SQL fragmentation affects database performance and is mainly divided into internal fragments and external fragments. The detection method is to use the sys.dm_db_index_physical_stats function. The processing method selects reorganization or reconstruction according to the fragmentation rate. It is recommended to maintain it regularly to avoid performance degradation.
The database runs for a long time and the query speed becomes slower. Many times, it is not because the hardware is not strong enough, but because SQL fragmentation is at work. Especially for users using SQL Server, index fragmentation is a common problem that affects performance. Simply put, if there are too many index fragments, it will be more difficult for the query optimizer to find data, and the efficiency will naturally decrease.

To solve this problem, the key is to identify the fragments first and then deal with them according to the situation.
What is SQL fragmentation?
SQL fragmentation mainly refers to the discontinuity of physical storage in database indexes, and is divided into two types:

- Internal Fragmentation : There is too much free space in the page, resulting in wasted storage.
- External Fragmentation : The physical and logical order of index pages on disk are inconsistent, which affects reading efficiency.
To give a simple example, when you organize your bookshelf, the originally neatly arranged books are constantly drawn out and inserted into new books, and finally become messy and find books slowly.
How to detect index fragmentation?
SQL Server provides a system function sys.dm_db_index_physical_stats
, which can be used to view index fragmentation.

You can run a query like this:
SELECT OBJECT_NAME(object_id) AS TableName, index_id, avg_fragmentation_in_percent, avg_page_space_used_in_percent FROM sys.dm_db_index_physical_stats(DB_ID(), NULL, NULL, NULL, 'LIMITED') WHERE avg_fragmentation_in_percent > 10 AND index_id > 0;
This query lists all indexes with fragmentation rates exceeding 10%. This threshold can be adjusted according to actual conditions, for example, for systems with high performance requirements, it can be set to 5%.
How to deal with fragmentation? Rebuild or reorganization?
There are two main ways to deal with fragmentation: Rebuild and Reorganize
- Rebuild : will completely rebuild the index structure, free up unused space, suitable for situations where fragmentation rate is high (for example > 30%)
- Reorganize : organize existing pages, compress free space, suitable for medium fragmentation rate (such as 10% - 30%)
Their comparison:
- Rebuilding locks tables may affect running queries
- Reorganization is milder, but the effect is relatively limited
- Rebuild can be used online in supported versions with
ONLINE = ON
parameter.
It is usually recommended to automatically select the processing method according to the fragmentation rate, such as:
- avg_fragmentation_in_percent
- 10% - 30%: Reorganization
30%: Rebuild
Maintenance strategy recommendations
In order to prevent fragmentation problems from occurring repeatedly, it is recommended to establish a regular maintenance plan:
- Check index fragmentation once a week
- Schedule rebuild or reorganization tasks according to the thresholds above
- For large tables that are frequently updated, you can consider maintaining them more frequently
- Use SQL Agent Jobs or third-party maintenance scripts, such as those of Ola Hallengren, to automate
In addition, pay attention to some points during maintenance:
- Avoid peak business execution
- Monitor execution time and resource usage
- Record logs to facilitate subsequent analysis and adjustment of strategies
Basically that's it. SQL fragmentation analysis and processing are not complicated, but are easily overlooked. Regular maintenance not only improves performance, but also extends the life of the database.
The above is the detailed content of SQL Fragmentation Analysis and Resolution. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

In database design, use the CREATETABLE statement to define table structures and constraints to ensure data integrity. 1. Each table needs to specify the field, data type and primary key, such as user_idINTPRIMARYKEY; 2. Add NOTNULL, UNIQUE, DEFAULT and other constraints to improve data consistency, such as emailVARCHAR(255)NOTNULLUNIQUE; 3. Use FOREIGNKEY to establish the relationship between tables, such as orders table references the primary key of the users table through user_id.

SQLfunctionsandstoredproceduresdifferinpurpose,returnbehavior,callingcontext,andsecurity.1.Functionsreturnasinglevalueortableandareusedforcomputationswithinqueries,whileproceduresperformcomplexoperationsanddatamodifications.2.Functionsmustreturnavalu

Pattern matching functions in SQL include LIKE operator and REGEXP regular expression matching. 1. The LIKE operator uses wildcards '%' and '_' to perform pattern matching at basic and specific locations. 2.REGEXP is used for more complex string matching, such as the extraction of email formats and log error messages. Pattern matching is very useful in data analysis and processing, but attention should be paid to query performance issues.

LAG and LEAD in SQL are window functions used to compare the current row with the previous row data. 1. LAG (column, offset, default) is used to obtain the data of the offset line before the current line. The default value is 1. If there is no previous line, the default is returned; 2. LEAD (column, offset, default) is used to obtain the subsequent line. They are often used in time series analysis, such as calculating sales changes, user behavior intervals, etc. For example, obtain the sales of the previous day through LAG (sales, 1, 0) and calculate the difference and growth rate; obtain the next visit time through LEAD (visit_date) and calculate the number of days between them in combination with DATEDIFF;

To find columns with specific names in SQL databases, it can be achieved through system information schema or the database comes with its own metadata table. 1. Use INFORMATION_SCHEMA.COLUMNS query is suitable for most SQL databases, such as MySQL, PostgreSQL and SQLServer, and matches through SELECTTABLE_NAME, COLUMN_NAME and combined with WHERECOLUMN_NAMELIKE or =; 2. Specific databases can query system tables or views, such as SQLServer uses sys.columns to combine sys.tables for JOIN query, PostgreSQL can be used through inf

Create a user using the CREATEUSER command, for example, MySQL: CREATEUSER'new_user'@'host'IDENTIFIEDBY'password'; PostgreSQL: CREATEUSERnew_userWITHPASSWORD'password'; 2. Grant permission to use the GRANT command, such as GRANTSELECTONdatabase_name.TO'new_user'@'host'; 3. Revoke permission to use the REVOKE command, such as REVOKEDELETEONdatabase_name.FROM'new_user

Backing up and restoring SQL databases is a key operation to prevent data loss and system failure. 1. Use SSMS to visually back up the database, select complete and differential backup types and set a secure path; 2. Use T-SQL commands to achieve flexible backups, supporting automation and remote execution; 3. Recovering the database can be completed through SSMS or RESTOREDATABASE commands, and use WITHREPLACE and SINGLE_USER modes if necessary; 4. Pay attention to permission configuration, path access, avoid overwriting the production environment and verifying backup integrity. Mastering these methods can effectively ensure data security and business continuity.

TheSQLLIKEoperatorisusedforpatternmatchinginSQLqueries,allowingsearchesforspecifiedpatternsincolumns.Ituseswildcardslike'%'forzeroormorecharactersand'_'forasinglecharacter.Here'showtouseiteffectively:1)UseLIKEwithwildcardstofindpatterns,e.g.,'J%'forn
