最近一段时间，负责公司的产品日志埋点与收集工作，搭建了基于Flume+HDFS+Hive日志搜集系统。
一、日志搜集系统架构：
简单画了一下日志搜集系统的架构图，可以看出，flume承担了agent与collector角色，HDFS承担了数据持久化存储的角色。
作者搭建的服务器是个demo版，只用到了一个flume_collector，数据只存储在HDFS。当然高可用的日志搜集处理系统架构是需要多台flume collector做负载均衡与容错处理的。
二、日志产生：
1、log4j配置，每隔1分钟roll一个文件，如果1分钟之内文件大于5M，则再生成一个文件。
<!-- 产品数据分析日志 按分钟分 --> <RollingRandomAccessFile name="RollingFile_product_minute" fileName="${STAT_LOG_HOME}/${SERVER_NAME}_product.log" filePattern="${STAT_LOG_HOME}/${SERVER_NAME}_product.log.%d{yyyy-MM-dd-HH-mm}-%i"> <PatternLayout charset="UTF-8" pattern="%d{yyyy-MM-dd HH:mm:ss.SSS} %level - %msg%xEx%n" /> <Policies> <TimeBasedTriggeringPolicy interval="1" modulate="true" /> <SizeBasedTriggeringPolicy size="${EVERY_FILE_SIZE}" /> </Policies> <Filters> <ThresholdFilter level="INFO" onMatch="ACCEPT" onMismatch="NEUTRAL" /> </Filters> </RollingRandomAccessFile>
roll后的文件格式如下
![clipboard[1]](http://images2015.cnblogs.com/blog/429922/201612/429922-20161202150806318-1359554351.png "clipboard[1]")
2、日志内容
json格式文件，最外层json按顺序为:tableName,logRequest,timestamp,statBody,logResponse,resultCode,resultMsg
2016-11-30 09:18:21.916 INFO - { "tableName": "ReportView", "logRequest": { *** }, "timestamp": 1480468701432, "statBody": { *** }, "logResponse": { *** }, "resultCode": 1, "resultFailMsg": "" }
三、flume配置
虚拟机环境，请见我的博客http://www.cnblogs.com/xckk/p/6000881.html
hadoop环境，请见我的另一篇博客http://www.cnblogs.com/xckk/p/6124553.html
此处flume环境是
centos1:flume-agent
centos2:flume-collector
1、flume agent配置，conf文件
a1.sources = skydataSource a1.channels = skydataChannel a1.sinks = skydataSink a1.sources.skydataSource.type = spooldir a1.sources.skydataSource.channels = skydataChannel #日志目录 a1.sources.skydataSource.spoolDir = /opt/flumeSpool a1.sources.skydataSource.fileHeader = true #日志内容处理完后，会生成.COMPLETED后缀的文件，同时.log文件每一分钟roll一个，此处忽略.log文件与.COMPLETED文件 a1.sources.skydataSource.ignorePattern=([^_]+)|(.*(.log)$)|(.*(.COMPLETED)$) a1.sources.skydataSource.basenameHeader=true a1.sources.skydataSource.deserializer.maxLineLength=102400 #自定义拦截器，对json格式的源日志进行字段分隔，并添加timestamp，为后面的hdfsSink做处理，拦截器代码见后面 a1.sources.skydataSource.interceptors=i1 a1.sources.skydataSource.interceptors.i1.type=com.skydata.flume_interceptor.HiveLogInterceptor2$Builder a1.sinks.skydataSink.type = avro a1.sinks.skydataSink.channel = skydataChannel a1.sinks.skydataSink.hostname = centos2 a1.sinks.skydataSink.port = 4545 #此处配置deflate压缩后，hive collector那边一定也要相应配置解压缩 a1.sinks.skydataSink.compression-type=deflate a1.channels.skydataChannel.type=memory a1.channels.skydataChannel.capacity=10000 a1.channels.skydataChannel.transactionCapacity=1000
2、flume collector配置
a1.sources = avroSource a1.channels = memChannel a1.sinks = hdfsSink a1.sources.avroSource.type = avro a1.sources.avroSource.channels = memChannel a1.sources.avroSource.bind=centos2 a1.sources.avroSource.port=4545 #与flume agent配置对应 a1.sources.avroSource.compression-type=deflate a1.sinks.hdfsSink.type = hdfs a1.sinks.hdfsSink.channel = memChannel # skydata_hive_log为hive表，按年-月-日分区存储， a1.sinks.hdfsSink.hdfs.path=hdfs://centos1:9000/flume/skydata_hive_log/dt=%Y-%m-%d a1.sinks.hdfsSink.hdfs.batchSize=10000 a1.sinks.hdfsSink.hdfs.fileType=DataStream a1.sinks.hdfsSink.hdfs.writeFormat=Text a1.sinks.hdfsSink.hdfs.rollSize=10240000 a1.sinks.hdfsSink.hdfs.rollCount=0 a1.sinks.hdfsSink.hdfs.rollInterval=300 a1.channels.memChannel.type=memory a1.channels.memChannel.capacity=100000 a1.channels.memChannel.transactionCapacity=10000
