Hive是基于Hadoop的数据管理系统,作为分析人员的即时分析工具和ETL等工作的执行引擎,对于如今的大数据管理与分析、处理有着非常大的 意义。GeoIP是一套IP映射数据库,它定时更新,并且提供了各种语言的API,非常适合在做地域相关数据分析时的一个数据源。
Precondition:通过 IP 地址获得用户的地理位置信息
也就是根据用户的IP,通过IP数据库查询获得信息。一般IP数据库中,
每条记录的基本结构:
IP地址段(起始、结束),以及对应的信息数据 一般包含的信息:国家、区域(省/州)、城市、街道、经纬度、ISP提供商等信息
因为IP数据库随着时间经常变化(不过一段时间内变化很小),所以需要有人经常维护和更新。这个数据也不可能完全准确、也不可能覆盖全。这是maxmind的城市准确度 http://www.maxmind.com/app/city_accuracy 。 因为没有权威的数据组织机构,且经常有变化。各家数据供应商,基本上做着做着就形成自己的一套数据了。
目前,国内用的比较有名的是“纯真IP数据库”,国外常用的是 maxmind、ip2location。
IP数据库是否收费:收费、免费都有。一般有人维护的数据往往都是收费的,准确率和覆盖率会稍微高一些。
质量方面:
因为IP数据库随着时间经常变化(不过一段时间内变化很小),所以需要有人经常维护和更新。这个数据也不可能完全准确、也不可能覆盖全。这是maxmind的城市准确度 http://www.maxmind.com/app/city_accuracy 。 因为没有权威的数据组织机构,且经常有变化。各家数据供应商,基本上做着做着就形成自己的一套数据了。
目前,国内用的比较有名的是“纯真IP数据库”,国外常用的是 maxmind、ip2location。
IP数据库是否收费:收费、免费都有。一般有人维护的数据往往都是收费的,准确率和覆盖率会稍微高一些。
质量方面:
- 主要概念是准确率和覆盖率。
- 记录数据总条数。纯真现在是38万条(2010年07月30日更新)
- 是否有人维护。
- 数据库更新频率:每月、每周。数据库会定期更新的,maxmind开源版是每月更新一次。
查询形式:
是否提供经纬度:纯真IP数据库不提供经纬度,Maxmind提供,如果做地图应用,一般是需要经纬度的。
- 本地,将IP数据库下载到本地使用,查询效率高、性能好。常用在统计分析方面。具体形式又分为:
- 内存查询:将全部数据直接加载到内存中,便于高性能查询。或者二进制的数据文件本身就是经过优化的索引文件,可以直接对文件做查询。
- 数据库查询:将数据导入到数据库,再用数据库查询。效率没有内存查询快。
- 远程(web service或ajax),调用远程第三方服务。查询效率自然比较低,一般用在网页应用中。
查询的本质:输入一个IP,找到其所在的IP段,一般都是采用二分搜索实现的。
是否提供API:有的IP数据库提供API,支持多语言(java、javascript、C#等),这样你就不用自己直接分析数据格式、整理、写查询代码了。是否提供经纬度:纯真IP数据库不提供经纬度,Maxmind提供,如果做地图应用,一般是需要经纬度的。
而UDF是Hive提供的用户自定义函数的接口,通过实现它可以扩展Hive目前已有的内置函数。而为Hive加入一个IP映射函数,我们只需要简单地在UDF中调用GeoIP的Java API即可。
GeoIP的数据文件可以从这里下载:http://www.maxmind.com/download/geoip/database/,由于需 要国家和城市的信息,我这里下载的是http://www.maxmind.com/download/geoip/database /GeoLiteCity.dat.gz
GeoIP的各种语言的API可以从这里下载:http://www.maxmind.com/download/geoip/api/
操作Steps如下:
Step 1:Hive所需添加的IP地址信息识别UDF函数如下:
package org.hadoop.hive.additionalUDF; import java.io.File; import java.io.IOException; import org.apache.hadoop.hive.ql.exec.UDF; import com.maxmind.geoip.Location; import com.maxmind.geoip.LookupService; import com.maxmind.geoip.regionName; import com.maxmind.geoip.timeZone; import java.util.regex.*; public class IPToCC extends UDF { private static LookupService cl = null; private static String ipPattern = "\d+\.\d+\.\d+\.\d+"; private static String ipNumPattern = "\d+"; static LookupService getLS(String dbfile) throws IOException{ //String sep = System.getProperty("file.separator"); //String dir = "/home/landen/UntarFile/GeoIP"; //String dbfile = dir + sep + "GeoLiteCity.dat"; //String dbfile = "GeoLiteCity.dat"; if(new File(dbfile).exists()) { if(cl == null) { cl = new LookupService(dbfile,LookupService.GEOIP_MEMORY_CACHE); } } return cl; } /** * @param str like "114.43.181.143" * */ public String evaluate(String str,String ipDBInfo) { try { Location l1 = null; Matcher mIP = Pattern.compile(ipPattern).matcher(str); Matcher mIPNum = Pattern.compile(ipNumPattern).matcher(str); if(mIP.matches()) l1 = getLS(ipDBInfo).getLocation(str); else if(mIPNum.matches()) l1 = getLS(ipDBInfo).getLocation(Long.parseLong(str)); /*System.out.println("countryCode: " + l1.countryCode + " countryName: " + l1.countryName + " region: " + l1.region + " regionName: " + regionName.regionNameByCode(l1.countryCode, l1.region) + " city: " + l1.city + " latitude: " + l1.latitude + " longitude: " + l1.longitude + " timezone: " + timeZone.timeZoneByCountryAndRegion(l1.countryCode, l1.region));*/ return String.format("%s %s %s %s %s %s %s %s",l1.countryCode,l1.countryName,l1.region,regionName.regionNameByCode(l1.countryCode, l1.region),l1.city,l1.latitude,l1.longitude,timeZone.timeZoneByCountryAndRegion(l1.countryCode, l1.region)); } catch(Exception e) { e.printStackTrace(); if(cl != null) cl.close(); return null; } } public static void main(String[] args) { String dbfile = "GeoLiteCity.dat"; IPToCC ipTocc = new IPToCC(); String ipAdress = "221.12.10.218"; System.out.println(ipTocc.evaluate(ipAdress,dbfile)); } } Step 2.将以上程序和GeoIP的API程序,一起打成JAR包IPToCC.jar,和数据文件(GeoLiteCity.dat)一起放到Hive所在的服务器的一个位置。然后可以按照以下两种方式将以上资源添加到Hive中: 1> 打开Hive执行以下语句: landen@Master:~/UntarFile/hive-0.10.0$ bin/hive WARNING: org.apache.hadoop.metrics.jvm.EventCounter is deprecated. Please use org.apache.hadoop.log.metrics.EventCounter in all the log4j.properties files. Logging initialized using configuration in jar:file:/home/landen/UntarFile/hive-0.10.0/lib/hive-common-0.10.0.jar!/hive-log4j.properties Hive history file=/home/landen/UntarFile/hive-0.10.0/logs/hive_job_log_landen_201312081638_1930432077.txt hive (default)> use stuchoosecourse; OK Time taken: 5.251 seconds hive (stuchoosecourse)> add file /home/landen/UntarFile/GeoIP/GeoLiteCity.dat; Added resource: /home/landen/UntarFile/GeoIP/GeoLiteCity.dat hive (stuchoosecourse)> add jar /home/landen/UntarFile/hive-0.10.0/lib/IPTocc.jar; Added /home/landen/UntarFile/hive-0.10.0/lib/IPTocc.jar to class path Added resource: /home/landen/UntarFile/hive-0.10.0/lib/IPTocc.jar hive (stuchoosecourse)> create temporary function IP4Tocc as 'org.hadoop.hive.additionalUDF.IPToCC'; OK Time taken: 0.107 seconds 2> 在启动hive shell命令前,在$HIVE_HOME/conf目录下添加.hiverc文件,然后添加如下内容: add file /home/landen/UntarFile/GeoIP/GeoLiteCity.dat; add jar /home/landen/UntarFile/hive-0.10.0/lib/IPTocc.jar; create temporary function IP4Tocc as 'org.hadoop.hive.additionalUDF.IPToCC'; 当启动hive shell命令后,hive会将加载.hiverc文件内容并添加到全局内容中,便于client使用 Step 3:Hive测试内容如下: hive (stuchoosecourse)> select * from ipidentifier; OK ipadress 221.12.10.218 60.180.248.201 125.111.251.118 Time taken: 0.099 seconds hive (stuchoosecourse)> select IP4Tocc(ipadress,'./GeoLiteCity.dat') from ipidentifier; Total MapReduce jobs = 1 Launching Job 1 out of 1 Number of reduce tasks is set to 0 since there's no reduce operator Starting Job = job_201312042044_0020, Tracking URL = http://Master:50030/jobdetails.jsp?jobid=job_201312042044_0020 Kill Command = /home/landen/UntarFile/hadoop-1.0.4/libexec/../bin/hadoop job -kill job_201312042044_0020 Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0 2013-12-08 20:54:10,276 Stage-1 map = 0%, reduce = 0% 2013-12-08 20:54:18,308 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.55 sec 2013-12-08 