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1·Listing 5 is a graph of the database CPU usage with the write-through use case.

列表5是一幅采用write - through产生的数据库cpu占用图。

2·The second change was adding another write-through Row Cache containing database records: users and tweets.

第二个改动是加入另一个直写式行缓存，它包含了数据库记录：用户和tweets。

3·We ran the sample code using write-through and write-behind for a comparison in response time and database CPU utilization.

我们分别使用了write - through和write - behind技术运行示例代码，比较了各自的响应时间以及数据库的CPU占用率。

4·In a write-through cache, the data is written to the cache and to the disk before returning to the writing process to continue.

在write - through cache模式下，在返回到写入流程并继续之前，数据将写入到缓存然后再写到磁盘。

5·8K stripe size RA-WT (write ahead/write-through) cache (C: for OS; D: for Utils; E: for Scanmail quarantine; F: for Domino files and data)

8K stripe size RA-WT (write ahead/write-through) 缓存（C：用于 OS；D：用于实用程序；E：用于Scanmail 隔离；F：用于 Domino 文件和数据）

6·The first architectural change was to create a write-through Vector Cache containing an array of tweet IDs which are serialized 64 bit integers.

第一个架构改动是创建一个直写式向量缓存包含了一个tweet ID的数组，tweet ID是序列化的64位整数。

7·Write-through caching pattern is used by the applications to write data to the cache which causes writes to an underlying resource (e.g. a database).

直写式缓存模式被用于将数据写入底层资源(如数据库)的缓存。

8·Listing 3 shows the responses times for the write-through scenario with reads taking less than 10 ms compared to the writes which are taking around 450-550 ms.

列表3展示了write - throug h场景下的响应时间，读取数据花费了不到10 ms，而写花费的时间达到了450——550 ms。

9·This scenario also has faster response times than the write-through caching scenario where an update to the cache results in an immediate update to the database.

它比write - through缓存具有更快的响应时间，后者对缓存的更新会导致对数据库的及时更新。

10·In a write-behind cache, data reads and updates are all serviced by the cache, but unlike a write-through cache, updates are not immediately propagated to the data store.

在write - behind缓存中，数据的读取和更新通过缓存进行，与write - through缓存不同，更新的数据并不会立即传到数据库。