基于SolrCloud的内容搜索和热点推送

原文出自【听云技术博客】：http://blog.tingyun.com/web/article/detail/556

什么是热点

我认为热点有时效性和受众面

用户关注从低到高再到低的内容 。有公共热点和分类热点。例如医辽养老全民关注，科技汽车等只有特定的人群关注。

推送的条件

搜索频次达到一定数量

单位时间内搜索频次上升一定倍数。例如1000一周内达到100万，这样就达到推送标准了。

问题背景

自动提示功能是所有搜索应用的标准配置，目的主要有两个

1．提供更好的用户体验，降低输入的复杂度。

2．避免用户输入错误的词，将用户的输入引导向正确的词。弱化同义词处理的重要性

需求分析

• 海量数据的快速搜索

• 支持自动提示功能

• 支持自动纠错

• 在输入舌尖时，要自动提示舌尖上的中国，舌尖上的小吃等

• 支持拼音和缩写笔错拼例如shejian sjsdzg shenjianshang shejiashang 

• 查询记录，按照用户的搜索历史优先上排查询频率最高的。

• 分类热点进行推送

解决方案

索引

Solr的全文件检索有两步

1、创建索引

2、搜索索引

索引是如何创建的又是如何查找的？

Solr采用的一种策略是倒排索引，什么是倒排索引。Solr的倒排索引是如何实现的

大家参考以下三篇文章写的很全。

http://www.cnblogs.com/
forfuture1978/p/3940965.html

http://www.cnblogs.com/
forfuture1978/p/3944583.html

http://www.cnblogs.com/
forfuture1978/p/3945755.html

汉字转拼音

用户输入的关键字可能是汉字、数字，英文，拼音，特殊字符等等，由于需要实现拼音提示，我们需要把汉字转换成拼音，java中考虑使用pinyin4j组件实现转换。

拼音缩写提取

考虑到需要支持拼音缩写，汉字转换拼音的过程中，顺便提取出拼音缩写，如“shejian”,--->"sj”。

自动提示功能

方案一：

在solr中内置了智能提示功能，叫做Suggest模块，该模块可选择基于提示词文本做智能提示，还支持通过针对索引的某个字段建立索引词库做智能提示。使用说明http://wiki.apache.org/solr/Suggester

Suggest存在一些问题，它完全使用freq排序算法，返回的结果完全基于索引中出现的次数，没有兼容搜索的频率，但是我们必须要得到搜索的频率。

我们可以定制SuggestWordScoreComparator重写compare(SuggestWord first, SuggestWord second)方法来实现自己的排序算法。笔者使用了搜索频率和freq权重7:3的方式

方案二：

我们考虑专门为关键字建立一个索引collection，利用solr前缀查询实现。solr中的copyField能很好解决我们同时索引多个字段(汉字、pinyin, abbre)的需求，且field的multiValued属性设置为true时能解决同一个关键字的多音字组合问题。配置如下：

schema.xml:

<field name="keyword" type="string" indexed="true" stored="true" />
<field name="pinyin" type="string" indexed="true" stored="false" multiValued="true"/>
<field name="abbre" type="string" indexed="true" stored="false" multiValued="true"/>
<field name="kwfreq" type="int" indexed="true" stored="true" />
<field name="_version_" type="long" indexed="true" stored="true"/>
<field name="suggest" type="suggest_text" indexed="true" stored="false" multiValued="true" />

<!--multiValued表示字段是多值的-->

<uniqueKey>keyword</uniqueKey>
<defaultSearchField>suggest</defaultSearchField>

<copyField source="kw" dest="suggest" />
<copyField source="pinyin" dest="suggest" />
<copyField source="abbre" dest="suggest" />

