聚集索引意味着您告诉数据库在磁盘上存储实际上彼此接近的接近值。这样做的好处是可以快速扫描/检索某些聚集索引值范围内的记录。

例如，你有两个表，Customer和Order:

Customer
----------
ID
Name
Address

Order
----------
ID
CustomerID
Price

如果希望快速检索某个特定客户的所有订单，则可能希望在订单表的“CustomerID”列上创建聚集索引。这样，具有相同CustomerID的记录将在物理上彼此靠近地存储在磁盘上(集群)，从而加快了它们的检索速度。

附注:CustomerID上的索引显然不是唯一的，因此您要么需要添加第二个字段来“唯一”索引，要么让数据库为您处理，但这是另一回事。

Regarding multiple indexes. You can have only one clustered index per table because this defines how the data is physically arranged. If you wish an analogy, imagine a big room with many tables in it. You can either put these tables to form several rows or pull them all together to form a big conference table, but not both ways at the same time. A table can have other indexes, they will then point to the entries in the clustered index which in its turn will finally say where to find the actual data.

我知道这是一个非常古老的问题，但我想我可以提供一个类比来帮助说明上面的答案。

聚集索引

If you walk into a public library, you will find that the books are all arranged in a particular order (most likely the Dewey Decimal System, or DDS). This corresponds to the "clustered index" of the books. If the DDS# for the book you want was 005.7565 F736s, you would start by locating the row of bookshelves that is labeled 001-099 or something like that. (This endcap sign at the end of the stack corresponds to an "intermediate node" in the index.) Eventually you would drill down to the specific shelf labelled 005.7450 - 005.7600, then you would scan until you found the book with the specified DDS#, and at that point you have found your book.

非聚簇索引

But if you didn't come into the library with the DDS# of your book memorized, then you would need a second index to assist you. In the olden days you would find at the front of the library a wonderful bureau of drawers known as the "Card Catalog". In it were thousands of 3x5 cards -- one for each book, sorted in alphabetical order (by title, perhaps). This corresponds to the "non-clustered index". These card catalogs were organized in a hierarchical structure, so that each drawer would be labeled with the range of cards it contained (Ka - Kl, for example; i.e., the "intermediate node"). Once again, you would drill in until you found your book, but in this case, once you have found it (i.e, the "leaf node"), you don't have the book itself, but just a card with an index number (the DDS#) with which you could find the actual book in the clustered index.

当然，没有什么能阻止图书管理员复印所有的卡片，并将它们按不同的顺序分类在一个单独的卡片目录中。(通常至少有两个这样的目录:一个按作者姓名排序，另一个按标题排序。)原则上，您可以拥有任意数量的这些“非聚集”索引。

下面是聚类索引和非聚类索引的一些特征:

聚集索引

聚集索引是唯一标识SQL表中的行的索引。 每个表只能有一个聚集索引。 可以创建包含多个列的聚集索引。例如:create Index index_name(col1, col2, col.....)。 默认情况下，具有主键的列已经具有聚集索引。

非聚簇索引

非聚集索引类似于简单索引。它们只是用于快速检索数据。不一定有唯一的数据。

聚集索引

聚集索引根据表或视图中的键值对数据行进行排序和存储。这些是包含在索引定义中的列。每个表只能有一个聚集索引，因为数据行本身只能按一种顺序排序。

只有当表中包含聚集索引时，表中的数据行才会按排序顺序存储。当一个表具有聚集索引时，这个表称为聚集表。如果表没有聚集索引，则其数据行存储在称为堆的无序结构中。

非聚集

Nonclustered indexes have a structure separate from the data rows. A nonclustered index contains the nonclustered index key values and each key value entry has a pointer to the data row that contains the key value. The pointer from an index row in a nonclustered index to a data row is called a row locator. The structure of the row locator depends on whether the data pages are stored in a heap or a clustered table. For a heap, a row locator is a pointer to the row. For a clustered table, the row locator is the clustered index key.

可以将非键列添加到非聚集索引的叶级，以绕过现有的索引键限制，并执行完全覆盖的索引查询。有关更多信息，请参见创建包含列的索引。有关索引键限制的详细信息，请参见SQL Server最大容量规格。

参考:https://learn.microsoft.com/en-us/sql/relational-databases/indexes/clustered-and-nonclustered-indexes-described

聚集索引

聚类索引决定了表中DATA的物理顺序。因此，一个表只有一个聚集索引(主键/组合键)。

“字典”不需要任何其他索引，它已经根据单词索引

非聚集索引

非聚集索引类似于Book中的索引。数据存储在一个地方。索引存储在另一个位置，并且索引具有指向存储位置的指针。这有助于快速搜索数据。因此，一个表有超过1个非聚集索引。

“生物书”在开头有一个单独的索引指向章节的位置，在“结尾”有另一个索引指向常用单词的位置

聚集索引——聚集索引定义了数据在表中物理存储的顺序。表数据只能按某种方式排序，因此，每个表只能有一个聚集索引。在SQL Server中，主键约束自动在特定列上创建聚集索引。

Non-Clustered Index - A non-clustered index doesn’t sort the physical data inside the table. In fact, a non-clustered index is stored at one place and table data is stored in another place. This is similar to a textbook where the book content is located in one place and the index is located in another. This allows for more than one non-clustered index per table.It is important to mention here that inside the table the data will be sorted by a clustered index. However, inside the non-clustered index data is stored in the specified order. The index contains column values on which the index is created and the address of the record that the column value belongs to.When a query is issued against a column on which the index is created, the database will first go to the index and look for the address of the corresponding row in the table. It will then go to that row address and fetch other column values. It is due to this additional step that non-clustered indexes are slower than clustered indexes

聚类索引和非聚类索引的区别

每个表只能有一个聚集索引。但是，你可以 在一个表上创建多个非聚集索引。 聚集索引只对表进行排序。因此，他们不消费 额外的存储。非聚集索引存储在单独的位置 从实际表中占用更多的存储空间。 聚集索引比非聚集索引快，因为它们 不要涉及任何额外的查找步骤。

有关更多信息，请参阅本文。