mapreduce实现"浏览该商品的人大多数还浏览了"经典应用
输入:
日期 ...cookie id. ...商品id..
xx xx xx
输出:
商品id 商品id列表(按优先级排序,用逗号分隔)
xx xx
比如:
id1 id3,id0,id4,id2
id2 id0,id5
整个计算过程分为4步
1、提取原始日志日期,cookie id,商品id信息,按天计算,最后输出数据格式
商品id-0 商品id-1
xx x x
这一步做了次优化,商品id-0一定比商品id-1小,为了减少存储,在最后汇总数据转置下即可
reduce做局部排序及排重
2、基于上次的结果做汇总,按天计算
商品id-0 商品id-1 关联值(关联值即同时访问这两个商品的用户数)
xx x x xx
3、汇总最近三个月数据,同时考虑时间衰减,时间越久关联值的贡献越低,最后输出两两商品的关联值(包括转置后)
4、行列转换,生成最后要的推荐结果数据,按关联值排序生成
第一个MR
<strong>import java.io.IOException; import java.util.ArrayList; 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.io.WritableComparable; import org.apache.hadoop.io.WritableComparator; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Partitioner; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; import org.apache.log4j.Logger; /* * 输入:原始数据,会有重复 *日期 cookie 楼盘id * * 输出: * 日期 楼盘id1 楼盘id2 //楼盘id1一定小于楼盘id2 ,按日期 cookie进行分组 * */ public class HouseMergeAndSplit { public static class Partitioner1 extends Partitioner<TextPair, Text> { @Override public int getPartition(TextPair key, Text value, int numParititon) { return Math.abs((new Text(key.getFirst().toString()+key.getSecond().toString())).hashCode() * 127) % numParititon; } } public static class Comp1 extends WritableComparator { public Comp1() { super(TextPair.class, true); } @SuppressWarnings("unchecked") public int compare(WritableComparable a, WritableComparable b) { TextPair t1 = (TextPair) a; TextPair t2 = (TextPair) b; int comp= t1.getFirst().compareTo(t2.getFirst()); if (comp!=0) return comp; return t1.getSecond().compareTo(t2.getSecond()); } } public static class TokenizerMapper extends Mapper<LongWritable, Text, TextPair, Text>{ Text val=new Text("test"); public void map(LongWritable key, Text value, Context context ) throws IOException, InterruptedException { String s[]=value.toString().split("\001"); TextPair tp=new TextPair(s[0],s[1],s[4]+s[3]); //thedate cookie city+houseid context.write(tp, val); } } public static class IntSumReducer extends Reducer<TextPair,Text,Text,Text> { private static String comparedColumn[] = new String[3]; ArrayList<String> houselist= new ArrayList<String>(); private static Text keyv = new Text(); private static Text valuev = new Text(); static Logger logger = Logger.getLogger(HouseMergeAndSplit.class.getName()); public void reduce(TextPair key, Iterable<Text> values, Context context ) throws IOException, InterruptedException { houselist.clear(); String thedate=key.getFirst().toString(); String cookie=key.getSecond().toString(); for (int i=0;i<3;i++) comparedColumn[i]=""; //first+second为分组键,每次不同重新调用reduce函数 for (Text val:values) { if (thedate.equals(comparedColumn[0]) && cookie.equals(comparedColumn[1])&& !key.getThree().toString().equals(comparedColumn[2])) { // context.write(new Text(key.getFirst()+" "+key.getSecond().toString()), new Text(key.getThree().toString()+" first"+ " "+comparedColumn[0]+" "+comparedColumn[1]+" "+comparedColumn[2])); houselist.add(key.getThree().toString()); comparedColumn[0]=key.getFirst().toString(); comparedColumn[1]=key.getSecond().toString(); comparedColumn[2]=key.getThree().toString(); } if (!thedate.equals(comparedColumn[0])||!cookie.equals(comparedColumn[1])) { // context.write(new Text(key.getFirst()+" "+key.getSecond().toString()), new Text(key.getThree().toString()+" second"+ " "+comparedColumn[0]+" "+comparedColumn[1]+" "+comparedColumn[2])); houselist.add(key.getThree().toString()); comparedColumn[0]=key.getFirst().toString(); comparedColumn[1]=key.getSecond().toString(); comparedColumn[2]=key.getThree().toString(); } } keyv.set(comparedColumn[0]); //日期 //valuev.set(houselist.toString()); //logger.info(houselist.toString()); //context.write(keyv,valuev); for (int i=0;i<houselist.size()-1;i++) { for (int j=i+1;j<houselist.size();j++) { valuev.set(houselist.get(i)+" "+houselist.get(j)); //关联的楼盘 