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    ASP.NET Core 2.0 MVC项目实战

    毕业后入职现在的公司快有一个月了,公司主要的产品用的是C/S架构,再加上自己现在还在学习维护很老的delphi项目,还是有很多不情愿的。之前实习时主要是做.NET的B/S架构的项目,主要还是用的那种传统的开发模式,只有一个项目用到了Web API,自己负责后端的接口功能实现。既然现在没办法改变现状,那就先改变自己吧。定了个计划,下班后慢慢的开始学习ASP.NET Core Web API和Vue,准备从前端到后端自己写一个小项目玩玩,毕竟代码这个东西,时间长了是会忘的。 嗯,有点扯远了。这个MVC项目是我的毕业设计,虽然写的比较烂,而且当时为了赶紧写完,代码的冗余程度有点高,但还是希望能给一些准备入门ASP.NET Core MVC的童鞋提供些借鉴吧。代码我放到Github上了,源码地址https://github.com/Lanesra712/Danvic.PSU,欢迎大神们拍砖,指出不足处。

    03

    一行代码, Java 怎样把List 转成 Map 的方法( Java 8 中的Stream API )

    java.util.stream public interface Collector<T, A, R> A mutable reduction operation that accumulates input elements into a mutable result container, optionally transforming the accumulated result into a final representation after all input elements have been processed. Reduction operations can be performed either sequentially or in parallel. Examples of mutable reduction operations include: accumulating elements into a Collection; concatenating strings using a StringBuilder; computing summary information about elements such as sum, min, max, or average; computing "pivot table" summaries such as "maximum valued transaction by seller", etc. The class Collectors provides implementations of many common mutable reductions. A Collector is specified by four functions that work together to accumulate entries into a mutable result container, and optionally perform a final transform on the result. They are: creation of a new result container (supplier()) incorporating a new data element into a result container (accumulator()) combining two result containers into one (combiner()) performing an optional final transform on the container (finisher()) Collectors also have a set of characteristics, such as Collector.Characteristics.CONCURRENT, that provide hints that can be used by a reduction implementation to provide better performance. A sequential implementation of a reduction using a collector would create a single result container using the supplier function, and invoke the accumulator function once for each input element. A parallel implementation would partition the input, create a result container for each partition, accumulate the contents of each partition into a subresult for that partition, and then use the combiner function to merge the subresults into a combined result. To ensure that sequential and parallel executions produce equivalent results, the collector functions must satisfy an identity and an associativity constraints. The identity constraint says that for any partially accumulated result, combi

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