本文選自《Pro SQL Server Internals》

 

作者: Dmitri Korotkevitch

 

出版社: Apress

 

出版年: 2016-12-29

 

頁數: 804

 

作者簡介：Dmitri Korotkevitchis是微軟SQL Server MVP和微軟認證大師。作為應用程式和資料庫開發人員、資料庫管理員和資料庫架構師，他具有多年使用SQL Server的經驗。他專門從事OLTP系統在高負載下的設計、開發和效能調優。Dmitri經常在各種Microsoft和SQL PASS活動上發言，他為世界各地的客戶提供

SQL Server培訓。

 

原文連結：http://www.doc88.com/p-4042504089228.html

 

CHAPTER 2 ■ TABLES AND INDEXES: INTERNAL STRUCTURE AND ACCESS METHODS

第2章■表格和索引：內部結構和訪問方法

 

 

 Figure 2-4. Forwarding pointers and I/O: Reading data when forwarding pointers exist

圖2-4。轉發指標和I / O：存在轉發指標時讀取資料

As you can see, the large number of forwarding pointers leads to extra I/O operations and significantly reduces the performance of the queries accessing the data. Companion materials for this book include the script that demonstrates this problem in a large scope with a table that includes a large amount of data.

如您所見，大量轉發指標會導致額外的I / O操作，並顯著降低訪問資料的查詢的效能。本書的伴隨材料包括在大範圍內使用包含大量資料的表來演示此問題的指令碼。

When the size of the forwarded row is reduced by another update and the data page with forwarding pointer has enough space to accommodate the updated version of the row, SQL Server may move it back to its original data page and remove the forwarding pointer row. Nevertheless, the only reliable way to get rid of all of the forwarding pointers is by rebuilding the heap table. You can do that by using an ALTER TABLE REBUILD statement.

當通過另一次更新減少轉發行的大小並且具有轉發指標的資料頁具有足夠的空間來容納該行的更新版本時，SQL Server可以將其移回其原始資料頁並移除轉發指標行。然而，擺脫所有轉發指標的唯一可靠方法是重建堆表。您可以使用ALTER TABLE REBUILD語句執行此操作。

   Heap tables can be useful in staging environments, where you want to import a large amount of data into the system as fast as possible. Inserting data into heap tables can often be faster than inserting it into tables with clustered indexes. Nevertheless, during a regular workload, tables with clustered indexes usually outperform heap tables, which have suboptimal space control and extra I/O operations introduced by forwarding pointers.

堆疊表在暫存環境中非常有用，您希望儘可能快地將大量資料匯入系統。將資料插入堆表通常比將其插入具有聚簇索引的表更快。然而，在常規工作負載期間，具有聚簇索引的表通常優於堆表，堆表具有次優的空間控制和轉發指標引入的額外I / O操作。

 Clustered Indexes 

聚集索引

 

   A clustered index dictates the physical order of the data in a table, which is sorted according to the clustered index key. The table can have only one clustered index defined.

 聚簇索引指示表中資料的物理順序，該表根據聚簇索引鍵進行排序。 該表只能定義一個聚簇索引。

   Let’s assume that you want to create a clustered index on the heap table with the data. As a first step, which is shown in Figure  2-5 , SQL Server creates another copy of the data that is then sorted based on the value of the clustered key. The data pages are linked in a double-linked list where every page contains pointers to the next and previous pages in the chain. This list is called the leaf level of the index, and it contains the actual table data.

       假設您要在堆表上使用資料建立聚簇索引。 作為第一步，如圖2-5所示，SQL Server會建立另一個數據副本，然後根據群集金鑰的值對其進行排序。 資料頁連結在雙鏈表中，其中每個頁面都包含指向鏈中下一頁和上一頁的指標。 此列表稱為索引的葉級，它包含實際的表資料。

 

 

 

 

 

 Figure 2-5. Clustered index structure: Leaf level

圖2-5。 聚集的索引結構：葉級

■ Note The sort order on the page is controlled by a slot array. Actual data on the page is unsorted.

注意頁面上的排序順序由插槽陣列控制。 頁面上的實際資料未排序。

When the leaf level consists of multiple pages, SQL Server starts to build an intermediate level of the index, as shown in Figure  2-6 .

