手写一个垂直领域的搜索引擎

前言：想要谷歌和百度已经够用了，这里实现的搜索只是为了方便自己做后续的事情的一个小实践。

理论架构

想要实现一个搜索引擎，首先需要考虑出完整的架构。

页面抓取
存储
分析
搜索实现
展现

页面抓取

首先，页面抓取我打算采取最简单的HttpClient的方式，可能有人会说，你这样做会漏掉大量使用Web2.0的网站的，是的，没错，最开始我为了验证架构的可用性，就是要漏掉一些复杂的点。

存储

然后，存储，我打算直接使用文件系统进行实体存储，在搜索使用的时候，全部将结果加载到内存中。可能有的人会说，你这样好消耗内存哦，是的，没错，我可以用大量的swap空间，用性能换内存。

分析

分析部分，我打算直接使用分词算法，解析出词频，建立文章的倒排索引，但是不存储文章的所有词语的倒排索引，毕竟要考虑到未优化的文件系统的存取性能。我这里的方案是直接取词频在20~50范围内的词以及网站标题的分词结果作为网站的关键词，建立倒排系统而存在。为了描述不显得那么空白和抽象，这里贴出最后的结构：image.png
文件的标题名就是分词的词语名，文件里面存储的是所有关键词有该词的网站域名，如下：

有点类似elasticsearch底层的存储原理，不过我没有做什么优化。

搜索实现

搜索实现部分，我打算直接将上述文件加载到内存中，直接使用HashMap存储，方便读取。

展现

为了方便随点随用，我打算直接使用谷歌浏览器插件的形式进行展现实现。

好了，现在理论架构差不多了，那么就开始动手实现吧

动手实现

页面抓取

刚才提到了，这里直接使用HttpClient进行页面抓取，除此之外，还涉及对页面的外链解析。在说外链解析之前，我打算先说说我的抓取思路。

将整个互联网想象成一张巨大的网，网站间通过链接的方式相互串联，虽然这里面有大量的网站是孤岛，但是不妨碍对绝大多数网站的抓取。所以这里采取的方案就是多点为主要节点的广度优先遍历，对单个网站只抓取首页的内容，分析其中的所有外链，然后作为目标进行抓取。

