Tracking data changes using UDI counters.
使用udi计数器跟踪数据变化。
The data changes infrequently relative to the request volume.
数据不经常地相对于请求容量发生变化。
Data changes made during every user request would need to be saved.
每个用户请求期间所做的数据变更都要被保存。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
Further, when the back-end data changes, the updated data is automatically presented to users in these charts.
此外，当后端数据发生变化时，更新后的数据将自动反应到这些图表中并呈现给用户。
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。
Data is loaded into the MDM Server software repository mostly as data changes from existing legacy systems.
此时数据被加载到MDMServer储存库，但大部分数据更改仍然来自现有的遗留系统。
But it is also possible that the data changes are fast enough to use up the log space before the log is truncated.
但是，由于数据改变的足够快以至于在日志被截断以前而用尽了日志的空间，这种情况也是可能发生的。
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。
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。
Migrations combine power and simplicity to coordinate both schema changes and data changes using a wrapping approach.
迁移用包装的方式组合了协调模式变化和数据变化的威力和简单性。
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。
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。
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。
Rather, when data changes, the database should just add another record and note duly the previous value for that record.
当数据发生变化，数据库应该只添加另一条记录，并标记删除该记录的前一个值。
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。
If you have a time cyclic database schema where data changes are localized to rows with certain specific column values.
您的数据库模式是周期性的，数据更改主要针对具有特定列值的行。
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。
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。
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。
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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