I am a rookie, so I can only summarize the experience of Daniel, so that I can improve my efficiency in the following work.
The popular SQL optimization methods on the Internet are as follows:
1. Try to avoid using it in the where clause! = or <> operator, otherwise the engine will give up using the index and perform a full table scan.
2. To optimize the query, full table scan should be avoided as much as possible. First, index should be considered on the columns involved in where and order by.
3. Try to avoid the null value judgment of the field in the where clause, otherwise the engine will give up the use of the index and perform a full table scan, such as:
select id from t where num is null
You can set the default value of 0 on num to 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
4. Try to avoid using or in the where clause to connect conditions, otherwise the engine will give up the use of the index and perform a full table scan, such as:
select id from t where num=10 or num=20
can be queried like this:
select id from t where num=10
union all
select id from t where num=20
5. The following query will also result in a full table scan: (cannot be prepended percent sign)
select id from t where name like '?c%'
To improve efficiency, you can consider full-text search.
6. In and not in should also be used 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
7. If a parameter is used in the where clause, it will also cause a full table scan. Because SQL resolves local variables only at runtime, the optimizer cannot defer the choice of an access plan to runtime; it must choose it at compile time. However, if the access plan is built at compile time, the value of the variable is 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
can be changed to force the query to use the index:
select id from t with(index(index name)) where num=@num
8. Try to avoid where The expression operation on the field in the clause will cause the engine to give up the use of 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 performing functional operations on fields in the where clause, which will cause the engine to abandon the use of indexes and Do a full table scan. Such as:
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 The id generated by -11-30'
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 will likely not be able to use the index 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 a condition to ensure that the system uses the index, otherwise the index will not be used and should be used. As much as possible, make the field order consistent with the index order.
12. Don't write some meaningless queries. If you need to generate an empty table structure:
select col1,col2 into #t from t where 1=0
This kind of code will not return any result set, but it will consume system resources and should be changed It is like 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 optimizes the query based on the data in the table. When a large amount of data in the index column is repeated, the SQL query may not use it. Index, if there are fields sex in a table, male and female are almost equal, then even if an index is built on sex, it will not affect the query efficiency.
15. The more indexes the better, the index can certainly improve the efficiency of the corresponding select, but it also reduces the efficiency of insert and update, because the index may be rebuilt during insert or update, so how to build an index needs to be carefully considered. As the case may be. The number of indexes in a table should not exceed 6. If there are too many indexes, you should consider whether it is necessary to build indexes on some infrequently used columns.
16. Avoid updating the clustered index data column as much as possible, because the order of the clustered index data column is the physical storage order of the table records. Once the value of this column changes, the order of the entire table records will be adjusted, which will consume considerable resources. If the application system needs to update the clustered index data column frequently, it needs to consider whether the index should be built as a clustered index.
17. Try to use numeric fields as much as possible. If the fields only contain numeric information, try not to design them as character fields, which will reduce the performance of query and connection and increase the storage overhead. This is because the engine compares each character of the string one by one when processing queries and joins, whereas only one comparison is required for numbers.
18. Use /n instead of /n as much as possible, because first of all, the storage space of variable-length fields is small, which can save storage space. Secondly, for queries, the search efficiency in a relatively small field is obviously higher.
19. Don't use select * from t anywhere, replace "*" with a list of specific fields, and don't return any unused fields.
20. Try to use table variables instead of temporary tables. If the table variable contains a lot of data, be aware that the indexes are 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 their proper use can make certain routines more efficient, for example, when a large table or a dataset in a frequently used table needs to be repeatedly referenced. However, for one-time events, it is better to use an export table.
23. When creating a new temporary table, if a large amount of data is inserted at one time, you can use select into instead of create table to avoid causing a large number of logs to improve the speed; if the amount of data is not large, in order to ease the resources of the system table, you should first create table, then insert.
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, which can avoid long-term locking of system tables.
25. Try to avoid using the cursor, because the efficiency of the cursor is poor, if the data operated by the cursor exceeds 10,000 rows, then you should consider rewriting.
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 efficient.
27. Like temporary tables, cursors are not unavailable. Using FAST_FORWARD cursors on small datasets is often preferable to other row-by-row processing methods, especially when several tables must be referenced to obtain the required data. Routines that include "totals" in the result set are usually faster than using cursors. If development time allows, try both the cursor-based approach and the set-based approach to see which one 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 is executed.
29. Try to avoid returning a large amount of data to the client. If the amount of data is too large, you should consider whether the corresponding demand is reasonable.
30. Try to avoid large transaction operations and improve system concurrency capabilities.