抓取页面的代码如下：

import com.chaojilaji.auto.autocode.generatecode.GenerateFile;
import com.chaojilaji.auto.autocode.standartReq.SendReq;
import com.chaojilaji.auto.autocode.utils.Json;
import com.chaojilaji.moneyframework.model.OnePage;
import com.chaojilaji.moneyframework.model.Word;
import com.chaojilaji.moneyframework.service.Nlp;
import com.chaojilaji.moneyframework.utils.DomainUtils;
import com.chaojilaji.moneyframework.utils.HtmlUtil;
import com.chaojilaji.moneyframework.utils.MDUtils;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.springframework.stereotype.Service;
import org.springframework.util.StringUtils;import java.io.*;
import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentSkipListSet;public class HttpClientCrawl {private static Log logger = LogFactory.getLog(HttpClientCrawl.class);public Set<String> oldDomains = new ConcurrentSkipListSet<>();public Map<String, OnePage> onePageMap = new ConcurrentHashMap<>(400000);public Set<String> ignoreSet = new ConcurrentSkipListSet<>();public Map<String, Set<String>> siteMaps = new ConcurrentHashMap<>(50000);public String domain;public HttpClientCrawl(String domain) {this.domain = DomainUtils.getDomainWithCompleteDomain(domain);String[] ignores = {"gov.cn", "apac.cn", "org.cn", "twitter.com", "baidu.com", "google.com", "sina.com", "weibo.com", "github.com", "sina.com.cn", "sina.cn", "edu.cn", "wordpress.org", "sephora.com"};ignoreSet.addAll(Arrays.asList(ignores));loadIgnore();loadWord();}private Map<String, String> defaultHeaders() {Map<String, String> ans = new HashMap<>();ans.put("Accept", "application/json, text/plain, */*");ans.put("Content-Type", "application/json");ans.put("Connection", "keep-alive");ans.put("Accept-Language", "zh-CN,zh;q=0.9");ans.put("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.71 Safari/537.36");return ans;}public SendReq.ResBody doRequest(String url, String method, Map<String, Object> params) {String urlTrue = url;SendReq.ResBody resBody = SendReq.sendReq(urlTrue, method, params, defaultHeaders());return resBody;}public void loadIgnore() {File directory = new File(".");try {String file = directory.getCanonicalPath() + "/moneyframework/generate/ignore/demo.txt";BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(new File(file))));String line = "";while ((line = reader.readLine()) != null) {String x = line.replace("[", "").replace("]", "").replace(" ", "");String[] y = x.split(",");ignoreSet.addAll(Arrays.asList(y));}} catch (IOException e) {e.printStackTrace();}}public void loadDomains(String file) {File directory = new File(".");try {File file1 = new File(directory.getCanonicalPath() + "\\" + file);logger.info(directory.getCanonicalPath() + "\\" + file);if (!file1.exists()) {file1.createNewFile();}BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(file1)));String line = "";while ((line = reader.readLine()) != null) {line = line.trim();OnePage onePage = new OnePage(line);if (!oldDomains.contains(onePage.getDomain())) {onePageMap.put(onePage.getDomain(), onePage);oldDomains.add(onePage.getDomain());}}} catch (FileNotFoundException e) {e.printStackTrace();} catch (IOException e) {e.printStackTrace();}}public void handleWord(List<String> s, String domain, String title) {for (String a : s) {String x = a.split(" ")[0];String y = a.split(" ")[1];Set<String> z = siteMaps.getOrDefault(x, new ConcurrentSkipListSet<>());if (Integer.parseInt(y) >= 10) {if (z.contains(domain)) continue;z.add(domain);siteMaps.put(x, z);GenerateFile.appendFileWithRelativePath("moneyframework/domain/markdown", x + ".md", MDUtils.getMdContent(domain, title, s.toString()));}}Set<Word> xxxx = Nlp.separateWordAndReturnUnit(title);for (Word word : xxxx) {String x = word.getWord();Set<String> z = siteMaps.getOrDefault(x, new ConcurrentSkipListSet<>());if (z.contains(domain)) continue;z.add(domain);siteMaps.put(x, z);GenerateFile.appendFileWithRelativePath("moneyframework/domain/markdown", x + ".md", MDUtils.getMdContent(domain, title, s.toString()));}}public void loadWord() {File directory = new File(".");try {File file1 = new File(directory.getCanonicalPath() + "\\moneyframework/domain/markdown");if (file1.isDirectory()) {int fileCnt = 0;File[] files = file1.listFiles();for (File file : files) {fileCnt ++;try {BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(file)));String line = "";siteMaps.put(file.getName().replace(".md", ""), new ConcurrentSkipListSet<>());while ((line = reader.readLine()) != null) {line = line.trim();if (line.startsWith("####")) {siteMaps.get(file.getName().replace(".md", "")).add(line.replace("#### ", "").trim());}}}catch (Exception e){}if ((fileCnt % 1000 ) == 0){logger.info((fileCnt * 100.0) / files.length + "%");}}}for (Map.Entry<String,Set<String>> xxx : siteMaps.entrySet()){oldDomains.addAll(xxx.getValue());}} catch (FileNotFoundException e) {e.printStackTrace();} catch (IOException e) {e.printStackTrace();}}public void doTask() {String root = "http://" + this.domain + "/";Queue<String> urls = new LinkedList<>();urls.add(root);Set<String> tmpDomains = new HashSet<>();tmpDomains.addAll(oldDomains);tmpDomains.add(DomainUtils.getDomainWithCompleteDomain(root));int cnt = 0;while (!urls.isEmpty()) {String url = urls.poll();SendReq.ResBody html = doRequest(url, "GET", new HashMap<>());cnt++;if (html.getCode().equals(0)) {ignoreSet.add(DomainUtils.getDomainWithCompleteDomain(url));try {GenerateFile.createFile2("moneyframework/generate/ignore", "demo.txt", ignoreSet.toString());} catch (IOException e) {e.printStackTrace();}continue;}OnePage onePage = new OnePage();onePage.setUrl(url);onePage.setDomain(DomainUtils.getDomainWithCompleteDomain(url));onePage.setCode(html.getCode());String title = HtmlUtil.getTitle(html.getResponce()).trim();if (!StringUtils.hasText(title) || title.length() > 100 || title.contains("�")) {title = "没有";}onePage.setTitle(title);String content = HtmlUtil.getContent(html.getResponce());Set<Word> words = Nlp.separateWordAndReturnUnit(content);List<String> wordStr = Nlp.print2List(new ArrayList<>(words), 10);handleWord(wordStr, DomainUtils.getDomainWithCompleteDomain(url), title);onePage.setContent(wordStr.toString());if (html.getCode().equals(200)) {List<String> domains = HtmlUtil.getUrls(html.getResponce());for (String domain : domains) {int flag = 0;String[] aaa = domain.split(".");if (aaa.length>=4){continue;}for (String i : ignoreSet) {if (domain.endsWith(i)) {flag = 1;break;}}if (flag == 1) continue;if (StringUtils.hasText(domain.trim())) {if (!tmpDomains.contains(domain)) {tmpDomains.add(domain);urls.add("http://" + domain + "/");}}}logger.info(this.domain + " 队列的大小为 " + urls.size());if (cnt >= 2000) {break;}} else {if (url.startsWith("http:")){urls.add(url.replace("http:","https:"));}}}}
}