context.write(keyv,valuev); } } } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } FileSystem fstm = FileSystem.get(conf); Path outDir = new Path(otherArgs[1]); fstm.delete(outDir, true); conf.set("mapred.textoutputformat.separator", "\t"); //reduce输出时key value中间的分隔符 Job job = new Job(conf, "HouseMergeAndSplit"); job.setNumReduceTasks(4); job.setJarByClass(HouseMergeAndSplit.class); job.setMapperClass(TokenizerMapper.class); job.setMapOutputKeyClass(TextPair.class); job.setMapOutputValueClass(Text.class); // 设置partition job.setPartitionerClass(Partitioner1.class); // 在分区之后按照指定的条件分组 job.setGroupingComparatorClass(Comp1.class); // 设置reduce // 设置reduce的输出 job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); //job.setNumReduceTasks(18); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }</strong>
TextPair
import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.WritableComparable; public class TextPair implements WritableComparable<TextPair> { private Text first; private Text second; private Text three; public TextPair() { set(new Text(), new Text(),new Text()); } public TextPair(String first, String second,String three) { set(new Text(first), new Text(second),new Text(three)); } public TextPair(Text first, Text second,Text Three) { set(first, second,three); } public void set(Text first, Text second,Text three) { this.first = first; this.second = second; this.three=three; } public Text getFirst() { return first; } public Text getSecond() { return second; } public Text getThree() { return three; } public void write(DataOutput out) throws IOException { first.write(out); second.write(out); three.write(out); } public void readFields(DataInput in) throws IOException { first.readFields(in); second.readFields(in); three.readFields(in); } public int compareTo(TextPair tp) { int cmp = first.compareTo(tp.first); if (cmp != 0) { return cmp; } cmp= second.compareTo(tp.second); if (cmp != 0) { return cmp; } return three.compareTo(tp.three); } }
TextPairSecond
import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.FloatWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.WritableComparable; public class TextPairSecond implements WritableComparable<TextPairSecond> { private Text first; private FloatWritable second; public TextPairSecond() { set(new Text(), new FloatWritable()); } public TextPairSecond(String first, float second) { set(new Text(first), new FloatWritable(second)); } public TextPairSecond(Text first, FloatWritable second) { set(first, second); } public void set(Text first, FloatWritable second) { this.first = first; this.second = second; } public Text getFirst() { return first; } public FloatWritable getSecond() { return second; } public void write(DataOutput out) throws IOException { first.write(out); second.write(out); } public void readFields(DataInput in) throws IOException { first.readFields(in); second.readFields(in); } public int compareTo(TextPairSecond tp) { int cmp = first.compareTo(tp.first); if (cmp != 0) { return cmp; } return second.compareTo(tp.second); } }
第二个MR
<strong>import java.io.IOException; import java.text.SimpleDateFormat; import java.util.ArrayList; import java.util.Date; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.WritableComparable; import org.apache.hadoop.io.WritableComparator; import org.apache.hadoop.mapred.OutputCollector; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Partitioner; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.Mapper.Context; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; import org.apache.log4j.Logger; /* * 统计楼盘之间共同出现的次数 * 输入: * 日期 楼盘1 楼盘2 * * 输出: * 日期 楼盘1 楼盘2 共同出现的次数 * */ public class HouseCount { public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable>{ IntWritable iw=new IntWritable(1); public void map(LongWritable