MySQL SQL optimization

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 null-valued fields in the WHERE clause, which will cause the engine to discard full-table scans using the index, such as:

Select ID from t where num is null

You can set the default value of 0 on NUM, make sure that the NUM column in the table does not have a null value, and then query:

Select ID from t where num=0

3. Try to avoid using the! = or <> operator in the WHERE clause, or discard the engine for a full table scan using the index.

4. You should try to avoid using or in the WHERE clause to join the condition, otherwise it will cause the engine to abandon using the index for a full table scan, such as:

Select ID from t where num=10 or num=20

You can query this:

Select ID from t where num=10

Union AlL

Select ID from t where num=20

5.in and not in should also be used with caution, otherwise it will result in full table scans, 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 access the planned

The selection is deferred to run; 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 indexed

The selected entry. 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 system use

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 optimized for queries based on data in the table, and when there is a large amount of data duplication in the index columns, SQL queries may not

Index, such as a table with fields Sex,male, female almost half, so even if you build an index on sex to query the effect

Rate does not work.

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 and UPDATE, because the insert

or update, it is possible to rebuild the index, so how to build the index requires careful consideration, depending on the situation. The number of indexes on a table should not be more than 6, if too many

You should consider whether it is necessary to index the indexes that are not commonly used on the columns.

16. Whenever possible, avoid updating clustered index data columns, because the order of the clustered index data columns is the physical storage order of the table records, once the column value

Changing the order of the entire table records will result in a significant amount of resources being spent. 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

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.

as far as possible to use Varchar/nvarchar instead of Char/nchar, because the first variable length field storage space is small, you can save storage space, followed by queries to

, it is clear that search efficiency is higher in a relatively small field.

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. In the new temporary table???? , you can use SELECT INTO instead of CREATE table to avoid creating a large number of logs to

High speed, if the amount of data is small, to mitigate the resources of the system table, create TABLE and insert.

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, then drop table, which

To avoid locking the system table for a longer period of time.

25. Avoid using the cursor???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 when you must

When you reference several tables to get the data you want. Routines that include "totals" in the result set are typically faster than using cursors. If development time permits, the base

Both the cursor method and the set-based approach can 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. No need to execute stored procedures and trigger

Sends a DONE_IN_PROC message to the client after each statement of the device.

29. Try to avoid large transaction operation and improve the system concurrency ability.

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.

MySQL SQL optimization