其中，这里的_SendReq.sendReq_是自己实现的一个下载页面你的方法，调用了HttpClient的方法。如果你想实现对Web2.0的抓取，可以考虑在里面封装一个PlayWrite。
然后是格式化Html，去除标签和由于特殊字符引起的各种乱码的工具类HtmlUtils。

import org.apache.commons.lang3.StringEscapeUtils;import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.regex.Matcher;
import java.util.regex.Pattern;public class HtmlUtil {public static String getContent(String html) {String ans = "";try {html = StringEscapeUtils.unescapeHtml4(html);html = delHTMLTag(html);html = htmlTextFormat(html);return html;} catch (Exception e) {e.printStackTrace();}return ans;}public static String delHTMLTag(String htmlStr) {String regEx_script = "<script[^>]*?>[\\s\\S]*?<\\/script>"; //定义script的正则表达式String regEx_style = "<style[^>]*?>[\\s\\S]*?<\\/style>"; //定义style的正则表达式String regEx_html = "<[^>]+>"; //定义HTML标签的正则表达式Pattern p_script = Pattern.compile(regEx_script, Pattern.CASE_INSENSITIVE);Matcher m_script = p_script.matcher(htmlStr);htmlStr = m_script.replaceAll(""); //过滤script标签Pattern p_style = Pattern.compile(regEx_style, Pattern.CASE_INSENSITIVE);Matcher m_style = p_style.matcher(htmlStr);htmlStr = m_style.replaceAll(""); //过滤style标签Pattern p_html = Pattern.compile(regEx_html, Pattern.CASE_INSENSITIVE);Matcher m_html = p_html.matcher(htmlStr);htmlStr = m_html.replaceAll(""); //过滤html标签return htmlStr.trim();}public static String htmlTextFormat(String htmlText) {return htmlText.replaceAll(" +", " ").replaceAll("\n", " ").replaceAll("\r", " ").replaceAll("\t", " ").replaceAll("　　"," ").replaceAll("                 "," ").replaceAll("              "," ").replaceAll("            "," ").replaceAll("        "," ").replaceAll("       "," ").replaceAll("      "," ").replaceAll("     "," ").replaceAll("    "," ").replaceAll("   "," ").replaceAll("  "," ").replaceAll("  "," ").replaceAll(" • "," ").replaceAll("⎛⎝"," ").replaceAll("⎠⎞"," ").replaceAll("  "," ").replaceAll("!!"," ").replaceAll("✔        "," ");}public static List<String> getUrls(String htmlText) {Pattern pattern = Pattern.compile("(http|https):\\/\\/[A-Za-z0-9_\\-\\+.:?&@=\\/%#,;]*");Matcher matcher = pattern.matcher(htmlText);Set<String> ans = new HashSet<>();while (matcher.find()) {ans.add(DomainUtils.getDomainWithCompleteDomain(matcher.group()));}return new ArrayList<>(ans);}public static String getTitle(String htmlText) {Pattern pattern = Pattern.compile("(?<=title\\>).*(?=</title)");Matcher matcher = pattern.matcher(htmlText);Set<String> ans = new HashSet<>();while (matcher.find()) {return matcher.group();}return "";}
}