四、hive表创建与分区
1、hive表创建
在hive中执行建表语句后，hdfs://centos1:9000/flume/目录下新生成了skydata_hive_log目录。（建表语句里面有location关键字）
u0001表示hive通过该分隔符进行字段分离，该字符在linux用vim编辑器打开是^A。
由于日志格式是JSON格式，因为需要将JSON格式转换成u0001字符分隔，并通过dt进行分区。这一步通过flume自定义拦截器来完成。
CREATE TABLE `skydata_hive_log`( `tableNmae` string, `logRequest` string, `timestamp` bigint, `statBody` string, `logResponse` string, `resultCode` int, `resultFailMsg` string ) PARTITIONED BY ( `dt` string) ROW FORMAT DELIMITED FIELDS TERMINATED BY 'u0001' STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat' LOCATION 'hdfs://centos1:9000/flume/skydata_hive_log';
2、hive表分区
日志flume sink到hdfs上时，如果没有对hive表预先进行分区，会出现日志已经上传到hdfs目录，但是hive表却无法加载数据的情况。
这是因为hive表的分区没有创建。因此要对表进行分区添加，这里对最近一年左右时间进行分区添加
分区脚本 init_flume_hive_table.sh
for ((i=-1;i<=365;i++)) do dt=$(date -d "$(date +%F) ${i} days" +%Y-%m-%d) echo date=$dt hive -e "ALTER TABLE skydata_hive_log ADD PARTITION(dt='${dt}')" >> logs/init_skydata_hive_log.out 2>>logs/init_skydata_hive_log.err done
五、自定义flume拦截器
新建maven工程，拦截器HiveInterceptor2代码如下。
package com.skydata.flume_interceptor; import java.util.ArrayList; import java.util.List; import java.util.Map; import org.apache.flume.Context; import org.apache.flume.Event; import org.apache.flume.interceptor.Interceptor; import org.apache.flume.interceptor.TimestampInterceptor.Constants; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.alibaba.fastjson.JSONObject; import com.google.common.base.Charsets; import com.google.common.base.Joiner; public class HiveLogInterceptor2 implements Interceptor { private static Logger logger = LoggerFactory.getLogger(HiveLogInterceptor2.class); public static final String HIVE_SEPARATOR = " 01"; public void close() { // TODO Auto-generated method stub } public void initialize() { // TODO Auto-generated method stub } public Event intercept(Event event) { String orginalLog = new String(event.getBody(), Charsets.UTF_8); try { String log = this.parseLog(orginalLog); // 设置时间,用于hdfsSink long now = System.currentTimeMillis(); Map headers = event.getHeaders(); headers.put(Constants.TIMESTAMP, Long.toString(now)); event.setBody(log.getBytes()); } catch (Throwable throwable) { logger.error(("errror when intercept,log [ " + orginalLog + " ] "), throwable); return null; } return event; } public List<Event> intercept(List<Event> list) { List<Event> events = new ArrayList<Event>(); for (Event event : list) { Event interceptedEvent = this.intercept(event); if (interceptedEvent != null) { events.add(interceptedEvent); } } return events; } private static String parseLog(String log) { List<String> logFileds = new ArrayList<String>(); String dt = log.substring(0, 10); String keyStr = "INFO - "; int index = log.indexOf(keyStr); String content = ""; if (index != -1) { content = log.substring(index + keyStr.length(), log.length()); } //针对不同OS，使用不同回车换行符号 content = content.replaceAll(" ", ""); content = content.replaceAll(" ", "\\" + System.getProperty("line.separator")); JSONObject jsonObj = JSONObject.parseObject(content); String tableName = jsonObj.getString("tableName"); String logRequest = jsonObj.getString("logRequest"); String timestamp = jsonObj.getString("timestamp"); String statBody = jsonObj.getString("statBody"); String logResponse = jsonObj.getString("logResponse"); String resultCode = jsonObj.getString("resultCode"); String resultFailMsg = jsonObj.getString("resultFailMsg"); //字段分离 logFileds.add(tableName); logFileds.add(logRequest); logFileds.add(timestamp); logFileds.add(statBody); logFileds.add(logResponse); logFileds.add(resultCode); logFileds.add(resultFailMsg); logFileds.add(dt); return Joiner.on(HIVE_SEPARATOR).join(logFileds); } public static class Builder implements Interceptor.Builder { public Interceptor build() { return new HiveLogInterceptor2(); } public void configure(Context arg0) { } } }
pom.xml增加如下配置，将flume拦截器工程进行maven打包，jar包与依赖包均拷到${flume-agent}/lib目录
<build> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-dependency-plugin</artifactId> <configuration> <outputDirectory> ${project.build.directory} </outputDirectory> </configuration> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-dependency-plugin</artifactId> <executions> <execution> <id>copy-dependencies</id> <phase>prepare-package</phase> <goals> <goal>copy-dependencies</goal> </goals> <configuration> <outputDirectory>${project.build.directory}/lib</outputDirectory> <overWriteReleases>true</overWriteReleases> <overWriteSnapshots>true</overWriteSnapshots> <overWriteIfNewer>true</overWriteIfNewer> </configuration> </execution> </executions> </plugin> </plugins> </build>
对日志用分隔符" 01"进行分隔，。经拦截器处理后的日志格式如下，^A即是" 01"
ReportView^A{"request":{},"requestBody":{"detailInfos":[],"flag":"","reportId":7092,"pageSize":0,"searchs":[],"orders":[],"pageNum":1}}^A1480468701432^A{"sourceId":22745,"reportId":7092,"projectId":29355,"userId":2532}^A{"responseBody":{"statusCodeValue":200,"httpHeaders":{},"body":{"msg":"请求成功","httpCode":200,"timestamp":1480468701849},"statusCode":"OK"},"response":{}}^A1^A^A2016-11-30
至此，flume+Hdfs+Hive的配置均已完成。
后续可以通过mapreduce或者HQL对数据进行分析。
六、启动运行与结果
1、启动hadoop hdfs
参考我的前一篇文章：hadoop 1.2 集群搭建与环境配置 http://www.cnblogs.com/xckk/p/6124553.html
2、启动flume_collector和flume_agent，由于flume启动命令参数太多，自己写了一个启动脚本
start-Flume.sh
#!/bin/bash jps -l|grep org.apache.flume.node.Application|awk '{print $1}'|xargs kill -9 2>&1 >/dev/null cd "$(dirname "$0")" cd .. nohup bin/flume-ng agent --conf conf --conf-file conf/flume-conf.properties --name a1 2>&1 > /dev/null &
3、hdfs查看数据
可以看到搜集的日志已经上传到HDFS上
[root@centos1 bin]# rm -rf FlumeData.1480587273016.tmp [root@centos1 bin]# hadoop fs -ls /flume/skydata_hive_log/dt=2016-12-01/ Found 3 items -rw-r--r-- 3 root supergroup 5517 2016-12-01 08:12 /flume/skydata_hive_log/dt=2016-12-01/FlumeData.1480608753042.tmp -rw-r--r-- 3 root supergroup 5517 2016-12-01 08:40 /flume/skydata_hive_log/dt=2016-12-01/FlumeData.1480610453116 -rw-r--r-- 3 root supergroup 5517 2016-12-01 08:44 /flume/skydata_hive_log/dt=2016-12-01/FlumeData.1480610453117 [root@centos1 bin]#