<!--suggest_text-->

<fieldType name="suggest_text" class="solr.TextField" positionIncrementGap="100" autoGeneratePhraseQueries="true">
<analyzer type="index">
<tokenizer class="solr.KeywordTokenizerFactory" />
<filter class="solr.SynonymFilterFactory"
synonyms="synonyms.txt"
ignoreCase="true"
expand="true" />
<filter class="solr.StopFilterFactory"
ignoreCase="true"
words="stopwords.txt"
enablePositionIncrements="true" />
<filter class="solr.LowerCaseFilterFactory" />
<filter class="solr.KeywordMarkerFilterFactory" protected="protwords.txt" />
</analyzer>
<analyzer type="query">
<tokenizer class="solr.KeywordTokenizerFactory" />
<filter class="solr.StopFilterFactory"
ignoreCase="true"
words="stopwords.txt"
enablePositionIncrements="true" />
<filter class="solr.LowerCaseFilterFactory" />
<filter class="solr.KeywordMarkerFilterFactory" protected="protwords.txt" />
</analyzer>
</fieldType>

SpellCheckComponent拼写纠错

拼写检查的核心是求相似度

两个给定字符串S1和S2的Jaro Distance为：

• m是匹配的字符数；

• t是换位的数目。

两个分别来自S1和S2的字符如果相距不超过时，我们就认为这两个字符串是匹配的；而这些相互匹配的字符则决定了换位的数目t，简单来说就是不同顺序的匹配字符的数目的一半即为换位的数目t，举例来说，MARTHA与MARHTA的字符都是匹配的，但是这些匹配的字符中，T和H要换位才能把MARTHA变为MARHTA,那么T和H就是不同的顺序的匹配字符，t=2/2=1.

那么这两个字符串的Jaro Distance即为:

而Jaro-Winkler则给予了起始部分就相同的字符串更高的分数，他定义了一个前缀p，给予两个字符串，如果前缀部分有长度为  的部分相同，则Jaro-Winkler Distance为：

• dj是两个字符串的Jaro Distance

• 是前缀的相同的长度，但是规定最大为4

• p则是调整分数的常数，规定不能超过0.25，不然可能出现dw大于1的情况，Winkler将这个常数定义为0.1

这样，上面提及的MARTHA和MARHTA的Jaro-Winkler Distance为：

dw = 0.944 + (3 * 0.1(1 − 0.944)) = 0.961

以上资料来源于维基百科：

http://en.wikipedia.org/wiki/Jaro-Winkler_distance

solr内置了自动纠错的实现spellchecker

我们来分析一下spellchecker的源码

package org.apache.lucene.search.spell;

import java.io.Closeable;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.Iterator;
import java.util.List;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;
import org.apache.lucene.document.Field.Store;
import org.apache.lucene.document.FieldType;
import org.apache.lucene.document.StringField;
import org.apache.lucene.index.AtomicReader;
import org.apache.lucene.index.AtomicReaderContext;
import org.apache.lucene.index.DirectoryReader;
import org.apache.lucene.index.FieldInfo.IndexOptions;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.index.IndexWriterConfig.OpenMode;
import org.apache.lucene.index.Term;
import org.apache.lucene.index.Terms;
import org.apache.lucene.index.TermsEnum;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanClause.Occur;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.store.AlreadyClosedException;
import org.apache.lucene.store.Directory;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.BytesRefIterator;
import org.apache.lucene.util.Version;

public class SpellChecker implements Closeable {
/*
* DEFAULT_ACCURACY表示默认的最小分数
* SpellCheck会对字典里的每个词与用户输入的搜索关键字进行一个相似度打分
* 默认该值是0.5，相似度分值范围是0到1之间，数字越大表示越相似。
*/
public static final float DEFAULT_ACCURACY = 0.5F;
public static final String F_WORD = "word";
//拼写索引目录
Directory spellIndex;

