Lisp的本质(The Nature of Lisp)
http://www.defmacro.org/ramblings/lisp.html
简介
最初在web的某些角落偶然看到有人赞美Lisp时, 我那时已经是一个颇有经验的程序员。
在我的履历上, 掌握的语言范围相当广泛, 象C++, Java, C#主流语言等等都不在话下, 我觉得我差不多知道所有的有关编程语言的事情。对待编程语言的问题上, 我觉得自己不太会遇到什么大问题。其实我大错特错了。
我试着学了一下Lisp, 结果马上就撞了墙。我被那些范例代码吓坏了。我想很多初次接触Lisp语言的人, 一定也有过类似的感受。Lisp的语法太次了。一个语言的发明人, 居然不肯用心弄出一套漂亮的语法, 那谁还会愿意学它。反正, 我是确确实实被那些难看的无数
的括号搞蒙了。
回过神来之后, 我和Lisp社区的那伙人交谈, 诉说我的沮丧心情。结果, 立马就有一大套理论砸过来, 这套理论在Lisp社区处处可见, 几成惯例。
比如说:
Lisp的括号只是表面现象;
Lisp的代码和数据的表达方式没有差别, 而且比XML语法高明许多, 所以有无穷的好处;
Lisp有强大无比的元语言能力, 程序员可以写出自我维护的代码; Lisp可以创造出针对特定应用的语言子集DSL;
Lisp的运行时和编译时没有明确的分界;
等等, 等等, 等等。
这么长的赞美词虽然看起来相当动人, 不过对我毫无意义。没人能给我演示这些东西是如何应用的, 因为这些东西一般来说只有在大型系统才会用到。我争辩说, 这些东西传统语言一样办得到。在和别人争论了数个小时之后, 我最终还是放弃了学Lisp的念头。为什么要花费几个月的时间学习语法这么难看的语言呢? 这种语言的概念这么晦涩, 又没什么好懂的例子。也许这语言不是该我这样的人学的。
几个月来, 我承受着这些Lisp辩护士对我心灵的重压。我一度陷入了困惑。我认识一些绝顶聪明的人, 我对他们相当尊敬, 我看到他们对Lisp的赞美达到了宗教般的高度。这就是说, Lisp中一定有某种神秘的东西存在, 我不能忍受自己对此的无知, 好奇心和求知欲最
终不可遏制。我于是咬紧牙关埋头学习Lisp, 经过几个月的时间费劲心力的练习, 终于,我看到了那无穷无尽的泉水的源头。在经过脱胎换骨的磨练之后, 在经过七重地狱的煎熬之后, 终于, 我明白了。
顿悟在突然之间来临。曾经许多次, 我听到别人引用雷蒙德(译者注: 论文<<大教堂和市集>>的作者, 著名的黑客社区理论家)的话: “Lisp语言值得学习。
当你学会Lisp之后, 你会拥有深刻的体验。就算你平常并不用Lisp编程, 它也会使你成为更加优秀的程序员”。
过去, 我根本不懂这些话的含义, 我也不相信这是真的。可是现在我懂得了。这些话蕴含的真理远远超过我过去的想像。我内心体会到一种神圣的情感, 一瞬间的顿悟, 几乎使我对电脑科学的观念发生了根本的改变。
顿悟的那一刻, 我成了Lisp的崇拜者。我体验到了宗教大师的感受: 一定要把我的知识传布开来, 至少要让10个迷失的灵魂得到拯救。按照通常的办法, 我把这些道理(就是刚开始别人砸过来的那一套, 不过现在我明白了真实的含义)告诉旁人。结果太令人失望了,
只有少数几个人在我坚持之下, 发生了一点兴趣, 但是仅仅看了几眼Lisp代码, 他们就退却了。照这样的办法, 也许费数年功夫能造就了几个Lisp迷, 但我觉得这样的结果太差强人意了, 我得想一套有更好的办法。
我深入地思考了这个问题。是不是Lisp有什么很艰深的东西, 令得那么多老练的程序员都不能领会? 不是, 没有任何绝对艰深的东西。因为我能弄懂, 我相信其他人也一定能。那么问题出在那里? 后来我终于找到了答案。我的结论就是, 凡是教人学高级概念, 一定要
从他已经懂得的东西开始。如果学习过程很有趣, 学习的内容表达得很恰当, 新概念就会变得相当直观。这就是我的答案。
所谓元编程, 所谓数据和代码形式合一, 所谓自修改代码, 所谓特定应用的子语言, 所有这些概念根本就是同族概念, 彼此互为解释, 肯定越讲越不明白。还是从实际的例子出发最有用。
我把我的想法说给Lisp程序员听, 遭到了他们的反对。”这些东西本身当然不可能用熟悉的知识来解释, 这些概念完全与众不同, 你不可能在别人已有的经验里找到类似的东西”,可是我认为这些都是遁词。他们又反问我, “你自己为啥不试一下?” 好吧, 我来试一下。
这篇文章就是我尝试的结果。我要用熟悉的直观的方法来解释Lisp, 我希望有勇气的人读完它, 拿杯饮料, 深呼吸一下, 准备被搞得晕头转向。来吧, 愿你获得大能。
重新审视XML
千里之行始于足下。让我们的第一步从XML开始。可是XML已经说得更多的了, 还能有什么新意思可说呢? 有的。XML自身虽然谈谈不上有趣, 但是XML和Lisp的关系却相当有趣。
光剑评注：其实，说了这么多废话，无非就是： 一切皆是映射。不管是嵌套 XML，还是 Lisp 嵌套括号，还是 XXX 的 Map 数据结构，一切都是树形结构——映射。
XML和Lisp的概念有着惊人的相似之处。XML是我们通向理解Lisp的桥梁。好吧, 我们且把
XML当作活马医。让我们拿好手杖, 对XML的无人涉及的荒原地带作一番探险。我们要从一
个全新的视角来考察这个题目。
表面上看, XML是一种标准化语法, 它以适合人阅读的格式来表达任意的层次化数据
(hirearchical data)。象任务表(to-do list), 网页, 病历, 汽车保险单, 配置文件等
等, 都是XML用武的地方。比如我们拿任务表做例子:
<todo name=”housework”>
<item priority=”high”>Clean the house.</item>
<item priority=”medium”>Wash the dishes.</item>
