使用suggest的原因，最主要就是相比于search速度快，In general, we need the autosuggest feature to satisfy two main requirements:
■ It must be fast; there are few things that are more annoying than a clunky type- ahead solution that cannot keep up with users as they type. The Suggester must be able to update the suggestions as the user types each character, so millisec- onds matter.
■ It should return ranked suggestions ordered by term frequency, as there is little benefit to suggesting rare terms that occur in only a few documents in your index, especially when the user has typed only a few characters.
lucene Suggest
其中分析了AnalyzingInfixSuggester类的相关源码，建立测试用例帮助理解整体过程。Suggest中手动根据其建立索引，在AnalyzingInfixSuggester类中，主要涉及到的属性有：
- text：搜索关键字域，用户输入的搜索关键字是在该域上进行匹配，使用TextField，并进行store；
- exacttext: 与text的唯一区别是使用StringField并且不进行Store；
- contexts: 该域也是用于过滤的，只不过它为比较次要的过滤条件域；
先根据InputIterator建立索引，示例中手写了一个InputIterator来进行，InputIterator接口决定了用于suggest搜索的索引数据来源，用于suggest搜索的索引的每个默认域的域值都需要用户自定义，建立的过程中涉及到下面几个概念：
- key: 用于搜索字域，用户输入的搜索关键字分词后的Term在这个域上进行匹配；
- content: 就是一个Term集合，用于contexts上的域进行TermQuery，在关键词的基础上再加个限制条件让返回的热词列表更符合要求，例如分类，分组等信息（给定限定范围，搜索衬衫，在男装范围内）；
- weight：指定一个数字类型(int, long)的域，搜索结果将按照该域进行降序排序；
- payload：存储一个额外信息，以ByteBuf存储（其实就是byte[]方式存入索引），当搜索返回后，可以通过LookupResult结果对象的payload属性返回并反序列化该值。
- allTermRequired: 搜索阶段，是否所有用户输入的关键词都需要全部匹配；
LookupResult包含了如下信息：
- key:用户输入的搜索关键字，再返回给你
- highlightKey：其实就是经过高亮的搜索关键字文本，假如你在搜索的时候设置了需要关键字高亮
- value：即InputInterator接口中weight方法的返回值，即返回的当前热词的权重值，排序就是根据这个值排的
- payload：就是InputInterator接口中payload方法中指定的payload信息，设计这个payload就是用来让你存一些任意你想存的信息，这就留给你们自己去发挥想象了。
- contexts：同理即InputInterator接口中contexts方法的返回值再原样返回给你。
Suggest索引的建立
从lucene suggester的源码中可以看出，suggest在内部存在一个SearchManager和一个IndexWriter，建立索引：
@Override
public void build(InputIterator iter) throws IOException {
if (searcherMgr != null) {
searcherMgr.close();
searcherMgr = null;
}
if (writer != null) {
writer.close();
writer = null;
}
boolean success = false;
try {
// First pass: build a temporary normal Lucene index,
// just indexing the suggestions as they iterate:
writer = new IndexWriter(dir,
getIndexWriterConfig(getGramAnalyzer(), IndexWriterConfig.OpenMode.CREATE));
//long t0 = System.nanoTime();
// TODO: use threads?
BytesRef text;
while ((text = iter.next()) != null) {
BytesRef payload;
if (iter.hasPayloads()) {
payload = iter.payload();
} else {
payload = null;
}
add(text, iter.contexts(), iter.weight(), payload);
}
public void add(BytesRef text, Set<BytesRef> contexts, long weight, BytesRef payload) throws IOException {
ensureOpen();
writer.addDocument(buildDocument(text, contexts, weight, payload));
}
关键是其中的buildDocument，可以看出是通过在其中建立内部的Document并存储来实现的
private Document buildDocument(BytesRef text, Set<BytesRef> contexts, long weight, BytesRef payload) throws IOException {
String textString = text.utf8ToString();
Document doc = new Document();
FieldType ft = getTextFieldType();
doc.add(new Field(TEXT_FIELD_NAME, textString, ft));
doc.add(new Field("textgrams", textString, ft));
doc.add(new StringField(EXACT_TEXT_FIELD_NAME, textString, Field.Store.NO));
doc.add(new BinaryDocValuesField(TEXT_FIELD_NAME, text));
doc.add(new NumericDocValuesField("weight", weight));
if (payload != null) {
doc.add(new BinaryDocValuesField("payloads", payload));
}
if (contexts != null) {
for(BytesRef context : contexts) {
doc.add(new StringField(CONTEXTS_FIELD_NAME, context, Field.Store.NO));
doc.add(new SortedSetDocValuesField(CONTEXTS_FIELD_NAME, context));
}
}
return doc;
}
Suggest查询
使用suggest查询是通过lookup方法来完成的，查询过程使用的SORT是根据weight字段来定义的：
private static final Sort SORT = new Sort(new SortField("weight", SortField.Type.LONG, true));