key, Text value, Context context ) throws IOException, InterruptedException { context.write(value, iw); } } public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { IntWritable result=new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum=0; for (IntWritable iw:values) { sum+=iw.get(); } result.set(sum); context.write(key, result) ; } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } FileSystem fstm = FileSystem.get(conf); Path outDir = new Path(otherArgs[1]); fstm.delete(outDir, true); conf.set("mapred.textoutputformat.separator", "\t"); //reduce输出时key value中间的分隔符 Job job = new Job(conf, "HouseCount"); job.setNumReduceTasks(2); job.setJarByClass(HouseCount.class); job.setMapperClass(TokenizerMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); // 设置reduce // 设置reduce的输出 job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); //job.setNumReduceTasks(18); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }</strong>
第三个MR
import java.io.IOException; import java.text.ParseException; import java.text.SimpleDateFormat; import java.util.ArrayList; import java.util.Calendar; import java.util.Date; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.FloatWritable; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.WritableComparable; import org.apache.hadoop.io.WritableComparator; import org.apache.hadoop.mapred.OutputCollector; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Partitioner; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.Mapper.Context; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; import org.apache.log4j.Logger; /* * 汇总近三个月统计楼盘之间共同出现的次数,考虑衰减系数, 并最后a b 转成 b a输出一次 * 输入: * 日期 楼盘1 楼盘2 共同出现的次数 * * 输出 * 楼盘1 楼盘2 共同出现的次数(考虑了衰减系数,每天的衰减系数不一样) * */ public class HouseCountHz { public static class HouseCountHzMapper extends Mapper<LongWritable, Text, Text, FloatWritable>{ Text keyv=new Text(); FloatWritable valuev=new FloatWritable(); public void map(LongWritable key, Text value, Context context ) throws IOException, InterruptedException { String[] s=value.toString().split("\t"); keyv.set(s[1]+" "+s[2]);//楼盘1,楼盘2 Calendar date1=Calendar.getInstance(); Calendar d2=Calendar.getInstance(); Date b = null; SimpleDateFormat sdf=new SimpleDateFormat("yyyy-MM-dd"); try { b=sdf.parse(s[0]); } catch (ParseException e) { e.printStackTrace(); } d2.setTime(b); long n=date1.getTimeInMillis(); long birth=d2.getTimeInMillis(); long sss=n-birth; int day=(int)((sss)/(3600*24*1000)); //该条记录的日期与当前日期的日期差 float factor=1/(1+(float)(day-1)/10); //衰减系数 valuev.set(Float.parseFloat(s[3])*factor); context.write(keyv, valuev); } } public static class HouseCountHzReducer extends Reducer<Text,FloatWritable,Text,FloatWritable> { FloatWritable result=new FloatWritable(); Text keyreverse=new Text(); public void reduce(Text key, Iterable<FloatWritable> values, Context context ) throws IOException, InterruptedException { float sum=0; for (FloatWritable iw:values) { sum+=iw.get(); } result.set(sum); String[] keys=key.toString().split("\t"); keyreverse.set(keys[1]+" "+keys[0]); context.write(key, result) ; context.write(keyreverse, result) ; } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } FileSystem fstm = FileSystem.get(conf); Path outDir = new Path(otherArgs[1]); fstm.delete(outDir, true); conf.set("mapred.textoutputformat.separator", "\t"); //reduce输出时key value中间的分隔符 Job job = new Job(conf, "HouseCountHz"); job.setNumReduceTasks(2); job.setJarByClass(HouseCountHz.class); job.setMapperClass(HouseCountHzMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(FloatWritable.class); // 设置reduce // 设置reduce的输出 job.setReducerClass(HouseCountHzReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(FloatWritable.class); //job.setNumReduceTasks(18); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
第四个MR
