层次索引模板通过一组常驻显存的索引缓存序列，隐式地表示地形块细节层次。
It represents terrain blocks detail levels by using an index buffer sequence, which resides in video memeory.
系统采用客户资源索引缓存机制和“迭代深入”的查询转发机制，提高了搜索的效率和成功率。
Client resource index caching mechanism and the iterative query forward mechanism are adopted in the system design to enhance the search efficiency and success ratio.
例如，编译器为了优化一个循环索引变量，可能会选择把它存储到一个寄存器中，或者缓存会延迟到一个更适合的时间，才把一个新的变量值存入主存。
For example, a compiler may choose to optimize a loop index variable by storing it in a register, or the cache may delay flushing a new value of a variable to main memory until a more opportune time.
如果选择这个选项，将为主题视图创建一个内部缓存或索引。
Selecting this option creates an internal cache or index for the threaded views.
例如，某些机制允许您在数据上创建索引，而其他机制能够配置缓存和缓冲选项。
For example, some allow you to create indexes on the data, while others have the capability to configure caching and buffering options.
我们可以对缓存状态加索引，这样就能搜索整个网格了。
Cached state can optionally be indexed, allowing the entire grid to be searched.
通过利用索引、缓存、登录开销的减少、汇总表以及取数据的减少来减少I/O。
Reduce I/O by taking the advantage of indexes, caching, reduced logging overhead, summary tables, and reduced fetches.
因为db2缓冲池是系统主内存的一部分，DB 2数据库管理器在它读取或写入磁盘介质时将它分配给缓存表和索引数据。
Since a DB2 buffer pool is a part of the main memory of the system, the DB2 database manager allocates it for caching tables and index data when it reads or writes to and from media disks.
面向服务提倡使用存储过程(存储过程可以减少数据传输)和使用各种优化(例如视图缓存、索引等等)。
Service orientation encourages the use of stored procedures, which reduce data transfer, and also the use of various optimizations (such as view caching, indexing, and so on).
因为MQT是缓存查询结果的本地表，所以在本地表上创建索引的相同步骤也可应用于在 MQT 上创建索引。
Since MQTs are local tables that cache the results of a query, the same considerations for creating an index on a local table apply to creating indexes on MQTs.
当从硬盘驱动器读写表和索引数据时，DB 2使用缓冲池来缓存它们。
DB2 USES buffer pools to cache the table and index data as they are being read or written to the hard disk drive.
在该页面被重新抓取和索引之后，搜索结果及更新后的摘要及缓存页面(基于新内容)将可见。
After the page has been re-crawled and re-indexed, the search result with an updated snippet and cached page (based on the new content) can be visible.
在这种情况下，缓存索引视图可能会弊大于利。
In this case, caching the index view might prove to be more of a hindrance than a help.
对索引所做的更改最初缓存在内存中，并周期性转储到索引目录。
The changes made to the index are initially buffered in the memory and periodically flushed to the index directory.
如果没找到任何索引，那么就没有缓存的tweet，所以就没有展示的东西，并且没有可对查询进行的优化(您返回一个0值以指示这一点)。
If no index is found, then there are no cached tweets, so there is nothing to show and no optimization can be made on the query (and you return a value of 0 to indicate this).
但是，首先，我想在域模型上花费几分钟的时间，这样我可以介绍Objectify 的用于索引和缓存的功能。
First, I want to spend just a few minutes more on the domain model, however, so that I can introduce Objectify's features for indexing and caching.
我们不仅通过利用一个操作码缓存和优化PHP配置探究了php级别的技术，而且探究了如何优化您的数据库设计来实现合理的索引编制。
We looked at techniques not only at the PHP level, by leveraging an opcode cache and optimizing the PHP configuration, but also looked at optimizing your database design for proper indexing.
这个计算考虑了缓冲池高速缓存的所有页(索引和数据)。
This calculation takes into account all of the pages (index and data) that are cached by the buffer pool.
即使你随后就将内容彻底删除，这些内容仍可能缓存在搜索引擎，服务商，或网络存档中。
Even if you subsequently delete the post, it may have been cached in a search engine archive, a company server or in the Internet archive.
尽管几个小时候这些东西消失了，但是它们仍留在搜索引擎的缓存里。
Though they disappeared several hours later, they remained cached in search engines.
为了高速缓存一个来自Web服务的响应，您需要生成一个用来对它进行索引的唯一的密钥。
To cache a response from a web service, you need to generate a unique key to use for indexing it.
IndexWriter公开了几个控制如何在内存中缓存索引并写入磁盘的字段。
IndexWriter exposes several fields that control how indices are buffered in the memory and written to disk.
Google的搜索引擎缓存已经成为一件很有价值的搜索利器，因为用户可以利用此项功能穿越时间，找到也已不存在的页面。
Google's search engine cache has become one of the most valuable research features of Google search, since it enables users to travel back in time and find pages that may not exist anymore.
Google的搜索引擎缓存已经成为一件很有价值的搜索利器，因为用户可以利用此项功能穿越时间，找到也已不存在的页面。
Google's search engine cache has become one of the most valuable research features of Google search, since it enables users to travel back in time and find pages that may not exist anymore.
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