MySQL · 引擎介绍 · Sphinx源码剖析(三)

在本节中我会介绍Sphinx在构建索引之前做的一些事情，主要是从mysql拉取数据保存，然后分词排序保存到内存等等一系列的操作。下面是几个相关指令

    sql_query = \SELECT id, group_id, UNIX_TIMESTAMP(date_added) AS date_added, \title, content \FROM documentssql_query_range = SELECT MIN(id),MAX(id) FROM documentssql_range_step = 1000

其中sql_query是sphinx每次从mysql拉取数据的sql，而sql_query_range则是取得需要从mysql拉取的数据条目，而sql_rang_step则是表示每次从mysql拉取多少数据。sql_rang_range执行分两种情况，第一种是第一次拉取数据的时候，第二种是当当前的range数据读取完毕之后。

首先来看CSphSource_SQL::NextDocument函数，这个函数的主要作用是从mysql读取数据然后切分保存，首先我们来看读取数据这一部分，这里步骤很简单，就是执行对应的sql，然后判断当前range的数据是否读取完毕，如果读取完毕则继续执行sql_query_rang(RunQueryStep)。这里要注意的是，sphinx读取数据是一条一条的读取然后执行的.

	do{// try to get next rowbool bGotRow = SqlFetchRow ();// when the party's over...while ( !bGotRow ){// is that an error?if ( SqlIsError() ){sError.SetSprintf ( "sql_fetch_row: %s", SqlError() );m_tDocInfo.m_uDocID = 1; // 0 means legal eofreturn NULL;}// maybe we can do next step yet?if ( !RunQueryStep ( m_tParams.m_sQuery.cstr(), sError ) ){// if there's a message, there's an error// otherwise, we're just overif ( !sError.IsEmpty() ){m_tDocInfo.m_uDocID = 1; // 0 means legal eofreturn NULL;}} else{// step went fine; try to fetchbGotRow = SqlFetchRow ();continue;}SqlDismissResult ();// ok, we're overARRAY_FOREACH ( i, m_tParams.m_dQueryPost ){if ( !SqlQuery ( m_tParams.m_dQueryPost[i].cstr() ) ){sphWarn ( "sql_query_post[%d]: error=%s, query=%s",i, SqlError(), m_tParams.m_dQueryPost[i].cstr() );break;}SqlDismissResult ();}m_tDocInfo.m_uDocID = 0; // 0 means legal eofreturn NULL;}// get him!m_tDocInfo.m_uDocID = VerifyID ( sphToDocid ( SqlColumn(0) ) );m_uMaxFetchedID = Max ( m_uMaxFetchedID, m_tDocInfo.m_uDocID );} while ( !m_tDocInfo.m_uDocID );

上面的代码我们可以看到一个很关键的字段m_uDocID,这个字段表示当前doc的id(因此数据库的表设计必须有这个id字段).

读取完毕数据之后，开始处理读取的数据，这里会按照字段来切分，主要是将对应的数据库字段保存到索引fielld

	// split columns into fields and attrsfor ( int i=0; i<m_iPlainFieldsLength; i++ ){// get that field#if USE_ZLIBif ( m_dUnpack[i]!=SPH_UNPACK_NONE ){DWORD uUnpackedLen = 0;m_dFields[i] = (BYTE*) SqlUnpackColumn ( i, uUnpackedLen, m_dUnpack[i] );m_dFieldLengths[i] = (int)uUnpackedLen;continue;}#endifm_dFields[i] = (BYTE*) SqlColumn ( m_tSchema.m_dFields[i].m_iIndex );m_dFieldLengths[i] = SqlColumnLength ( m_tSchema.m_dFields[i].m_iIndex );}

紧接着就是处理attribute，后续我们会详细介绍attribute，现在我们只需要知道它是一个类似二级索引的东西(不进入全文索引).

		switch ( tAttr.m_eAttrType ){case SPH_ATTR_STRING:case SPH_ATTR_JSON:// memorize string, fixup NULLsm_dStrAttrs[i] = SqlColumn ( tAttr.m_iIndex );if ( !m_dStrAttrs[i].cstr() )m_dStrAttrs[i] = "";m_tDocInfo.SetAttr ( tAttr.m_tLocator, 0 );break;
..................................default:// just store as uint by defaultm_tDocInfo.SetAttr ( tAttr.m_tLocator, sphToDword ( SqlColumn ( tAttr.m_iIndex ) ) ); // FIXME? report conversion errors maybe?break;}

然后我们来看Sphinx如何处理得到的数据,核心代码在 RtIndex_t::AddDocument中，这个函数主要是用来分词(IterateHits中)然后保存数据到对应的数据结构,而核心的数据结构是RtAccum_t，也就是最终sphinx在写索引到文件之前，会将数据保存到这个数据结构，这里要注意一般来说sphinx会保存很多数据，然后最后一次性提交给索引引擎来处理.而索引引擎中处理的就是这个数据结构.因此最终会调用RtAccum_t::AddDocument.

