file caching造句
例句与造句
- Lists the contents of the downloaded files cache
列出下载文件缓存的内容。 - Db2 memory and file cache performance tuning on linux
Linux上的db2内存和文件缓存性能调优 - Figure 2 illustrates the aggressive nature of linux file caching
图2说明了linux文件缓存的贪婪性。 - This problem is accentuated when there is a large system file cache
当需要缓存一个大型文件时，此问题尤为突出。 - D size of the file cache
D文件缓存的大小 - It's difficult to find file caching in a sentence. 用file caching造句挺难的
- This article summarizes issues related to memory utilization and file caching on linux
本文总结了linux上与内存的使用和文件缓存相关的一些问题。 - The only exception is linux raw device tablespace containers , which inherently bypass the file cache
惟一的例外是linux原始设备表空间容器，因为它本身就绕过文件缓存。 - Linux , under normal circumstances , uses a system file cache to buffer , read , and write requests from disk
在一般情况下， linux使用一个文件系统缓存为磁盘请求进行缓冲和读写。 - The entire amount of memory allocated to all processes , kernel , and file cache is your total working set
分配给所有进程、内核和文件缓存的总内存量就是您的全部工作区（ working set ） 。 - Lob and long varchar data fields will not typically benefit from direct i o and will continue to use the file cache
Lob和long varchar数据字段通常不能从直接i / o受益，因此将继续使用文件缓存。 - Db2 bufferpools and the linux file cache perform largely the same function ; that is , cache a copy of data read from disk
Db2缓冲池与linux文件缓存基本上执行相同的功能，也就是缓存从磁盘读出的数据的拷贝。 - As noted earlier , the flow of reads under normal circumstances is to read into the file cache and then copy into the bufferpool
如前所述，一般情况下，读操作的流程是先将数据读到文件缓存中，然后再复制到缓冲池。 - Direct i o solves this issue by directing reads to be made directly from disk into db2 memory areas , bypassing the file cache entirely
直接i / o直接将数据从磁盘读到db2内存区域，完全绕过文件缓存，从而解决了上述问题。 - Prior to the linux 2 . 6 kernel , file caching on a linux system could go awry , using a huge number of cycles trying to manage the file cache
对于linux 2 . 6之前版本的内核， linux系统上的文件缓存可能会出差错，需要花费大量的周期来管理文件缓存。 - Memory utilization and file caching are related elements that affect performance and are important to consider when tuning a database system
内存的使用和文件缓存是彼此相关的两个部分，它们一起影响着性能，在对数据库系统进行调优时，要重点考虑这两个方面。
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