分布式日志收集框架Flume

时间：2023-03-09 06:48:27
分布式日志收集框架Flume

分布式日志收集框架Flume

1.业务现状分析

分布式日志收集框架Flume

  • WebServer/ApplicationServer分散在各个机器上

  • 想在大数据平台Hadoop进行统计分析

  • 日志如何收集到Hadoop平台上

  • 解决方案及存在的问题

  • 如何解决我们的数据从其他的server上移动到Hadoop之上？

    1. shell: cp --> Hadoop集群的机器上，hdfs dfs -put ....（有很多问题不好解决，容错、负载均衡、时效性、压缩）
    2. Flume，从 A --> B 移动日志

2.Flume概述

Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.

Flume是由Apache基金会提供的一个分布式、高可靠、高可用的服务，用于分布式的海量日志的高效收集、聚合、移动系统。

  • Flume设计目标

    1. 可靠性：高科要
    2. 扩展性：模块可扩展
    3. 管理性：agent管理

  • 界同类产品对比

    1. Flume: Cloudera/Apache, Java语言开发。
    2. Logstash: ELK(ElasticsSearch, Logstash, Kibana)
    3. Scribe: Facebook, 使用C/C++开发, 负载均衡不是很好, 已经不维护了。
    4. Chukwa: Yahoo/Apache, 使用Java语言开发, 负载均衡不是很好, 已经不维护了。
    5. Fluentd: 和Flume类似, Ruby开发。

  • Flume发展史

    1. Cloudera公司提出0.9.2，叫Flume-OG
    2. 2011年Flume-728编号，重要里程碑(Flume-NG)，贡献给Apache社区
    3. 2012年7月 1.0版本
    4. 2015年5月 1.6版本
    5. ~ 1.7版本

3.Flume架构及核心组件

分布式日志收集框架Flume

Flume有三大组件

  • Source: 收集，指定数据源从哪里来（Avro, Thrift, Spooling, Kafka, Exec）
  • Channel: 聚集，把数据先存在（Memory, File, Kafka等用的比较多）
  • Sink: 把数据写到某个地方去（HDFS, Hive, Logger, Avro, Thrift, File, ES, HBase, Kafka等）

4.Flume环境部署

  • 前置条件
    • Java Runtime Environment - Java 1.8 or later（安装Java）
    • Memory - Sufficient memory for configurations used by sources, channels or sinks（足够内存）
    • Disk Space - Sufficient disk space for configurations used by channels or sinks（足够空间）
    • Directory Permissions - Read/Write permissions for directories used by agent（读写权限）
  • 1.安装JDK（下载，解压，安装，配置环境变量）
  • 2.安装Flume（下载，加压，安装，配置环境变量，检测：flume-ng version）

5.Flume实战

  • 需求1：从指定网络端口采集数据输出到控制台

    • flume-conf.properties
      • A) 配置Source
      • B) 配置Channel
      • C) 配置Sink
      • D) 把以上三个组件串起来
    # example.conf: A single-node Flume configuration

# a1: agent名称
    
# r1：source的名称
    
# k1：sink的名称
    
# c1：channel的名称

# 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 = c1
    • 启动Agent
    flume-ng agent \
    
--name $agent_name \
    
--conf conf \
    
--conf-file conf/flume-conf.properties \
    
-Dflume.root.logger=INFO,console

flume-ng agent \
    
--name a1 \
    
--conf $FLUME_HOME/conf \
    
--conf-file $FLUME_HOME/conf/example.conf \
    
-Dflume.root.logger=INFO,console

  • 需求2：监控一个文件实时采集新增的数据输出到控制台

    • 1.Agent选型：exec source + memory channel + logger sink
    • 2.配置文件
    # exec-memory-logger.conf: A single-node Flume configuration

# a1: agent名称
    
# r1：source的名称
    
# k1：sink的名称
    
# c1：channel的名称

# 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 /home/k.o/data/data.log
    
a1.sources.r1.shell = /bin/sh -c

# 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Agent
    flume-ng agent \
    
--name $agent_name \
    
--conf conf \
    
--conf-file conf/flume-conf.properties \
    
-Dflume.root.logger=INFO,console

flume-ng agent \
    
--name a1 \
    
--conf $FLUME_HOME/conf \
    
--conf-file $FLUME_HOME/conf/exec-memory-logger.conf \
    
-Dflume.root.logger=INFO,console

  • 需求3：将A服务器上的日志实时采集到B服务器

分布式日志收集框架Flume

  • 技术选型：

    1.exec source + memory channel + avro sink

    2.arro source + memory channel + logger sink
# exec-memory-avro.conf: A single-node Flume configuration

# exec-memory-avro: agent名称

# exec-source：source的名称

# avro-sink：sink的名称

# memory-channel：channel的名称

# Name the components on this agent

exec-memory-avro.sources = exec-source

exec-memory-avro.sinks = avro-sink

exec-memory-avro.channels = memory-channel

# Describe/configure the source

exec-memory-avro.sources.exec-source.type = exec

exec-memory-avro.sources.exec-source.command = tail -F /home/k.o/data/data.log

exec-memory-avro.sources.exec-source.shell = /bin/sh -c

# Describe the sink

exec-memory-avro.sinks.avro-sink.type = avro

exec-memory-avro.sinks.avro-sink.hostname = localhost

exec-memory-avro.sinks.avro-sink.port = 44444

# Use a channel which buffers events in memory

exec-memory-avro.channels.memory-channel.type = memory

exec-memory-avro.channels.memory-channel.capacity = 1000

exec-memory-avro.channels.memory-channel.transactionCapacity = 100

# Bind the source and sink to the channel

exec-memory-avro.sources.exec-source.channels = memory-channel

exec-memory-avro.sinks.avro-sink.channel = memory-channel
# avro-memory-logger.conf: A single-node Flume configuration

# avro-memory-logger: agent名称

# exec-source：source的名称

# logger-sink：sink的名称

# memory-channel：channel的名称

# Name the components on this agent

avro-memory-logger.sources = avro-source

avro-memory-logger.sinks = logger-sink

avro-memory-logger.channels = memory-channel

# Describe/configure the source

avro-memory-logger.sources.avro-source.type = avro

avro-memory-logger.sources.avro-source.bind = localhost

avro-memory-logger.sources.avro-source.port = 44444

# Describe the sink

avro-memory-logger.sinks.logger-sink.type = logger

# Use a channel which buffers events in memory

avro-memory-logger.channels.memory-channel.type = memory

avro-memory-logger.channels.memory-channel.capacity = 1000

avro-memory-logger.channels.memory-channel.transactionCapacity = 100

# Bind the source and sink to the channel

avro-memory-logger.sources.avro-source.channels = memory-channel

avro-memory-logger.sinks.logger-sink.channel = memory-channel
  • 启动Agent
# 先启动 avro-memory-logger

flume-ng agent \

--name avro-memory-logger \

--conf $FLUME_HOME/conf \

--conf-file $FLUME_HOME/conf/avro-memory-logger.conf \

-Dflume.root.logger=INFO,console

# 再启动 exec-memory-avro

flume-ng agent \

--name exec-memory-avro \

--conf $FLUME_HOME/conf \

--conf-file $FLUME_HOME/conf/exec-memory-avro.conf \

-Dflume.root.logger=INFO,console
  • 日志收集过程
    1. 机器A上监控一个文件，当我们访问主站时会有用户行为日志记录到access.log钟
    2. avro sink把新产生的日志输出到对应的avro source指定的hostname和port上
    3. 通过avro source对应的logger将我们收集的日志输出到控制台

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