使用MSSQL建立有效的索引（mssql建立索引）

With the rapid development of the Internet, the use of database systems in business and life is increasing. As one of the representative database systems, MS SQL is widely used in many businesses due to its high reliability, scalability and programmability.Compared to other databases, MS SQL requires developers to create indexes on tables that are properly correlated to database performance. Creating effective indexes can significantly improve database performance.

Creating an index in MS SQL is the same as creating a database table. The basic command syntax is “Create index name on table (column name);”. It is worth knowing that a single table can have multiple indexes. It is recommended to use a combined index of multiple columns for query tables with multiple conditions. In addition to non-clustered indexes, composite non-clustered indexes also play an important role in improving SQL query efficiency.

There are many best practices for creating indexes in MS SQL. For example, create a non-clustered index for cases where there is only one available selection criterion; the index can be used to avoid the table scan. Generally speaking, the index column should reduce the width of the data set. This is because a large index takes up more space and reduces the effective data set size. In addition, it is best not to select computed columns and columns with a large data type as index columns. Lastly, it is also important to clear out redundant indexes that are not essential.

The following is a sample code for creating an index in MS SQL.

//Create a combined index on three columns

CREATE INDEX ix_table1_composite_1

ON table1(col_1,col_2,col_3);

Through the above sample code, we can create combined indexes on three columns in the same table. In summary, creating appropriate indexes is an effective way to improve MS SQL query performance. The specific practices include creating non-clustered indexes for single field selections, reducing index column width, not selecting computed columns and columns with a large data type as index columns, and regularly cleaning out redundant indexes.