开始用“过去的冲击波”来思考

很久很久以前，有一个计算机王国 解释器和编译器。的优点引起了各种各样的争论 一个比另一个。当时的普遍意见是这样的:

解释器:快速开发(编辑和运行)。执行速度慢，因为每个语句都必须被解释为 每次执行的机器代码(想想这对于执行了数千次的循环意味着什么)。 编译器:开发(编辑、编译、链接和运行)缓慢。编译/链接步骤可能会花费大量时间)。快 来执行。整个程序已经是原生机器代码了。

运行时有一到两个数量级的差异 解释程序和编译程序之间存在性能差异。其他的区别 点，例如代码的运行时可变性，也有一些兴趣，但主要是 区别围绕着运行时性能问题。

今天的情况已经发展到这样的程度，编译/解释的区别是 几乎无关紧要。许多 编译语言调用的运行时服务并非如此 完全基于机器代码。而且，大多数解释型语言都被“编译”成字节码 之前执行。字节码解释器非常高效，可以与一些编译器生成的解释器相匹敌 从执行速度的角度来看代码。

典型的区别是编译器生成本机机器码，解释器读取源代码 使用某种运行时系统动态生成机器代码。 如今，经典的诠释者已所剩无几——几乎全部 编译成字节码(或其他一些半编译状态)，然后在虚拟“机器”上运行。

开始用“过去的冲击波”来思考

很久很久以前，有一个计算机王国 解释器和编译器。的优点引起了各种各样的争论 一个比另一个。当时的普遍意见是这样的:

解释器:快速开发(编辑和运行)。执行速度慢，因为每个语句都必须被解释为 每次执行的机器代码(想想这对于执行了数千次的循环意味着什么)。 编译器:开发(编辑、编译、链接和运行)缓慢。编译/链接步骤可能会花费大量时间)。快 来执行。整个程序已经是原生机器代码了。

运行时有一到两个数量级的差异 解释程序和编译程序之间存在性能差异。其他的区别 点，例如代码的运行时可变性，也有一些兴趣，但主要是 区别围绕着运行时性能问题。

今天的情况已经发展到这样的程度，编译/解释的区别是 几乎无关紧要。许多 编译语言调用的运行时服务并非如此 完全基于机器代码。而且，大多数解释型语言都被“编译”成字节码 之前执行。字节码解释器非常高效，可以与一些编译器生成的解释器相匹敌 从执行速度的角度来看代码。

典型的区别是编译器生成本机机器码，解释器读取源代码 使用某种运行时系统动态生成机器代码。 如今，经典的诠释者已所剩无几——几乎全部 编译成字节码(或其他一些半编译状态)，然后在虚拟“机器”上运行。

语言本身既不编译也不解释，只有语言的特定实现才是。Java就是一个很好的例子。有一个基于字节码的平台(JVM)、一个本机编译器(gcj)和一个用于Java超集(bsh)的互用器。那么Java现在是什么呢?字节码编译，本机编译还是解释?

其他既编译又解释的语言有Scala、Haskell或Ocaml。每种语言都有一个交互式解释器，以及一个字节码或本机机器码的编译器。

所以一般来说，用“编译型”和“解释型”来划分语言并没有多大意义。

Compile is the process of creating an executable program from code written in a compiled programming language. Compiling allows the computer to run and understand the program without the need of the programming software used to create it. When a program is compiled it is often compiled for a specific platform (e.g. IBM platform) that works with IBM compatible computers, but not other platforms (e.g. Apple platform). The first compiler was developed by Grace Hopper while working on the Harvard Mark I computer. Today, most high-level languages will include their own compiler or have toolkits available that can be used to compile the program. A good example of a compiler used with Java is Eclipse and an example of a compiler used with C and C++ is the gcc command. Depending on how big the program is it should take a few seconds or minutes to compile and if no errors are encountered while being compiled an executable file is created.check this information

The Python Book©2015 Imagine Publishing Ltd，简单地通过第10页中提到的以下提示来区分差异:

像Python这样的解释型语言是指将源代码转换为机器码，然后在每次程序运行时执行的语言。这与编译语言(如C)不同，后者只将源代码转换为机器代码一次——然后在程序每次运行时执行生成的机器代码。

编译器和解释器做同样的工作:将一种编程语言翻译成另一种编程语言，通常更接近硬件，通常是直接可执行的机器代码。

Traditionally, "compiled" means that this translation happens all in one go, is done by a developer, and the resulting executable is distributed to users. Pure example: C++. Compilation usually takes pretty long and tries to do lots of expensive optmization so that the resulting executable runs faster. End users don't have the tools and knowledge to compile stuff themselves, and the executable often has to run on a variety of hardware, so you can't do many hardware-specific optimizations. During development, the separate compilation step means a longer feedback cycle.

Traditionally, "interpreted" means that the translation happens "on the fly", when the user wants to run the program. Pure example: vanilla PHP. A naive interpreter has to parse and translate every piece of code every time it runs, which makes it very slow. It can't do complex, costly optimizations because they'd take longer than the time saved in execution. But it can fully use the capabilities of the hardware it runs on. The lack of a separrate compilation step reduces feedback time during development.

But nowadays "compiled vs. interpreted" is not a black-or-white issue, there are shades in between. Naive, simple interpreters are pretty much extinct. Many languages use a two-step process where the high-level code is translated to a platform-independant bytecode (which is much faster to interpret). Then you have "just in time compilers" which compile code at most once per program run, sometimes cache results, and even intelligently decide to interpret code that's run rarely, and do powerful optimizations for code that runs a lot. During development, debuggers are capable of switching code inside a running program even for traditionally compiled languages.