• Because the user base also grew, data changes became slower and slower.

    因为用户量也在增长,数据修改变得越来越慢。

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  • Normally data changes are immediately pushed from the control to the model.

    一般数据变更会立刻从控制器推给模型。

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  • This approach makes it easier to assess the impact of architecture and data changes.

    这种方法使得评估架构和数据更改的影响变得更加容易。

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  • Antipatterns: Manually applying database and data changes in each target environment.

    反模式:在每个目标环境中手动应用数据库和数据更改。

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  • Using this model, data changes on a page can be automatically reflected on another page.

    通过使用这个模型,在一个页面上进行的数据修改可以自动地反映在另一个页面上。

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  • Asynchronous updates let data changes propagate to the clients in a Web 2.0 environment.

    异步更新允许将数据更改传播到Web 2.0环境中的各个客户端。

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  • A complementary method is to invalidate the cached entry when the underlying data changes.

    一种补充方法是,当底层数据改变时,让缓存的数据项失效。

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  • Mark your transaction as read-only if it doesn't involve data changes as mentioned in Section 4.3.1.

    如果不涉及数据变更,将事务标记为只读的,就像4.3.1节提到的那样。

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  • In each scenario, data changes are synchronized automatically, transaction by transaction, or on demand.

    在每一种场景中,数据修改都将被自动同步,可以逐一执行同步,也可以按需同步。

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  • The service layer does have one method involving data changes on both databases. Here is the pseudo-code.

    服务层上有一个方法设计在两个数据库上执行数据变更。

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  • Do not expect the output of any of the session bean methods to reflect data changes from running other methods.

    请不要期望任何会话bean方法的输出能反映因运行其它方法而造成的数据变化。

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  • Er can be configured so that if data changes are made on any server, the other participants become updated too.

    可以对ER进行配置,因此如果在任何服务器上修改了数据,那么也将更新其他参与节点。

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  • First, we'd like to show how we were able to protect the JTable's UI code from being altered by any data changes.

    首先,我们展示如何保护JTable的UI代码不因任何数据变化而遭到更改。

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  • Only data changes are required to be replicated, so read transaction are unaffected by the implementation of HADR.

    由于只需复制被更改的数据,所以读事务不受 HADR实现的影响。

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  • There is also an experimental facility to receive 'push' notifications the instant that data changes on the server.

    还有一个实验工具,在服务器上的数据发生变化时能立即接收“推”过来的通知。

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  • Whether a complex data structure is overkill for a plain resume depends on your needs and how often the data changes.

    一个复杂数据结构对于一份简单的简历是否过度,要取决于您的需求和数据更改的频率。

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  • Storing a single profile item is useful, but most data changes over time, particularly when you talk about energy usage.

    存储单个配置项目很有用,但大多数数据随时间改变,特别是当您谈论能源用途时。

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  • Some database changes require changes in data as well as schema, and some of those data changes require logical changes.

    有些数据库变化要求数据和模式一起变化,有些数据变化要求逻辑变化。

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  • And yet a third possibility is that there is simply a tremendous amount of data changes taking place throughout the day.

    而第三种可能性是这一天中的确进行了大量数据更改。

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  • So when the interested data changes, the Grid infrastructure (on the server) will send a notification message to the client.

    所以当感兴趣的数据发生变化时,Grid基础结构(在服务器上)会向客户机发送一个通知消息。

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  • It is also possible that over time, as your data changes, old partitioning keys become less optimal than they were previously.

    随着时间的推移,因数据的改变,旧的分区键有可能会不如之前那么好。

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  • Consumers of XML data returned from data-mining queries are often interested only in the data changes made since a previous query.

    从数据挖掘查询返回的XML数据的消费者通常只对自上次查询以来发生的数据变化感兴趣。

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  • When data changes or the values need to be computed, the summaries can be used without necessarily rerunning all of the computations.

    当数据更改或值需要被计算时,可以使用各级汇总,而不必重新运行所有计算。

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  • You can synchronize changes in data and schema. You can also address data changes that involve logical operations on your model objects.

    可以同步数据和模式中的变化,也可以解决涉及对模型对象进行逻辑操作的数据变化。

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  • In fact, the line of reasoning should simply be how frequently your data changes with respect to how frequently you perform your lookups.

    事实上,正确的思路应该是:执行查找的频率与数据变化的频率有关。

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  • It may also improve performance, as not every item in the repeat will need to have its associated controls redrawn when the data changes.

    它也可以改善性能,因为当数据更改时,并非repeat中的每一条目都需要刷新其关联控件。

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  • If the nature of the data changes over time, the KB accuracy can be verified periodically, using Classification Workbench reporting tools.

    如果数据的性质会随时间变化,那么可以定期使用ClassificationWorkbench报告工具检查知识库的精确性。

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  • Raw data providers are not sufficient for data that is always changing, because the components do not receive notification of data changes.

    原始数据提供程序不能满足经常改变的数据的需求,因为组件不能收到数据变更通知。

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  • HADR protects against data loss by replicating data changes from a source database, called the primary, to a target database, called the standby.

    HADR把数据修改从源数据库(称为主数据库)复制到目标数据库(称为备用数据库),从而防止数据丢失。

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  • Grails also supports intercepting Auto reload Events that can be used to monitor resources for any data changes and then reload the changes when they occur.

    Grails还支持对自动重新加载事件的拦截,这可用来监控资源的改变,当发生变化时会重新加载资源。

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