//前缀ngram权重
private float bStart = 2.0F;
//后缀ngram的权重
private float bEnd = 1.0F;
//ngram算法：该算法基于这样一种假设，第n个词的出现只与前面N-1个词相关，而与其它任何词都不相关，整句的概率就是各个词出现概率的乘积。
//简单说ngram就是按定长来分割字符串成多个Term 例如 abcde 3ngram分会得到 abc bcd cde ，4ngram会得到abcd bcde
//索引的查询器对象
private IndexSearcher searcher;
private final Object searcherLock = new Object();
private final Object modifyCurrentIndexLock = new Object();
private volatile boolean closed = false;
private float accuracy = 0.5F;
private StringDistance sd;
private Comparator<SuggestWord> comparator;

public SpellChecker(Directory spellIndex, StringDistance sd) throws IOException {
this(spellIndex, sd, SuggestWordQueue.DEFAULT_COMPARATOR);
}

public SpellChecker(Directory spellIndex) throws IOException {
this(spellIndex, new LevensteinDistance());
}

public SpellChecker(Directory spellIndex, StringDistance sd, Comparator<SuggestWord> comparator)
throws IOException {
setSpellIndex(spellIndex);
setStringDistance(sd);
this.comparator = comparator;
}

public void setSpellIndex(Directory spellIndexDir) throws IOException {
synchronized (this.modifyCurrentIndexLock) {
ensureOpen();
if (!DirectoryReader.indexExists(spellIndexDir)) {
IndexWriter writer = new IndexWriter(spellIndexDir,
new IndexWriterConfig(Version.LUCENE_CURRENT, null));

writer.close();
}
swapSearcher(spellIndexDir);
}
}

public void setComparator(Comparator<SuggestWord> comparator) {
this.comparator = comparator;
}

public Comparator<SuggestWord> getComparator() {
return this.comparator;
}

public void setStringDistance(StringDistance sd) {
this.sd = sd;
}

public StringDistance getStringDistance() {
return this.sd;
}

public void setAccuracy(float acc) {
this.accuracy = acc;
}

public float getAccuracy() {
return this.accuracy;
}

public String[] suggestSimilar(String word, int numSug) throws IOException {
return suggestSimilar(word, numSug, null, null, SuggestMode.SUGGEST_WHEN_NOT_IN_INDEX);
}

public String[] suggestSimilar(String word, int numSug, float accuracy) throws IOException {
return suggestSimilar(word, numSug, null, null, SuggestMode.SUGGEST_WHEN_NOT_IN_INDEX, accuracy);
}

public String[] suggestSimilar(String word, int numSug, IndexReader ir, String field, SuggestMode suggestMode)
throws IOException {
return suggestSimilar(word, numSug, ir, field, suggestMode, this.accuracy);
}

/*
* 核心重点
*/
public String[] suggestSimilar(String word, int numSug, IndexReader ir, String field, SuggestMode suggestMode,
float accuracy) throws IOException {
IndexSearcher indexSearcher = obtainSearcher();
try {
if ((ir == null) || (field == null)) {
//SuggestMode.SUGGEST_ALWAYS永远建议
suggestMode = SuggestMode.SUGGEST_ALWAYS;
}
if (suggestMode == SuggestMode.SUGGEST_ALWAYS) {
ir = null;
field = null;
}
int lengthWord = word.length();

int freq = (ir != null) && (field != null) ? ir.docFreq(new Term(field, word)) : 0;
int goalFreq = suggestMode == SuggestMode.SUGGEST_MORE_POPULAR ? freq : 0;
// freq > 0表示用记搜索的关键词在SuggestMode.SUGGEST_WHEN_NOT_IN_INDEX为空，才提供建议
if ((suggestMode == SuggestMode.SUGGEST_WHEN_NOT_IN_INDEX) && (freq > 0)) {
return new String[] { word };
}
BooleanQuery query = new BooleanQuery();
for (int ng = getMin(lengthWord); ng <= getMax(lengthWord); ng++) {
String key = "gram" + ng;

