1·Therefore, it is useful to get the data only once, cache it, and have all other components use the cached data, instead of accessing the backend system again.
因此，只一次获取数据，对数据进行缓存，并让所有其他组件使用该缓存的数据，而非再次访问后端系统，这是很有益的。
2·To speed this up, you can cache the results from a search query and use these cached results whenever a user submits a query.
为了提速，您可以高速缓存从搜索查询得到的结果，然后在用户每次提交查询时使用这些缓存的结果。
3·You can safely remove all files that are found under this folder because they are usually generated and cached for performance reasons.
您可以安全地删除在该文件夹下找到的所有文件，因为它们通常是出于性能原因生成并缓存的。
4·This request retrieves theme information that can be cached from the server.
这个请求检索可以从服务器缓存的主题信息。
5·Subsequent requests can then be inspected to see if they match any previously cached entry.
然后检查随后的请求，以查看它们是否匹配先前缓存的任何项。
1·You may begin to wonder if there's a way to provide cached, local copies of the referenced pieces of content or another way to circumvent the entity-resolution process.
您可能想知道是否存在一种方法在本地提供了被引用内容的高速缓存副本，或有另一种方法能绕过实体解析进程。
2·This calculation takes into account all of the pages (index and data) that are cached by the buffer pool.
这个计算考虑了缓冲池高速缓存的所有页(索引和数据)。
3·You might decide to update the time of the cached item to keep it around longer.
您可以决定更新高速缓存项的时间来使其时间更久些。
4·To address this, the logs are placed on fast write cached disks, reducing the disk write time.
为了解决这个问题，把系统放在快速写入高速缓存磁盘上，以减少磁盘写入时间。
5·Potentially, a later version might know how to target those Python operations that could actually benefit most, and discard cached machine code for non-optimizable sections.
可能今后的版本会知道如何针对那些能真正最大获益Python，并且丢弃为不可优化部分高速缓存的机器码。