MSSQL 深度清理数据库宝典

1. Introduction

Microsoft SQL Server is one of the most popular relational database management systems in the world. It provides a reliable and efficient way for organizations to store, manage, and retrieve large amounts of data. However, as the size of a database grows, it can become bogged down with unused or redundant information, increasing its overall size and slowing down performance. This is where database cleanup comes in. In this article, we will explore the various techniques and tools available for deep cleaning an MSSQL database.

2. Identifying Unused Data

2.1 Running Queries

The first step in cleaning up an MSSQL database is to identify unused or redundant data. One way to do this is by running queries to find tables that may have grown too large or contain information that is no longer needed. For example, we can identify tables with a high number of rows but low selectivity by running the following query:

SELECT 
    [schema].[table].name AS [Table Name], 
    COUNT_BIG(*) AS [Number of Rows], 
    SUM(CASE 
        WHEN [stats].last_user_seek IS NOT NULL 
            THEN 1 
        ELSE 0 
    END) AS [Number of Seeks], 
    CAST((SUM(CASE 
                WHEN [stats].last_user_seek IS NOT NULL 
                    THEN 1 
                ELSE 0 
            END) * 100.0) / COUNT_BIG(*) AS DECIMAL(5, 2)) AS [Selectivity (%)], 
    [stats].last_user_seek, 
    [stats].last_user_scan 
FROM 
    sys.tables AS [table] 
    INNER JOIN sys.indexes AS [index] 
        ON [table].[object_id] = [index].[object_id] 
    LEFT JOIN sys.dm_db_index_usage_stats AS [stats] 
        ON [index].[object_id] = [stats].[object_id] 
            AND [index].[index_id] = [stats].[index_id] 
GROUP BY 
    [schema].[table].name, 
    [stats].last_user_seek, 
    [stats].last_user_scan 
HAVING 
    COUNT_BIG(*) > 1000 
    AND (SUM(CASE 
                WHEN [stats].last_user_seek IS NOT NULL 
                    THEN 1 
                ELSE 0 
            END) * 100.0) / COUNT_BIG(*) < 5.00 
    AND [stats].last_user_seek IS NULL 
    AND [stats].last_user_scan IS NULL 
ORDER BY 
    [Selectivity (%)] ASC;

This query will return tables with more than 1000 rows, a selectivity of less than 5%, and no recent seeks or scans, indicating that they may contain unused data.

2.2 Using SQL Server Profiler

Another way to identify unused data is to use SQL Server Profiler to monitor database activity. This tool allows you to track queries and events in real time, giving you a better understanding of how your database is being used. By analyzing this information, you can identify tables or indexes that have not been accessed recently and may be candidates for cleanup.

3. Removing Unused Data

3.1 Truncating Tables

Once you have identified unused data, the next step is to remove it. One way to do this is by truncating tables. This operation removes all data from a table, allowing you to quickly free up space and improve performance. However, it's important to note that truncating a table also resets identity columns and can't be undone, so be sure to back up your data before proceeding.

USE [database_name]
GO
TRUNCATE TABLE [table_name];

3.2 Dropping Indexes

Another way to remove unused data is by dropping indexes. Indexes are used to speed up searches against a table, but they can also take up a lot of space and slow down insert/update/delete operations. By dropping indexes that are no longer needed, you can free up space and improve overall performance. However, be sure to test your queries after dropping indexes to make sure they still perform adequately.

USE [database_name]
GO
DROP INDEX [index_name] ON [table_name];

3.3 Compact and Shrink Database

Finally, you can further optimize your database by performing a database shrink operation. This operation releases unused space from your database files, reducing their overall size and improving performance. To do this, you can use the DBCC SHRINKDATABASE or DBCC SHRINKFILE command. However, be aware that shrinking a database can be resource-intensive and should be done during off-peak hours.

USE [database_name]
GO
DBCC SHRINKDATABASE([database_name]);

4. Conclusion

By following these techniques and tools, you can effectively clean up your MSSQL database and improve its overall performance. However, it's important to carefully analyze your database structure and usage patterns before making any significant changes to avoid data loss or negatively impacting performance. Additionally, it's always a good idea to back up your data before performing any database cleanup operation.