String[] grams = formGrams(word, ng);
if (grams.length != 0) {
if (this.bStart > 0.0F) {
add(query, "start" + ng, grams[0], this.bStart);
}
if (this.bEnd > 0.0F) {
add(query, "end" + ng, grams[(grams.length - 1)], this.bEnd);
}
for (int i = 0; i < grams.length; i++) {
add(query, key, grams[i]);
}
}
}
int maxHits = 10 * numSug;

ScoreDoc[] hits = indexSearcher.search(query, null, maxHits).scoreDocs;

SuggestWordQueue sugQueue = new SuggestWordQueue(numSug, this.comparator);

int stop = Math.min(hits.length, maxHits);
SuggestWord sugWord = new SuggestWord();
for (int i = 0; i < stop; i++) {
sugWord.string = indexSearcher.doc(hits[i].doc).get("word");
if (!sugWord.string.equals(word)) {
sugWord.score = this.sd.getDistance(word, sugWord.string);
//求关键字和索引中的Term的相似度
if (sugWord.score >= accuracy) {
if ((ir != null) && (field != null)) {
sugWord.freq = ir.docFreq(new Term(field, sugWord.string));
//如果相似度小于设置的默认值则也不返回
if (((suggestMode == SuggestMode.SUGGEST_MORE_POPULAR) && (goalFreq > sugWord.freq))
|| (sugWord.freq < 1)) {
}
} else {
//条件符合那就把当前索引中的Term存入拼写建议队列中
//如果队列满了则把队列顶部的score(即相似度)缓存到accuracy即该值就表示了当前最小的相似度值，
//当队列满了，把相似度最小的移除
sugQueue.insertWithOverflow(sugWord);
if (sugQueue.size() == numSug) {
accuracy = ((SuggestWord) sugQueue.top()).score;
}
sugWord = new SuggestWord();
}
}
}
}
String[] list = new String[sugQueue.size()];
for (int i = sugQueue.size() - 1; i >= 0; i--) {
list[i] = ((SuggestWord) sugQueue.pop()).string;
}
return list;
} finally {
releaseSearcher(indexSearcher);
}
}

private static void add(BooleanQuery q, String name, String value, float boost) {
Query tq = new TermQuery(new Term(name, value));
tq.setBoost(boost);
q.add(new BooleanClause(tq, BooleanClause.Occur.SHOULD));
}

private static void add(BooleanQuery q, String name, String value) {
q.add(new BooleanClause(new TermQuery(new Term(name, value)), BooleanClause.Occur.SHOULD));
}

/*
* 根据ng的长度对text字符串进行 ngram分词
*/

private static String[] formGrams(String text, int ng) {
int len = text.length();
String[] res = new String[len - ng + 1];
for (int i = 0; i < len - ng + 1; i++) {
res[i] = text.substring(i, i + ng);
}
return res;
}

public void clearIndex() throws IOException {
synchronized (this.modifyCurrentIndexLock) {
ensureOpen();
Directory dir = this.spellIndex;
IndexWriter writer = new IndexWriter(dir,
new IndexWriterConfig(Version.LUCENE_CURRENT, null).setOpenMode(IndexWriterConfig.OpenMode.CREATE));

writer.close();
swapSearcher(dir);
}
}

public boolean exist(String word) throws IOException {
IndexSearcher indexSearcher = obtainSearcher();
try {
return indexSearcher.getIndexReader().docFreq(new Term("word", word)) > 0;
} finally {
releaseSearcher(indexSearcher);
}
}

/*
* 这个比较难理解
*/
public final void indexDictionary(Dictionary dict, IndexWriterConfig config, boolean fullMerge)
throws IOException
{
synchronized (this.modifyCurrentIndexLock)
{
ensureOpen();
Directory dir = this.spellIndex;
IndexWriter writer = new IndexWriter(dir, config);
IndexSearcher indexSearcher = obtainSearcher();
List<TermsEnum> termsEnums = new ArrayList();
//读取索引目录
IndexReader reader = this.searcher.getIndexReader();
if (reader.maxDoc() > 0) {
//加载word域上的所有Term存入TermEnum集合
for (AtomicReaderContext ctx : reader.leaves())
{
Terms terms = ctx.reader().terms("word");

