前一段时间使用repair命令修复线上的数据库,发现数据库中碎片巨大,占用200多G的数据在repair之后只有50多G,然后就研究了一下Mongodb是如何利用已经删除了的空间的。
分析下源码(源码版本2.2.2,新版本可能随时更新):
Mongodb在执行删除(文档)操作时,并不会进行物理删除,而是将他们放入每个命名空间维护的删除列表里。
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//pdfile.cpp delete()
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/* add to the free list */
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{
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....
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d->addDeletedRec((DeletedRecord*)todelete, dl);
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}
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}
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//namespace_detail.cpp addDeletedRec(..)
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....
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else {
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int b = bucket(d->lengthWithHeaders());
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DiskLoc& list = deletedList[b];
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DiskLoc oldHead = list;
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getDur().writingDiskLoc(list) = dloc;
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d->nextDeleted() = oldHead;
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}
上面的deletedList就是维护的删除数据列表。
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//namespace_detail.h
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/* deleted lists -- linked lists of deleted records -- are placed in 'buckets' of various sizes so you can look for a deleterecord about the right size.
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*/
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const int Buckets = 19;
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const int MaxBucket = 18;
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DiskLoc deletedList[Buckets];
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int bucketSizes[] = { 32, 64, 128, 256, 0x200, 0x400, 0x800, 0x1000, 0x2000, 0x4000, 0x8000, 0x10000, 0x20000, 0x40000, 0x80000, 0x100000, 0x200000,0x400000, 0x800000};
可以看到,deleteList数组实际保存的是DiskLoc,长度19,跟bucketSizes[]的长度一致。DiskLoc就是文档在磁盘上的位置,并且有后指针,可以指向下一个DiskLoc,从而组成一个列表。deleteList中实际就保存了19个列表,每个列表就是已经被删除文档地址,且这些文档都在bucketSizes所规定的的范围内。描述不太清楚,上图吧:
插入文档时,Mongodb会先计算需要开辟多大的空间,然后去找deleteList中的位置,如果deleteList中不满足,那么才会去开辟新的空间。
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//pdfile.cpp
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int lenWHdr = d->getRecordAllocationSize( len + Record::HeaderSize );
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DiskLoc loc;
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if( addID || tableToIndex || d->isCapped() ) {
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// if need id, we don't do the early indexing. this is not the common case so that is sort of ok
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earlyIndex = false;
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loc = allocateSpaceForANewRecord(ns, d, lenWHdr, god);
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}
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else {
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loc = d->allocWillBeAt(ns, lenWHdr);
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if( loc.isNull() ) {
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// need to get a new extent so we have to do the true alloc now (not common case)
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earlyIndex = false;
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loc = allocateSpaceForANewRecord(ns, d, lenWHdr, god);
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}
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}
我们暂时不讨论cappedCollection(固定大小的集合),只看常规集合
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/* predetermine location of the next alloc without actually doing it.
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if cannot predetermine returns null (so still call alloc() then)
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*/
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DiskLoc NamespaceDetails::allocWillBeAt(const char *ns, int lenToAlloc) {
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if ( ! isCapped() ) {
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lenToAlloc = (lenToAlloc + 3) & 0xfffffffc;
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return __stdAlloc(lenToAlloc, true);
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}
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return DiskLoc();
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}
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/* for non-capped collections.
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@param peekOnly just look up where and don't reserve
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returned item is out of the deleted list upon return
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*/
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DiskLoc NamespaceDetails::__stdAlloc(int len, bool peekOnly) {
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DiskLoc *prev;
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DiskLoc *bestprev = 0;
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DiskLoc bestmatch;
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int bestmatchlen = 0x7fffffff;
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int b = bucket(len);
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DiskLoc cur = deletedList[b];
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prev = &deletedList[b];
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int extra = 5; // look for a better fit, a little.
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int chain = 0;
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while ( 1 ) {
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{
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int a = cur.a();
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if ( a < -1 || a >= 100000 ) {
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problem() << "~~ Assertion - cur out of range in _alloc() " <<
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cur.toString() <<
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" a:" << a << " b:" << b << " chain:" << chain << '\n';
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logContext();
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if ( cur == *prev )
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prev->Null();
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cur.Null();
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}
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}
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if ( cur.isNull() ) {
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// move to next bucket. if we were doing "extra", just break
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if ( bestmatchlen < 0x7fffffff )
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break;
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b++;
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if ( b > MaxBucket ) {
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// out of space. alloc a new extent.