<item priority=”medium”>Buy more soap.</item>
</todo>
解析这段数据时会发生什么情况? 解析之后的数据在内存中怎样表示? 显然, 用树来表示
这种层次化数据是很恰当的。说到底, XML这种比较容易阅读的数据格式, 就是树型结构
数据经过序列化之后的结果。任何可以用树来表示的数据, 同样可以用XML来表示, 反之
亦然。希望你能懂得这一点, 这对下面的内容极其重要。
再进一步。还有什么类型的数据也常用树来表示? 无疑列表(list)也是一种。上过编译课
吧? 还模模糊糊记得一点吧? 源代码在解析之后也是用树结构来存放的, 任何编译程序都
会把源代码解析成一棵抽象语法树, 这样的表示法很恰当, 因为源代码就是层次结构的:
函数包含参数和代码块, 代码快包含表达式和语句, 语句包含变量和运算符等等。
我们已经知道, 任何树结构都可以轻而易举的写成XML, 而任何代码都会解析成树, 因此,
任何代码都可以转换成XML, 对不对? 我举个例子, 请看下面的函数:
int add(int arg1, int arg2)
{
return arg1+arg2;
}
能把这个函数变成对等的XML格式吗? 当然可以。我们可以用很多种方式做到, 下面是其
中的一种, 十分简单:
<define-function return-type=”int” name=”add”>
<arguments>
<argument type=”int”>arg1</argument>
<argument type=”int”>arg2</argument>
</arguments>
<body>
<return>
<add value1=”arg1″ value2=”arg2″ />
</return>
</body>
</define>
这个例子非常简单, 用哪种语言来做都不会有太大问题。我们可以把任何程序码转成XML,
也可以把XML转回到原来的程序码。我们可以写一个转换器, 把Java代码转成XML, 另一个
转换器把XML转回到Java。一样的道理, 这种手段也可以用来对付C++(这样做跟发疯差不
多么。可是的确有人在做, 看看GCC-XML(http://www.gccxml.org)就知道了)。进一步说,
凡是有相同语言特性而语法不同的语言, 都可以把XML当作中介来互相转换代码。实际上
几乎所有的主流语言都在一定程度上满足这个条件。我们可以把XML作为一种中间表示法,
在两种语言之间互相译码。比方说, 我们可以用Java2XML把Java代码转换成XML, 然后用
XML2CPP再把XML转换成C++代码, 运气好的话, 就是说, 如果我们小心避免使用那些C++不
具备的Java特性的话, 我们可以得到完好的C++程序。这办法怎么样, 漂亮吧?
这一切充分说明, 我们可以把XML作为源代码的通用存储方式, 其实我们能够产生一整套
使用统一语法的程序语言, 也能写出转换器, 把已有代码转换成XML格式。如果真的采纳
这种办法, 各种语言的编译器就用不着自己写语法解析了, 它们可以直接用XML的语法解
析来直接生成抽象语法树。
说到这里你该问了, 我们研究了这半天XML, 这和Lisp有什么关系呢? 毕竟XML出来之时,
Lisp早已经问世三十年了。这里我可以保证, 你马上就会明白。不过在继续解释之前, 我
们先做一个小小的思维练习。看一下上面这个XML版本的add函数例子, 你怎样给它分类,
是代码还是数据? 不用太多考虑都能明白, 把它分到哪一类都讲得通。它是XML, 它是标
准格式的数据。我们也知道, 它可以通过内存中的树结构来生成(GCC-XML做的就是这个事
情)。它保存在不可执行的文件中。我们可以把它解析成树节点, 然后做任意的转换。显
而易见, 它是数据。不过且慢, 虽然它语法有点陌生, 可它又确确实实是一个add函数,
对吧? 一旦经过解析, 它就可以拿给编译器编译执行。我们可以轻而易举写出这个XML
代码解释器, 并且直接运行它。或者我们也可以把它译成Java或C++代码, 然后再编译运
行。所以说, 它也是代码。
我们说到那里了? 不错, 我们已经发现了一个有趣的关键之点。过去被认为很难解的概念
已经非常直观非常简单的显现出来。代码也是数据, 并且从来都是如此。这听起来疯疯癫
癫的, 实际上却是必然之事。我许诺过会以一种全新的方式来解释Lisp, 我要重申我的许
诺。但是我们此刻还没有到预定的地方, 所以还是先继续上边的讨论。
刚才我说过, 我们可以非常简单地实现XML版的add函数解释器, 这听起来好像不过是说说
而已。谁真的会动手做一下呢? 未必有多少人会认真对待这件事。随便说说, 并不打算真
的去做, 这样的事情你在生活中恐怕也遇到吧。你明白我这样说的意思吧, 我说的有没有
打动你? 有哇, 那好, 我们继续。
重新审视Ant
我们现在已经来到了月亮背光的那一面, 先别忙着离开。再探索一下, 看看我们还能发现
什么东西。闭上眼睛, 想一想2000年冬天的那个雨夜, 一个名叫James Duncan Davidson
的杰出的程序员正在研究Tomcat的servlet容器。那时, 他正小心地保存好刚修改过的文
件, 然后执行make。结果冒出了一大堆错误, 显然有什么东西搞错了。经过仔细检查, 他
想, 难道是因为tab前面加了个空格而导致命令不能执行吗? 确实如此。老是这样, 他真
的受够了。乌云背后的月亮给了他启示, 他创建了一个新的Java项目, 然后写了一个简单
但是十分有用的工具, 这个工具巧妙地利用了Java属性文件中的信息来构造工程, 现在
James可以写makefile的替代品, 它能起到相同的作用, 而形式更加优美, 也不用担心有
