row-overflow data造句
例句与造句
- Cumulative count of row - overflow data bytes retrieved
检索到的行溢出数据字节数的累积计数。 - Index , in - row , lob , and row - overflow data pages are correctly linked
是否已正确链接索引、行内、 lob以及行溢出数据页。 - 3 row - overflow data
3 =行溢出数据 - Cumulative count of column values for lob data and row - overflow data that is pulled in - row
已请求到行内的lob数据和行溢出数据的列值的累积计数。 - To obtain information about tables or indexes that might contain row - overflow data , use the
若要获得有关可能包含行溢出数据的表或索引的信息，请使用 - It's difficult to find row-overflow data in a sentence. 用row-overflow data造句挺难的
- Cumulative count of row - overflow data pages retrieved from the row overflow data allocation unit
从row _ overflow _ data分配单元检索到的行溢出数据页数的累积计数。 - You can include columns that contain row - overflow data as key or nonkey columns of a nonclustered index
可以包括包含行溢出数据的列，作为非聚集索引的键列或非键列。 - In - row data , lob data , and row - overflow data represent the three allocation units that make up a partition
行内数据、 lob数据以及行溢出数据表示构成分区的三个分配单元。 - If row - overflow data exists in the table , one row is returned for the row overflow data allocation unit in each partition
如果表中存在行溢出数据，则针对每个分区中的row _ overflow _ data分配单元，返回与其对应的一行。 - Cumulative count of column values for lob data and row - overflow data that is pushed off - row to make an inserted or updated row fit within a page
已推出行外以使插入或更新的行可容纳在页中的lob数据和行溢出数据的列值累积计数。 - Displays information about the space used to store and manage in - row data lob data , and row - overflow data for all partitions in a database
Sys . dm _ db _ partition _ stats显示用于存储和管理数据库中全部分区的行内数据lob数据和行溢出数据的空间的有关信息。 - If there are likely to be frequent queries on many rows of row - overflow data , consider normalizing the table so that some columns are moved to another table
如果可能需要经常查询行溢出数据中的许多行，请考虑对表格进行规范化处理，以使某些列移动到另一个表中。 - Also , querying and performing other select operations , such as sorts or joins on large records that contain row - overflow data slows processing time , because these records are processed synchronously instead of asynchronously
此外，执行查询和其他选择操作（例如，对包含行溢出数据的大型记录进行排序或合并）将延长处理时间，因为这些记录将同步处理，而不是异步处理。