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 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：