當葉級別由多個頁面組成時，SQL Server開始構建索引的中間級別，如圖2-6所示。

 

 

 Figure 2-6. Clustered index structure: Intermediate and leaf levels

圖2-6。 聚集的索引結構：中級和葉級

 The intermediate level stores one row per leaf-level page. It stores two pieces of information: the physical address and the minimum value of the index key from the page it references. The only exception is the very first row on the first page, where SQL Server stores NULL rather than the minimum index key value. With such optimization, SQL Server does not need to update non-leaf-level rows when you insert the row with the lowest key value in the table.

中間級別為每個葉級頁面儲存一行。 它儲存兩條資訊：它引用的頁面中的索引鍵的實體地址和最小值。 唯一的例外是第一頁上的第一行，其中SQL Server儲存NULL而不是最小索引鍵值。 通過這種優化，當您在表中插入具有最低鍵值的行時，SQL Server不需要更新非葉級行。

 The pages on the intermediate levels are also linked to the double-linked list. SQL Server adds more and more intermediate levels until there is a level that includes just the single page. This level is called the root level , and it becomes the entry point to the index, as shown in Figure  2-7 .

  中間級別的頁面也連結到雙鏈表。 SQL Server添加了越來越多的中間級別，直到只包含單個頁面的級別。 此級別稱為根級別，它將成為索引的入口點，如圖2-7所示。

 

 

Figure 2-7. Clustered index structure: Root level

 As you can see, the index always has one leaf level, one root level, and zero or more intermediate levels. The only exception is when the index data fits into a single page. In that case, SQL Server does not create the separate root-level page, and the index consists of just the single leaf-level page.

如您所見，索引始終具有一個葉級別，一個根級別和零個或多箇中間級別。唯一的例外是索引資料適合單個頁面。在這種情況下，SQL Server不會建立單獨的根級頁面，索引只包含單個葉級頁面。

 The number of levels in the index largely depends on the row and index key sizes. For example, the index on the 4-byte integer column will require 13 bytes per row on the intermediate and root levels. Those 13 bytes consist of a 2-byte slot-array entry, a 4-byte index-key value, a 6-byte page pointer, and a 1-byte row overhead, which is adequate because the index key does not contain variable-length and NULL columns.

索引中的級別數很大程度上取決於行和索引鍵的大小。例如，4位元組整數列上的索引在中間和根級別上每行需要13個位元組。這13個位元組由一個2位元組的插槽陣列條目，一個4位元組的索引鍵值，一個6位元組的頁面指標和一個1位元組的行開銷組成，這是足夠的，因為索引鍵不包含變數 - length和NULL列。

 As a result, you can accommodate 8,060 bytes / 13 bytes per row = 620 rows per page. This means that, with the one intermediate level, you can store information about up to 620 * 620 = 384,400 leaf-level pages. If your data row size is 200 bytes, you can store 40 rows per leaf-level page and up to 15,376,000 rows in the index with just three levels. Adding another intermediate level to the index would essentially cover all possible integer values.  

 因此，每行可容納8,060位元組/ 13位元組=每頁620行。這意味著，使用一箇中間級別，您可以儲存最多620 * 620 = 384,400個葉級頁面的資訊。如果資料行大小為200位元組，則每個葉級頁面可儲存40行，索引中最多可儲存15,376,000行，只有三個級別。向索引新增另一箇中間級別將基本上涵蓋所有可能的整數值。

 Note In real life, index fragmentation would reduce those numbers. We will talk about index fragmentation in Chapter 6 .

注意在現實生活中，索引碎片會減少這些數字。我們將在第6章討論索引碎片。

 There are three different ways in which SQL Server can read data from the index. The first one is by an ordered scan. Let’s assume that we want to run the SELECT Name FROM dbo.Customers ORDER BY CustomerId query. The data on the leaf level of the index is already sorted based on the CustomerId column value. As a result, SQL Server can scan the leaf level of the index from the first to the last page and return the rows in the order in which they were stored.

SQL Server可以通過三種不同的方式從索引中讀取資料。第一個是有序掃描。假設我們想要執行SELECT Name FROM dbo.Customers ORDER BY CustomerId查詢。索引的葉級別上的資料已根據CustomerId列值進行排序。因此，SQL Server可以從第一頁到最後一頁掃描索引的葉級，並按儲存順序返回行。

 SQL Server starts with the root page of the index and reads the first row from there. That row references the intermediate page with the minimum key value from the table. SQL Server reads that page and repeats the process until it finds the first page on the leaf level. Then, SQL Server starts to read rows one by one, moving through the linked list of the pages until all rows have been read. Figure  2-8 illustrates this process.  

