Flume + HDFS + Hive日志收集系统

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





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 = "\001";

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("\r", "");

content = content.replaceAll("\n", "\\\\" + 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"\001"进行分隔，。经拦截器处理后的日志格式如下，^A即是"\001"

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