除了上面提到的去除标签和特殊字符外，还实现了获取所有url和标题的方法（Java有一些库也提供了相同的方法）。

存储

在上面的代码中，其实包含了存储和分析的调用代码，现在单独拎出来分析一下。

public void handleWord(List<String> s, String domain, String title) {for (String a : s) {String x = a.split(" ")[0];String y = a.split(" ")[1];Set<String> z = siteMaps.getOrDefault(x, new ConcurrentSkipListSet<>());if (Integer.parseInt(y) >= 10) {if (z.contains(domain)) continue;z.add(domain);siteMaps.put(x, z);GenerateFile.appendFileWithRelativePath("moneyframework/domain/markdown", x + ".md", MDUtils.getMdContent(domain, title, s.toString()));}}Set<Word> xxxx = Nlp.separateWordAndReturnUnit(title);for (Word word : xxxx) {String x = word.getWord();Set<String> z = siteMaps.getOrDefault(x, new ConcurrentSkipListSet<>());if (z.contains(domain)) continue;z.add(domain);siteMaps.put(x, z);GenerateFile.appendFileWithRelativePath("moneyframework/domain/markdown", x + ".md", MDUtils.getMdContent(domain, title, s.toString()));}}

存储的方法就是这个handleWord，其中，这里的s就是某个页面的分词结果（这里没有存储词语出现的偏移量，所以也不算是倒排索引），domain是域名本身，title是标题。
其中，这里调用了GenerateFile，是自定义实现的创建文件工具类。部分代码如下：

public static void createFileRecursion(String fileName, Integer height) throws IOException {Path path = Paths.get(fileName);if (Files.exists(path)) {// TODO: 2021/11/13 如果文件存在return;}if (Files.exists(path.getParent())) {// TODO: 2021/11/13 如果父级文件存在，直接创建文件if (height == 0) {Files.createFile(path);} else {Files.createDirectory(path);}} else {createFileRecursion(path.getParent().toString(), height + 1);// TODO: 2021/11/13 这一步能保证path的父级一定存在了，现在需要把自己也建一下createFileRecursion(fileName, height);}
}public static void appendFileWithRelativePath(String folder, String fileName, String value) {File directory = new File(".");try {fileName = directory.getCanonicalPath() + "/" + folder + "/" + fileName;createFileRecursion(fileName, 0);} catch (IOException e) {e.printStackTrace();}try {BufferedOutputStream bufferedOutputStream = new BufferedOutputStream(new FileOutputStream(fileName, true));bufferedOutputStream.write(value.getBytes());bufferedOutputStream.flush();bufferedOutputStream.close();} catch (IOException e) {e.printStackTrace();}
}