if (terms != null) {
termsEnums.add(terms.iterator(null));
}
}
}
boolean isEmpty = termsEnums.isEmpty();
try
{
//加载字典文件
BytesRefIterator iter = dict.getEntryIterator();
BytesRef currentTerm;
//遍历字典文件里的每个词
while ((currentTerm = iter.next()) != null)
{
String word = currentTerm.utf8ToString();
int len = word.length();
if (len >= 3)
{
if (!isEmpty)
{
Iterator i$ = termsEnums.iterator();
for (;;)
{

if (!i$.hasNext()) {
break label235;
}
//遍历索引目录里word域上的每个Term
TermsEnum te = (TermsEnum)i$.next();

if (te.seekExact(currentTerm)) {
break;
}
}
}
label235:
//通过ngram分成多个Term
Document doc = createDocument(word, getMin(len), getMax(len));
//将字典文件里当前词写入索引
writer.addDocument(doc);
}
}
}
finally
{
releaseSearcher(indexSearcher);
}
if (fullMerge) {
writer.forceMerge(1);
}
writer.close();

swapSearcher(dir);
}
}

private static int getMin(int l) {
if (l > 5) {
return 3;
}
if (l == 5) {
return 2;
}
return 1;
}

private static int getMax(int l) {
if (l > 5) {
return 4;
}
if (l == 5) {
return 3;
}
return 2;
}

private static Document createDocument(String text, int ng1, int ng2) {
Document doc = new Document();

Field f = new StringField("word", text, Field.Store.YES);
doc.add(f);
addGram(text, doc, ng1, ng2);
return doc;
}

private static void addGram(String text, Document doc, int ng1, int ng2) {
int len = text.length();
for (int ng = ng1; ng <= ng2; ng++) {
String key = "gram" + ng;
String end = null;
for (int i = 0; i < len - ng + 1; i++) {
String gram = text.substring(i, i + ng);
FieldType ft = new FieldType(StringField.TYPE_NOT_STORED);
ft.setIndexOptions(FieldInfo.IndexOptions.DOCS_AND_FREQS);
Field ngramField = new Field(key, gram, ft);

doc.add(ngramField);
if (i == 0) {
Field startField = new StringField("start" + ng, gram, Field.Store.NO);
doc.add(startField);
}
end = gram;
}
if (end != null) {
Field endField = new StringField("end" + ng, end, Field.Store.NO);
doc.add(endField);
}
}
}

private IndexSearcher obtainSearcher() {
synchronized (this.searcherLock) {
ensureOpen();
this.searcher.getIndexReader().incRef();
return this.searcher;
}
}

private void releaseSearcher(IndexSearcher aSearcher) throws IOException {
aSearcher.getIndexReader().decRef();
}

private void ensureOpen() {
if (this.closed) {
throw new AlreadyClosedException("Spellchecker has been closed");
}
}

public void close() throws IOException {
synchronized (this.searcherLock) {
ensureOpen();
this.closed = true;
if (this.searcher != null) {
this.searcher.getIndexReader().close();
}
this.searcher = null;
}
}

private void swapSearcher(Directory dir) throws IOException {
IndexSearcher indexSearcher = createSearcher(dir);
synchronized (this.searcherLock) {
if (this.closed) {
indexSearcher.getIndexReader().close();
throw new AlreadyClosedException("Spellchecker has been closed");
}
if (this.searcher != null) {
this.searcher.getIndexReader().close();
}
this.searcher = indexSearcher;
this.spellIndex = dir;
}
}
IndexSearcher createSearcher(Directory dir) throws IOException {
return new IndexSearcher(DirectoryReader.open(dir));
}

boolean isClosed() {
return this.closed;
}
}

以上我们就建立的一个符合要求的检索功能，然后再从中筛选热点，根据用户画像分类推送就可以了。