Model Computed Data模型计算数据

Overview概述

Often, an application needs to derive a value from source data stored in a database. 通常，应用程序需要从存储在数据库中的源数据中派生值。Computing a new value may require significant CPU resources, especially in the case of large data sets or in cases where multiple documents must be examined.计算一个新值可能需要大量的CPU资源，尤其是在大型数据集或必须检查多个文档的情况下。

If a computed value is requested often, it can be more efficient to save that value in the database ahead of time. 如果经常请求计算值，那么提前将该值保存在数据库中会更有效。This way, when the application requests data, only one read operation is required.这样，当应用程序请求数据时，只需要一次读取操作。

Computed Pattern计算模式

If your reads significantly outnumber your writes, the computed pattern reduces the frequency of having to perform computations. 如果您的读取次数明显多于写入次数，则计算模式会降低执行计算的频率。Instead of attaching the burden of computation to every read, the application stores the computed value and recalculates it as needed. 应用程序存储计算值并根据需要重新计算，而不是将计算负担附加到每次读取。The application can either recompute the value with every write that changes the computed value's source data, or as part of a periodic job.应用程序可以在每次写入更改计算值的源数据时重新计算值，也可以作为周期性作业的一部分。

Example实例

An application displays movie viewer and revenue information.应用程序显示电影查看器和收入信息。

Consider the following screenings collection:考虑以下screenings集合：

// screenings collection

{
    "theater": "Alger Cinema",
    "location": "Lakeview, OR",
    "movie_title": "Reservoir Dogs",
    "num_viewers": 344,
    "revenue": 3440
}
{
    "theater": "City Cinema",
    "location": "New York, NY",
    "movie_title": "Reservoir Dogs",
    "num_viewers": 1496,
    "revenue": 22440
}
{
    "theater": "Overland Park Cinema",
    "location": "Boise, ID",
    "movie_title": "Reservoir Dogs",
    "num_viewers": 760,
    "revenue": 7600
}

Users often want to know how many people saw a certain movie and how much money that movie made. 用户通常想知道有多少人看过某部电影，以及这部电影赚了多少钱。In this example, to total num_viewers and revenue, you must perform a read for theaters that screened a movie with the title "Reservoir Dogs" and sum the values of those fields. 在本例中，为了合计num_viewers和revenue，必须对放映标题为“Reservoir Dogs”的电影的影院执行读取操作，并对这些字段的值求和。To avoid performing that computation every time the information is requested, you can compute the total values and store them in a movies collection with the movie record itself:为了避免每次请求信息时都执行这种计算，您可以计算总值，并将它们与movies记录本身一起存储在电影集合中：

// movies collection

{
    "title": "Reservoir Dogs",
    "total_viewers": 2600,
    "total_revenue": 33480,
    ...
}

In a low write environment, the computation could be done in conjunction with any update of the screenings data.在低写环境中，可以结合screenings数据的任何更新来进行计算。

In an environment with more regular writes, the computations could be done at defined intervals - every hour for example. 在具有更规则写入的环境中，计算可以按定义的间隔进行，例如每小时一次。The source data in screenings isn't affected by writes to the movies collection, so you can run calculations at any time.screenings中的源数据不受写入movies集合的影响，因此您可以随时运行计算。

This is a common design pattern that reduces CPU workload and increases application performance. 这是一种常见的设计模式，可以减少CPU工作负载并提高应用程序性能。Whenever you are performing the same calculations repeatedly and you have a high read to write ratio, consider the Computed Pattern.每当重复执行相同的计算并且读写比较高时，请考虑“计算模式”。