2. 科技
    3. Hadoop基础-11-用户行为日志分析

    Hadoop基础-11-用户行为日志分析

    科技2025-06-06  68

    源码见：https://github.com/hiszm/hadoop-train

    用户行为日志概述

    用户每次搜索和点击的记录历史行为数据,从历史订单

    ==> 然后进行推荐/ 从而 提高用户的转化量 (最终目的)

    日志内容

    20979872853^Ahttp://www.yihaodian.com/1/?type=3&tracker_u=10974049258^A^A^A3^ABAWG49VCYYTMZ6VU9XX74KPV5CCHPAQ2A4A5^A^A^A^A^APPG68XWJNUSSX649S4YQTCT6HBMS9KBA^A10974049258^A\N^A27.45.216.128^A^A,unionKey:10974049258^A^A2013-07-21 18:58:21^A\N^A^A1^A^A\N^Anull^A247^A^A^A^A^AMozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; WOW64; Trident/6.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C)^AWin32^A^A^A^A^A^A广东省^A20^A^A^A惠州 市^A^A^A^A^A^A^A\N^A\N^A\N^A\N^A2013-07-21 20977690754^Ahttp://www.yihaodian.com/1/?type=3&tracker_u=10781349260^A^A^A3^A49FDCP696X2RDDRC2ED6Y4JVPTEVFNDADF1D^A^A^A^A^APPGCTKD92UT3DR7KY1VFZ92ZU4HEP479^A10781349260^A\N^A101.80.223.6^A^A,unionKey:10781349260^A^A2013-07-21 18:11:46^A\N^A^A1^A^A\N^Anull^A-10^A^A^A^A^AMozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.1; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; InfoPath.3; .NET4.0E)^AWin32^A^A^A^A^A^A上海市^A1^A^A2013-07-21 18:11:46^A上海市^A^A^A^A^A^A^A\N^A\N^A\N^A\N^A2013-07-21 第二个字段:url=> 页面ID第十四字段:ip => 地点第十八字段:time

    电商常用术语

    1.Ad Views（广告浏览）:网上广告被用户浏览的次数。 2.PV(访问量)：即Page View。页面浏览量，用户每次刷新即被计算一次。网站各网页被浏览的总次数。一个访客有可能创造十几个甚至更多的浏览量。或者这样理解：用户在你的网站上打开网页的次数，浏览了多少个页面，或者刷新了的次数。 3.Impression (印象数):指受用户要求的网页每一次的显示，就是一个Impression；广告主希望10万人次看到广告，即10万次Impression；也是评估广告效果的元素之一。 4.UV（独立访客数）：即Unique Visitor,访问网站或看到广告的一台电脑客户端为一个访客。24小时内相同的客户端只被计算一次。 5.IP (独立IP)： 即Internet Protocol,指独立IP数。24小时内相同IP地址之被计算一次。 6.URL（统一资源定位器）: URL给出任何服务器、文件、图象在网上的位置。用户可以通过超文本协议链接特定的URL而找到所需信息。也就是着陆页网址。 7.Key Word(关键字) 8.HTML（超文本标识语言）: 一种基于文本格式的页面描述语言，是网页通用的编辑语言。 9.Band Width (带宽):在某一时刻能够通过传播线路传输的信息(文字、图片、音、视频)容量。带宽越高，网页的调用就越快。有限的带宽导致了尽可能地要使网页中的图片文件小。 10.Browser Cache(浏览器缓存):为了加速网页的浏览，浏览器在硬盘中储存了最近访问的页面，如果重新访问该站点，浏览器就从硬盘中显示这个页面，而不是从服务器中。 11.Cookie:电脑中记录用户在网络中的行为的文件；网站可通过Cookie来识别用户是否曾经访问过该网站。 12.Database(数据库):通常指的利用现代计算机技术，将各类信息有序分类整理，便于查找和管理。在网络营销中，指利用互联网收集用户个人信息，并存 档、管理；如：姓名、性别、年龄、地址、电话、兴趣爱好、消费行为等等。 13.Targeting(定向): 通过内容匹配、用户构成或者过滤传递最适宜的广告给用户。也是百度所说的寻找精准客户，是广告定向，客户定向。 14.Traffic(流量):用户访问站点的数字和种类