makefile那样可恨的空格问题。这个工具能够自动解释属性文件, 然后采取正确的动作来
编译工程。真是简单而优美。
(作者注: 我不认识James, James也不认识我, 这个故事是根据网上关于Ant历史的帖子
虚构的)
使用Ant构造Tomcat之后几个月, 他越来越感到Java的属性文件不足以表达复杂的构造指
令。文件需要检出, 拷贝, 编译, 发到另外一台机器, 进行单元测试。要是出错, 就发邮
件给相关人员, 要是成功, 就继续在尽可能高层的卷(volumn)上执行构造。追踪到最后,
卷要回复到最初的水平上。确实, Java的属性文件不够用了, James需要更有弹性的解决
方案。他不想自己写解析器(因为他更希望有一个具有工业标准的方案)。XML看起来是个
不错的选择。他花了几天工夫把Ant移植到XML，于是，一件伟大的工具诞生了。
Ant是怎样工作的？原理非常简单。Ant把包含有构造命令的XML文件(算代码还是算数据,
你自己想吧)，交给一个Java程序来解析每一个元素，实际情况比我说的还要简单得多。
一个简单的XML指令会导致具有相同名字的Java类装入，并执行其代码。
<copy todir="../new/dir">
<fileset dir="src_dir" />
</copy>
这段文字的含义是把源目录复制到目标目录，Ant会找到一个”copy”任务(实际上就是一个
Java类), 通过调用Java的方法来设置适当参数(todir和fileset)，然后执行这个任务。
Ant带有一组核心类, 可以由用户任意扩展, 只要遵守若干约定就可以。Ant找到这些类,
每当遇到XML元素有同样的名字, 就执行相应的代码。过程非常简单。Ant做到了我们前面
所说的东西: 它是一个语言解释器, 以XML作为语法, 把XML元素转译为适当的Java指令。
我们可以写一个”add”任务, 然后, 当发现XML中有add描述的时候, 就执行这个add任务。
由于Ant是非常流行的项目, 前面展示的策略就显得更为明智。毕竟, 这个工具每天差不
多有几千家公司在使用。
到目前为之, 我还没有说Ant在解析XML时所遇到困难。你也不用麻烦去它的网站上去找答
案了, 不会找到有价值的东西。至少对我们这个论题来说是如此。我们还是继续下一步讨
论吧。我们答案就在那里。
为什么是XML
有时候正确的决策并非完全出于深思熟虑。我不知道James选择XML是否出于深思熟虑。也
许仅仅是个下意识的决定。至少从James在Ant网站上发表的文章看起来, 他所说的理由完
全是似是而非。他的主要理由是移植性和扩展性, 在Ant案例上, 我看不出这两条有什么
帮助。使用XML而不是Java代码, 到底有什么好处? 为什么不写一组Java类, 提供api来满
足基本任务(拷贝目录, 编译等等), 然后在Java里直接调用这些代码? 这样做仍然可以保
证移植性, 扩展性也是毫无疑问的。而且语法也更为熟悉, 看着顺眼。那为什么要用 XML
呢? 有什么更好的理由吗?
有的。虽然我不确定James是否确实意识到了。在语义的可构造性方面, XML的弹性是Java
望尘莫及的。我不想用高深莫测的名词来吓唬你, 其中的道理相当简单, 解释起来并不费
很多功夫。好, 做好预备动作, 我们马上就要朝向顿悟的时刻做奋力一跃。
上面的那个copy的例子, 用Java代码怎样实现呢? 我们可以这样做:
CopyTask copy = new CopyTask();
Fileset fileset = new Fileset();
fileset.setDir("src_dir");
copy.setToDir("../new/dir");
copy.setFileset(fileset);
copy.excute();
这个代码看起来和XML的那个很相似, 只是稍微长一点。差别在那里? 差别在于XML构造了
一个特殊的copy动词, 如果我们硬要用Java来写的话, 应该是这个样子:
copy("../new/dir");
{
fileset("src_dir");
}
看到差别了吗? 以上代码(如果可以在Java中用的化), 是一个特殊的copy算符, 有点像
for循环或者Java5中的foreach循环。如果我们有一个转换器, 可以把XML转换到Java, 大
概就会得到上面这段事实上不可以执行的代码。因为Java的技术规范是定死的, 我们没有
办法在程序里改变它。我们可以增加包, 增加类, 增加方法, 但是我们没办法增加算符,
而对于XML, 我们显然可以任由自己增加这样的东西。对于XML的语法树来说, 只要原意,
我们可以任意增加任何元素, 因此等于我们可以任意增加算符。如果你还不太明白的话,
看下面这个例子, 加入我们要给Java引入一个unless算符:
unless(someObject.canFly())
{
someObject.transportByGround():
}
在上面的两个例子中, 我们打算给Java语法扩展两个算符, 成组拷贝文件算符和条件算符
unless, 我们要想做到这一点, 就必须修改Java编译器能够接受的抽象语法树, 显然我们
无法用Java标准的功能来实现它。但是在XML中我们可以轻而易举地做到。我们的解析器
根据 XML元素, 生成抽象语法树, 由此生成算符, 所以, 我们可以任意引入任何算符。
对于复杂的算符来说, 这样做的好处显而易见。比如, 用特定的算符来做检出源码, 编译
文件, 单元测试, 发送邮件等任务, 想想看有多么美妙。对于特定的题目, 比如说构造软
件项目, 这些算符的使用可以大幅减低少代码的数量。增加代码的清晰程度和可重用性。
解释性的XML可以很容易的达到这个目标。XML是存储层次化数据的简单数据文件, 而在
