An indexed database has two parts: a set of physical records, which are arranged in some arbitrary order, and a set of indexes which identify the sequence in which records should be read to yield a result sorted by some criterion. If there is no correlation between the physical arrangement and the index, then reading out all the records in order may require making lots of independent single-record read operations. Because a database may be able to read dozens of consecutive records in less time than it would take to read two non-consecutive records, performance may be improved if records which are consecutive in the index are also stored consecutively on disk. Specifying that an index is clustered will cause the database to make some effort (different databases differ as to how much) to arrange things so that groups of records which are consecutive in the index will be consecutive on disk.

For example, if one were to start with an empty non-clustered database and add 10,000 records in random sequence, the records would likely be added at the end in the order they were added. Reading out the database in order by the index would require 10,000 one-record reads. If one were to use a clustered database, however, the system might check when adding each record whether the previous record was stored by itself; if it found that to be the case, it might write that record with the new one at the end of the database. It could then look at the physical record before the slots where the moved records used to reside and see if the record that followed that was stored by itself. If it found that to be the case, it could move that record to that spot. Using this sort of approach would cause many records to be grouped together in pairs, thus potentially nearly doubling sequential read speed.

In reality, clustered databases use more sophisticated algorithms than this. A key thing to note, though, is that there is a tradeoff between the time required to update the database and the time required to read it sequentially. Maintaining a clustered database will significantly increase the amount of work required to add, remove, or update records in any way that would affect the sorting sequence. If the database will be read sequentially much more often than it will be updated, clustering can be a big win. If it will be updated often but seldom read out in sequence, clustering can be a big performance drain, especially if the sequence in which items are added to the database is independent of their sort order with regard to the clustered index.

聚集索引物理地存储在表上。这意味着它们是最快的，并且每个表只能有一个聚集索引。

非聚集索引单独存储，您可以拥有任意数量的索引。

最好的选择是在最常用的唯一列上设置聚集索引，通常是PK。在表中应该始终有一个精心选择的聚集索引，除非有一个非常令人信服的理由——想不出一个，但是，嘿，可能有——不这样做。

聚类索引本质上是索引列中数据的排序副本。

聚集索引的主要优点是，当查询(seek)在索引中定位数据时，不需要额外的IO来检索该数据。

维护聚集索引的开销，特别是在频繁更新的表中，可能会导致性能较差，因此最好创建非聚集索引。

优点:

聚集索引适用于范围(例如:select * from my_table where my_key在@min和@max之间)

在某些情况下，如果使用orderderby语句，DBMS将不需要做排序工作。

缺点:

聚集索引可能会减慢插入速度，因为如果新键不是按顺序排列的，那么在放入记录时必须修改记录的物理布局。

聚集索引

一个表只能有一个聚集索引。 通常在主键上进行。 聚集索引的叶节点包含数据页。

非聚簇索引

一个表只能有249个非聚集索引(直到sql version 2005的后续版本支持多达999个非聚集索引)。 通常在任意键上。 非聚集索引的叶节点不包含数据页。相反，叶节点包含索引行。

聚类基本上意味着数据在表中的物理顺序。这就是为什么每个表只能有一个。

非聚集意味着它“只是”一个逻辑顺序。