In order to improve query efficiency in sql query, we often take some measures to optimize the query statement. Some methods summarized below can be referred to if necessary. 1. To optimize the query, full table scan should be avoided as much as possible. First, you should consider building indexes on the columns involved in where and order by . 2. 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 a default value of 0 on num, make sure there are no null values in the num column in the table, and then query like this: select id from t where num=0 3. Try to avoid using the != or <> operator in the where clause , otherwise the engine will give up the use of the index and perform a full table scan. 4. Try to avoid using or to join conditions in the where clause , otherwise it will cause the engine to 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 should also be used with caution, otherwise it will cause a full table scan, such as : select id from t where num in ( 1 , 2 , 3 ) For continuous values, use between instead of 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% ' 7. The expression operation on the field in the where clause should be avoided as much as possible , which will cause the engine to give up the use of the index and perform a full table scan. Such as: select id from t where num / 2 = 100 Should be changed to: select id from t where num=100*2 8. You should try to avoid functional operations on fields in the where clause, which will cause the engine to give up the use of indexes and perform full table scans. For example: select id from t where substring (name, 1 , 3 ) = ' abc ' -- id whose name starts with abc should be changed to: select id from t where name like ' abc% ' 9. Do not perform functions, arithmetic operations or other expression operations on the left side of the " = " in the where clause , otherwise the system may not be able to use the index correctly. 10. 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 can use the index, Otherwise the index will not be used, and the field order should be as consistent as possible with the index order. 11. Don't write some meaningless queries, such as generating 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 will consume system resources, it should be changed to this: create table #t(...) 12. Many times it is a good choice to use exists instead of in : 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) 13. Not all indexes are valid for queries. SQL optimizes queries 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 the index. If there are fields sex in a table, male and female are almost half and half, then even if an index is built on sex, it will not affect the query efficiency. 14. The more indexes the better, the index can 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 , how to build the index needs careful consideration, depending on the specific situation. 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. 15. 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. 16. Use varchar instead of char as much as possible , because the storage space of variable-length fields is small at first, which can save storage space, Secondly, for the query, the search efficiency in a relatively small field is obviously higher. 17. Don't use select * from t anywhere , replace " * " with a list of specific fields, and don't return any fields that are not used. 18. Avoid frequent creation and deletion of temporary tables to reduce the consumption of system table resources. 19. 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. 20. 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 . In order to improve the speed; if the amount of data is not large, in order to ease the resources of the system table, you should create table first , and then insert. 21. 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-time locking of system tables. 22. 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 rewriting should be considered. 23. Before using the cursor-based method or the temporary table method, you should look for a set-based solution to solve the problem, the set-based method is usually more efficient. 24. Like temporary tables, cursors are not unusable. 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. 25. Try to avoid large transaction operations and improve system concurrency capabilities. 26. 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.