Java中, 由于层次结构是定死的(你很快就会看到, Lisp的情况与此截然不同), 我们就没
法达到上述目标。也许这正是Ant的成功之处呢。
你可以注意一下最近Java和C#的变化(尤其是C#3.0的技术规范), C#把常用的功能抽象出
来, 作为算符增加到C#中。C#新增加的query算符就是一个例子。它用的还是传统的作法:
C#的设计者修改抽象语法树, 然后增加对应的实现。如果程序员自己也能修改抽象语法树
该有多好! 那样我们就可以构造用于特定问题的子语言(比如说就像Ant这种用于构造项目
的语言), 你能想到别的例子吗? 再思考一下这个概念。不过也不必思考太甚, 我们待会
还会回到这个题目。那时候就会更加清晰。
离Lisp越来越近
我们先把算符的事情放一放, 考虑一下Ant设计局限之外的东西。我早先说过, Ant可以通
过写Java类来扩展。Ant解析器会根据名字来匹配XML元素和Java类, 一旦找到匹配, 就执
行相应任务。为什么不用Ant自己来扩展Ant呢? 毕竟核心任务要包含很多传统语言的结构
(例如”if”), 如果Ant自身就能提供构造任务的能力(而不是依赖java类), 我们就可以得
到更高的移植性。我们将会依赖一组核心任务(如果你原意, 也不妨把它称作标准库), 而
不用管有没有Java 环境了。这组核心任务可以用任何方式来实现, 而其他任务建筑在这
组核心任务之上, 那样的话, Ant就会成为通用的, 可扩展的, 基于XML的编程语言。考虑
下面这种代码的可能性:
<task name="Test">
<echo message="Hello World" />
</task>
<Test />
如果XML支持”task”的创建, 上面这段代码就会输出”Hello World!”. 实际上, 我们可以
用Java写个”task”任务, 然后用Ant-XML来扩展它。Ant可以在简单原语的基础上写出更复
杂的原语, 就像其他编程语言常用的作法一样。这也就是我们一开始提到的基于XML的编
程语言。这样做用处不大(你知道为甚么吗?), 但是真的很酷。
再看一回我们刚才说的Task任务。祝贺你呀, 你在看Lisp代码!!! 我说什么? 一点都不像
Lisp吗? 没关系, 我们再给它收拾一下。
比XML更好
前面一节说过, Ant自我扩展没什么大用, 原因在于XML很烦琐。对于数据来说, 这个问题
还不太大, 但如果代码很烦琐的话, 光是打字上的麻烦就足以抵消它的好处。你写过Ant
的脚本吗? 我写过, 当脚本达到一定复杂度的时候, XML非常让人厌烦。想想看吧, 为了
写结束标签, 每个词都得打两遍, 不发疯算好的!
为了解决这个问题, 我们应当简化写法。须知, XML仅仅是一种表达层次化数据的方式。
我们并不是一定要使用尖括号才能得到树的序列化结果。我们完全可以采用其他的格式。
其中的一种(刚好就是Lisp所采用的)格式, 叫做s表达式。s表达式要做的和XML一样, 但
它的好处是写法更简单, 简单的写法更适合代码输入。后面我会详细讲s表达式。这之前
我要清理一下XML的东西。考虑一下关于拷贝文件的例子:
<copy toDir="../new/dir">
<fileset dir="src_dir">
</copy>
想想看在内存里面, 这段代码的解析树在内存会是什么样子? 会有一个”copy”节点, 其下
有一个 “fileset”节点, 但是属性在哪里呢? 它怎样表达呢? 如果你以前用过XML, 并且
弄不清楚该用元素还是该用属性, 你不用感到孤单, 别人一样糊涂着呢。没人真的搞得清
楚。这个选择与其说是基于技术的理由, 还不如说是闭着眼瞎摸。从概念上来讲, 属性也
是一种元素, 任何属性能做的, 元素一样做得到。XML引入属性的理由, 其实就是为了让
XML写法不那么冗长。比如我们看个例子:
<copy>
<toDir>../new/dir</toDir>
<fileset>
<dir>src_dir</dir>
</fileset>
</copy>
两下比较, 内容的信息量完全一样, 用属性可以减少打字数量。如果XML没有属性的话,
光是打字就够把人搞疯掉。
说完了属性的问题, 我们再来看一看s表达式。之所以绕这么个弯, 是因为s表达式没有属
性的概念。因为s表达式非常简练, 根本没有必要引入属性。我们在把XML转换成s表达式
的时候, 心里应该记住这一点。看个例子, 上面的代码译成s表达式是这样的:
(copy
(todir "../new/dir")
(fileset (dir "src_dir")))
仔细看看这个例子, 差别在哪里? 尖括号改成了圆括号, 每个元素原来是有一对括号标记
包围的, 现在取消了后一个(就是带斜杠的那个)括号标记。表示元素的结束只需要一个”)”
就可以了。不错, 差别就是这些。这两种表达方式的转换, 非常自然, 也非常简单。s表
达式打起字来, 也省事得多。第一次看s表达式(Lisp)时, 括号很烦人是吧? 现在我们明
白了背后的道理, 一下子就变得容易多了。至少, 比XML要好的多。用s表达式写代码, 不
单是实用, 而且也很让人愉快。s表达式具有XML的一切好处, 这些好处是我们刚刚探讨过
的。现在我们看看更加Lisp风格的task例子:
(task (name "Test")
(echo (message "Hellow World!")))
(Test)
用Lisp的行话来讲, s表达式称为表(list)。对于上面的例子, 如果我们写的时候不加换
行, 用逗号来代替空格, 那么这个表达式看起来就非常像一个元素列表, 其中又嵌套着其
他标记。
(task, (name, "test"), (echo, (message, "Hello World!")))