 SQL Server從索引的根頁開始，從那裡讀取第一行。該行使用表中的最小鍵值引用中間頁面。 SQL Server讀取該頁面並重復該過程，直到找到葉級別的第一頁。然後，SQL Server開始逐個讀取行，遍歷頁面的連結列表，直到讀取了所有行。圖2-8說明了這個過程。

 

 

Figure 2-8. Ordered index scan

圖2-8。 有序索引掃描

 

 

 The execution plan for the preceding query shows the Clustered Index Scan operator with the Orderedproperty set to true, as shown in Figure  2-9 .

上述查詢的執行計劃顯示了“叢集索引掃描”操作符，其中Orderedproperty設定為true，如圖2-9所示。

 Figure 2-9. Ordered index scan execution plan

圖2-9。 有序索引掃描執行計劃

It is worth mentioning that the order by clause is not required for an ordered scan to be triggered. An ordered scan just means that SQL Server reads the data based on the order of the index key.

值得一提的是，觸發有序掃描不需要order by子句。 有序掃描只意味著SQL Server根據索引鍵的順序讀取資料。

 SQL Server can navigate through indexes in both directions, forward and backward. However, there is one important aspect that you must keep in mind: SQL Server does not use parallelism during backward index scans.

  SQL Server可以向前和向後兩個方向導航索引。 但是，您必須記住一個重要方面：SQL Server在向後索引掃描期間不使用並行性。

 

 Tip： You can check scan direction by examining the INDEX SCAN or INDEX SEEK operator properties in the execution plan. Keep in mind, however, that Management Studio does not display these properties in the graphical representation of the execution plan. You need to open the Properties window to see it by selecting the operator in the execution plan and choosing the View/Properties Window menu item or by pressing the F4 key

提示：您可以通過檢查執行計劃中的INDEX SCAN或INDEX SEEK運算子屬性來檢查掃描方向。 但請記住，Management Studio不會在執行計劃的圖形表示中顯示這些屬性。 您需要開啟“屬性”視窗以通過在執行計劃中選擇運算子並選擇“檢視/屬性視窗”選單項或按F4鍵來檢視它。

 The Enterprise Edition of SQL Server has an optimization feature called merry-go-round scan that allows multiple tasks to share the same index scan. Let’s assume that you have session S1, which is scanning the index. At some point in the middle of the scan, another session, S2, runs a query that needs to scan the same index. With a merry-go-round scan, S2 joins S1 at its current scan location. SQL Server reads each page only once, passing rows to both sessions.

SQL Server企業版具有稱為旋轉木馬掃描的優化功能，允許多個任務共享相同的索引掃描。假設您有會話S1，它正在掃描索引。在掃描中間的某個時刻，另一個會話S2執行需要掃描相同索引的查詢。通過旋轉木馬掃描，S2將S1連線到當前掃描位置。 SQL Server只讀取每個頁面一次，將行傳遞給兩個會話。

 When the S1 scan reaches the end of the index, S2 starts scanning data from the beginning of the index until the point where the S2 scan started. A merry-go-round scan is another example of why you cannot rely on the order of the index keys and why you should always specify an ORDER BY clause when it matters.

當S1掃描到達索引的末尾時，S2開始從索引的開頭掃描資料，直到S2掃描開始的點。旋轉木馬掃描是另一個例子，說明為什麼不能依賴索引鍵的順序以及為什麼在重要時應始終指定ORDER BY子句。

 The next access method after the ordered scan is called an allocation order scan . SQL Server accesses the table data through the IAM pages, similar to how it does so with heap tables. The SELECT Name FROM dbo.Customers WITH (NOLOCK) query and Figure  2-10 illustrate this method. Figure  2-11 shows the query execution plan.  

 有序掃描之後的下一個訪問方法稱為分配順序掃描。 SQL Server通過IAM頁面訪問表資料，類似於使用堆表的方式。 SELECT名稱FROM dbo.Customers WITH（NOLOCK）查詢和圖2-10說明了這種方法。圖2-11顯示了查詢執行計劃。

 

 

 Figure 2-10. Allocation order scan

圖2-10。 分配訂單掃描

 

 

 Figure 2-11. Allocation order scan execution plan

圖2-11。 分配訂單掃描執行計劃

Unfortunately, it is not easy to detect when SQL Server uses an allocation order scan. Even though the Ordered property in the execution plan shows false , it indicates that SQL Server does not care whether the rows were read in the order of the index key, not that an allocation order scan was used.