这里需要注意两个地方，第一个是m_dAccum这个域，这个域是一个vector，而这个vector里面保存了CSphWordHit这个结构，我们来看这个结构的定义

    struct CSphWordHit{SphDocID_t		m_uDocID;		///< document IDSphWordID_t		m_uWordID;		///< word ID in current dictionaryHitpos_t		m_uWordPos;		///< word position in current document};

可以看到其实这个结构也就是保存了对应分词的信息.

然后我们来看核心代码，这里主要是便利刚才从mysql得到的数据，去重然后保存数据.

	int iHits = 0;if ( pHits && pHits->Length() ){CSphWordHit tLastHit;tLastHit.m_uDocID = 0;tLastHit.m_uWordID = 0;tLastHit.m_uWordPos = 0;iHits = pHits->Length();m_dAccum.Reserve ( m_dAccum.GetLength()+iHits );for ( const CSphWordHit * pHit = pHits->First(); pHit<=pHits->Last(); pHit++ ){// ignore duplicate hitsif ( pHit->m_uDocID==tLastHit.m_uDocID && pHit->m_uWordID==tLastHit.m_uWordID && pHit->m_uWordPos==tLastHit.m_uWordPos )continue;// update field lengthsif ( pFieldLens && HITMAN::GetField ( pHit->m_uWordPos )!=HITMAN::GetField ( tLastHit.m_uWordPos ) )pFieldLens [ HITMAN::GetField ( tLastHit.m_uWordPos ) ] = HITMAN::GetPos ( tLastHit.m_uWordPos );// accumulatem_dAccum.Add ( *pHit );tLastHit = *pHit;}if ( pFieldLens )pFieldLens [ HITMAN::GetField ( tLastHit.m_uWordPos ) ] = HITMAN::GetPos ( tLastHit.m_uWordPos );}

做完上面这些事情之后，就需要提交数据给索引处理引擎了，这里核心的代码都是在RtIndex_t::Commit中.

这个函数主要做两个事情，第一个提取出前面我们构造好的RtAccum_t,然后对于所有的doc进行排序，创建segment，也就是对应的索引块(ram chunk)，最后调用CommitReplayable来提交ram chunk到磁盘.

其实可以这么理解，保存在内存中的索引也就是segment,然后当内存的大小到达限制后就会刷新内存中的索引到磁盘.

    void RtIndex_t::Commit ( int * pDeleted, ISphRtAccum * pAccExt ){assert ( g_bRTChangesAllowed );MEMORY ( MEM_INDEX_RT );RtAccum_t * pAcc = AcquireAccum ( NULL, pAccExt, true );if ( !pAcc )return;...................................pAcc->Sort();RtSegment_t * pNewSeg = pAcc->CreateSegment ( m_tSchema.GetRowSize(), m_iWordsCheckpoint );.............................................// now on to the stuff that needs locking and recoveryCommitReplayable ( pNewSeg, pAcc->m_dAccumKlist, pDeleted );......................................}

然后我们来看RtAccum_t::CreateSegment函数，这个函数用来将分词好的数据保存到ram chunk，这里需要注意两个数据结构分别是RtDoc_t和RtWord_t,这两个数据结构分别表示doc信息和分词信息.

结构很简单，后面的注释都很详细

    template < typename DOCID = SphDocID_t >struct RtDoc_T{DOCID						m_uDocID;	///< my document idDWORD						m_uDocFields;	///< fields maskDWORD						m_uHits;	///< hit countDWORD						m_uHit;		///< either index into segment hits, or the only hit itself (if hit count is 1)};template < typename WORDID=SphWordID_t >struct RtWord_T{union{WORDID					m_uWordID;	///< my keyword idconst BYTE *			m_sWord;};DWORD						m_uDocs;	///< document count (for stats and/or BM25)DWORD						m_uHits;	///< hit count (for stats and/or BM25)DWORD						m_uDoc;		///< index into segment docs};

然后来看代码，首先是初始化对应的写结构,可以看到都是会写到我们创建好的segment中.

	RtDocWriter_t tOutDoc ( pSeg );RtWordWriter_t tOutWord ( pSeg, m_bKeywordDict, iWordsCheckpoint );RtHitWriter_t tOutHit ( pSeg );

然后就是写数据了，这里主要是做一个聚合，也就是将相同的keyword对应的属性聚合起来.

	ARRAY_FOREACH ( i, m_dAccum ){.......................................// new keyword; flush current keywordif ( tHit.m_uWordID!=tWord.m_uWordID ){tOutDoc.ZipRestart ();if ( tWord.m_uWordID ){if ( m_bKeywordDict ){const BYTE * pPackedWord = pPacketBase + tWord.m_uWordID;assert ( pPackedWord[0] && pPackedWord[0]+1<m_pDictRt->GetPackedLen() );tWord.m_sWord = pPackedWord;}tOutWord.ZipWord ( tWord );}tWord.m_uWordID = tHit.m_uWordID;tWord.m_uDocs = 0;tWord.m_uHits = 0;tWord.m_uDoc = tOutDoc.ZipDocPtr();uPrevHit = EMPTY_HIT;}..................}

这次就分析到这里，下次我们将会分析最核心的部分就是Sphinx如何刷新数据到磁盘.