分析

这里的分析主要是对处理之后的网页内容进行分词和词频统计，这里使用的仍旧是之前推荐的Hanlp。

import com.chaojilaji.moneyframework.model.Word;
import com.hankcs.hanlp.HanLP;
import com.hankcs.hanlp.seg.Segment;
import com.hankcs.hanlp.seg.common.Term;
import java.util.*;
import java.util.regex.Matcher;
import java.util.regex.Pattern;public class Nlp {private static Pattern ignoreWords = Pattern.compile("[,.0-9_\\- ，、：。；;\\]\\[\\/！（）【】*？“”()+:|\"%~<>——]+");public static Set<Word> separateWordAndReturnUnit(String text) {Segment segment = HanLP.newSegment().enableOffset(true);Set<Word> detectorUnits = new HashSet<>();Map<Integer, Word> detectorUnitMap = new HashMap<>();List<Term> terms = segment.seg(text);for (Term term : terms) {Matcher matcher = ignoreWords.matcher(term.word);if (!matcher.find() && term.word.length() > 1 && !term.word.contains("�")) {Integer hashCode = term.word.hashCode();Word detectorUnit = detectorUnitMap.get(hashCode);if (Objects.nonNull(detectorUnit)) {detectorUnit.setCount(detectorUnit.getCount() + 1);} else {detectorUnit = new Word();detectorUnit.setWord(term.word.trim());detectorUnit.setCount(1);detectorUnitMap.put(hashCode, detectorUnit);detectorUnits.add(detectorUnit);}}}return detectorUnits;}public static List<String> print2List(List<Word> tmp,int cnt){PriorityQueue<Word> words = new PriorityQueue<>();List<String> ans = new ArrayList<>();for (Word word : tmp) {words.add(word);}int count = 0;while (!words.isEmpty()) {Word word = words.poll();if (word.getCount()<50){ans.add(word.getWord() + " " + word.getCount());count ++;if (count >= cnt){break;}}}return ans;}
}

其中，separateWordAndReturnUnit是对文本进行分词和进行词频统计，其结构如下：

public class Word implements Comparable{private String word;private Integer count = 0;... ...@Overridepublic int compareTo(Object o) {if (this.count >= ((Word)o).count){return -1;}else {return 1;}}
}

print2List方法是为了对List进行排序后输出，直接使用自带的排序方法也可以，这里使用优先队列的目的是觉得可能大顶堆的时间复杂度比快排低一些，不过这里的数据量不大，优化过头了。

搜索实现

搜索实现，本质上就是利用两个HashMap以两种不同的维度去看待这些结果，分别以网站域名的角度和词语的角度看待。那么我在使用谷歌插件实现展示的时候，就可以做两个功能

在每个网站右上角点击插件的时候，读取到当前的网站的关键词情况，已经得到其相关网站
在插件选项里面做多关键词搜索

加载的代码比较生硬，就是简单的文件读取，字符串处理，这里就不贴了。不过这里有一个值得注意的点就是需要定期去重启加载，因为内容是变化的，爬虫一直在写入数据，而这里也需要进行反馈，告诉爬虫，哪些网站可以成为新的目标去爬取。

需要提供如下的方法提供给插件

通过域名获取网站关键词

@GetMapping("/api/v1/keywords")
@ResponseBody
public String getKeyWords(String domain) {try {Site site = demoService.stringSiteMap.get(DomainUtils.getDomainWithCompleteDomain(domain));if (Objects.nonNull(site)) {String keyWords = site.getKeywords();keyWords = keyWords.replace("[", "").replace("]", "");String[] keyWordss = keyWords.split(", ");StringBuffer ans = new StringBuffer();for (int i = 0; i < keyWordss.length; i++) {ans.append(keyWordss[i]).append("\n");}return ans.toString();}} catch (Exception e) {}return "该网站没有入库";
}

通过域名获取相似网站

@GetMapping("/api/v1/relations")
@ResponseBody
public String getRelationDomain(String domain) {try {Site site = demoService.stringSiteMap.get(DomainUtils.getDomainWithCompleteDomain(domain));String keyWords = site.getKeywords();keyWords = keyWords.replace("[", "").replace("]", "");String[] keyWordss = keyWords.split(", ");Set<String> tmp = new HashSet<>();int cnt = 0;for (int i = 0; i < keyWordss.length; i++) {String keyword = keyWordss[i];String key = keyword.split(" ")[0];if (IgnoreUtils.checkIgnore(key)) continue;cnt++;Set<String> x = demoService.siteMaps.get(key);if (Objects.nonNull(x)) {for (String y : x) {String yy = demoService.stringSiteMap.get(y).getKeywords();int l = yy.indexOf(key);if (l != -1) {String yyy = "";int flag = 0;for (int j = l; j < yy.length(); j++) {if (yy.charAt(j) == ',' || yy.charAt(j) == ']') {break;}if (flag == 1) {yyy = yyy + yy.charAt(j);}if (yy.charAt(j) == ' ') {flag = 1;}}if (Integer.parseInt(yyy) >= 20) {tmp.add(y + "----" + key + "----" + yyy);}} else {//                            Boolean titleContains = demoService.stringSiteMap.get(y).getTitle().contains(key);//                            if (titleContains) {//                                tmp.add(y + "----" + key + "----标题含有");//                            }}}}if (cnt >= 4) {break;}}StringBuffer ans = new StringBuffer();for (String s : tmp) {ans.append("<a href=\"http://" + s.split("----")[0] + "\">" + s + "</a><br>");}return ans.toString();} catch (Exception e) {//            e.printStackTrace();}return "该网站暂无相似网站";
}