不幸的是，當SQL Server使用分配順序掃描時，檢測起來並不容易。 即使執行計劃中的Ordered屬性顯示為false，也表示SQL Server不關心是否按索引鍵的順序讀取行，而不是使用分配順序掃描。

   An allocation order scan can be faster for scanning large tables, although it has a higher startup cost. SQL Server does not use this access method when the table is small. Another important consideration is data consistency. SQL Server does not use forwarding pointers in tables that have a clustered index, and an allocation order scan can produce inconsistent results. Rows can be skipped or read multiple times due to the data movement caused by page splits. As a result, SQL Server usually avoids using allocation order scans unless it reads the data in READ UNCOMMITTED or SERIALIZABLE transaction-isolation levels.

儘管掃描大型表的啟動成本較高，但分配順序掃描可以更快地掃描大型表。 當表很小時，SQL Server不使用此訪問方法。 另一個重要的考慮是資料一致性 SQL Server不在具有聚簇索引的表中使用轉發指標，並且分配順序掃描可能會產生不一致的結果。 由於頁面拆分導致的資料移動，可以多次跳過或讀取行。 因此，SQL Server通常會避免使用分配順序掃描，除非它讀取READ UNCOMMITTED或SERIALIZABLE事務隔離級別中的資料。

 Note We will talk about page splits and fragmentation in Chapter 6 , “Index Fragmentation,” and discuss locking and data consistency in Part III, “Locking, Blocking, and Concurrency.”

注意我們將在第6章“索引碎片”中討論頁面拆分和碎片，並討論第三部分“鎖定，阻塞和併發”中的鎖定和資料一致性。

 The last index access method is called index seek . The SELECT Name FROM dbo.Customers WHERE CustomerId BETWEEN 4 AND 7 query and Figure  2-12 illustrate the operation.

最後一個索引訪問方法稱為索引查詢。 SELECT名稱來自dbo.Customers WHERE CustomerId BETWEEN 4和7查詢以及圖2-12說明了操作。

 

 

Figure 2-12. Index seek

圖2-12。 索引尋求

In order to read the range of rows from the table, SQL Server needs to find the row with the minimum value of the key from the range, which is 4. SQL Server starts with the root page, where the second row references the page with the minimum key value of 350. It is greater than the key value that we are looking for (4), and SQL Server reads the intermediate-level data page (1:170) referenced by the first row on the root page.

為了從表中讀取行的範圍，SQL Server需要從該範圍中找到具有最小鍵值的行，即4. SQL Server以根頁面開始，其中第二行引用該頁面的頁面。 最小鍵值350.它大於我們要查詢的鍵值（4），SQL Server讀取根頁面第一行引用的中間級資料頁（1：170）。

 Similarly, the intermediate page leads SQL Server to the first leaf-level page (1:176). SQL Server reads that page, then it reads the rows with CustomerIds equal to 4 and 5, and, finally, it reads the two remaining rows from the second page.

  同樣，中間頁面將SQL Server引導到第一個葉級頁面（1：176）。 SQL Server讀取該頁面，然後它讀取CustomerIds等於4和5的行，最後，它從第二頁讀取剩餘的兩行。

 The execution plan is shown in Figure  2-13 .

  執行計劃如圖2-13所示。

 

 

 Figure 2-13. Index seek execution plan

圖2-13。 索引尋求執行計劃

As you can guess, index seek is more efficient than index scan, because SQL Server processes just the subset of rows and data pages rather than scanning the entire table.

您可以猜測，索引搜尋比索引掃描更有效，因為SQL Server只處理行和資料頁的子集，而不是掃描整個表。

 Technically speaking, there are two kinds of index seek operations. The first is called a singleton lookup , or sometimes point-lookup , where SQL Server seeks and returns a single row. You can think about WHERE CustomerId = 2 predicate as an example. The other type of index seek operation is called a range scan , and it requires SQL Server to find the lowest or highest value of the key and scan (either forward or backward) the set of rows until it reaches the end of scan range. The predicate WHERE CustomerId BETWEEN 4 AND 7 leads to the range scan. Both cases are shown as INDEX SEEK operations in the execution plans.