XML自然也可以用这样的风格来写。当然上面这句并不是一般意义上的元素表。它实际上
是一个树。这和XML的作用是一样的。称它为列表, 希望你不会感到迷惑, 因为嵌套表和
树实际上是一码事。Lisp的字面意思就是表处理(list processing), 其实也可以称为树
处理, 这和处理XML节点没有什么不同。
经受这一番折磨以后, 现在我们终于相当接近Lisp了, Lisp的括号的神秘本质(就像许多
Lisp狂热分子认为的)逐渐显现出来。现在我们继续研究其他内容。
重新审视C语言的宏
到了这里, 对XML的讨论你大概都听累了, 我都讲累了。我们先停一停, 把树, s表达式,
Ant这些东西先放一放, 我们来说说C的预处理器。一定有人问了, 我们的话题和C有什么
关系? 我们已经知道了很多关于元编程的事情, 也探讨过专门写代码的代码。理解这问题
有一定难度, 因为相关讨论文章所使用的编程语言, 都是你们不熟悉的。但是如果只论概
念的话, 就相对要简单一些。我相信, 如果以C语言做例子来讨论元编程, 理解起来一定
会容易得多。好, 我们接着看。
一个问题是, 为什么要用代码来写代码呢? 在实际的编程中, 怎样做到这一点呢? 到底元
编程是什么意思? 你大概已经听说过这些问题的答案, 但是并不懂得其中缘由。为了揭示
背后的真理, 我们来看一下一个简单的数据库查询问题。这种题目我们都做过。比方说,
直接在程序码里到处写SQL语句来修改表(table)里的数据, 写多了就非常烦人。即便用
C#3.0的LINQ, 仍然不减其痛苦。写一个完整的SQL查询(尽管语法很优美)来修改某人的地
址, 或者查找某人的名字, 绝对是件令程序员倍感乏味的事情, 那么我们该怎样来解决这
个问题? 答案就是: 使用数据访问层。
概念挺简单, 其要点是把数据访问的内容(至少是那些比较琐碎的部分)抽象出来, 用类来
映射数据库的表, 然后用访问对象属性访问器(accessor)的办法来间接实现查询。这样就
极大地简化了开发工作量。我们用访问对象的方法(或者属性赋值, 这要视你选用的语言
而定)来代替写SQL查询语句。凡是用过这种方法的人, 都知道这很节省时间。当然, 如果
你要亲自写这样一个抽象层, 那可是要花非常多的时间的–你要写一组类来映射表, 把属
性访问转换为SQL查询, 这个活相当耗费精力。用手工来做显然是很不明智的。但是一旦
你有了方案和模板, 实际上就没有多少东西需要思考的。你只需要按照同样的模板一次又
一次重复编写相似代码就可以了。事实上很多人已经发现了更好的方法, 有一些工具可以
帮助你连接数据库, 抓取数据库结构定义(schema), 按照预定义的或者用户定制的模板来
自动编写代码。
如果你用过这种工具, 你肯定会对它的神奇效果深为折服。往往只需要鼠标点击数次, 就
可以连接到数据库, 产生数据访问源码, 然后把文件加入到你的工程里面, 十几分钟的工
作, 按照往常手工方式来作的话, 也许需要数百个小时人工(man-hours)才能完成。可是,
如果你的数据库结构定义后来改变了怎么办? 那样的话, 你只需把这个过程重复一遍就可
以了。甚至有一些工具能自动完成这项变动工作。你只要把它作为工程构造的一部分, 每
次编译工程的时候, 数据库部分也会自动地重新构造。这真的太棒了。你要做的事情基本
上减到了0。如果数据库结构定义发生了改变, 并在编译时自动更新了数据访问层的代码,
那么程序中任何使用过时的旧代码的地方, 都会引发编译错误。
数据访问层是个很好的例子, 这样的例子还有好多。从GUI样板代码, WEB代码, COM和
CORBA存根, 以及MFC和ATL等等。在这些地方, 都是有好多相似代码多次重复。既然这些
代码有可能自动编写, 而程序员时间又远远比CPU时间昂贵, 当然就产生了好多工具来自
动生成样板代码。这些工具的本质是什么呢? 它们实际上就是制造程序的程序。它们有一
个神秘的名字, 叫做元编程。所谓元编程的本义, 就是如此。
元编程本来可以用到无数多的地方, 但实际上使用的次数却没有那么多。归根结底, 我们
心里还是在盘算, 假设重复代码用拷贝粘贴的话, 大概要重复6,7次, 对于这样的工作量,
值得专门建立一套生成工具吗? 当然不值得。数据访问层和COM存根往往需要重用数百次,
甚至上千次, 所以用工具生成是最好的办法。而那些仅仅是重复几次十几次的代码, 是没
有必要专门做工具的。不必要的时候也去开发代码生成工具, 那就显然过度估计了代码生
成的好处。当然, 如果创建这类工具足够简单的话, 还是应当尽量多用, 因为这样做必然
会节省时间。现在来看一下有没有合理的办法来达到这个目的。
现在, C预处理器要派上用场了。我们都用过C/C++的预处理器, 我们用它执行简单的编译
指令, 来产生简单的代码变换(比方说, 设置调试代码开关), 看一个例子:
#define triple(X) X+X+X
这一行的作用是什么? 这是一个简单的预编译指令, 它把程序中的triple(X)替换称为
X+X+X。例如, 把所有的triple(5)都换成5+5+5, 然后再交给编译器编译。这就是一个简
单的代码生成的例子。要是C的预处理器再强大一点, 要是能够允许连接数据库, 要是能
多一些其他简单的机制, 我们就可以在我们程序的内部开发自己的数据访问层。下面这个
例子, 是一个假想的对C宏的扩展:
#get-db-schema("127.0.0.1")
#iterate-through-tables
#for-each-table
class #table-name
{
};
#end-for-each
我们连接数据库结构定义, 遍历数据表, 然后对每个表创建一个类, 只消几行代码就完成
了这个工作。这样每次编译工程的时候, 这些类都会根据数据库的定义同步更新。显而易