import java.io.IOException; import java.util.Iterator; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.FloatWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.WritableComparable; import org.apache.hadoop.io.WritableComparator; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Partitioner; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; /* * 输入数据: * 楼盘1 楼盘2 共同出现的次数 * * 输出数据 * 楼盘1 楼盘2,楼盘3,楼盘4 (按次数排序) */ public class HouseRowToCol { public static class Partitioner1 extends Partitioner<TextPairSecond, Text> { @Override //分区 public int getPartition(TextPairSecond key, Text value, int numParititon) { return Math.abs((new Text(key.getFirst().toString()+key.getSecond().toString())).hashCode() * 127) % numParititon; } } //分组 public static class Comp1 extends WritableComparator { public Comp1() { super(TextPairSecond.class, true); } @SuppressWarnings("unchecked") public int compare(WritableComparable a, WritableComparable b) { TextPairSecond t1 = (TextPairSecond) a; TextPairSecond t2 = (TextPairSecond) b; return t1.getFirst().compareTo(t2.getFirst()); } } //排序 public static class KeyComp extends WritableComparator { public KeyComp() { super(TextPairSecond.class, true); } @SuppressWarnings("unchecked") public int compare(WritableComparable a, WritableComparable b) { TextPairSecond t1 = (TextPairSecond) a; TextPairSecond t2 = (TextPairSecond) b; int comp= t1.getFirst().compareTo(t2.getFirst()); if (comp!=0) return comp; return -t1.getSecond().compareTo(t2.getSecond()); } } public static class HouseRowToColMapper extends Mapper<LongWritable, Text, TextPairSecond, Text>{ Text houseid1=new Text(); Text houseid2=new Text(); FloatWritable weight=new FloatWritable(); public void map(LongWritable key, Text value, Context context ) throws IOException, InterruptedException { String s[]=value.toString().split("\t"); weight.set(Float.parseFloat(s[2])); houseid1.set(s[0]); houseid2.set(s[1]); TextPairSecond tp=new TextPairSecond(houseid1,weight); context.write(tp, houseid2); } } public static class HouseRowToColReducer extends Reducer<TextPairSecond,Text,Text,Text> { Text valuev=new Text(); public void reduce(TextPairSecond key, Iterable<Text> values, Context context ) throws IOException, InterruptedException { Text keyv=key.getFirst(); Iterator<Text> it=values.iterator(); StringBuilder sb=new StringBuilder(it.next().toString()); while(it.hasNext()) { sb.append(","+it.next().toString()); } valuev.set(sb.toString()); context.write(keyv, valuev); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } FileSystem fstm = FileSystem.get(conf); Path outDir = new Path(otherArgs[1]); fstm.delete(outDir, true); conf.set("mapred.textoutputformat.separator", "\t"); //reduce输出时key value中间的分隔符 Job job = new Job(conf, "HouseRowToCol"); job.setNumReduceTasks(4); job.setJarByClass(HouseRowToCol.class); job.setMapperClass(HouseRowToColMapper.class); job.setMapOutputKeyClass(TextPairSecond.class); job.setMapOutputValueClass(Text.class); // 设置partition job.setPartitionerClass(Partitioner1.class); // 在分区之后按照指定的条件分组 job.setGroupingComparatorClass(Comp1.class); job.setSortComparatorClass(KeyComp.class); // 设置reduce // 设置reduce的输出 job.setReducerClass(HouseRowToColReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); //job.setNumReduceTasks(18); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
来自:http://blog.csdn.net/u011750989/article/details/12004065
mapreduce实现"浏览该商品的人大多数还浏览了"经典应用的更多相关文章
- Java遇见HTML——JSP篇之商品浏览记录的实现
一.项目总体介绍 使用Cookie实现商品浏览记录. 要实现这个程序采取的是Model1(Jsp+JavaBean)架构实现,具体步骤: 首先要有个数据库,商品表,操作数据库的一个类DBHelper类 ...
- javaWeb 使用cookie显示商品浏览记录
package de.bvb.cookie; import java.io.IOException; import java.io.PrintWriter; import java.util.Date ...
- 利用COOKIE保存历史浏览商品的一个简单思路
<?php //如是COOKIE 里面不为空,则往里面增加一个商品ID if (!empty($_COOKIE['SHOP']['history'])){ //取得COOKIE里面的值,并用逗号 ...