從技術上講，索引搜尋操作有兩種。第一種稱為單例查詢，有時稱為點查詢，其中SQL Server尋找並返回單行。您可以考慮將WHERE CustomerId = 2謂詞作為示例。另一種型別的索引查詢操作稱為範圍掃描，它要求SQL Server查詢鍵的最低值或最高值，並掃描（向前或向後）行集，直到達到掃描範圍的末尾。 CustomerId BETWEEN 4和7之間的謂詞WHERE導致範圍掃描。這兩種情況都在執行計劃中顯示為INDEX SEEK操作。

 As you can guess, it is entirely possible for range scans to force SQL Server to process a large number or even all data pages from the index. For example, if you changed the query to use a WHERE CustomerId > 0predicate, SQL Server would read all rows/pages, even though you would have an Index Seek operator displayed in the execution plan. You must keep this behavior in mind and always analyze the efficiency of range scans during query performance tuning.

您可以猜到，範圍掃描完全可以強制SQL Server處理索引中的大量甚至所有資料頁。例如，如果您將查詢更改為使用WHERE CustomerId> 0predicate，則SQL Server將讀取所有行/頁，即使您在執行計劃中顯示了Index Seek運算子。您必須牢記此行為，並始終在查詢效能調整期間分析範圍掃描的效率。

 There is a concept in relational databases called SARGable predicates , which stands for S earch Arg ument able . The predicate is SARGable if SQL Server can utilize an index seek operation, if an index exists. In a nutshell, predicates are SARGable when SQL Server can isolate the single value or range of index key values to process, thus limiting the search during predicate evaluation. Obviously, it is beneficial to write queries using SARGable predicates and utilize index seek whenever possible.

在關係資料庫中有一個名為SARGable謂詞的概念，它代表了S earch Arg ement的能力。如果索引存在，如果SQL Server可以使用索引查詢操作，則謂詞是SARGable。簡而言之，當SQL Server可以隔離要處理的索引鍵值的單個值或範圍時，謂詞是SARGable，因此在謂詞評估期間限制搜尋。顯然，使用SARGable謂詞編寫查詢並儘可能利用索引查詢是有益的。

 SARGable predicates include the following operators: = , > , >= , < , <= , IN , BETWEEN , and LIKE (in case of prefix matching). Non-SARGable operators include NOT , <> , LIKE (in case of non-prefix matching), and NOT IN . Another circumstance for making predicates non-SARGable is using functions or mathematical calculations against the table columns. SQL Server has to call the function or perform the calculation for every row it processes. Fortunately, in some of cases you can refactor the queries to make such predicates SARGable. Table  2-1 shows a few examples of this.

 SARGable謂詞包括以下運算子：=，>，> =，<，<=，IN，BETWEEN和LIKE（在字首匹配的情況下）。非SARGable運算子包括NOT，<>，LIKE（在非字首匹配的情況下）和NOT IN。使謂詞非SARGable的另一種情況是對錶列使用函式或數學計算。 SQL Server必須為其處理的每一行呼叫該函式或執行計算。幸運的是，在某些情況下，您可以重構查詢以生成此類謂詞優化搜尋。表2-1列出了一些例子。

 

 

Another important factor that you must keep in mind is type conversion . In some cases, you can make predicates non-SARGable by using incorrect data types. Let’s create a table with a varchar column and populate it with some data, as shown in Listing 2-6 .

您必須牢記的另一個重要因素是型別轉換。 在某些情況下，您可以使用不正確的資料型別使謂詞非SARGable。 讓我們建立一個帶有varchar列的表，並用一些資料填充它，如清單2-6所示。

 Listing 2-6. SARG predicates and data types: Test table creation

清單2-6 SARG謂詞和資料型別：測試表建立

create table dbo.Data

(

 VarcharKey varchar(10) not null,

 Placeholder char(200)

);

create unique clustered index IDX_Data_VarcharKey

on dbo.Data(VarcharKey);

;with N1(C) as (select 0 union all select 0) -- 2 rows

,N2(C) as (select 0 from N1 as T1 cross join N1 as T2) -- 4 rows

,N3(C) as (select 0 from N2 as T1 cross join N2 as T2) -- 16 rows

,N4(C) as (select 0 from N3 as T1 cross join N3 as T2) -- 256 rows

,N5(C) as (select 0 from N4 as T1 cross join N4 as T2) -- 65,536 rows

,IDs(ID) as (select row_number() over (order by (select null)) from N5)

insert into dbo.Data(VarcharKey)

 select convert(varchar(10),ID) from IDs;

The clustered index key column is defined as varchar, even though it stores integer values. Now, let’s run two selects, as shown in Listing 2-7 , and look at the execution plans.