4、启动hive，查看数据，可以看到hive已经可以加载hdfs数据
[root@centos1 lib]# hive Logging initialized using configuration in file:/root/apache-hive-1.2.1-bin/conf/hive-log4j.properties hive> select * from skydata_hive_log limit 2; OK ReportView {"request":{},"requestBody":{"detailInfos":[],"flag":"","reportId":7092,"pageSize":0,"searchs":[],"orders":[],"pageNum":1}} 1480468701432 {"sourceId":22745,"reportId":7092,"projectId":29355,"userId":2532} {"responseBody":{"statusCodeValue":200,"httpHeaders":{},"body":{"msg":"请求成功","httpCode":200,"timestamp":1480468701849},"statusCode":"OK"},"response":{}} 1 2016-12-01 ReportDesignResult {"request":{},"requestBody":{"sourceId":22745,"detailInfos":[{"colName":"月份","flag":"0","reportId":7092,"colCode":"col_2_22745","pageSize":20,"type":"1","pageNum":1,"rcolCode":"col_25538","colType":"string","formula":"","id":25538,"position":"row","colId":181664,"dorder":1,"pColName":"月份","pRcolCode":"col_25538"},{"colName":"综合利率(合计)","flag":"1","reportId":7092,"colCode":"col_11_22745","pageSize":20,"type":"1","pageNum":1,"rcolCode":"sum_col_25539","colType":"number","formula":"sum","id":25539,"position":"group","colId":181673,"dorder":1,"pColName":"综合利率","pRcolCode":"col_25539"}],"flag":"bar1","reportId":7092,"reportName":"iiiissszzzV","pageSize":100,"searchs":[],"orders":[],"pageNum":1,"projectId":29355}} 1480468703586{"reportType":"bar1","sourceId":22745,"reportId":7092,"num":5,"usedFields":"月份$$综合利率(合计)$$","projectId":29355,"userId":2532} {"responseBody":{"statusCodeValue":200,"httpHeaders":{},"body":{"msg":"请求成功","reportId":7092,"httpCode":200,"timestamp":1480468703774},"statusCode":"OK"},"response":{}} 1 2016-12-01 Time taken: 2.212 seconds, Fetched: 2 row(s) hive>
七、常见问题与处理方法
1、FATAL: Spool Directory source skydataSource: { spoolDir: /opt/flumeSpool }: Uncaught exception in SpoolDirectorySource thread. Restart or reconfigure Flume to continue processing.
java.nio.charset.MalformedInputException: Input length = 1
可能原因：
1、字符编码问题，spoolDir目录下的日志文件必须是UTF-8
2、使用Spooling Directory Source的时候，一定要避免同时读写一个文件的情况,conf文件增加如下配置
a1.sources.skydataSource.ignorePattern=([^_]+)|(.*(.log)$)|(.*(.COMPLETED)$)
2、日志导入到hadoop目录，但是hive表查询无数据。如hdfs://centos1:9000/flume/skydata_hive_log/dt=2016-12-01/下面有数据，
hive查询 select * from skydata_hive_log 却无数据
可能原因：
1、建表的时候，没有建立分区。即使flume进行了配置（a1.sinks.hdfsSink.hdfs.path=hdfs://centos1:9000/flume/skydata_hive_log/dt=%Y-%m-%d），但是表的分区结构没有建立，因此文件导入到HDFS上后，HIVE并不能读取。
解决方法：先创建分区，建立shell可执行文件，将该表的分区先建好
for ((i=-10;i<=365;i++)) do dt=$(date -d "$(date +%F) ${i} days" +%Y-%m-%d) echo date=$dt hive -e "ALTER TABLE skydata_hive_log ADD PARTITION(dt='${dt}')" >> logs/init_skydata_hive_log.out 2>>logs/init_skydata_hive_log.err done
2、也可能是文件在hdfs上还是.tmp文件，仍然被hdfs在写入。.tmp文件hive暂时无法读取，只能读取非.tmp文件。
解决方法：等待hdfs配置的roll间隔时间，或者达到一定大小后tmp文件重命名为hdfs上的日志文件后，再查询hive，即可查到。
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原文地址：http://www.cnblogs.com/xckk/p/6125838.html