20:54:19,313 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.55 sec 2013-12-08 20:54:20,317 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.55 sec 2013-12-08 20:54:21,322 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.55 sec 2013-12-08 20:54:22,326 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.55 sec 2013-12-08 20:54:23,331 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.55 sec 2013-12-08 20:54:24,402 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.55 sec MapReduce Total cumulative CPU time: 2 seconds 550 msec Ended Job = job_201312042044_0020 MapReduce Jobs Launched: Job 0: Map: 1 Cumulative CPU: 2.55 sec HDFS Read: 306 HDFS Write: 188 SUCCESS Total MapReduce CPU Time Spent: 2 seconds 550 msec OK _c0 CN China 02 Zhejiang Hangzhou 30.293594 120.16141 Asia/Shanghai CN China 02 Zhejiang Wenzhou 27.999405 120.66681 Asia/Shanghai CN China 02 Zhejiang Ningbo 29.878204 121.5495 Asia/Shanghai hive (stuchoosecourse)> select split(IP4Tocc(ipadress,'./GeoLiteCity.dat'),' ') from ipidentifier; Total MapReduce jobs = 1 Launching Job 1 out of 1 Number of reduce tasks is set to 0 since there's no reduce operator Starting Job = job_201312042044_0021, Tracking URL = http://Master:50030/jobdetails.jsp?jobid=job_201312042044_0021 Kill Command = /home/landen/UntarFile/hadoop-1.0.4/libexec/../bin/hadoop job -kill job_201312042044_0021 Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0 2013-12-08 21:12:46,717 Stage-1 map = 0%, reduce = 0% 2013-12-08 21:12:56,764 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 4.28 sec 2013-12-08 21:12:57,768 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 4.28 sec 2013-12-08 21:12:58,772 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 4.28 sec 2013-12-08 21:12:59,775 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 4.28 sec 2013-12-08 21:13:00,778 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 4.28 sec 2013-12-08 21:13:01,782 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 4.28 sec 2013-12-08 21:13:02,786 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 4.28 sec MapReduce Total cumulative CPU time: 4 seconds 280 msec Ended Job = job_201312042044_0021 MapReduce Jobs Launched: Job 0: Map: 1 Cumulative CPU: 4.28 sec HDFS Read: 306 HDFS Write: 188 SUCCESS Total MapReduce CPU Time Spent: 4 seconds 280 msec OK _c0 ["CN","China","02","Zhejiang","Hangzhou","30.293594","120.16141","Asia/Shanghai"] ["CN","China","02","Zhejiang","Wenzhou","27.999405","120.66681","Asia/Shanghai"] ["CN","China","02","Zhejiang","Ningbo","29.878204","121.5495","Asia/Shanghai"] Time taken: 45.037 seconds hive (stuchoosecourse)> create table HiddenIPInfo( > IP string,countrycode string,countryname string,region string,regionname string,city string, > latitude string,longitude string,timezone string); OK Time taken: 1.828 seconds hive (stuchoosecourse)> show tables; OK tab_name hbase_stu_course hiddenipinfo ipidentifier Time taken: 0.486 seconds hive (stuchoosecourse)> describe hiddenipinfo; OK col_name data_type