用户日志分析系统实战（二）

た入场券 2022-06-05 03:09 207阅读 0赞

### 用户日志分析系统实战（二） ###

#### 1 日志收集 ####

##### 1.1 常见数据来源 #####

*  非结构化数据  
    
    
     *  机器产生的数据
     *  用户访问日志
     *  用户购买日志
 *  结构化数据  
    
    
     *  RDBMS：MySQL、Oracle等  
        
        
         *  全量导入
         *  增量导入

##### 1.2 数据收集与入库要求 #####

*  分布式  
    
    
     *  数据源多样性
     *  数据源分散
 *  可靠性  
    
    
     *  保证数据不丢失
     *  允许丢失部分数据
 *  可扩展  
    
    
     *  数据源可能会不断增加
 *  通过并行提高性能

##### 1.3 Flume #####

*  Flume收集数据到HDFS的配置  
    flume-conf-logAnalysis.properties:

logAgent.sources = logSource
    logAgent.channels = fileChannel
    logAgent.sinks = hdfsSink
    
    # For each one of the sources, the type is defined
    logAgent.sources.logSource.type = exec
    logAgent.sources.logSource.command = tail -F /home/bigdata/datasource/record.list
    
    # The channel can be defined as follows.
    logAgent.sources.logSource.channels = fileChannel
    
    # Each sink's type must be defined
    logAgent.sinks.hdfsSink.type = hdfs
    logAgent.sinks.hdfsSink.hdfs.path = hdfs://bigdata:9000/flume/record/%Y-%m-%d/%H%M
    logAgent.sinks.hdfsSink.hdfs.filePrefix= transaction_log
    logAgent.sinks.hdfsSink.hdfs.rollInterval= 600
    logAgent.sinks.hdfsSink.hdfs.rollCount= 10000
    logAgent.sinks.hdfsSink.hdfs.rollSize= 0
    logAgent.sinks.hdfsSink.hdfs.round = true
    logAgent.sinks.hdfsSink.hdfs.roundValue = 10
    logAgent.sinks.hdfsSink.hdfs.roundUnit = minute
    logAgent.sinks.hdfsSink.hdfs.fileType = DataStream
    logAgent.sinks.hdfsSink.hdfs.useLocalTimeStamp = true
    #Specify the channel the sink should use
    logAgent.sinks.hdfsSink.channel = fileChannel
    
    # Each channel's type is defined.
    logAgent.channels.fileChannel.type = file
    logAgent.channels.fileChannel.checkpointDir= /home/bigdata/apache-flume-1.7.0-bin/dataCheckpointDir
    logAgent.channels.fileChannel.dataDirs= /home/bigdata/apache-flume-1.7.0-bin/dataDir

*  Flume收集数据到Kafka的配置  
    flume-conf-logAnalysis-kafka.properties:

logAgent.sources = logSource
    logAgent.channels = fileChannel
    logAgent.sinks = kafkaSink
    
    # For each one of the sources, the type is defined
    logAgent.sources.logSource.type = exec
    logAgent.sources.logSource.command = tail -F /home/bigdata/datasource/record.list
    
    # The channel can be defined as follows.
    logAgent.sources.logSource.channels = fileChannel
    
    # Each sink's type must be defined
    logAgent.sinks.kafkaSink.type = org.apache.flume.sink.kafka.KafkaSink
    logAgent.sinks.kafkaSink.topic = log
    logAgent.sinks.kafkaSink.brokerList= bigdata:9092
    logAgent.sinks.kafkaSink.batchSize= 10
    #Specify the channel the sink should use
    logAgent.sinks.kafkaSink.channel = fileChannel
    
    # Each channel's type is defined.
    logAgent.channels.fileChannel.type = file
    logAgent.channels.fileChannel.checkpointDir= /home/bigdata/apache-flume-1.7.0-bin/dataCheckpointDir_realtime
    logAgent.channels.fileChannel.dataDirs= /home/bigdata/apache-flume-1.7.0-bin/dataDir_realtime

*  Flume启动命令  
    start\_flume\_batch.sh:

#!/bin/bash 
    flume-ng agent --conf /home/bigdata/apache-flume-1.7.0-bin/conf --conf-file /home/bigdata/apache-flume-1.7.0-bin/conf/flume-conf-logAnalysis.properties --name logAgent -Dflume.root.logger=DEBUG,console -Dflume.monitoring.type=http -Dflume.monitoring.port=34545

start\_flume\_realtime.sh:

#!/bin/bash 
    flume-ng agent --conf /home/bigdata/apache-flume-1.7.0-bin/conf --conf-file /home/bigdata/apache-flume-1.7.0-bin/conf/flume-conf-logAnalysis-kafka.properties --name logAgent -Dflume.root.logger=DEBUG,console -Dflume.monitoring.type=http -Dflume.monitoring.port=34546

##### 1.4 Sqoop #####

*  Sqoop: SQL-to-Hadoop
    
     *  连接传统关系型数据和Hadoop的桥梁
     *  利用MR加快数据传输速度
     *  批处理方式进行数据传输
 *  Sqoop优势：
    
     *  高效、可控的利用资源  
        
        
         *  任务并发度，超时时间等
     *  数据类型映射与转换  
        
        
         *  可自行进行，用户也可自定义
     *  支持多种数据库  
        
        
         *  MySQL、Oracle、Postgresql等
 *  Sqoop导入数据到HDFS的命令  
    user\_dimension\_sqoop.sh:

#!/bin/bash 
     sqoop import --connect jdbc:mysql://bigdata:3306/log --username root --password root --table user_dimension --driver com.mysql.jdbc.Driver --m 10 --target-dir /warehouse/user_dimension

brand\_dimension\_sqoop.sh:

#!/bin/bash 
    sqoop import --connect jdbc:mysql://bigdata:3306/log --username root --password root --table brand_dimension --driver com.mysql.jdbc.Driver --m 10 --target-dir /warehouse/brand_dimension

#### 2 文件存储（HDFS） ####

##### 2.1 HDFS架构 #####

![这里写图片描述][SouthEast]

##### 2.2 文件存储 #####

*  数据分区
    
     *  年/月/日形式
 *  数据压缩
    
     *  占用较少存储空间

#### 3 HDFS优化小技巧 ####

##### 3.1 小文件优化 #####

*  合并成大文件  
    
    
     *  Sequence File
     *  Hadoop Archive

![这里写图片描述][SouthEast 1]

![这里写图片描述][SouthEast 2]

*  保存到key/value系统中  
    
    
     *  HBase
     *  TFS（Tao Bao FileSystem）

##### 3.2 压缩与归档 #####

![这里写图片描述][SouthEast 3]

*  Flume
 *  MR/Hive/Spark（MR压缩请参照我的博客[Shuffle揭秘][Shuffle]）

##### 3.3 纠删码编码 #####

![这里写图片描述][SouthEast 4]

*  通过引入纠删码，节省存储空间（节省一半空间）
 *  Hadoop 3.0可用

[SouthEast]: /images/20220605/3a28be47b25a45aaaf6efb24171f46cd.png
[SouthEast 1]: /images/20220605/8c1e77448d99499e8bae5d3c182266cb.png
[SouthEast 2]: /images/20220605/fe207757f714423db15606a32341c58f.png
[SouthEast 3]: /images/20220605/9cba741bf99f4ae98efa1b06794a5f29.png
[Shuffle]: http://blog.csdn.net/qq_17776287/article/details/78474354
[SouthEast 4]: /images/20220605/5c49cb9098524988a9cd489639c8ddba.png