海量日志采集Flume(HA)

提示：扫一扫查出行
【扫一扫了解最新限行尾号】
复制提示

海量日志采集Flume(HA)

1.介绍:

Flume是Cloudera提供的一个高可用的，高可靠的，分布式的海量日志采集、聚合和传输的系统，Flume支持在日志系统中定制各类数据发送方，用于收集数据；同时，Flume提供对数据进行简单处理，并写到各种数据接受方（可定制）的能力。

2.日志采集

Flume—对哪个ip 哪个端口进行监控 --- 数据监控—接收数据----内存—存储本地硬盘

3.数据处理

Flume提供对数据进行简单处理，并写到各种数据接受方（可定制）的能力。 Flume提供了从Console（控制台）、RPC（Thrift-RPC）、Text（文件）、Tail（UNIX tail）、Syslog（Syslog日志系统，支持TCP和UDP等2种模式），exec（命令执行）等数据源上收集数据的能力。

4.Flume原理：

Flume OG:

Flume逻辑上分三层架构：Agent，Collector，Storage。采用多Master，为保持数据一致项，使用zookeeper，保持数据高可用和一致性。

特点：

· 3个角色：代理节点(agent),收集节点(collector),主节点(master).

· gent 从各个数据源收集日志数据，将收集到的数据集中到 Collector，然后由收集节点汇总存入 HDFS。master 负责管理 agent，collector 的活动。
· agent、collector 由 source、sink 组成，代表在当前节点数据是从 source 传送到 sink。

Flume NG

NG只有一个节点：代理节点（agent）Flume NG的 agent 由 source、sink、Channel 组成。如下关系

· Source:完成对日志数据的收集，分成 transtion 和 event 打入到Channel之中。

Source类型	说明
Avro Source	支持Avro协议（实际上是Avro RPC），提供一个Avro的接口，需要往设置的地址和端口发送Avro消息，Source就能接收到,如：Log4j Appender通过Avro Source将消息发送到Agent
Thrift Source	支持Thrift协议，提供一个Thrift接口，类似Avro
Exec Source	Source启动的时候会运行一个设置的UNIX命令（比如 cat file），该命令会不断地往标准输出（stdout）输出数据，这些数据就会被打包成Event，进行处理
JMS Source	从JMS系统（消息、主题）中读取数据，类似ActiveMQ
Spooling Directory Source	监听某个目录，该目录有新文件出现时，把文件的内容打包成Event，进行处理
Netcat Source	监控某个端口，将流经端口的每一个文本行数据作为Event输入
Sequence Generator Source	序列生成器数据源，生产序列数据
Syslog Sources	读取syslog数据，产生Event，支持UDP和TCP两种协议
HTTP Source	基于HTTP POST或GET方式的数据源，支持JSON、BLOB表示形式
Legacy Sources	兼容老的Flume OG中Source（0.9.x版本）
自定义Source	使用者通过实现Flume提供的接口来定制满足需求的Source。

· Channel:主要提供一个队列的功能，对source提供中的数据进行简单的缓存。

Channel类型	说明
Memory Channel	Event数据存储在内存中
JDBC Channel	Event数据存储在持久化存储中，当前Flume Channel内置支持Derby
File Channel	Event数据存储在磁盘文件中
Spillable Memory Channel	Event数据存储在内存中和磁盘上，当内存队列满了，会持久化到磁盘文件（当前试验性的，不建议生产环境使用）
Pseudo Transaction Channel	测试用途
Custom Channel	自定义Channel实现

· Sink:取出Channel中的数据，进行相应的存储文件系统，数据库，或者提交到远程服务器。

Sink类型	说明
HDFS Sink	数据写入HDFS
Logger Sink	数据写入日志文件
Avro Sink	数据被转换成Avro Event，然后发送到配置的RPC端口上
Thrift Sink	数据被转换成Thrift Event，然后发送到配置的RPC端口上
IRC Sink	数据在IRC上进行回放
File Roll Sink	存储数据到本地文件系统
Null Sink	丢弃到所有数据
HBase Sink	数据写入HBase数据库
Morphline Solr Sink	数据发送到Solr搜索服务器（集群）
ElasticSearch Sink	数据发送到Elastic Search搜索服务器（集群）
Kite Dataset Sink	写数据到Kite Dataset，试验性质的
Custom Sink	自定义Sink实现

一个sink和channel只能收集一种类型数据日志，但是可以有多个sink和channel ，source则可以接受多种类型数据日志，如下：

Flume安装和使用：

安装

运行配置：

a1.sources = r1 a1.sinks = k1 a1.channels = c1# Describe configure the source a1.sources.r1.type = avro a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 4141# 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