见, 我们不费吹灰之力就在程序内部建立了一个完整的数据访问层, 根本用不着任何外部
工具。当然这种作法有一个缺点, 那就是我们得学习一套新的”编译时语言”, 另一个缺点
就是根本不存在这么一个高级版的C预处理器。需要做复杂代码生成的时候, 这个语言(译
者注: 这里指预处理指令, 即作者所说的”编译时语言”)本身也一定会变得相当复杂。它
必须支持足够多的库和语言结构。比如说我们想要生成的代码要依赖某些ftp服务器上的
文件, 预处理器就得支持ftp访问, 仅仅因为这个任务而不得不创造和学习一门新的语言,
真是有点让人恶心(事实上已经存在着有此能力的语言, 这样做就更显荒谬)。我们不妨再
灵活一点, 为什么不直接用 C/C++自己作为自己的预处理语言呢? 这样子的话, 我们可
以发挥语言的强大能力, 要学的新东西也只不过是几个简单的指示字 , 这些指示字用来
区别编译时代码和运行时代码。
<%
cout<<"Enter a number: ";
cin>>n;
%>
for(int i=0;i< <% n %>;i++)
{
cout<<"hello"<<endl;
}
你明白了吗? 在<%和%>标记之间的代码是在编译时运行的, 标记之外的其他代码都是普通
代码。编译程序时, 系统会提示你输入一个数, 这个数在后面的循环中会用到。而for循
环的代码会被编译。假定你在编译时输入5, for循环的代码将会是:
for(int i=0;i<5; i++)
{
cout<<"hello"<<endl;
}
又简单又有效率, 也不需要另外的预处理语言。我们可以在编译时就充分发挥宿主语言(
此处是C/C++)的强大能力, 我们可以很容易地在编译时连接数据库, 建立数据访问层, 就
像JSP或者ASP创建网页那样。我们也用不着专门的窗口工具来另外建立工程。我们可以在
代码中立即加入必要的工具。我们也用不着顾虑建立这种工具是不是值得, 因为这太容易
了, 太简单了。这样子不知可以节省多少时间啊。
你好, Lisp
到此刻为止, 我们所知的关于Lisp的指示可以总结为一句话: Lisp是一个可执行的语法更
优美的XML, 但我们还没有说Lisp是怎样做到这一点的, 现在开始补上这个话题。
Lisp有丰富的内置数据类型, 其中的整数和字符串和其他语言没什么分别。像71或者
“hello”这样的值, 含义也和C++或者Java这样的语言大体相同。真正有意思的三种类型是
符号(symbol), 表和函数。这一章的剩余部分, 我都会用来介绍这几种类型, 还要介绍
Lisp环境是怎样编译和运行源码的。这个过程用Lisp的术语来说通常叫做求值。通读这一
节内容, 对于透彻理解元编程的真正潜力, 以及代码和数据的同一性, 和面向领域语言的
观念, 都极其重要。万勿等闲视之。我会尽量讲得生动有趣一些, 也希望你能获得一些
启发。那好, 我们先讲符号。
大体上, 符号相当于C++或Java语言中的标志符, 它的名字可以用来访问变量值(例如
currentTime, arrayCount, n, 等等), 差别在于, Lisp中的符号更加基本。在C++或
Java里面, 变量名只能用字母和下划线的组合, 而Lisp的符号则非常有包容性, 比如, 加
号(+)就是一个合法的符号, 其他的像-, =, hello-world, *等等都可以是符号名。符号
名的命名规则可以在网上查到。你可以给这些符号任意赋值, 我们这里先用伪码来说明这
一点。假定函数set是给变量赋值(就像等号=在C++和Java里的作用), 下面是我们的例子:
set(test, 5) // 符号test的值为5
set(=, 5) // 符号=的值为5
set(test, "hello") // 符号test的值为字符串"hello"
set(test, =) // 此时符号=的值为5, 所以test的也为5
set(*, "hello") // 符号*的值为"hello"
好像有什么不对的地方? 假定我们对赋给整数或者字符串值, 那做乘法时怎么办? 不管
怎么说, 总是乘法呀? 答案简单极了。Lisp中函数的角色十分特殊, 函数也是一种数据
类型, 就像整数和字符串一样, 因此可以把它赋值给符号。乘法函数Lisp的内置函数, 默
认赋给, 你可以把其他函数赋值给, 那样*就不代表乘法了。你也可以把这函数的值存
到另外的变量里。我们再用伪码来说明一下:
*(3,4) // 3乘4, 结果是12
set(temp, *) // 把*的值, 也就是乘法函数, 赋值给temp
set(*, 3) // 把3赋予*
*(3,4) // 错误的表达式, *不再是乘法, 而是数值3
temp(3,4) // temp是乘法函数, 所以此表达式的值为3乘4等于12
set(*, temp) // 再次把乘法函数赋予*
*(3,4) // 3乘4等于12
再古怪一点, 把减号的值赋给加号:
set(+, -) // 减号(-)是内置的减法函数
+(5, 4) // 加号(+)现在是代表减法函数, 结果是5减4等于1
这只是举例子, 我还没有详细讲函数。Lisp中的函数是一种数据类型, 和整数, 字符串,
符号等等一样。一个函数并不必然有一个名字, 这和C++或者Java语言的情形很不相同。
在这里函数自己代表自己。事实上它是一个指向代码块的指针, 附带有一些其他信息(例
如一组参数变量)。只有在把函数赋予其他符号时, 它才具有了名字, 就像把一个数值或
字符串赋予变量一样的道理。你可以用一个内置的专门用于创建函数的函数来创建函数,
然后把它赋值给符号fn, 用伪码来表示就是:
fn [a]
{
return *(a, 2);
}
这段代码返回一个具有一个参数的函数, 函数的功能是计算参数乘2的结果。这个函数还