    项目需求描述

    统计访问量(pv)统计每个省份(ip)访问量统计每个页面(url)的访问量

    数据处理流程及技术架构

    浏览量统计功能实现

    统计页面的浏览量 count 一行记录做成一个固定的KET, value赋值为1

    package com.bigdata.hadoop.mr.project.mrv1; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import java.io.IOException; /** * 第一版本流量的统计 * */ public class PVStatApp { public static void main(String[] args) throws Exception { Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); job.setJarByClass(PVStatApp.class); job.setMapperClass(MyMapper.class); job.setReducerClass(MyReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); job.setOutputKeyClass(NullWritable.class); job.setOutputValueClass(LongWritable.class); FileInputFormat.setInputPaths(job, new Path("input/raw/trackinfo_20130721.data")); FileOutputFormat.setOutputPath(job, new Path("output/pv1")); job.waitForCompletion(true); } static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> { private Text KEY = new Text("key"); private LongWritable ONE = new LongWritable(1); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { context.write(KEY, ONE); } } static class MyReducer extends Reducer<Text, LongWritable, NullWritable, LongWritable> { @Override protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { long count = 0; for (LongWritable value : values) { count++; } context.write(NullWritable.get(), new LongWritable(count)); } } }

    output : 300000

    省份浏览量统计

    统计各个省份的流量 select province count(1) from xxx group by province 地市信息 <= IP解析 <= IP如何转换城市信息

    package com.bigdata.hadoop.hdfs; import com.bigdata.hadoop.mr.project.utils.IPParser; import org.junit.Test; public class Iptest { @Test public void testIP(){ IPParser.RegionInfo regionInfo =IPParser.getInstance().analyseIp("58.32.19.255"); System.out.println(regionInfo.getCountry()); System.out.println(regionInfo.getProvince()); System.out.println(regionInfo.getCity()); } } /Library/Java/JavaVirtualMachines/jdk1.8.0_1/commons-logging/commons-logging/1.1.3/commons-logging-1.1.3.jar:/Users/jacksun/.m2/repository/log4j/log4j/1.2.17/log4j-1.2.17.jar:/Users/jacksun/.m2/repository/commons-lang/commons-lang/2.6/commons-lang-2.6.jar:/Users/jackop.hdfs.Iptest,testIP 中国 上海市 null Process finished with exit code 0

    IP库解析

    import com.bigdata.hadoop.mr.project.utils.IPParser; import com.bigdata.hadoop.mr.project.utils.LogParser; package com.bigdata.hadoop.mr.project.utils; import org.apache.commons.lang.StringUtils; import java.util.HashMap; import java.util.Map; public class LogParser { public Map<String, String> parse(String log) { IPParser ipParser = IPParser.getInstance(); Map<String, String> info = new HashMap<>(); if (StringUtils.isNotBlank(log)) { String[] splits = log.split("\001"); String ip = splits[13]; String country = "-"; String province = "-"; String city = "-"; IPParser.RegionInfo regionInfo = ipParser.analyseIp(ip); if (regionInfo != null) { country = regionInfo.getCountry(); province = regionInfo.getProvince(); city = regionInfo.getCity(); } info.put("ip", ip); info.put("country", country); info.put("province", province); info.put("city", city); } return info; } }