运行：

在/home/bigdata/flume1.6目录下运行

flume-ng agent -n a1 -c . -f ./conf/avro.conf -Dflume.root.logger=INFO,console

当flume可以运行我们就体会下收集不同数据源(source)日志，并存放到hdfs上

source: avro

flume-ng avro-client -c /home/bigdata/flime1.6/ -H ry-hadoop1 -p4141 -F ./avro.txt

source: Exec

b1.sources=r1 b1.channels=c1 b1.sinks=k1b1.sources.r1.type=exec b1.sources.r1.command=tail -F /home/data/avro.txtb1.channels.c1.type=memory b1.channels.c1.capacity=1000 b1.channels.c1.transactionCapacity=100b1.sinks.k1.type=loggerb1.sources.r1.channels=c1 b1.sinks.k1.channel=c1

source: spooldir只能对一级目录进行收集

在数据Linux本地建一个文件夹log

agent.sources=r1 agent.channels=c1 agent.sinks=k1agent.sources.r1.type=spooldir agent.sources.r1.spooldir=/home/data/log agent.sources.r1.fileHeader=trueagent.channels.c1.type=memory agent.channels.c1.capacity=1000 agent.channels.c1.transactionCapacity=100agent.sinks.k1.type=loggeragent.sources.r1.channels=c1 agent.sinks.k1.channel=c1

启动：

flume-ng agent -n agent -c /home/bigdata/flime1.6/ -f /home/bigdata/flime1.6/conf/spoolDir.conf -Dflume.root.logger=INFO,console

source: TCP

a1.sources = r1 a1.sinks = k1 a1.channels = c1# Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = 0.0.0.0# 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

source：JSONHandler

a1.sources = r1 a1.sinks = k1 a1.channels = c1# Describe/configure the source a1.sources.r1.type = org.apache.flume.source.http.HTTPSource a1.sources.r1.port = 8888# 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

source 就讲5个。

然后讲存储

hdfsSinK.conf

配置：

a1.sources = r1 a1.sinks = k1 a1.channels = c1# Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = 0.0.0.0# Describe the sink a1.sinks.k1.type = hdfs a1.sinks.k1.hdfs.path = hdfs://ry-hadoop1:8020/flume a1.sinks.k1.hdfs.filePrefix = Syslog a1.sinks.k1.hdfs.fileSuffix = .log a1.sinks.k1.hdfs.round = true a1.sinks.k1.hdfs.roundValue = 1 a1.sinks.k1.hdfs.roundUnit = minute a1.sinks.k1.hdfs.fileType=DataStream a1.sinks.k1.hdfs.writeFormat=Text a1.sinks.k1.hdfs.rollInterval=0 a1.sinks.k1.hdfs.rollSize=10240 a1.sinks.k1.hdfs.rollCount=0 a1.sinks.k1.hdfs.idleTimeout=60# 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

运行：

flume-ng agent -n a1 -c . -f ./conf/hdfsSink.conf -Dflume.root.logger=INFO,console

写一个shell脚本，循环输出tcp数据，然后收集在hdfs种

#!/bin/sh int=1 while(( $int<=500000 )) do echo "this is message"$int | nc ry-hadoop1 5140 echo "this is message"$int let "int++" done

设定收集日志的具体时间。

那么有个问题，当hadoop维护期间不能存储数据时，我们的日志文件存在哪里呢？

本地，那么我们看看如何存在本地

通道类型为文本形式

a1.sources = r1 a1.sinks = k1 a1.channels = c1# Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.host = 0.0.0.0# Describe the sink a1.sinks.k1.type = file_roll a1.sinks.k1.sink.directory = /home/data/log/ a1.sinks.k1.sink.serializer=TEXT# 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

channels通道类型为文件形式

a1.sources = s1 a1.channels = c1 a1.sinks = k1# For each one of the sources, the type is defined a1.sources.s1.type = syslogtcp a1.sources.s1.host = localhost a1.sources.s1.port = 5180# Each sink's type must be defined a1.sinks.k1.type = logger# Each channel's type is defined. a1.channels.c1.type = file a1.channels.c1.checkpointDir = /home/data/log/checkpoint a1.channels.c1.dataDir = /home/data/log/data#Bind the source and sinks to channels a1.sources.s1.channels = c1 a1.sinks.k1.channel = c1

flume的HA：

Flume支持Fan out流从一个源到多个通道。有两种模式的Fan out，分别是复制和复用。在复制的情况下，流的事件被发送到所有的配置通道。在复用的情况下，事件被发送到可用的渠道中的一个子集。Fan out流需要指定源和Fan out通道的规则。大白话来说就是，当你采集日志的时候可以通过一个agent进行保存多份日志。启动多台集群讲多台的flume连接起来，可以同时接收到其中一台的数据进行备份，这个有点类似zookeeper。

