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1、不论是 聚集索引，还是非聚集索引，都是用B+树来实现的。我们在了解这两种索引之前，需要先了解B+树。如果你对B树不了解的话，建议参看以下几篇文章：BTree,B-Tree,B+Tree,B*Tree都是什么 http:/ B+ 树的结构图:B+ 树的特点: 所有关键字都出现在叶子结点的链表中（稠密索引），且链表中的关键字恰好是有序的； 不可能在非叶子结点命中； 非叶子结点相当于是叶子结点的索引（稀疏索引），叶子结点相当于是存储（关键字）数据的数据层；B+ 树中增加一个数据，或者删除一个数据，需要分多种情况处理，比较复杂，这里就不详述这个内容了。 聚集索引（Clustered Index） 聚集
2、索引的叶节点就是实际的数据页 在数据页中数据按照索引顺序存储 行的物理位置和行在索引中的位置是相同的 每个表只能有一个聚集索引 聚集索引的平均大小大约为表大小的5%左右下面是两副简单描述聚集索引的示意图： 在聚集索引中执行下面语句的的过程：select * from table where firstName = Ota 一个比较抽象点的聚集索引图示： 非聚集索引 （Unclustered Index） 非聚集索引的页，不是数据，而是指向数据页的页。 若未指定索引类型，则默认为非聚集索引 叶节点页的次序和表的物理存储次序不同 每个表最多可以有249个非聚集索引 在非聚集索引创建之前创建聚集索引
3、（否则会引发索引重建）在非聚集索引中执行下面语句的的过程：select * from employee where lname = Green一个比较抽象点的非聚集索引图示： 什么是 Bookmark Lookup 虽然SQL 2005 中已经不在提 Bookmark Lookup 了(换汤不换药)，但是我们的很多搜索都是用的这样的搜索过程，如下：先在非聚集中找，然后再在聚集索引中找。 在 http:/ 提供的一个例子中，就给我们演示了 Bookmark Lookup 比 Table Scan 慢的情况，例子的脚本如下：USE CREDITgo- These samples use the C
4、redit database. You can download and restore the- credit database from here:- http:/ NOTE: This is a SQL Server 2000 backup and MANY examples will work on - SQL Server 2000 in addition to SQL Server 2005.- (1) Create two tables which are copies of charge:- Create the HEAPSELECT * INTO ChargeHeap FRO
5、M Chargego- Create the CL TableSELECT * INTO ChargeCL FROM ChargegoCREATE CLUSTERED INDEX ChargeCL_CLInd ON ChargeCL (member_no, charge_no)go- (2) Add the same non-clustered indexes to BOTH of these tables:- Create the NC index on the HEAPCREATE INDEX ChargeHeap_NCInd ON ChargeHeap (Charge_no)go- Cr
6、eate the NC index on the CL TableCREATE INDEX ChargeCL_NCInd ON ChargeCL (Charge_no)go- (3) Begin to query these tables and see what kind of access and I/O returns- Get ready for a bit of analysis:SET STATISTICS IO ON- Turn Graphical Showplan ON (Ctrl+K)- First, a point query (also, see how a bookma
7、rk lookup looks in 2005)SELECT * FROM ChargeHeap WHERE Charge_no = 12345goSELECT * FROM ChargeCL WHERE Charge_no = 12345go- What if our query is less selective?- 1000 is .0625% of our data. (1,600,000 million rows)SELECT * FROM ChargeHeap WHERE Charge_no 1000goSELECT * FROM ChargeCL WHERE Charge_no
8、1000go- What if our query is less selective?- 16000 is 1% of our data. (1,600,000 million rows)SELECT * FROM ChargeHeap WHERE Charge_no 16000goSELECT * FROM ChargeCL WHERE Charge_no 16000go- (4) Whats the EXACT percentage where the bookmark lookup isnt worth it?- What happens here: Table Scan or Boo
9、kmark lookup?SELECT * FROM ChargeHeap WHERE Charge_no 4000goSELECT * FROM ChargeCL WHERE Charge_no 4000go- What happens here: Table Scan or Bookmark lookup?SELECT * FROM ChargeHeap WHERE Charge_no 3000goSELECT * FROM ChargeCL WHERE Charge_no 3000go- And - you can narrow it down by trying the middle
10、ground:- What happens here: Table Scan or Bookmark lookup?SELECT * FROM ChargeHeap WHERE Charge_no 3500goSELECT * FROM ChargeCL WHERE Charge_no 3500go- And again:SELECT * FROM ChargeHeap WHERE Charge_no 3250goSELECT * FROM ChargeCL WHERE Charge_no 3250go- And again:SELECT * FROM ChargeHeap WHERE Cha
11、rge_no 3375goSELECT * FROM ChargeCL WHERE Charge_no 3375go- Dont worry, I wont make you go through it all :)- For the Heap Table (in THIS case), the cutoff is: 0.21%SELECT * FROM ChargeHeap WHERE Charge_no 3383goSELECT * FROM ChargeHeap WHERE Charge_no 3384go- For the Clustered Table (in THIS case), the cut-off is: 0.21%SELECT * FROM ChargeCL WHERE Charge_no 3438SELECT * FROM ChargeCL WHERE Charge_no 3439go这个例子也就是 吴家震 在Teched 2007 上的那个演示例子。 小结： 这篇博客只是简单的用几个图表来介绍索引的实现方法：B+数， 聚集索引，非聚集索引，Bookmark Lookup 的信息而已。
