Summary of some common performance issues in SQL Server

1. To optimize the query, avoid full-table scanning as far as possible, and first consider establishing an index on the columns involved in the Where and order by.

2. Avoid using left joins and null values as far as possible. Left joins consume more resources than inner joins because they contain data that matches null (nonexistent) data, so if you can rewrite the query so that the query does not use any inner join, you get a corresponding return.
For example, there are two tables:
product (product_id int not null,product_type_id int null,...), Products table, product_id is an integer greater than 0, product_type_id with table product_ Type is associated, but can be empty, because some products do not have a category
Product_type (product_type_id not null,product_type_name null,...), Product category table
In order to query the content of the product after two tables are associated, you will immediately think of using inner join, but there is a way to avoid using inner join:
add a record in Product_type: 0, ',..., and set product's product_type_id to NOT NULL, and set its product_type_id to 0 when the product does not have a category, so the query can use the in The NER join.

3. Try to avoid using the! = or <> operator in the WHERE clause, or the engine may abandon full table scanning using the index.

4. You should try to avoid using or in the WHERE clause to join the condition, otherwise it may cause the engine to abandon the use of indexes for full table scanning, such as table T, Key1, Key2 on the index, need the following stored procedure:
CREATE PROCEDURE Select_proc1 @key1 int=0, @key2 int=0
as
begin
Select Key3 from T
where (@key1 =0 or [email protected])
and (@key2 =0 or [email protected])
End
Go
This stored procedure causes a full table scan and can be modified as follows:
CREATE PROCEDURE select_proc2 @key1 int=0, @key2 int=0
as
begin
if @key1 <>0 and @key2 <>0
Select Key3 from T
where [email protected] and [email protected]
Else
if @key1 <>0
Select Key3 from t where [email protected]
Else
Select Key3 from t where [email protected]
End
Go
after the change, the code increased, but the efficiency increased.

5.in and not in should also be used with caution, such as:
Select ID from t where num in
For consecutive values, you can use between instead of in:
Select ID from t where num between 1 and 3

6. The following query will also cause a full table scan:
Select ID from t where name like '%abc% '
To be more efficient, consider full-text indexing.

7. If you use a parameter in the WHERE clause, it also causes a full table scan. Because SQL resolves local variables only at run time, the optimizer cannot defer the selection of access plans to run time; it must be selected at compile time. However, if an access plan is established at compile time, the value of the variable is still unknown and therefore cannot be selected as an input for the index. The following statement will perform a full table scan:
Select ID from t where [email protected]
You can force the query to use the index instead:
Select ID from T with (index name) where [email protected]

8. You should try to avoid expression operations on the fields in the WHERE clause, which will cause the engine to discard the full table scan using the index. Such as:
Select ID from t where num/2=100
should read:
Select ID from t where num=100*2

9. You should try to avoid function operations on the fields in the WHERE clause, which will cause the engine to discard the full table scan using the index. Such as:
Select ID from t where substring (name,1,3) = ' abc '--name ID starting with ABC
Select ID from t where DATEDIFF (day,createdate, ' 2005-11-30 ') =0--' 2005-11-30 ' generated ID
should read:
Select ID from t where name like ' abc% '
Select ID from t where createdate>= ' 2005-11-30 ' and createdate< ' 2005-12-1 '

10. Do not perform functions, arithmetic operations, or other expression operations on the left side of "=" in the WHERE clause, or the index may not be used correctly by the system.

11. When using an indexed field as a condition, if the index is a composite index, you must use the first field in the index as a condition to guarantee that the system uses the index, otherwise the index will not be used, and the field order should be consistent with the index order as much as possible.

12. Do not write meaningless queries, such as the need to generate an empty table structure:
Select Col1,col2 into #t from T where 1=0
This type of code does not return any result sets, but consumes system resources and should be changed to this:
CREATE TABLE #t (...)

13. It is a good choice to replace in with exists in many cases:
Select num from a where num in (select num from B)
Replace with the following statement:
Select num from a where exists (select 1 from b where num=a.num)

14. Not all indexes are valid for queries, SQL is query-optimized based on data in the table, and when there is a large amount of data duplication in the index columns, SQL queries may not take advantage of the index, as there are fields in the table sex, male, female almost half, So even if you build an index on sex, it doesn't work for query efficiency.

15. The index is not the more the better, although the index can improve the efficiency of the corresponding select, but also reduce the efficiency of INSERT, update and Delete, because the insert or update when the index may be rebuilt, so how to build the index needs careful consideration, depending on the situation and Availability The number of indexes on a table should not be more than 6, if too many you should consider whether some of the indexes that are not commonly used are necessary.

16. You should avoid updating clustered index data columns as much as possible, because the order of the clustered index data columns is the physical storage order of the table records, which can consume considerable resources once the column values change to the order in which the entire table is recorded. If your application needs to update clustered index data columns frequently, you need to consider whether the index should be built as a clustered index.

17. Use numeric fields as much as possible, if the field containing only numeric information should not be designed as a character type, which will reduce the performance of queries and connections and increase storage overhead. This is because the engine compares each character in a string one at a time while processing queries and joins, and it is sufficient for a numeric type to be compared only once.

18. Use Varchar/nvarchar instead of Char/nchar as much as possible, because the first variable-length field has less storage space, Storage space can be saved (fixed-length fields require fixed-length storage (7.0 and higher) even when data is null, and second, for queries, the search efficiency in a relatively small field is obviously higher, and each page (8KB) may store more records, which can also reduce i/ o Consumption and improve performance.

19. Do not use SELECT * from t anywhere, replace "*" with a specific field list, and do not return any fields that are not available.

20. Try to use table variables instead of temporary tables. If the table variable contains a large amount of data, be aware that the index is very limited (only the primary key index).

21. Avoid frequent creation and deletion of temporary tables to reduce the consumption of system table resources.

22. Temporary tables are not unusable, and they can be used appropriately to make certain routines more efficient, for example, when you need to repeatedly reference a dataset in a large table or a common table. However, for one-time events, it is best to use an export table.

23. When creating a temporary table, if you insert a large amount of data at one time, you can use SELECT INTO instead of CREATE table to avoid creating a large number of logs to speed up, and if the amount of data is small, create table and inser to mitigate the resources of the system tables. T.

24. If a temporary table is used, be sure to explicitly delete all temporary tables at the end of the stored procedure, TRUNCATE table first, and then drop table, which avoids longer locking of the system tables.

25. Avoid using cursors as much as possible, because cursors are inefficient and should be considered for overwriting if the cursor is manipulating more than 10,000 rows of data.

26. Before using a cursor-based method or temporal table method, you should first look for a set-based solution to solve the problem, and the set-based approach is generally more efficient.

27. As with temporary tables, cursors are not unusable. Using Fast_forward cursors on small datasets is often preferable to other progressive processing methods, especially if you must reference several tables to obtain the required data. Routines that include "totals" in the result set are typically faster than using cursors. If development time permits, a cursor-based approach and a set-based approach can all be tried to see which method works better.

28. Set NOCOUNT on at the beginning of all stored procedures and triggers, set NOCOUNT OFF at the end. You do not need to send a DONE_IN_PROC message to the client after each statement that executes the stored procedure and trigger.

29. Try to avoid large transaction operation and improve the system concurrency ability. When using constraints and triggers to accomplish the same functionality, precedence is given to using constraints.

30. Try to avoid the return of large data to the client, if the amount of data is too large, should consider whether the corresponding demand is reasonable.

Summary of some common performance issues in SQL Server