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return DiskLoc();
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}
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cur = deletedList[b];
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prev = &deletedList[b];
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continue;
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}
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DeletedRecord *r = cur.drec();
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if ( r->lengthWithHeaders() >= len &&
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r->lengthWithHeaders() < bestmatchlen ) {
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bestmatchlen = r->lengthWithHeaders();
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bestmatch = cur;
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bestprev = prev;
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}
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if ( bestmatchlen < 0x7fffffff && --extra <= 0 )
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break;
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if ( ++chain > 30 && b < MaxBucket ) {
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// too slow, force move to next bucket to grab a big chunk
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//b++;
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chain = 0;
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cur.Null();
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}
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else {
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/*this defensive check only made sense for the mmap storage engine:
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if ( r->nextDeleted.getOfs() == 0 ) {
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problem() << "~~ Assertion - bad nextDeleted " << r->nextDeleted.toString()
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<<
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" b:" << b << " chain:" << chain << ", fixing.\n";
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r->nextDeleted.Null();
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}*/
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cur = r->nextDeleted();
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prev = &r->nextDeleted();
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}
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}
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/* unlink ourself from the deleted list */
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if( !peekOnly ) {
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DeletedRecord *bmr = bestmatch.drec();
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*getDur().writing(bestprev) = bmr->nextDeleted();
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bmr->nextDeleted().writing().setInvalid(); // defensive.
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verify(bmr->extentOfs() < bestmatch.getOfs());
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}
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return bestmatch;
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}
上面这段就是Mongodb在deleteList中寻找合适插入位置的算法.
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int b = bucket(len);
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DiskLoc cur = deletedList[b];
这是最初始的寻找位置的算法,解释一下,bucket函数就是寻找跟len(插入文档的大小)最接近的bucketSize,比如说len=68,那么应该在64-128这个范围内,在deleteList中应该是第3个列表,那么b=2,cur就是返回的第三个列表的起始位置。如果找到了,那么就是用列表中的值,如果找不到,就继续往下一个列表中寻找。找到之后,将找到的位置从deleteList中删除,返回。
如果所有的列表都遍历完成还是找不到,那么mongodb就会去硬盘上真的开辟一段空间。我们上面说过Mongodb会先计算需要开辟的空间大小,有两种方式
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//namespace_detail.cpp
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int NamespaceDetails::getRecordAllocationSize( int minRecordSize ) {
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if ( _paddingFactor == 0 ) {
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warning() << "implicit updgrade of paddingFactor of very old collection" << endl;
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setPaddingFactor(1.0);
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}
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verify( _paddingFactor >= 1 );
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if ( isUserFlagSet( Flag_UsePowerOf2Sizes ) ) {
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int allocationSize = bucketSizes[ bucket( minRecordSize ) ];
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if ( allocationSize < minRecordSize ) {
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// if we get here, it means we're allocating more than 8mb
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// the highest bucket is 8mb, so the above code will never return more than 8mb for allocationSize
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// if this happens, we are going to round up to the nearest megabyte
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fassert( 16439, bucket( minRecordSize ) == MaxBucket );
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allocationSize = 1 + ( minRecordSize | ( ( 1 << 20 ) - 1 ) );
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}
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return allocationSize;
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}
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return static_cast<int>(minRecordSize * _paddingFactor);
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}
第一种padding方式,Mongodb会计算一个_paddingFactor,开辟doclen*(1+paddingFactor)大小,以防止update引起的长度变大,需要移动数据。第二种方式usePowerOf2Size,Mongodb为文档开辟的空间总是2的倍数,如之前我们说过的,文档大小68字节,那么就会开辟128字节,bucket函数就是从bucketSize数组中寻找最接近文档长度的那个2的次方值。
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//namespace_detail.cpp
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int bucketSizes[] = {
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32, 64, 128, 256, 0x200, 0x400, 0x800, 0x1000, 0x2000, 0x4000,
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0x8000, 0x10000, 0x20000, 0x40000, 0x80000, 0x100000, 0x200000,
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0x400000, 0x800000
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};
这两种方式各有优劣,padding方式会为文档开辟更合适的大小,而且paddingFactor比较小,一般为0.01-0.09,不会浪费空间,文档更新小的话也不会移动文档位置。但是当大量更新和删除的时候,这种方式重复利用空间的能力就比较小,因为在deleteList中,不太容易找到合适的已删除文档,而且一旦更新就会又移动位置,磁盘重复利用率低,增长快,碎片多。相比之下,usePowerOf2Size方式,Mongodb每次都会开辟比文档大的多的空间,使用空间变多,但是更新和删除的容错率就会比较高,因为在deleteList列表中更容易找到合适的删除文档(每个列表中的文档大小都是相同的固定的),更新的时候也不会大量移动位置,磁盘重复利用率高,增长慢。
所以,在读操作较多的应用中,可以使用padding方式,也是mongodb默认的方式,在写操作较多的应用中,可以使用usePowerOf2Size方式。
usePowerOf2Size是在创建集合的时候指定的
db.runCommand( {collMod: "products", usePowerOf2Sizes : true }) //enable
db.runCommand( {collMod: "products", usePowerOf2Sizes : false })//disable
usePowerOf2Size只影响新插入和更新引起的分配空间大小,对之前的文档不起作用。
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