Flume+Kafka+SparkStreaming+Hbase+可视化（一）

一、前置准备：

Linux命令基础

Scala、Python其中一门

Hadoop、Spark、Flume、Kafka、Hbase基础知识

 

二、分布式日志收集框架Flume

业务现状分析：服务器、web服务产生的大量日志，怎么使用，怎么将大量日志导入到集群

1、shell脚本批量，再传到Hdfs：实效性不高，容错率低，网络/磁盘IO，监控

2、Flume：

Flume：关键在于写配置文件

1）配置 agent

2）配置 Source

3）配置 Channel

4）配置 Sink

1-netcat-mem-logger.conf ：监听端口数据

#example for source=netcat, channel=memory, sink=logger
# Name the components on this agent
a1.sources = r1
a1.channels = c1
a1.sinks = k1

# configure for sources
a1.sources.r1.type = netcat
a1.sources.r1.bind = localhost
a1.sources.r1.port = 44444

# configure for channels
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# configure for sinks
a1.sinks.k1.type = logger

# configure 
a1.sinks.k1.channel = c1
a1.sources.r1.channels = c1

启动 flume-ng agent \

-n a1 \

-c conf -f ./1-netcat-mem-logger.conf \

-Dflume.root.logger=INFO,console

 

exec-mem-logger.conf ：监控文件

# Name the components on this agent
a1.sources = r1
a1.channels = c1
a1.sinks = k1

# configure for sources
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /opt/datas/flume_data/exec_tail.log

# configure for channels
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# configure for sinks
a1.sinks.k1.type = logger
a1.sinks.k1.channel = c1
a1.sources.r1.channels = c1

flume-ng agent \

-n a1 \

-c conf -f ./4-exec-mem-logger.conf \

-Dflume.root.logger=INFO,console

日志收集过程：

1. 日志服务器，启动agent，exec-source, memory-channel,avro-sink（数据服务器）, 将收集到的日志数据，写到数据服务器

2. 数据服务器，启动agent，avro-aource，memory-channel，logger-sink/kafka-sink

conf1：exec-mem-avro.conf

# Name the components on this agent
a1.sources = exec-source
a1.channels = memory-channel
a1.sinks = avro-sink

# configure for sources
a1.sources.exec-source.type = exec
a1.sources.exec-source.command = tail -F /opt/datas/log-collect-system/log_server.log

# configure for channels
a1.channels.memory-channel.type = memory
a1.channels.memory-channel.capacity = 1000
a1.channels.memory-channel.transactionCapacity = 100

# configure for sinks
a1.sinks.avro-sink.type = avro
a1.sinks.avro-sink.hostname = localhost
a1.sinks.avro-sink.port = 44444

# configure 
a1.sinks.avro-sink.channel = memory-channel
a1.sources.exec-source.channels = memory-channel

conf2：avro-mem-logger.conf

# Name the components on this agent
a1.sources = avro-source
a1.channels = memory-channel
a1.sinks = logger-sink

# configure for sources
a1.sources.avro-source.type = avro
a1.sources.avro-source.bind = localhost
a1.sources.avro-source.port = 44444

# configure for channels
a1.channels.memory-channel.type = memory
a1.channels.memory-channel.capacity = 1000
a1.channels.memory-channel.transactionCapacity = 100

# configure for sinks
a1.sinks.logger-sink.type = logger

# configure 
a1.sinks.logger-sink.channel = memory-channel
a1.sources.avro-source.channels = memory-channel

（非常重要！！！）启动顺序：先启动exec-mem-avro.conf再启动exec-mem-avro.conf