没有名字, 你可以把此函数赋值给别的符号:
set(times-two, fn [a] {return *(a, 2)})
我们现在可以这样调用这个函数:
time-two(5) // 返回10
我们先跳过符号和函数, 讲一讲表。什么是表? 你也许已经听过好多相关的说法。表, 一
言以蔽之, 就是把类似XML那样的数据块, 用s表达式来表示。表用一对括号括住, 表中元
素以空格分隔, 表可以嵌套。例如(这回我们用真正的Lisp语法, 注意用分号表示注释):
() ; 空表
(1) ; 含一个元素的表
(1 "test") ; 两元素表, 一个元素是整数1, 另一个是字符串
(test "hello") ; 两元素表, 一个元素是符号, 另一个是字符串
(test (1 2) "hello") ; 三元素表, 一个符号test, 一个含有两个元素1和2的
; 表, 最后一个元素是字符串
当Lisp系统遇到这样的表时, 它所做的, 和Ant处理XML数据所做的, 非常相似, 那就是试
图执行它们。其实, Lisp源码就是特定的一种表, 好比Ant源码是一种特定的XML一样。
Lisp执行表的顺序是这样的, 表的第一个元素当作函数, 其他元素当作函数的参数。如果
其中某个参数也是表, 那就按照同样的原则对这个表求值, 结果再传递给最初的函数作为
参数。这就是基本原则。我们看一下真正的代码:
(* 3 4) ; 相当于前面列举过的伪码*(3,4), 即计算3乘4
(times-two 5) ; 返回10, times-two按照前面的定义是求参数的2倍
(3 4) ; 错误, 3不是函数
(time-two) ; 错误, times-two要求一个参数
(times-two 3 4) ; 错误, times-two只要求一个参数
(set + -) ; 把减法函数赋予符号+
(+ 5 4) ; 依据上一句的结果, 此时+表示减法, 所以返回1
(* 3 (+ 2 2)) ; 2+2的结果是4, 再乘3, 结果是12
上述的例子中, 所有的表都是当作代码来处理的。怎样把表当作数据来处理呢? 同样的,
设想一下, Ant是把XML数据当作自己的参数。在Lisp中, 我们给表加一个前缀’来表示数
据。
(set test '(1 2)) ; test的值为两元素表
(set test (1 2)) ; 错误, 1不是函数
(set test '(* 3 4)) ; test的值是三元素表, 三个元素分别是*, 3, 4
我们可以用一个内置的函数head来返回表的第一个元素, tail函数来返回剩余元素组成的
表。
(head '(* 3 4)) ; 返回符号*
(tail '(* 3 4)) ; 返回表(3 4)
(head (tal '(* 3 4))) ; 返回3
(head test) ; 返回*
你可以把Lisp的内置函数想像成Ant的任务。差别在于, 我们不用在另外的语言中扩展
Lisp(虽然完全可以做得到), 我们可以用Lisp自己来扩展自己, 就像上面举的times-two
函数的例子。Lisp的内置函数集十分精简, 只包含了十分必要的部分。剩下的函数都是作
为标准库来实现的。
Lisp宏
我们已经看到, 元编程在一个类似jsp的模板引擎方面的应用。我们通过简单的字符串处
理来生成代码。但是我们可以做的更好。我们先提一个问题, 怎样写一个工具, 通过查找
目录结构中的源文件来自动生成Ant脚本。
用字符串处理的方式生成Ant脚本是一种简单的方式。当然, 还有一种更加抽象, 表达能
力更强, 扩展性更好的方式, 就是利用XML库在内存中直接生成XML节点, 这样的话内存中
的节点就可以自动序列化成为字符串。不仅如此, 我们的工具还可以分析这些节点, 对已
有的XML文件做变换。通过直接处理XML节点。我们可以超越字符串处理, 使用更高层次的
概念, 因此我们的工作就会做的更快更好。
我们当然可以直接用Ant自身来处理XML变换和制作代码生成工具。或者我们也可以用Lisp
来做这项工作。正像我们以前所知的, 表是Lisp内置的数据结构, Lisp含有大量的工具来
快速有效的操作表(head和tail是最简单的两个)。而且, Lisp没有语义约束, 你可以构造
任何数据结构, 只要你原意。
Lisp通过宏(macro)来做元编程。我们写一组宏来把任务列表(to-do list)转换为专用领
域语言。
回想一下上面to-do list的例子, 其XML的数据格式是这样的:
<todo name = "housework">
<item priority = "high">Clean the hose</item>
<item priority = "medium">Wash the dishes</item>
<item priority = "medium">Buy more soap</item>
</todo>
相应的s表达式是这样的:
(todo "housework"
(item (priority high) "Clean the house")
(item (priority medium) "Wash the dishes")
(item (priority medium) "Buy more soap"))
假设我们要写一个任务表的管理程序, 把任务表数据存到一组文件里, 当程序启动时, 从
文件读取这些数据并显示给用户。在别的语言里(比如说Java), 这个任务该怎么做? 我们
会解析XML文件, 从中得出任务表数据, 然后写代码遍历XML树, 再转换为Java的数据结构
(老实讲, 在Java里解析XML真不是件轻松的事情), 最后再把数据展示给用户。现在如果
用Lisp, 该怎么做?