    功能实现

    package com.bigdata.hadoop.mr.project.mrv1; import com.bigdata.hadoop.mr.project.utils.IPParser; import com.bigdata.hadoop.mr.project.utils.LogParser; import org.apache.commons.lang.StringUtils; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import java.io.IOException; import java.util.Map; public class ProvinceStatApp { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { Configuration configuration = new Configuration(); FileSystem fileSystem = FileSystem.get(configuration); Path outputPath = new Path("output/v1/provincestat"); if (fileSystem.exists(outputPath)) { fileSystem.delete(outputPath, true); } Job job = Job.getInstance(configuration); job.setJarByClass(ProvinceStatApp.class); job.setMapperClass(MyMapper.class); job.setReducerClass(MyReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); FileInputFormat.setInputPaths(job, new Path("input/raw/trackinfo_20130721.data")); FileOutputFormat.setOutputPath(job, new Path("output/v1/provincestat")); job.waitForCompletion(true); } static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> { private LongWritable ONE = new LongWritable(1); private LogParser logParser; @Override protected void setup(Context context) throws IOException, InterruptedException { logParser = new LogParser(); } @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String log = value.toString(); Map<String, String> info = logParser.parse(log); String ip = info.get("ip"); if (StringUtils.isNotBlank(ip)) { IPParser.RegionInfo regionInfo = IPParser.getInstance().analyseIp(ip); if (regionInfo != null) { String province = regionInfo.getProvince(); if(StringUtils.isNotBlank(province)){ context.write(new Text(province), ONE); }else { context.write(new Text("-"), ONE); } } else { context.write(new Text("-"), ONE); } } else { context.write(new Text("-"), ONE); } } } static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> { @Override protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { long count = 0; for (LongWritable value : values) { count++; } context.write(key, new LongWritable(count)); } } } - 923 上海市 72898 云南省 1480 内蒙古自治区 1298 北京市 42501 台湾省 254 吉林省 1435 四川省 4442 天津市 11042 宁夏 352 安徽省 5429 山东省 10145 山西省 2301 广东省 51508 广西 1681 新疆 840 江苏省 25042 江西省 2238 河北省 7294 河南省 5279 浙江省 20627 海南省 814 湖北省 7187 湖南省 2858 澳门特别行政区 6 甘肃省 1039 福建省 8918 西藏 110 贵州省 1084 辽宁省 2341 重庆市 1798 陕西省 2487 青海省 336 香港特别行政区 45 黑龙江省 1968

    页面浏览量统计

    统计页面的访问量 把符合规则的pageID获取到，然后进行统计即可

    页面编号获取

    package com.bigdata.hadoop.mr.project.utils; import org.apache.commons.lang.StringUtils; import java.util.HashMap; import java.util.Map; public class LogParser { public Map<String, String> parse(String log) { IPParser ipParser = IPParser.getInstance(); Map<String, String> info = new HashMap<>(); if (StringUtils.isNotBlank(log)) { String[] splits = log.split("\001"); String ip = splits[13]; String country = "-"; String province = "-"; String city = "-"; IPParser.RegionInfo regionInfo = ipParser.analyseIp(ip); if (regionInfo != null) { country = regionInfo.getCountry(); province = regionInfo.getProvince(); city = regionInfo.getCity(); } info.put("ip", ip); info.put("country", country); info.put("province", province); info.put("city", city); String url = splits[1]; info.put("url", url); String time = splits[17]; info.put("time", time); } return info; } public Map<String, String> parseV2(String log) { IPParser ipParser = IPParser.getInstance(); Map<String, String> info = new HashMap<>(); if (StringUtils.isNotBlank(log)) { String[] splits = log.split("\t"); String ip = splits[0]; String country = splits[1]; String province = splits[2]; String city = splits[3]; IPParser.RegionInfo regionInfo = ipParser.analyseIp(ip); info.put("ip", ip); info.put("country", country); info.put("province", province); info.put("city", city); String url = splits[4]; info.put("url", url); } return info; } }