建立一个比较大的BooleanQuery，其连接方式取决于allTermsRequired属性：
if (allTermsRequired) {
occur = BooleanClause.Occur.MUST;
} else {
occur = BooleanClause.Occur.SHOULD;
}
使用QueryAnalyzer进行切词，在最终的query加入单个TermQuery，注意这些Term都是以text为关键词的，
try (TokenStream ts = queryAnalyzer.tokenStream("", new StringReader(key.toString()))) {
//long t0 = System.currentTimeMillis();
ts.reset();
final CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class);
final OffsetAttribute offsetAtt = ts.addAttribute(OffsetAttribute.class);
String lastToken = null;
query = new BooleanQuery.Builder();
int maxEndOffset = -1;
matchedTokens = new HashSet<>();
while (ts.incrementToken()) {
if (lastToken != null) {
matchedTokens.add(lastToken);
query.add(new TermQuery(new Term(TEXT_FIELD_NAME, lastToken)), occur);
}
lastToken = termAtt.toString();
if (lastToken != null) {
maxEndOffset = Math.max(maxEndOffset, offsetAtt.endOffset());
}
}
我们的示例中查询contexts的时候，需要将region的字符串转换为BytesRef数组。
Set<BytesRef> contexts = new HashSet<>();
contexts.add(new BytesRef(region.getBytes("UTF8")));
List<Lookup.LookupResult> results = suggester.lookup(name, contexts, 2, true, false);
至此，Suggest组件的基本流程梳理完成。
Solr Suggest组件
在Solr中是如何定义并使用suggest组件的，可以参考：https://cwiki.apache.org/confluence/display/solr/Suggester
首先，建立一个SearchComponent，用来设置提供suggest功能的组件
<searchComponent name="suggest" class="solr.SuggestComponent">
<lst name="suggester">
<str name="name">default</str>
<str name="lookupImpl">FuzzyLookupFactory</str>
<str name="dictionaryImpl">DocumentDictionaryFactory</str>
<str name="field">suggest</str>
<str name="weightField"></str>
<str name="suggestAnalyzerFieldType">string</str>
<str name="buildOnStartup">false</str>
</lst>
</searchComponent>
根据当前使用到的suggest组件，来绘制一份类图帮助理解整体过程：
LookupFactory可以根据当前使用到的SolrCore和配置项来创建一个Lucene Suggester（Lookup）组件，我们使用到的InputIterator是根据Directory类来提供的，这两个类均存在对应的工厂类。
我可以根据需要，选择不同的Suggester类，以及对应Directionary组合来共同完成suggest提示。
在requestHandler中也需要加入声明来进行/suggest，以相应http GET请求：
<requestHandler name="/suggest" class="org.apache.solr.handler.component.SearchHandler"
startup="lazy" >
<lst name="defaults">
<str name="suggest">true</str>
<str name="suggest.count">10</str>
</lst>
<arr name="components">
<str>suggest</str>
</arr>
</requestHandler>
为了验证各种类型的Suggester，我们可以在本地加入测试用例，开展测试相关工作。
在AnalyzingInfixSuggester中，InputIterator的使用方式如下：
writer = new IndexWriter(dir,
getIndexWriterConfig(getGramAnalyzer(), IndexWriterConfig.OpenMode.CREATE));
BytesRef text;
while ((text = iter.next()) != null) {
BytesRef payload;
if (iter.hasPayloads()) {
payload = iter.payload();
} else {
payload = null;
}
add(text, iter.contexts(), iter.weight(), payload);
}
FieldType中存在两种Analyzer，index和query，在fieldType中进行配置。type string和text的主要区别在于是否会进行analyze，string是不需要的，当做一整个单词，而text需要。
<fieldType name="text_general" class="solr.TextField" positionIncrementGap="100">
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="stopwords.txt" />
<!-- in this example, we will only use synonyms at query time
<filter class="solr.SynonymFilterFactory" synonyms="index_synonyms.txt" ignoreCase="true" expand="false"/>
-->
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
<analyzer type="query">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.StopFilterFactory" ignoreCase="true" words="stopwords.txt" />
<filter class="solr.SynonymFilterFactory" synonyms="synonyms.txt" ignoreCase="true" expand="true"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
</fieldType>
应用场景示例
假设我们有一张品牌关键字表，需要可以根据品牌的拼音搜索到对应的品牌名称，我们在solr中使用下面的db-data-import语句来进行导入操作：
<entity name="gt_brand" query="
