以0开头
    a1.sinks.k1.serializer.colNames = ROW_KEY,name,age
    a1.sinks.k1.serializer.regex = (.*),(.*),(.*)
    a1.sinks.k1.serializer.rowKeyIndex=0

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1


5、asynchbaseSink        //异步hbaseSink
                //异步机制，写入速度快
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888
    
    # 配置sink
    a1.sinks.k1.type = asynchbase
    a1.sinks.k1.table = flume_hbase
    a1.sinks.k1.columnFamily = f1
    a1.sinks.k1.serializer = org.apache.flume.sink.hbase.SimpleAsyncHbaseEventSerializer

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

channel：缓冲区

1、memorychannel
    a1.channels.c1.type = memory
    # 缓冲区中存留的最大event个数
    a1.channels.c1.capacity = 1000
    # channel从source中每个事务提取的最大event数
    # channel发送给sink每个事务发送的最大event数
    a1.channels.c1.transactionCapacity = 100

2、fileChannel：    //检查点和数据存储在默认位置时，当多个channel同时开启
            //会导致文件冲突，引发其他channel会崩溃
    
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888

    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels = c1
    a1.channels.c1.type = file
    a1.channels.c1.checkpointDir = /home/centos/flume/checkpoint
    a1.channels.c1.dataDirs = /home/centos/flume/data

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1


memoryChannel：快速，但是当设备断电，数据会丢失

FileChannel：  速度较慢，即使设备断电，数据也不会丢失

Avro

source
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = avro
    a1.sources.r1.bind = 0.0.0.0
    a1.sources.r1.port = 4444

    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

***********************************************************************************************    
*启动avro客户端，发送数据：                                      *
*    flume-ng avro-client -H localhost -p 4444 -R ~/avro/header.txt -F ~/avro/user0.txt    *
*                 指定ip                   指定端口 指定header文件      指定数据文件          *
***********************************************************************************************


sink
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = TAILDIR
    a1.sources.r1.filegroups = f1
    a1.sources.r1.filegroups.f1 = /home/centos/taildir/.*

    # 配置sink
    a1.sinks.k1.type = avro
    a1.sinks.k1.bind = 192.168.23.101
    a1.sinks.k1.port = 4444


    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

Flume跃点：

1、将s101的flume发送到其他节点
    xsync.sh /soft/flume
    xsync.sh /soft/apache-flume-1.8.0-bin/

2、切换到root用户，分发环境变量文件
    su root
    xsync.sh /etc/profile
    exit

3、配置文件
    1)配置s101    //hop.conf
        设置source:avro
        设置sink： hdfs

        # 将agent组件起名
        a1.sources = r1
        a1.sinks = k1
        a1.channels = c1

        # 配置source
        a1.sources.r1.type = avro
        a1.sources.r1.bind = 0.0.0.0
        a1.sources.r1.port = 4444

        # 配置sink
        a1.sinks.k1.type = hdfs
        a1.sinks.k1.hdfs.path = /flume/hop/%y-%m-%d/
        a1.sinks.k1.hdfs.filePrefix = events-
        a1.sinks.k1.hdfs.rollInterval = 0
        a1.sinks.k1.hdfs.rollSize = 1024
        a1.sinks.k1.hdfs.useLocalTimeStamp = true
        a1.sinks.k1.hdfs.fileType = DataStream

        # 配置channel
        a1.channels.c1.type = memory
        a1.channels.c1.capacity = 1000
        a1.channels.c1.transactionCapacity = 100

        # 绑定channel-source, channel-sink
        a1.sources.r1.channels = c1
        a1.sinks.k1.channel = c1


    2)配置s102-s104        //hop2.conf
        设置source:taildir
        设置sink： avro

        # 将agent组件起名
        a1.sources = r1
        a1.sinks = k1
        a1.channels = c1

        # 配置source
        a1.sources.r1.type = TAILDIR
        a1.sources.r1.filegroups = f1
        a1.sources.r1.filegroups.f1 = /home/centos/taildir/.*