聚簇索引鍵列定義為varchar，即使它儲存整數值。 現在，讓我們執行兩個選擇，如清單2-7所示，並檢視執行計劃。

 Listing 2-7. SARG predicates and data types: Select with integer parameter

清單2-7 SARG謂詞和資料型別：使用整數引數選擇

declare

 @IntParam int = '200'

select * from dbo.Data where VarcharKey = @IntParam;

select * from dbo.Data where VarcharKey = convert(varchar(10),@IntParam);

 As you can see in Figure  2-14 , in the case of the integer parameter, SQL Server scans the clustered index, converting varchar to an integer for every row. In the second case, SQL Server converts the integer parameter to a varchar at the beginning and utilizes a much more efficient clustered index seek operation.

如圖2-14所示，對於整數引數，SQL Server掃描聚簇索引，將varchar轉換為每行的整數。 在第二種情況下，SQL Server在開始時將整數引數轉換為varchar，並使用更高效的聚簇索引查詢操作。

 

 

Figure 2-14. SARG predicates and data types: Execution plans with integer parameter

圖2-14。 SARG謂詞和資料型別：帶整數引數的執行計劃

 Tip Pay attention to the column data types in the join predicates. Implicit or explicit data type conversions can significantly decrease the performance of the queries.

提示：請注意連線謂詞中的列資料型別。 隱式或顯式資料型別轉換可能會顯著降低查詢的效能。

 You will observe very similar behavior in the case of unicode string parameters. Let’s run the queries shown in Listing 2-8 . Figure  2-15 shows the execution plans for the statements.

在unicode字串引數的情況下，您將觀察到非常類似的行為。 讓我們執行清單2-8中所示的查詢。 圖2-15顯示了語句的執行計劃。

Listing 2-8. SARG predicates and data types: Select with string parameter

清單2-8 SARG謂詞和資料型別：使用字串引數選擇

select * from dbo.Data where VarcharKey = '200';

select * from dbo.Data where VarcharKey = N'200'; -- unicode parameter

 

 

Figure 2-15. SARG predicates and data types: Execution plans with string parameter

圖2-15。 SARG謂詞和資料型別：帶字串引數的執行計劃

 As you can see, a unicode string parameter is non-SARGable for varchar columns. This is a much bigger issue than it appears to be. While you rarely write queries in this way, as shown in Listing 2-8 , most application development environments nowadays treat strings as unicode. As a result, SQL Server client libraries generate unicode ( nvarchar ) parameters for string objects unless the parameter data type is explicitly specified as varchar . This makes the predicates non-SARGable, and it can lead to major performance hits due to unnecessary scans, even when varchar columns are indexed.

如您所見，對於varchar列，unicode字串引數是非SARGable。 這是一個比看起來更大的問題。 雖然您很少以這種方式編寫查詢，如清單2-8所示，但現在大多數應用程式開發環境都將字串視為unicode。 因此，除非將引數資料型別顯式指定為varchar，否則SQL Server客戶端庫會為字串物件生成unicode（nvarchar）引數。 這使得謂詞不具有SARG，並且由於不必要的掃描，它可能導致主要的效能命中，即使對varchar列進行索引也是如此。

■ Important Always specify parameter data types in client applications. For example, in ADO.Net, use

Parameters.Add("@ParamName",SqlDbType.Varchar, <Size>).Value = stringVariable instead of

Parameters.Add("@ParamName").Value = stringVariable overload. Use mapping in ORM frameworks to

explicitly specify non-unicode attributes in the classes.

 It is also worth mentioning that varchar parameters are SARGable for nvarchar unicode data columns.

值得一提的是，對於nvarchar unicode資料列，varchar引數是SARGable。

 Composite

Indexes Indexes with multiple key columns are called composite (or compound) indexes . The data in the composite indexes is sorted on a per-column basis from leftmost to rightmost columns. Figure  2-16 shows the structure of a composite index.

綜合

  索引具有多個鍵列的索引稱為複合（或複合）索引。 複合索引中的資料按從最左列到最右列的每列進行排序。 圖2-16顯示了複合索引的結構。