comment ip string countrycode string countryname string region string regionname string city string latitude string longitude string timezone string Time taken: 0.33 seconds hive (stuchoosecourse)> from(select ipadress,split(IP4Tocc(ipadress,'./GeoLiteCity.dat'),' ') as IPInfo from ipidentifier)e > insert overwrite table hiddenipinfo > select e.ipadress,e.IPInfo[0] as countrycode,e.IPInfo[1] as countryname,e.IPInfo[2] as region, > e.IPInfo[3] as regionname,e.IPInfo[4] as city,e.IPInfo[5] as latitude,e.IPInfo[6] as longitude, > e.IPInfo[7] as timezone; Total MapReduce jobs = 3 Launching Job 1 out of 3 Number of reduce tasks is set to 0 since there's no reduce operator Starting Job = job_201312042044_0023, Tracking URL = http://Master:50030/jobdetails.jsp?jobid=job_201312042044_0023 Kill Command = /home/landen/UntarFile/hadoop-1.0.4/libexec/../bin/hadoop job -kill job_201312042044_0023 Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0 2013-12-08 21:58:12,406 Stage-1 map = 0%, reduce = 0% 2013-12-08 21:58:18,449 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.48 sec 2013-12-08 21:58:19,454 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.48 sec 2013-12-08 21:58:20,458 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.48 sec 2013-12-08 21:58:21,462 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.48 sec 2013-12-08 21:58:22,466 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.48 sec 2013-12-08 21:58:23,470 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.48 sec 2013-12-08 21:58:24,474 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 1.48 sec MapReduce Total cumulative CPU time: 1 seconds 480 msec Ended Job = job_201312042044_0023 Ended Job = 39195028, job is filtered out (removed at runtime). Ended Job = 1695434910, job is filtered out (removed at runtime). Moving data to: hdfs://Master:9000/home/landen/UntarFile/hive-0.10.0/warehouse/hive_2013-12-08_21-57-40_106_7083774091282915969/-ext-10000 Loading data to table stuchoosecourse.hiddenipinfo Deleted hdfs://Master:9000/home/landen/UntarFile/hive-0.10.0/warehouse/stuchoosecourse.db/hiddenipinfo Table stuchoosecourse.hiddenipinfo stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 233, raw_data_size: 0] Rows loaded to hiddenipinfo MapReduce Jobs Launched: Job 0: Map: 1 Cumulative CPU: 1.48 sec HDFS Read: 306 HDFS Write: 233 SUCCESS Total MapReduce CPU Time Spent: 1 seconds 480 msec OK ipadress countrycode countryname region regionname city latitude longitude timezone Time taken: 45.692 seconds hive (stuchoosecourse)> show tables; OK tab_name hbase_stu_course hiddenipinfo ipidentifier Time taken: 0.053 seconds hive (stuchoosecourse)> select * from hiddenipinfo; OK ip countrycode countryname region regionname city latitude longitude timezone 221.12.10.218 CN China 02 Zhejiang Hangzhou 30.293594 120.16141 Asia/Shanghai 60.180.248.201 CN China 02 Zhejiang Wenzhou 27.999405 120.66681 Asia/Shanghai 125.111.251.118 CN China 02 Zhejiang Ningbo 29.878204 121.5495 Asia/Shanghai Time taken: 0.083 seconds