        # 配置sink
        a1.sinks.k1.type = avro
        a1.sinks.k1.hostname = 192.168.23.101
        a1.sinks.k1.port = 4444


        # 配置channel
        a1.channels.c1.type = memory
        a1.channels.c1.capacity = 1000
        a1.channels.c1.transactionCapacity = 100

        # 绑定channel-source, channel-sink
        a1.sources.r1.channels = c1
        a1.sinks.k1.channel = c1

4、在s102-s104创建~/taildir文件夹
    xcall.sh "mkdir ~/taildir"


5、启动s101的flume
    flume-ng agent -n a1 -f /soft/flume/conf/hop.conf

6、分别启动s102-s104的flume,并将其放在后台运行
    flume-ng agent -n a1 -f /soft/flume/conf/hop2.conf &


7、进行测试，分别在s102-s104的taildir中创建数据，观察hdfs数据情况
    s102]$ echo 102 > taildir/1.txt 
    s103]$ echo 103 > taildir/1.txt
    s104]$ echo 104 > taildir/1.txt

interceptor：拦截器

是source端组件：负责修改或删除event
每个source可以配置多个拦截器    ===> interceptorChain



1、Timestamp Interceptor    //时间戳拦截器    + header

    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888
    # 给拦截器起名
    a1.sources.r1.interceptors = i1
    # 指定拦截器类型
    a1.sources.r1.interceptors.i1.type = timestamp


    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

    
2、Static Interceptor    //静态拦截器    + header

3、Host Interceptor    //主机拦截器    + header

4、设置拦截器链：
    
    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888

    a1.sources.r1.interceptors = i1 i2 i3
    a1.sources.r1.interceptors.i1.type = timestamp
    a1.sources.r1.interceptors.i2.type = host
    a1.sources.r1.interceptors.i3.type = static
    a1.sources.r1.interceptors.i3.key = location
    a1.sources.r1.interceptors.i3.value = NEW_YORK


    # 配置sink
    a1.sinks.k1.type = logger

    # 配置channel
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 100

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1

channel selector：通道挑选器

是source端组件：负责将event发送到指定的channel，相当于分区
    
当一个source设置多个channel时，默认以副本形式向每个channel发送一个event拷贝


1、replication副本通道挑选器    //默认挑选器，source将所有channel发送event副本
                //设置source x 1, channel x 3, sink x 3 
                //    nc       memory    file

    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1 k2 k3
    a1.channels = c1 c2 c3

    # 配置source
    a1.sources.r1.type = netcat
    a1.sources.r1.bind = localhost
    a1.sources.r1.port = 8888
    a1.sources.r1.selector.type = replicating

    # 配置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

    a1.channels.c3.type = memory
    a1.channels.c3.capacity = 1000
    a1.channels.c3.transactionCapacity = 100

    
    # 配置sink
    a1.sinks.k1.type = file_roll
    a1.sinks.k1.sink.directory = /home/centos/file1
    a1.sinks.k1.sink.rollInterval = 0

    a1.sinks.k2.type = file_roll
    a1.sinks.k2.sink.directory = /home/centos/file2
    a1.sinks.k2.sink.rollInterval = 0

    a1.sinks.k3.type = file_roll
    a1.sinks.k3.sink.directory = /home/centos/file3
    a1.sinks.k3.sink.rollInterval = 0

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1 c2 c3
    a1.sinks.k1.channel = c1
    a1.sinks.k2.channel = c2
    a1.sinks.k3.channel = c3



2、Multiplexing 多路复用通道挑选器    //选择avro源发送文件
                    
                    
                    
                    

    # 将agent组件起名
    a1.sources = r1
    a1.sinks = k1 k2 k3
    a1.channels = c1 c2 c3
    
    # 配置source
    a1.sources.r1.type = avro
    a1.sources.r1.bind = 0.0.0.0
    a1.sources.r1.port = 4444
    # 配置通道挑选器
    a1.sources.r1.selector.type = multiplexing
    a1.sources.r1.selector.header = country
    a1.sources.r1.selector.mapping.CN = c1
    a1.sources.r1.selector.mapping.US = c2
    a1.sources.r1.selector.default = c3
    
    # 配置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

    a1.channels.c3.type = memory
    a1.channels.c3.capacity = 1000
    a1.channels.c3.transactionCapacity = 100

