你会ToList()吗? [英] Do you ToList()?

问题描述

您是否希望在处理LINQ查询结果时使用默认类型?

Do you have a default type that you prefer to use in your dealings with the results of LINQ queries?

默认情况下，LINQ将返回IEnumerable<>或IOrderedEnumerable<>.我们发现List<>通常对我们更有用，因此习惯了大部分时间ToList()进行查询，并且肯定在函数参数和返回值中使用List<>.

By default LINQ will return an IEnumerable<> or maybe an IOrderedEnumerable<>. We have found that a List<> is generally more useful to us, so have adopted a habit of ToList()ing our queries most of the time, and certainly using List<> in our function arguments and return values.

唯一的例外是在LINQ to SQL中，其中调用.ToList()会过早枚举IEnumerable.

The only exception to this has been in LINQ to SQL where calling .ToList() would enumerate the IEnumerable prematurely.

我们还广泛使用WCF，其默认集合类型为System.Array.为了与代码库的其余部分保持一致，我们始终在VS2008的服务引用设置"对话框中将其更改为System.Collections.Generic.List.

We are also using WCF extensively, the default collection type of which is System.Array. We always change this to System.Collections.Generic.List in the Service Reference Settings dialog in VS2008 for consistency with the rest of our codebase.

你做什么?

推荐答案

ToList 始终会立即评估序列-不仅仅是在LINQ to SQL中.如果您愿意的话，那很好-但这并不总是合适的.

ToList always evaluates the sequence immediately - not just in LINQ to SQL. If you want that, that's fine - but it's not always appropriate.

就我个人而言，我会尽量避免声明您直接返回List<T>-通常IList<T>更合适，并且允许您稍后更改为其他实现.当然，有些操作只能在List<T>本身上指定...这种决定总是很棘手.

Personally I would try to avoid declaring that you return List<T> directly - usually IList<T> is more appropriate, and allows you to change to a different implementation later on. Of course, there are some operations which are only specified on List<T> itself... this sort of decision is always tricky.

(我会在注释中添加它，但它太庞大了.)延迟执行使您可以处理太大而无法容纳在内存中的数据源.例如，如果您正在处理日志文件-将其从一种格式转换为另一种格式，将其上传到数据库中，计算一些统计信息或类似内容-您很可能能够通过流化处理任意数量的数据，但是您真的不想想要将所有内容都吸收到内存中.对于您的特定应用程序，这可能不是要关心的问题，但是要牢记这一点.

(I would have put this in a comment, but it would be too bulky.) Deferred execution allows you to deal with data sources which are too big to fit in memory. For instance, if you're processing log files - transforming them from one format to another, uploading them into a database, working out some stats, or something like that - you may very well be able to handle arbitrary amounts of data by streaming it, but you really don't want to suck everything into memory. This may not be a concern for your particular application, but it's something to bear in mind.

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