High Logical Reads in Windowed Aggregate Function Execution Plans with Common SubExpression Spools
High reported logical reads for large tables are often observed in execution plans utilizing common subexpression spools. The formula for worktable logical reads is:
Worktable Logical Reads = 1 + NumberOfRows * 2 + NumberOfGroups * 4
Explanation
Unlike conventional spool tables, worktables count logical reads per row read, resulting in inflated logical read counts. This is because worktables are internal to the server, and hashed page counting is deemed less valuable for analysis.
The formula breaks down as follows:
- 1x Logical Read: The worktable is created and initialized.
- 2x Logical Reads per Row: The primary spool reads each row twice, once to insert into the worktable and again to read from the worktable for aggregation. The secondary spools also read each row twice.
- 4x Logical Reads per Group: The primary spool emits a row to indicate the start of each new partition, and it outputs a dummy row to finalize the processing for the final group. These additional rows account for the 4x count.
Additional Insights
Paul White, in his blog post, explains that the formula aligns with the execution plan, where the two secondary spools are fully read twice and the primary spool emits (number of groups 1) rows. The extra row is emitted by the primary spool to signify the final group's end.
Conclusion
The formula for worktable logical reads is a useful tool for understanding the inflated logical read counts observed in execution plans with common subexpression spools. By recognizing that worktables count logical reads differently, it becomes easier to interpret the read statistics and assess the efficiency of the plan.
The above is the detailed content of Why Are My Windowed Aggregate Queries Showing High Logical Reads?. For more information, please follow other related articles on the PHP Chinese website!

MySQLstringtypesimpactstorageandperformanceasfollows:1)CHARisfixed-length,alwaysusingthesamestoragespace,whichcanbefasterbutlessspace-efficient.2)VARCHARisvariable-length,morespace-efficientbutpotentiallyslower.3)TEXTisforlargetext,storedoutsiderows,

MySQLstringtypesincludeVARCHAR,TEXT,CHAR,ENUM,andSET.1)VARCHARisversatileforvariable-lengthstringsuptoaspecifiedlimit.2)TEXTisidealforlargetextstoragewithoutadefinedlength.3)CHARisfixed-length,suitableforconsistentdatalikecodes.4)ENUMenforcesdatainte

MySQLoffersvariousstringdatatypes:1)CHARforfixed-lengthstrings,2)VARCHARforvariable-lengthtext,3)BINARYandVARBINARYforbinarydata,4)BLOBandTEXTforlargedata,and5)ENUMandSETforcontrolledinput.Eachtypehasspecificusesandperformancecharacteristics,sochoose

TograntpermissionstonewMySQLusers,followthesesteps:1)AccessMySQLasauserwithsufficientprivileges,2)CreateanewuserwiththeCREATEUSERcommand,3)UsetheGRANTcommandtospecifypermissionslikeSELECT,INSERT,UPDATE,orALLPRIVILEGESonspecificdatabasesortables,and4)

ToaddusersinMySQLeffectivelyandsecurely,followthesesteps:1)UsetheCREATEUSERstatementtoaddanewuser,specifyingthehostandastrongpassword.2)GrantnecessaryprivilegesusingtheGRANTstatement,adheringtotheprincipleofleastprivilege.3)Implementsecuritymeasuresl

ToaddanewuserwithcomplexpermissionsinMySQL,followthesesteps:1)CreatetheuserwithCREATEUSER'newuser'@'localhost'IDENTIFIEDBY'password';.2)Grantreadaccesstoalltablesin'mydatabase'withGRANTSELECTONmydatabase.TO'newuser'@'localhost';.3)Grantwriteaccessto'

The string data types in MySQL include CHAR, VARCHAR, BINARY, VARBINARY, BLOB, and TEXT. The collations determine the comparison and sorting of strings. 1.CHAR is suitable for fixed-length strings, VARCHAR is suitable for variable-length strings. 2.BINARY and VARBINARY are used for binary data, and BLOB and TEXT are used for large object data. 3. Sorting rules such as utf8mb4_unicode_ci ignores upper and lower case and is suitable for user names; utf8mb4_bin is case sensitive and is suitable for fields that require precise comparison.

The best MySQLVARCHAR column length selection should be based on data analysis, consider future growth, evaluate performance impacts, and character set requirements. 1) Analyze the data to determine typical lengths; 2) Reserve future expansion space; 3) Pay attention to the impact of large lengths on performance; 4) Consider the impact of character sets on storage. Through these steps, the efficiency and scalability of the database can be optimized.


Hot AI Tools

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

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

Undress AI Tool
Undress images for free

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

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

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

Atom editor mac version download
The most popular open source editor

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.