    功能实现

    package com.bigdata.hadoop.mr.project.mrv1; import com.bigdata.hadoop.mr.project.utils.ContentUtils; import com.bigdata.hadoop.mr.project.utils.LogParser; import org.apache.commons.lang.StringUtils; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import java.io.IOException; import java.util.Map; public class PageStatApp { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { Configuration configuration = new Configuration(); FileSystem fileSystem = FileSystem.get(configuration); Path outputPath = new Path("output/v1/pagestat"); if (fileSystem.exists(outputPath)) { fileSystem.delete(outputPath, true); } Job job = Job.getInstance(configuration); job.setJarByClass(PageStatApp.class); job.setMapperClass(MyMapper.class); job.setReducerClass(MyReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); FileInputFormat.setInputPaths(job, new Path("input/raw/trackinfo_20130721.data")); FileOutputFormat.setOutputPath(job, new Path("output/v1/pagestat")); job.waitForCompletion(true); } static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> { private LongWritable ONE = new LongWritable(1); private LogParser logParser; @Override protected void setup(Context context) throws IOException, InterruptedException { logParser = new LogParser(); } @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String log = value.toString(); Map<String, String> info = logParser.parse(log); String url = info.get("url"); if (StringUtils.isNotBlank(url)) { String pageId = ContentUtils.getPageId(url); context.write(new Text(pageId), ONE); } } } static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> { @Override protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { long count = 0; for (LongWritable value : values) { count++; } context.write(key, new LongWritable(count)); } } } - 298827 13483 19 13506 15 13729 9 13735 2 13736 2 14120 28 14251 1 14572 14 14997 2 15065 1 17174 1 17402 1 17449 2 17486 2 17643 7 18952 14 18965 1 18969 32 18970 27 18971 1 18972 3 18973 8 18977 10 18978 5 18979 11 18980 8 18982 50 18985 5 18988 2 18991 27 18992 4 18994 3 18996 3 18997 3 18998 2 18999 4 19000 5 19004 23 19006 4 19009 1 19010 1 19013 1 20154 2 20933 1 20953 5 21208 11 21340 1 21407 1 21484 1 21826 8 22068 1 22107 4 22114 4 22116 5 22120 6 22123 13 22125 1 22127 16 22129 3 22130 3 22140 1 22141 5 22142 8 22143 5 22144 1 22146 5 22169 1 22170 20 22171 51 22180 4 22196 75 22249 4 22331 6 22372 1 22373 1 22805 3 22809 3 22811 5 22813 11 23203 1 23481 194 23541 1 23542 1 23704 1 23705 1 3541 2 8101 36 8121 32 8122 38 9848 2 9864 1

    数据处理之ETL

    ----------------------- [INFO ] method:org.apache.hadoop.mapred.MapTask$MapOutputBuffer.init(MapTask.java:1008) kvstart = 26214396; length = 6553600 Counters: 30 File System Counters FILE: Number of bytes read=857754791 FILE: Number of bytes written=23557997 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 Map-Reduce Framework Map input records=300000 Map output records=299797 Map output bytes=3001739 Map output materialized bytes=3601369 Input split bytes=846 Combine input records=0 Combine output records=0 Reduce input groups=92 Reduce shuffle bytes=3601369 Reduce input records=299797 Reduce output records=92 Spilled Records=599594 Shuffled Maps =6 Failed Shuffles=0 Merged Map outputs=6 GC time elapsed (ms)=513 Total committed heap usage (bytes)=3870818304 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=173576072 File Output Format Counters Bytes Written=771

    我们在发现在处理原生数据的时候,会花费大量的时间,我们需要对数据进行加工

    所以这里我们引入ETL

    ETL，是英文Extract-Transform-Load的缩写，用来描述将数据从来源端经过抽取（extract）、转换（transform）、加载（load）至目的端的过程。

