我猜，大家最大的疑问就是：不是已经有那么多Query实现类吗，为什么又设计一个FunctionQuery,它的设计初衷是什么，或者说它是用来解决什么问题的？我们还是来看看源码里是怎么解释FunctionQuery的：
意思就是基于ValueSource来返回每个文档的评分即valueSourceScore,那ValueSource又是怎么东东？接着看看ValueSource源码里的注释说明：
ValueSource是用来根据指定的IndexReader来实例化FunctionValues的，那FunctionValues又是啥？
从接口中定义的函数可以了解到，FunctionValues提供了根据文档ID获取各种类型的DocValuesField域的值的方法，那这些接口返回的域值用来干嘛的，翻看FunctionQuery源码，你会发现：
从上面几张图，我们会发现，FunctionQuery构造的时候需要提供一个ValueSource,然后在FunctionQuery的内部类AllScorer中通过valueSource实例化了FunctionValues,然后在计算FunctionQuery评分的时候通过FunctionValues获取DocValuesField的域值，域值和FunctionQuery的权重值相乘得到FunctionQuery的评分。
float score = qWeight * vals.floatVal(doc);
那这里ValueSource又起什么作用呢，为什么不直接让FunctionQuery来构建FunctionValues，而是要引入一个中间角色ValueSource呢？
因为FunctionQuery应该线程安全的，即允许多次查询共用同一个FunctionQuery实例，如果让FunctionValues直接依赖FunctionQuery,那可能会导致某个线程通过FunctionValues得到的docValuesField域值被另一个线程修改了，所以引入了一个ValuesSource，让每个FunctionQuery对应一个ValueSource,再让ValueSource去生成FunctionValues,因为docValuesField域值的正确性会影响到最后的评分。另外出于缓存原因，因为每次通过FunctionValues去加载docValuesField的域值，其实还是通过IndexReader去读取的，这就意味着有磁盘IO行为，磁盘IO次数可是程序性能杀手哦，所以设计CachingDoubleValueSource来包装ValueSource.不过CachingDoubleValueSource貌似还处在捐献模块，不知道下个版本是否会考虑为ValueSource添加Cache功能。
ValueSource构造很简单:
public DoubleFieldSource(String field) { super(field); } 你只需要提供一个域的名称即可，不过要注意，这里的域必须是DocValuesField,不能是普通的StringField,TextField,IntField,FloatField,LongField。
那FunctionQuery可以用来解决什么问题？举个例子：比如你索引了N件商品，你希望通过某个关键字搜索时，出来的结果优先按最近上架的商品显示，再按商品和搜索关键字匹配度高低降序显示，即你希望最近上架的优先靠前显示，评分高的靠前显示。
下面是一个FunctionQuery使用示例，模拟类似这样的场景：
书籍的出版日期越久远，其权重因子会按天数一天天衰减，从而实现让新书自动靠前显示
import java.io.IOException; import java.util.Map; import org.apache.lucene.index.DocValues; import org.apache.lucene.index.LeafReaderContext; import org.apache.lucene.index.NumericDocValues; import org.apache.lucene.queries.function.FunctionValues; import org.apache.lucene.queries.function.valuesource.FieldCacheSource; import com.yida.framework.lucene5.util.score.ScoreUtils; /** * 自定义ValueSource[计算日期递减时的权重因子，日期越近权重值越高] * @author Lanxiaowei * */ public class DateDampingValueSouce extends FieldCacheSource { //当前时间 private static long now; public DateDampingValueSouce(String field) { super(field); //初始化当前时间 now = System.currentTimeMillis(); } /** * 这里Map里存的是IndexSeacher,context.get("searcher");获取 */ @Override public FunctionValues getValues(Map context, LeafReaderContext leafReaderContext) throws IOException { final NumericDocValues numericDocValues = DocValues.getNumeric(leafReaderContext.reader(), field); return new FunctionValues() { @Override public float floatVal(int doc) { return ScoreUtils.getNewsScoreFactor(now, numericDocValues,doc); } @Override public int intVal(int doc) { return (int) ScoreUtils.getNewsScoreFactor(now, numericDocValues,doc); } @Override public String toString(int doc) { return description() + '=' + intVal(doc); } }; } } import org.apache.lucene.index.NumericDocValues; import com.yida.framework.lucene5.util.Constans; /** * 计算衰减因子[按天为单位] * @author Lanxiaowei * */ public class ScoreUtils { /**存储衰减因子-按天为单位*/ private static float[] daysDampingFactor = new float[120]; /**降级阀值*/ private static float demoteboost = 0.9f; static { daysDampingFactor[0] = 1; //第一周时权重降级处理 for (int i = 1; i < 7; i++) { daysDampingFactor[i] = daysDampingFactor[i - 1] * demoteboost; } //第二周 for (int i = 7; i < 31; i++) { daysDampingFactor[i] = daysDampingFactor[i / 7 * 7 - 1] * demoteboost; } //第三周以后 for (int i = 31; i < daysDampingFactor.length; i++) { daysDampingFactor[i] = daysDampingFactor[i / 31 * 31 - 1] * demoteboost; } } //根据相差天数获取当前的权重衰减因子 private static float dayDamping(int delta) { float factor = delta < daysDampingFactor.length ? daysDampingFactor[delta] : daysDampingFactor[daysDampingFactor.length - 1]; System.out.println("delta:" + delta + "-->" + "factor:" + factor); return factor; } public static float getNewsScoreFactor(long now, NumericDocValues numericDocValues, int docId) { long time = numericDocValues.get(docId); float factor = 1; int day = (int) (time / Constans.DAY_MILLIS); int nowDay = (int) (now / Constans.DAY_MILLIS); System.out.println(day + ":" + nowDay + ":" + (nowDay - day)); // 如果提供的日期比当前日期小，则计算相差天数，传入dayDamping计算日期衰减因子 if (day < nowDay) { factor = dayDamping(nowDay - day); } else if (day > nowDay) { //如果提供的日期比当前日期还大即提供的是未来的日期 factor = Float.MIN_VALUE; } else if (now - time <= Constans.HALF_HOUR_MILLIS && now >= time) { //如果两者是同一天且提供的日期是过去半小时之内的，则权重因子乘以2 factor = 2; } return factor; } public static float getNewsScoreFactor(long now, long time) { float factor = 1; int day = (int) (time / Constans.DAY_MILLIS); int nowDay = (int) (now / Constans.DAY_MILLIS); // 如果提供的日期比当前日期小，则计算相差天数，传入dayDamping计算日期衰减因子 if (day < nowDay) { factor = dayDamping(nowDay - day); } else if (day > nowDay) { //如果提供的日期比当前日期还大即提供的是未来的日期 factor = Float.MIN_VALUE; } else if (now - time <= Constans.HALF_HOUR_MILLIS && now >= time) { //如果两者是同一天且提供的日期是过去半小时之内的，则权重因子乘以2 factor = 2; } return factor; } public static float getNewsScoreFactor(long time) { long now = System.currentTimeMillis(); return getNewsScoreFactor(now, time); } } import java.io.IOException; import java.nio.file.Paths; import java.text.DateFormat; import java.text.ParseException; import java.text.SimpleDateFormat; import java.util.Date; import org.apache.lucene.analysis.Analyzer; import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.lucene.document.Document; import org.apache.lucene.document.Field; import org.apache.lucene.document.Field.Store; import org.apache.lucene.document.LongField; import org.apache.lucene.document.NumericDocValuesField; import org.apache.lucene.document.TextField; import org.apache.lucene.index.DirectoryReader; 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.queries.CustomScoreQuery; import org.apache.lucene.queries.function.FunctionQuery; import org.apache.lucene.search.IndexSearcher; import org.apache.lucene.search.ScoreDoc; import org.apache.lucene.search.Sort; import org.apache.lucene.search.SortField; import org.apache.lucene.search.TermQuery; import org.apache.lucene.search.TopDocs; import org.apache.lucene.store.Directory; import org.apache.lucene.store.FSDirectory; /** * FunctionQuery测试 * @author Lanxiaowei * */ public class FunctionQueryTest { private static final DateFormat formate = new SimpleDateFormat("yyyy-MM-dd"); public static void main(String[] args) throws Exception { String indexDir = "C:/lucenedir-functionquery"; Directory directory = FSDirectory.open(Paths.get(indexDir)); //System.out.println(0.001953125f * 100000000 * 