其中，Server是http服务器，聚合了Connector(http请求接收器)和请求处理器Hanlder，Server本身是一个handler和一个线程池，Connector使用线程池来调用handle方法。

/** Jetty HTTP Servlet Server.

* This class is the main class for the Jetty HTTP Servlet server.

* It aggregates Connectors (HTTP request receivers) and request Handlers.

* The server is itself a handler and a ThreadPool. Connectors use the ThreadPool methods

* to run jobs that will eventually call the handle method.

*/

其工作流程如下图所示

因其不是本文重点，故略去不述。

7.solr调用lucene过程

上篇文章已经论述，可对照上面的整体流程图进行解读，故略去不述

 8.lucene调用过程

从上图可以看出分两个阶段

8.1 创建Weight

   8.1.1 创建BooleanWeight

BooleanWeight.java

BooleanWeight(BooleanQuery query, IndexSearcher searcher,booleanneedsScores,floatboost)throws IOException {

super(query);

this.query = query;

this.needsScores = needsScores;

this.similarity = searcher.getSimilarity(needsScores);

weights =newArrayList ();

for (BooleanClause c : query) {

Query q=c.getQuery(); Weight w = searcher.createWeight(q, needsScores && c.isScoring(), boost);weights.add(w); } }

  8.1.2 同义词权重分析

SynonymQuery.java

@Override

publicWeight createWeight(IndexSearcher searcher,booleanneedsScores,floatboost)throws IOException {

if (needsScores) {

returnnewSynonymWeight(this, searcher, boost);

} else {

// if scores are not needed, let BooleanWeight deal with optimizing that case.BooleanQuery.Builder bq =new BooleanQuery.Builder();

for (Term term : terms) {

bq.add(new TermQuery(term), BooleanClause.Occur.SHOULD);

}

return searcher.rewrite(bq.build()).createWeight(searcher, needsScores, boost);} }

8.1.3 TermQuery.java

@Override

publicWeight createWeight(IndexSearcher searcher,booleanneedsScores,floatboost)throws IOException {

finalIndexReaderContext context = searcher.getTopReaderContext();

final TermContext termState;

if(perReaderTermState ==null|| perReaderTermState.wasBuiltFor(context) ==false) {

if (needsScores) {

// make TermQuery single-pass if we don't have a PRTS or if the context

// differs!termState = TermContext.build(context, term);

} else {

// do not compute the term state, this will help save seeks in the terms

// dict on segments that have a cache entry for this querytermState =null;

}

} else {

// PRTS was pre-build for this IStermState =this.perReaderTermState;

}

return new TermWeight(searcher, needsScores, boost, termState);}

调用TermWeight,计算CollectionStatistics和TermStatistics

publicTermWeight(IndexSearcher searcher,boolean needsScores,

floatboost, TermContext termStates)throws IOException {

super(TermQuery.this);

if(needsScores && termStates ==null) {

thrownewIllegalStateException("termStates are required when scores are needed");

}

this.needsScores = needsScores;

this.termStates = termStates;

this.similarity = searcher.getSimilarity(needsScores);

final CollectionStatistics collectionStats;

final TermStatistics termStats;

if (needsScores) {

termStates.setQuery(this.getQuery().getKeyword());

collectionStats = searcher.collectionStatistics(term.field());

termStats = searcher.termStatistics(term, termStates);

} else {

// we do not need the actual stats, use fake stats with docFreq=maxDoc and ttf=-1finalintmaxDoc = searcher.getIndexReader().maxDoc();

collectionStats =newCollectionStatistics(term.field(), maxDoc, -1, -1, -1);

termStats =newTermStatistics(term.bytes(), maxDoc, -1,term.bytes());

}

this.stats = similarity.computeWeight(boost, collectionStats, termStats);}

调用Similarity的computeWeight

BM25Similarity.java

@Override

publicfinalSimWeight computeWeight(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) {

Explanation idf = termStats.length == 1 ? idfExplain(collectionStats, termStats[0]) : idfExplain(collectionStats, termStats);

floatavgdl = avgFieldLength(collectionStats);

float[] oldCache =newfloat[256];

float[] cache =newfloat[256];

for(inti = 0; i < cache.length; i++) {

oldCache[i] = k1 * ((1 - b) + b * OLD_LENGTH_TABLE[i] / avgdl);

cache[i] = k1 * ((1 - b) + b * LENGTH_TABLE[i] / avgdl);

}

returnnew BM25Stats(collectionStats.field(), boost, idf, avgdl, oldCache, cache);

}