How to improve sql query efficiency

1. To optimize the query, try to avoid full table scans. First, consider creating indexes on the columns involved in where and order by.

2. Try to avoid making null value judgments on fields in the where clause, otherwise the engine will give up using the index and perform a full table scan, such as:
select id from t where num is null
You can set the default value 0 on num, ensure that there is no null value in the num column in the table, and then query like this:
select id from t where num=0

3. Try to avoid where clauses Use the != or <> operator, otherwise the engine will give up using the index and perform a full table scan.

4. Try to avoid using or in the where clause to connect conditions, otherwise the engine will give up using the index and perform a full table scan, such as:
select id from t where num=10 or num =20
You can query like this:
select id from t where num=10
union all
select id from t where num=20

5.in and not in are also required Use with caution, otherwise it will lead to a full table scan, such as:
select id from t where num in (1,2,3)
For continuous values, if you can use between, do not use in:
select id from t where num between 1 and 3

6. The following query will also result in a full table scan:
select id from t where name like '%abc%'
To improve efficiency, Consider full-text search.

7. if in where Using parameters in the clause will also result in a full table scan. Because SQL resolves local variables only at runtime, the optimizer cannot defer selection of an access plan until runtime; it must make the selection at compile time. Ran However, if the access plan is built at compile time, the value of the variable is still unknown and cannot be used as an input for index selection. For example, the following statement will perform a full table scan:
select id from t where num=@num
You can change it to force the query to use the index:
select id from t with(index(index name)) where num= @num

8. Try to avoid performing expression operations on fields in the where clause, which will cause the engine to give up using the index and perform a full table scan. For example:
select id from t where num/2=100
should be changed to:
select id from t where num=100*2

9. Try to avoid using the where clause Function operations are performed on fields in the field, which will cause the engine to give up using the index and perform a full table scan. For example:
select id from t where substring(name,1,3)='abc'--name starts with abc id
select id from t where datediff(day,createdate,'2005-11-30 ')=0--'2005-11-30'The generated id
should be changed to:
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, otherwise the system may not be able to Use indexes correctly.

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

12. Do not write meaningless queries. For example, if you need to generate an empty table structure:
select col1,col2 into #t from t where 1=0
This type of code will not return anything The result set, but it will consume system resources, should be changed to this:
create table #t(...)

13. In many cases, using exists instead of in is a good choice:
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 effective for queries. SQL optimizes queries based on the data in the table. When there is a large amount of duplicate data in the index column, the SQL query may not use the index, such as a table with fields sex, male, and female. Almost half and half, then even if an index is built on sex, it will not have any effect on query efficiency.

15. The more indexes, the better. Although the index can improve the efficiency of the corresponding select, it also reduces the efficiency of insert and update, because insert or update update The index may need to be rebuilt from time to time, so how to build the index needs to be carefully considered and depends on the specific situation. It is best not to have more than 6 indexes on a table. If there are too many, you should consider whether there are indexes built on some columns that are not commonly used. necessary.

16. Avoid updating clustered index data columns as much as possible, because clustered The order of index data columns is the physical storage order of table records. Once the column value changes, the order of the entire table records will be adjusted, which will consume considerable resources. If the application system requires frequent updates clustered index data column, then you need to consider whether the index should be built as a clustered index.

17. Try to use numeric fields. If the fields contain only numerical information, try not to design them as character fields. This will reduce the performance of queries and connections, and increase storage overhead. This is because the engine will compare each character in the string one by one when processing queries and connections, and only one comparison is enough for numeric types.

18. Use varchar/nvarchar instead of char/nchar as much as possible, because first of all, variable length fields have small storage space and can save storage space. Secondly, for queries, search efficiency in a relatively small field is high. Obviously higher.

19. Do not use select * from t anywhere, replace "*" with a specific field list, and do not return any unused fields.

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

21. Avoid frequently creating and deleting temporary tables to reduce the consumption of system table resources.

22. Temporary tables are not unusable, and using them appropriately can make certain routines more efficient, for example, when you need to repeatedly reference a large table or a certain data set in a commonly used table. However, for one-off events, it's better to use an export table.

23. When creating a temporary table, if the amount of data inserted at one time is large, you can use select into instead of create table to avoid causing a large number of logs to increase speed; if the amount of data is not large, in order to ease the system For table resources, you should first create the table and then insert it.

24. If temporary tables are used, all temporary tables must be explicitly deleted at the end of the stored procedure, first truncate table, and then drop table. This can avoid long-term locking of system tables.

25. Try to avoid using cursors, because cursors are less efficient. If the data operated by the cursor exceeds 10,000 rows, you should consider rewriting it.

26. Before using the cursor-based method or the temporary table method, you should first look for a set-based solution to solve the problem. The set-based method is usually more effective.

27. Like temporary tables, cursors are not unusable. Use FAST_FORWARD for small data sets Cursors are often superior to other row-by-row processing methods, especially when several tables must be referenced to obtain the required data. Routines that include "totals" in a result set are usually faster than using a cursor. If during development Time permitting, you can try both the cursor-based method and the set-based method to see which method works better.

28. Set SET NOCOUNT ON at the beginning of all stored procedures and triggers, and set SET NOCOUNT OFF at the end. There is no need to send a DONE_IN_PROC message to the client after each statement of stored procedures and triggers.

29. Try to avoid large transaction operations and improve system concurrency.

30. Try to avoid returning large amounts of data to the client. If the amount of data is too large, you should consider whether the corresponding requirements are reasonable

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