通过多关键词获取相关的网站

@GetMapping("/api/v1/keyresult")
@ResponseBody
public String getKeyResult(String key, String key2, String key3,Integer page, Integer size) {Set<String> x = new HashSet<>(demoService.siteMaps.get(key));if (StringUtils.hasText(key2)) {key2 = key2.trim();if (StringUtils.hasText(key2)){Set<String> x2 = demoService.siteMaps.get(key2);x.retainAll(x2);}}if (StringUtils.hasText(key3)) {key3 = key3.trim();if (StringUtils.hasText(key3)){Set<String> x3 = demoService.siteMaps.get(key3);x.retainAll(x3);}}if (Objects.nonNull(x) && x.size() > 0) {Set<String> tmp = new HashSet<>();for (String y : x) {String yy = demoService.stringSiteMap.get(y).getKeywords();int l = yy.indexOf(key);if (l != -1) {String yyy = "";int flag = 0;for (int j = l; j < yy.length(); j++) {if (yy.charAt(j) == ',') {break;}if (flag == 1) {yyy = yyy + yy.charAt(j);}if (yy.charAt(j) == ' ') {flag = 1;}}tmp.add(y + "----" + demoService.stringSiteMap.get(y).getTitle() + "----" + key + "----" + yyy);} else {Boolean titleContains = demoService.stringSiteMap.get(y).getTitle().contains(key);if (titleContains) {tmp.add(y + "----" + demoService.stringSiteMap.get(y).getTitle() + "----" + key + "----标题含有");}}}StringBuffer ans = new StringBuffer();List<String> temp = new ArrayList<>(tmp);for (int i = (page - 1) * size; i < temp.size() && i < page * size; i++) {String s = temp.get(i);ans.append("<a href=\"http://" + s.split("----")[0] + "\" style=\"font-size: 20px\">"+ s.split("----")[1] + "</a> <p style=\"font-size: 15px\">" + s.split("----")[0] + "&nbsp;&nbsp;&nbsp;" + s.split("----")[3]+ "</p><hr color=\"silver\" size=1/>");}return ans.toString();}return "暂未收录";
}

告知爬虫作为爬取目标的网站

@GetMapping("/api/v1/demo")
@ResponseBody
public void demo(String key) {new Thread(new Runnable() {@Overridepublic void run() {HttpClientCrawl clientCrawl = new HttpClientCrawl(key);try {clientCrawl.doTask();} catch (Exception e) {e.printStackTrace();}finally {clientCrawl.oldDomains.clear();clientCrawl.siteMaps.clear();clientCrawl.onePageMap.clear();clientCrawl.ignoreSet.clear();}}}).start();
}

这是一个非正式的项目，所以写得比较简陋和随意，见谅。

展现

展现部分使用的是谷歌插件，最快捷的方式就是去github上下一个线程的插件来修修补补实现。跑成功了再去深入研究原理和奥秘。传送门

中间的实现过程就和写个普通的页面没啥区别，所以省略了。

最终的结果如下：

然后搜索部分如下：

在这里我想拜托各位朋友一件事：如果你们有收藏了好久的技术网站，可以分享在评论区，我目前陷入了抓取目标瓶颈。各行各业都可以。