1) Replicating Channel Selector 多个Channel
在3台机器上启动flume的avor，然后复制master连接启动source为：replicating的flume

a1.sources = r1 a1.sinks = k1 a1.channels = c1# Describe/configure the source a1.sources.r1.type = avro a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 5555# 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

在master启动连接：

a1.sources = r1 a1.channels = c1 c2 a1.sinks = k1 k2# Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.host = 0.0.0.0 a1.sources.r1.port = 5140 a1.sources.r1.channels = c1 c2 a1.sources.r1.selector.type = replicating# Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname = master a1.sinks.k1.port = 5555a1.sinks.k2.type = avro a1.sinks.k2.channel = c2 a1.sinks.k2.hostname = slave1 a1.sinks.k2.port = 5555# Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100a1.channels.c2.type = memory a1.channels.c2.capacity = 1000 a1.channels.c2.transactionCapacity = 100

当你写一条数据进入日志时，其他3台机器都会有反应

1) MulChnSel_a1.conf

输入数据映射的匹配。

a1.sources = s1 a1.channels = c1 c2 a1.sinks = k1 k2# For each one of the sources, the type is defined a1.sources.s1.type = org.apache.flume.source.http.HTTPSource a1.sources.s1.port = 8887 a1.sources.s1.channels = c1 c2 a1.sources.s1.selector.type = multiplexinga1.sources.s1.selector.header = company a1.sources.s1.selector.mapping.ali = c1 a1.sources.s1.selector.mapping.baidu = c2 a1.sources.s1.selector.default = c2# Each sink's type must be defined a1.sinks.k1.type = avro a1.sinks.k1.hostname = master a1.sinks.k1.port = 5555 a1.sinks.k1.channel = c1a1.sinks.k2.type = avro a1.sinks.k2.hostname = slave1 a1.sinks.k2.port = 5555 a1.sinks.k2.channel = c2# Each channel's type is defined. a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100a1.channels.c2.type = memory a1.channels.c2.capacity = 1000 a1.channels.c2.transactionCapacity = 100

3）Flume Sink Processors

failover的机器是一直发送给其中一个sink，当这个sink不可用的时候，自动发送到下一个sink。

a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1 c2#这个是配置failover的关键，需要有一个sink group a1.sinkgroups = g1 a1.sinkgroups.g1.sinks = k1 k2 #处理的类型是failover a1.sinkgroups.g1.processor.type = failover #优先级，数字越大优先级越高，每个sink的优先级必须不相同 a1.sinkgroups.g1.processor.priority.k1 = 5 a1.sinkgroups.g1.processor.priority.k2 = 10 #设置为10秒，当然可以根据你的实际状况更改成更快或者很慢 a1.sinkgroups.g1.processor.maxpenalty = 10000# Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.channels = c1 c2 a1.sources.r1.selector.type = replicating# Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname = m1 a1.sinks.k1.port = 5555a1.sinks.k2.type = avro a1.sinks.k2.channel = c2 a1.sinks.k2.hostname = m2 a1.sinks.k2.port = 5555# Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100a1.channels.c2.type = memory a1.channels.c2.capacity = 1000 a1.channels.c2.transactionCapacity = 100在hadoop1创建Flume_Sink_Processors_avro.conf配置文件 a1.sources = r1 a1.sinks = k1 a1.channels = c1# Describe/configure the source a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 5555# 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

启动：

flume-ng agent -c . -f /home/bigdata/flume/conf/Flume_Sink_Processors_avro.conf -n a1 -Dflume.root.logger=INFO,console

测试：

然后在hadoop1或hadoop2的任意一台机器上，测试产生log

# echo "idoall.org test1 failover" | nc localhost 5140

4) Load balancing Sink Processor

load balance type和failover不同的地方是，load balance有两个配置，一个是轮询，一个是随机。两种情况下如果被选择的sink不可用，就会自动尝试发送到下一个可用的sink上面。

a1.sources = r1 a1.sinks = k1 k2 a1.channels = c1#这个是配置Load balancing的关键，需要有一个sink group a1.sinkgroups = g1 a1.sinkgroups.g1.sinks = k1 k2 a1.sinkgroups.g1.processor.type = load_balance a1.sinkgroups.g1.processor.backoff = true a1.sinkgroups.g1.processor.selector = round_robin# Describe/configure the source a1.sources.r1.type = syslogtcp a1.sources.r1.port = 5140 a1.sources.r1.channels = c1# Describe the sink a1.sinks.k1.type = avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname = m1 a1.sinks.k1.port = 5555a1.sinks.k2.type = avro a1.sinks.k2.channel = c1 a1.sinks.k2.hostname = m2 a1.sinks.k2.port = 5555# Use a channel which buffers events in memory a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.channels.c1.transactionCapacity = 100