    
    # 配置sink
    a1.sinks.k1.type = file_roll
    a1.sinks.k1.sink.directory = /home/centos/file1
    a1.sinks.k1.sink.rollInterval = 0

    a1.sinks.k2.type = file_roll
    a1.sinks.k2.sink.directory = /home/centos/file2
    a1.sinks.k2.sink.rollInterval = 0

    a1.sinks.k3.type = file_roll
    a1.sinks.k3.sink.directory = /home/centos/file3
    a1.sinks.k3.sink.rollInterval = 0

    # 绑定channel-source, channel-sink
    a1.sources.r1.channels = c1 c2 c3
    a1.sinks.k1.channel = c1
    a1.sinks.k2.channel = c2
    a1.sinks.k3.channel = c3


    1、创建file1 file2 file3文件夹，家目录
        mkdir file1 file2 file3

    2、创建文件夹country，并放入头文件和数据
        创建头文件CN.txt、US.txt、OTHER.txt 
            CN.txt ===> country CN              
            US.txt ===> country US              
            OTHER.txt ===> country OTHER   
        
        创建数据 1.txt 
            1.txt ====> helloworld

    3、运行flume
        flume-ng agent -n a1 -f /soft/flume/selector_multi.conf

    4、运行Avro客户端
        flume-ng avro-client -H localhost -p 4444 -R ~/country/US.txt -F ~/country/1.txt    ===> 查看file2
        flume-ng avro-client -H localhost -p 4444 -R ~/country/CN.txt -F ~/country/1.txt    ===> 查看file1
        flume-ng avro-client -H localhost -p 4444 -R ~/country/OTHER.txt -F ~/country/1.txt    ===> 查看file3

sinkProcessor

sink Runner 运行一个 sink Group

sink Group 是由一个或多个 sink 构成

sink Runner 告诉 sink Group 处理下一批 event

sink Group 含有一个 sink Processor , 负责指定一个 sink 来处理这批数据


2、failover 容灾    //将所有sink设置一个优先级
            //数量越大，优先级越高
            //当数据传入时，优先级最高的sink负责处理
            //当sink挂掉，次高优先级的sink被激活，继续处理数据
            //channel和sink必须一对一

    a1.sources = r1
    a1.sinks = s1 s2 s3
    a1.channels = c1 c2 c3

    # Describe/configure the source
    a1.sources.r1.type = seq

    a1.sinkgroups = g1
    a1.sinkgroups.g1.sinks = s1 s2 s3
    a1.sinkgroups.g1.processor.type = failover
    a1.sinkgroups.g1.processor.priority.s1 = 5
    a1.sinkgroups.g1.processor.priority.s2 = 10
    a1.sinkgroups.g1.processor.priority.s3 = 15
    a1.sinkgroups.g1.processor.maxpenalty = 10000

    # Describe the sink
    a1.sinks.s1.type = file_roll
    a1.sinks.s1.sink.directory = /home/centos/file1
    a1.sinks.s2.type = file_roll
    a1.sinks.s2.sink.directory = /home/centos/file2
    a1.sinks.s3.type = file_roll
    a1.sinks.s3.sink.directory = /home/centos/file3

    # Use a channel which buffers events in memory
    a1.channels.c1.type = memory
    a1.channels.c2.type = memory
    a1.channels.c3.type = memory

    # Bind the source and sink to the channel
    a1.sources.r1.channels = c1 c2 c3
    a1.sinks.s1.channel = c1
    a1.sinks.s2.channel = c2
    a1.sinks.s3.channel = c3

Event事件是由Source端封装输入数据的字节数组得来的
Event event = EventBuilder.withBody(body);

Sink中的process方法返回两种状态：
1、READY //一个或多个event成功分发
2、BACKOFF //channel中没有数据提供给sink

flume中事务的生命周期：

tx.begin()    //开启事务，之后执行操作
tx.commit()    //提交事务，操作完成后由此提交
tx.rollback()    //回滚事务，出现异常可以采取回滚措施
tx.close()    //关闭事务，最后一定要关闭事务

本文章来源自 https://www.cnblogs.com/zyde/p/8946069.html

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