Asynchronous processing in gae.
在GAE中异步处理。
GAE provides a management interface.
GAE提供了一个管理界面。
In GAE, it also requires queuing.
在GAE 中，它也需要排队。
A task in a GAE queue is just a servlet.
gae队列上的任务刚好是一个servlet。
Overall, GAE provides a well-designed and scalable PaaS.
总体而言，GAE提供了精心设计并可伸缩的PaaS。
The GAE is a platform for creating Web applications.
GAE是创建Web应用程序的平台。
The GAE datastore leverages an index for any query issued.
GAE数据存储对发出的任何查询使用索引。
GAE provides excellent integration with other Google services.
GAE提供与其他Google服务的出色集成。
That's fine, except that GAE allows only 100 indexes per table.
这很好，除了GAE只允许每个表100个索引以外。
That makes GAE an unreliable platform for mashup-type applications.
这使GAE 成为不可靠混合类型应用程序平台。
To make the GAE scalable, it cannot be tied up by long-running requests.
要让GAE具有可伸缩性，就需要减少长时间运行的请求。
GAE promises and delivers scalability but not necessarily raw performance.
GAE承诺并传递可伸缩性，但不一定是原始性能。
Deciding which index to create is a significant burden for GAE developers.
决定要创建哪个索引对于GAE开发人员来说是一个很大的负担。
For its part, GAE limits the returned dataset of each query to 1,000 rows.
就其本身而言，GAE将每次查询的返回数据集限定为1000行。
If you want to build your apps on GAE, there are a few rules you must obey.
如果您想在GAE上建立您的应用程序，则您必须遵守一些规则。
Your application should be deployed to GAE successfully after a short while.
您的应用程序稍后就会被成功部署到GAE。
GAE provides excellent scalability as measured by a consistent response time.
GAE提供出色的可伸缩性就像通过一致响应时间所衡量的那样。
In GAE, you can't write files to disk because there's no useable file system.
在GAE 上，您是无法将文件写入到磁盘的，因为 GAE根本没有可用的文件系统。
This managed-session object database is transparent to developers - much like GAE.
此托管会话对象数据库对开发人员透明—这很像GAE。
Now, the logging could be viewed using the Logs console in GAE, as shown below.
现在，可以使用GAE中的日志控制台查看日志记录，如下所示。
As it turns out, GAE is an excellent example of a message-oriented middleware system.
事实证明，GAE是面向消息中间件系统的一个很好的例子。
To create each employee, you will use a SOAP service running on GAE, one at a time.
要创建每个员工，您将使用运行在GAE上的一个SOAP服务—一次一个。
Keys in the GAE datastore are also unique, just as they would be in a traditional database.
GAE数据存储中的键也是惟一的，正如在传统的数据库中一样。
Making matters worse, GAE provides no easy way to delete indexes that are no longer in use.
更糟的是，GAE没有提供简单的方式来删除不再使用的索引。
Beanstalk's advantages over GAE are rooted in fundamentally different cloud-based service models.
Beanstalk对GAE的优势植根于完全不同的基于云的服务模型。
In this article, we'll build an example application that implements each GAE storage option in turn.
在本文中，我们将创建一个示例应用程序，依次实现每一种GAE存储方法。
It also could be prohibitive in cases where the client is running on GAE, where CPU hours are billable.
在运行于GAE的客户端上，由于CPU时间是会产生费用的，所以它也可能是不适合使用的。
In my own anecdotal experience, GAE often takes 1 to 3 seconds to respond to database-related requests.
以我的经验，GAE常常用1到3秒对数据库相关请求作出响应。
In Part 1, we built a small application for aggregating content feeds and serving them through the GAE.
在第1部分中，我们构建了一个小型应用程序，用来聚集内容提要并通过GAE处理它们。
The server simulates a Datastore and GAE services so the developer can test most of the application locally.
服务器会模拟数据库和GAE服务，从而开发者可以在本地测试应用程序的大部分功能。
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