select brand_id, brand_name, brand_pinyin, brand_name_second, sort from gt_goods_brand
" >
<field column="brand_id" name="id"/>
<field column="brand_name" name="brand_name"/>
<field column="brand_pinyin" name="brand_pinyin"/>
<field column="brand_name_second" name="brand_name_second"/>
<field column="sort" name="sort"/>
</entity>
其中brand_pinyin作为关键词，sort作为权重（weight），brand_name为搜索后真正显示的文本
Directory indexDir = FSDirectory.open(Paths.get("/Users/xxx/develop/tools/solr-5.5.0/server/solr/suggest/data/index"));
StandardAnalyzer analyzer = new StandardAnalyzer();
AnalyzingInfixSuggester suggester = new AnalyzingInfixSuggester(indexDir, analyzer);
DirectoryReader directoryReader = DirectoryReader.open(indexDir);
DocumentDictionary documentDictionary = new DocumentDictionary(directoryReader, "brand_pinyin", "sort", "brand_name");
suggester.build(documentDictionary.getEntryIterator());
List<Lookup.LookupResult> cha = suggester.lookup("nijiazhubao", 5, false, false);
for (Lookup.LookupResult lookupResult : cha) {
// System.out.println(lookupResult.key);
// System.out.println(lookupResult.value);
System.out.println(new String(lookupResult.payload.bytes, "UTF8"));
}
<str name="field">brand_pinyin</str>
<str name="weightField">sort</str>
<str name="payloadField">brand_name</str>
<str name="suggestAnalyzerFieldType">string</str>
<str name="buildOnStartup">true</str>
注意，处理的field一定需要有相应的analyzer(index, search)才能suggest出来：
视图去建立多个searchComponent，因为searchHandler可以包含多个searchComponent的名称，但并没有奏效：
<searchComponent name="suggest" class="solr.SuggestComponent">
<lst name="suggester">
<str name="name">default</str>
<str name="lookupImpl">FuzzyLookupFactory</str> <!-- org.apache.solr.spelling.suggest.fst -->
<str name="dictionaryImpl">DocumentDictionaryFactory</str> <!-- org.apache.solr.spelling.suggest.HighFrequencyDictionaryFactory -->
<str name="field">category_name</str>
<str name="weightField"></str>
<str name="suggestAnalyzerFieldType">string</str>
</lst>
</searchComponent>
<searchComponent name="suggest1" class="solr.SuggestComponent">
<lst name="suggester">
<str name="name">default</str>
<str name="lookupImpl">FuzzyLookupFactory</str> <!-- org.apache.solr.spelling.suggest.fst -->
<str name="dictionaryImpl">DocumentDictionaryFactory</str> <!-- org.apache.solr.spelling.suggest.HighFrequencyDictionaryFactory -->
<str name="field">brand_name</str>
<str name="weightField"></str>
<str name="suggestAnalyzerFieldType">string</str>
</lst>
</searchComponent>
<requestHandler name="/suggest" class="solr.SearchHandler" startup="lazy">
<lst name="defaults">
<str name="suggest">true</str>
<str name="suggest.count">5</str>
</lst>
<arr name="components">
<str>suggest</str>
<str>suggest1</str>
</arr>
</requestHandler>
出现问题：suggest: org.apache.solr.common.SolrException:org.apache.solr.common.SolrException: org.apache.lucene.store.LockObtainFailedException: Lock held by this virtual machine: /Users/xxx/develop/tools/solr-5.5.0/server/solr/suggest/data/analyzingInfixSuggesterIndexDir/write.lock
这其实也是indexPath导致的问题，当存在多个suggester配置的时候，需要将其索引对应的目录分开（至少使用AnalyzingInfixLookupFactory的时候是这样的，看源码可以设置为相对于core/data目录的相对路径：
String indexPath = params.get(INDEX_PATH) != null
? params.get(INDEX_PATH).toString()
: DEFAULT_INDEX_PATH;
if (new File(indexPath).isAbsolute() == false) {
indexPath = core.getDataDir() + File.separator + indexPath;
}
但我们加入<str name=“indexPath”>xxx</str>，虽然Exception已经消除，但是查询也没有起作用，只能采用另外的方案来处理，将多个字段copy至同一个字段，以便能够对单独的字段进行suggest提示，参考：http://stackoverflow.com/questions/7712606/solr-suggester-multiple-field-autocomplete
https://issues.apache.org/jira/browse/SOLR-5529，该ISSUE中也提供了解决方案，但是没有试验成功~