    全量的原始数据不方便进行计算，需要做一步处理进行相应的维度统计分析解析需要的数据：ip ==》 城市信息去除不需要的字段ip/time/url/page_id/coutry/province/city package com.bigdata.hadoop.mr.project.mrv2; import com.bigdata.hadoop.mr.project.mrv1.PageStatApp; import com.bigdata.hadoop.mr.project.utils.ContentUtils; import com.bigdata.hadoop.mr.project.utils.LogParser; import org.apache.commons.lang.StringUtils; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import java.io.IOException; import java.util.Map; public class ETLApp { public static void main(String[] args) throws Exception { Configuration configuration = new Configuration(); FileSystem fileSystem = FileSystem.get(configuration); Path outputPath = new Path("input/etl"); if (fileSystem.exists(outputPath)) { fileSystem.delete(outputPath, true); } Job job = Job.getInstance(configuration); job.setJarByClass(ETLApp.class); job.setMapperClass(MyMapper.class); job.setMapOutputKeyClass(NullWritable.class); job.setMapOutputValueClass(Text.class); FileInputFormat.setInputPaths(job, new Path("input/raw/trackinfo_20130721.data")); FileOutputFormat.setOutputPath(job, new Path("input/etl")); job.waitForCompletion(true); } static class MyMapper extends Mapper<LongWritable, Text, NullWritable, Text> { private LongWritable ONE = new LongWritable(1); private LogParser logParser; @Override protected void setup(Context context) throws IOException, InterruptedException { logParser = new LogParser(); } @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String log = value.toString(); Map<String, String> info = logParser.parse(log); String ip = info.get("ip"); String country = info.get("country"); String province = info.get("province"); String city = info.get("city"); String url = info.get("url"); String time = info.get("time"); String pageId = ContentUtils.getPageId(url); StringBuilder builder = new StringBuilder(); builder.append(ip).append("\t"); builder.append(country).append("\t"); builder.append(province).append("\t"); builder.append(city).append("\t"); builder.append(url).append("\t"); builder.append(time).append("\t"); builder.append(pageId); context.write(NullWritable.get(), new Text(builder.toString())); } } } 106.3.114.42 中国 北京市 null http://www.yihaodian.com/2/?tracker_u=10325451727&tg=boomuserlist%3A%3A2463680&pl=www.61baobao.com&creative=30392663360&kw=&gclid=CPC2idPRv7gCFQVZpQodFhcABg&type=2 2013-07-21 11:24:56 - 58.219.82.109 中国 江苏省 无锡市 http://www.yihaodian.com/5/?tracker_u=2225501&type=4 2013-07-21 13:57:11 - 58.219.82.109 中国 江苏省 无锡市 http://search.yihaodian.com/s2/c0-0/k%25E7%25A6%258F%25E4%25B8%25B4%25E9%2597%25A8%25E9%2587%2591%25E5%2585%25B8%25E7%2589%25B9%25E9%2580%2589%25E4%25B8%259C%25E5%258C%2597%25E7%25B1%25B35kg%2520%25E5%259B%25BD%25E4%25BA%25A7%25E5%25A4%25A7%25E7%25B1%25B3%2520%25E6%2599%25B6%25E8%258E%25B9%25E5%2589%2594%25E9%2580%258F%2520%25E8%2587%25AA%25E7%2584%25B6%2520%2520/5/ 2013-07-21 13:50:48 - 58.219.82.109 中国 江苏省 无锡市 http://search.yihaodian.com/s2/c0-0/k%25E8%258C%25B6%25E8%258A%25B1%25E8%2582%25A5%25E7%259A%2582%25E7%259B%2592%25202213%2520%25E5%258D%25AB%25E7%2594%259F%25E7%259A%2582%25E7%259B%2592%2520%25E9%25A6%2599%25E7%259A%2582%25E7%259B%2592%2520%25E9%25A2%259C%25E8%2589%25B2%25E9%259A%258F%25E6%259C%25BA%2520%2520/5/ 2013-07-21 13:57:16 - 58.219.82.109 中国 江苏省 无锡市 http://www.yihaodian.com/5/?tracker_u=2225501&type=4 2013-07-21 13:50:13 - 218.11.179.22 中国 河北省 邢台市 http://www.yihaodian.com/2/?tracker_u=10861423206&type=1 2013-07-21 08:00:13 - 218.11.179.22 中国 河北省 邢台市 http://www.yihaodian.com/2/?tracker_u=10861423206&type=1 2013-07-21 08:00:20 - 123.123.202.45 中国 北京市 null http://search.1mall.com/s2/c0-0/k798%25203d%25E7%2594%25BB%25E5%25B1%2595%2520%25E5%259B%25A2%25E8%25B4%25AD/2/ 2013-07-21 11:55:28 - 123.123.202.45 中国 北京市 null http://t.1mall.com/100?grouponAreaId=3&uid=1ahrua02b8mvk0952dle&tracker_u=10691821467 2013-07-21 11:55:21 - ...........

    功能升级

    再etl的基础上改一下log即可

    public Map<String, String> parseV2(String log) { IPParser ipParser = IPParser.getInstance(); Map<String, String> info = new HashMap<>(); if (StringUtils.isNotBlank(log)) { String[] splits = log.split("\t"); String ip = splits[0]; String country = splits[1] ; String province = splits[2]; if(province.equals("null")){ province="其它"; } String city = splits[3]; IPParser.RegionInfo regionInfo = ipParser.analyseIp(ip); info.put("ip", ip); info.put("country", country); info.put("province", province); info.put("city", city); String url = splits[4]; info.put("url", url); String time = splits[5]; info.put("time", time); String client = splits[6]; info.put("client", client); } return info; }

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