- 用JS中的cookie实现商品的浏览记录
最近在做一个购物车效果,为了实现商品的浏览记录效果可是让我百般周折,避免以后忘记特写此随笔与大家共享,希望博友们看后有所收获. 第一步:在一个公用的js文件下getCookie(“liulan”),c ...
- react 从商品详情页返回到商品列表页,列表自动滚动上次浏览的位置
现状:目前从商品详情页返回到商品列表页,还需要再去请求服务数据,还需要用户再去等待获取数据的过程,这样用户体验非常不好, 遇到的问题: 1:如何将数据缓存, 2:如何获取和保存列表滑动的高度, 3:判 ...
- 使用Cookie实现用户商品历史浏览记录
该功能分为四个模块: 1. 获取所有商品并以链接的形式显示 out.write("网站商品: <br/>"); Map<String, Book> book ...
- 天猫浏览型应用的CDN静态化架构演变
原文链接:http://www.csdn.net/article/2014-01-22/2818227-CDN-Architecture 在天猫双11活动中,商品详情.店铺等浏览型系统,通常会承受超出 ...
- hadoop MapReduce 笔记
1. MapReduce程序开发步骤 编写map 和 reduce 程序–> 单元测试 -> 编写驱动程序进行验证-> 本地数据集调试 -> 部署到集群运行 用 ...
- php中如何实现网上商城用户历史浏览记录的代码
/如是COOKIE 里面不为空,则往里面增加一个商品ID if (!empty($_COOKIE['SHOP']['history'])){ //取得COOKIE里面的值,并用逗号把它切割成一个数组 ...
随机推荐
- 04斐波那契函数_Fibonacci--(栈与队列)
#include "stdio.h" int Fbi(int i) /* 斐波那契的递归函数 */ { ) ? : ; ) + Fbi(i - ); /* 这里Fbi就是函数自己, ...
- 详解Windows 7系统中IE8/IE9/IE10三个版本的关系(转)
今年(2013)年初,微软开放了姗姗来迟的 IE10 for Windows 7 版本下载.至此,Windows 7 平台上可以运行三个 IE 浏览器版本.虽然 Windows 与 IE 经历了诸多版 ...
- How to: Reading an ini config file from a batch file
Original Link: http://almanachackers.com/blog/2009/12/31/reading-an-ini-config-file-from-a-batch-fil ...
- Linux操作系统是如何工作的?破解操作系统的奥秘
学号:SA12**6112 研究笔记: 1:计算机是怎么样工作的 2:用户态到内核态切换之奥秘解析 3:进程切换之奥秘解析 本博文主要是根据前3篇笔记来总结Linux内核的工作机制. 一:操作系统工作 ...
- dd命令测试linux磁盘读写速度
1.先熟悉两个特殊的设备: (1)/dev/null:回收站.无底洞. (2)/dev/zero:产生字符. 2.测试磁盘写能力 time dd if=/dev/zero of=/t ...
- 【JSP&Servlet学习笔记】4.会话管理
Http本身是无状态通信协议,要进行会话管理的基本原理,就是将需要维护的状态回应给浏览器,由浏览器在下次请求时主动发送状态信息,让Web应用程序“得知”请求之间的关联. 隐藏字段是将状态信息以窗体中看 ...
- oracle日期格式数据修改
select * from INVOICE_NEW where ref_no='32308' update INVOICE_NEW set check_d=to_date('2015/11/16', ...
- PHPCMS如何实现后台访问限制?
修改phpcms 后台管理路径,可以有效的防止因为程序漏洞,或者是服务器安全问题所带来的 phpcms系统管理权限被黑客获取 文件路径:/caches/config/system.php 修改:'ad ...
- 基于等待队列及poll机制的按键驱动代码分析和测试代码
按键驱动分析: #include <linux/module.h> #include <linux/kernel.h> #include <linux/fs.h> ...
- 如何读懂Oracle文档中的语法图(转)
本文转载自:http://kyle.xlau.org/posts/syntax-diagrams.html Oracle文档中用到了两种表达语法的方法,语法图和BNF. BNF, Backus-Nau ...