0.001953125f / 100000000); //创建测试索引[注意：只用创建一次，第二次运行前请注释掉这行代码] //createIndex(directory); IndexReader reader = DirectoryReader.open(directory); IndexSearcher searcher = new IndexSearcher(reader); //创建一个普通的TermQuery TermQuery termQuery = new TermQuery(new Term("title", "solr")); //根据可以计算日期衰减因子的自定义ValueSource来创建FunctionQuery FunctionQuery functionQuery = new FunctionQuery(new DateDampingValueSouce("publishDate")); //自定义评分查询[CustomScoreQuery将普通Query和FunctionQuery组合在一起，至于两者的Query评分按什么算法计算得到最后得分，由用户自己去重写来干预评分] //默认实现是把普通查询评分和FunctionQuery高级查询评分相乘求积得到最终得分，你可以自己重写默认的实现 CustomScoreQuery customScoreQuery = new CustomScoreQuery(termQuery, functionQuery); //创建排序器[按评分降序排序] Sort sort = new Sort(new SortField[] {SortField.FIELD_SCORE}); TopDocs topDocs = searcher.search(customScoreQuery, null, Integer.MAX_VALUE, sort,true,false); ScoreDoc[] docs = topDocs.scoreDocs; for (ScoreDoc scoreDoc : docs) { int docID = scoreDoc.doc; Document document = searcher.doc(docID); String title = document.get("title"); String publishDateString = document.get("publishDate"); System.out.println(publishDateString); long publishMills = Long.valueOf(publishDateString); Date date = new Date(publishMills); publishDateString = formate.format(date); float score = scoreDoc.score; System.out.println(docID + " " + title + " " + publishDateString + " " + score); } reader.close(); directory.close(); } /** * 创建Document对象 * @param title 书名 * @param publishDateString 书籍出版日期 * @return * @throws ParseException */ public static Document createDocument(String title,String publishDateString) throws ParseException { Date publishDate = formate.parse(publishDateString); Document doc = new Document(); doc.add(new TextField("title",title,Field.Store.YES)); doc.add(new LongField("publishDate", publishDate.getTime(),Store.YES)); doc.add(new NumericDocValuesField("publishDate", publishDate.getTime())); return doc; } //创建测试索引 public static void createIndex(Directory directory) throws ParseException, IOException { Analyzer analyzer = new StandardAnalyzer(); IndexWriterConfig indexWriterConfig = new IndexWriterConfig(analyzer); indexWriterConfig.setOpenMode(OpenMode.CREATE_OR_APPEND); IndexWriter writer = new IndexWriter(directory, indexWriterConfig); //创建测试索引 Document doc1 = createDocument("Lucene in action 2th edition", "2010-05-05"); Document doc2 = createDocument("Lucene Progamming", "2008-07-11"); Document doc3 = createDocument("Lucene User Guide", "2014-11-24"); Document doc4 = createDocument("Lucene5 Cookbook", "2015-01-09"); Document doc5 = createDocument("Apache Lucene API 5.0.0", "2015-02-25"); Document doc6 = createDocument("Apache Solr 4 Cookbook", "2013-10-22"); Document doc7 = createDocument("Administrating Solr", "2015-01-20"); Document doc8 = createDocument("Apache Solr Essentials", "2013-08-16"); Document doc9 = createDocument("Apache Solr High Performance", "2014-06-28"); Document doc10 = createDocument("Apache Solr API 5.0.0", "2015-03-02"); writer.addDocument(doc1); writer.addDocument(doc2); writer.addDocument(doc3); writer.addDocument(doc4); writer.addDocument(doc5); writer.addDocument(doc6); writer.addDocument(doc7); writer.addDocument(doc8); writer.addDocument(doc9); writer.addDocument(doc10); writer.close(); } } 运行测试结果如图：