假定要用同样思路的化, 我们大概会用Lisp库来解析XML。XML对我们来说就是一个Lisp
的表(s表达式), 我们可以遍历这个表, 然后把相关数据提交给用户。可是, 既然我们用
Lisp, 就根本没有必要再用XML格式保存数据, 直接用s表达式就好了, 这样就没有必要做
转换了。我们也用不着专门的解析库, Lisp可以直接在内存里处理s表达式。注意, Lisp
编译器和.net编译器一样, 对Lisp程序来说, 在运行时总是随时可用的。
但是还有更好的办法。我们甚至不用写表达式来存储数据, 我们可以写宏, 把数据当作代
码来处理。那该怎么做呢? 真的简单。回想一下, Lisp的函数调用格式:
(function-name arg1 arg2 arg3)
其中每个参数都是s表达式, 求值以后, 传递给函数。如果我们用(+ 4 5)来代替arg1,
那么, 程序会先求出结果, 就是9, 然后把9传递给函数。宏的工作方式和函数类似。主要
的差别是, 宏的参数在代入时不求值。
(macro-name (+ 4 5))
这里, (+ 4 5)作为一个表传递给宏, 然后宏就可以任意处理这个表, 当然也可以对它求
值。宏的返回值是一个表, 然后有程序作为代码来执行。宏所占的位置, 就被替换为这个
结果代码。我们可以定义一个宏把数据替换为任意代码, 比方说, 替换为显示数据给用户
的代码。
这和元编程, 以及我们要做的任务表程序有什么关系呢? 实际上, 编译器会替我们工作,
调用相应的宏。我们所要做的, 仅仅是创建一个把数据转换为适当代码的宏。
例如, 上面曾经将过的C的求三次方的宏, 用Lisp来写是这样子:
(defmacro triple (x)
`(+ ~x ~x ~x))
(译注: 在Common Lisp中, 此处的单引号应当是反单引号, 意思是对表不求值, 但可以对
表中某元素求值, 记号~表示对元素x求值, 这个求值记号在Common Lisp中应当是逗号。
反单引号和单引号的区别是, 单引号标识的表, 其中的元素都不求值。这里作者所用的记
号是自己发明的一种Lisp方言Blaise, 和common lisp略有不同, 事实上, 发明方言是
lisp高手独有的乐趣, 很多狂热分子都热衷这样做。比如Paul Graham就发明了ARC, 许多
记号比传统的Lisp简洁得多, 显得比较现代)
单引号的用处是禁止对表求值。每次程序中出现triple的时候,
(triple 4)
都会被替换成:
(+ 4 4 4)
我们可以为任务表程序写一个宏, 把任务数据转换为可执行码, 然后执行。假定我们的输
出是在控制台:
(defmacro item (priority note)
`(block
(print stdout tab "Prority: " ~(head (tail priority)) endl)
(print stdout tab "Note: " ~note endl endl)))
我们创造了一个非常小的有限的语言来管理嵌在Lisp中的任务表。这个语言只用来解决特
定领域的问题, 通常称之为DSLs(特定领域语言, 或专用领域语言)。
特定领域语言
本文谈到了两个特定领域语言, 一个是Ant, 处理软件构造。一个是没起名字的, 用于处
理任务表。两者的差别在于, Ant是用XML, XML解析器, 以及Java语言合在一起构造出来
的。而我们的迷你语言则完全内嵌在Lisp中, 只消几分钟就做出来了。
我们已经说过了DSL的好处, 这也就是Ant用XML而不直接用Java的原因。如果使用Lisp,
我们可以任意创建DSL, 只要我们需要。我们可以创建用于网站程序的DSL, 可以写多用户
游戏, 做固定收益贸易(fixed income trade), 解决蛋白质折叠问题, 处理事务问题, 等
等。我们可以把这些叠放在一起, 造出一个语言, 专门解决基于网络的贸易程序, 既有网
络语言的优势, 又有贸易语言的好处。每天我们都会收获这种方法带给我们的益处, 远远
超过Ant所能给予我们的。
用DSL解决问题, 做出的程序精简, 易于维护, 富有弹性。在Java里面, 我们可以用类来
处理问题。这两种方法的差别在于, Lisp使我们达到了一个更高层次的抽象, 我们不再受
语言解析器本身的限制, 比较一下用Java库直接写的构造脚本和用Ant写的构造脚本其间
的差别。同样的, 比较一下你以前所做的工作, 你就会明白Lisp带来的好处。
接下来
学习Lisp就像战争中争夺山头。尽管在电脑科学领域, Lisp已经算是一门古老的语言, 直
到现在仍然很少有人真的明白该怎样给初学者讲授Lisp。尽管Lisp老手们尽了很大努力,
今天新手学习Lisp仍然是困难重重。好在现在事情正在发生变化, Lisp的资源正在迅速增
加, 随着时间推移, Lisp将会越来越受关注。
Lisp使人超越平庸, 走到前沿。学会Lisp意味着你能找到更好的工作, 因为聪明的雇主会
被你与众不同的洞察力所打动。学会Lisp也可能意味着明天你可能会被解雇, 因为你总是
强调, 如果公司所有软件都用Lisp写, 公司将会如何卓越, 而这些话你的同事会听烦的。
Lisp值得努力学习吗? 那些已经学会Lisp的人都说值得, 当然, 这取决于你的判断。
你的看法呢?
这篇文章写写停停, 用了几个月才最终完成。如果你觉得有趣, 或者有什么问题, 意见或
建议, 请给我发邮件coffeemug@gmail.com, 我会很高兴收到你的反馈。
作者 Slava Akhmechet
译者 Alec Jang
出处: http://www.defmacro.org/ramblings/lisp.html
The Nature of Lisp
Monday, May 8, 2006
Introduction
When I first stumbled into Lisp advocacy on various corners of the web I was already an experienced programmer. At that point I had grokked what seemed at the time a wide range of programming languages. I was proud to have the usual suspects (C++, Java, C#, etc.) on my service record and was under impression that I knew everything there is to know about programming languages. I couldn’t have possibly been more wrong.
My initial attempt to learn Lisp came to a crashing halt as soon as I saw some sample code. I suppose the same thought ran through my mind that ran through thousands of other minds who were ever in my shoes: “Why on Earth would anyone want to use a language with such horrific syntax?!” I couldn’t be bothered to learn a language if its creators couldn’t be bothered to give it a pleasant syntax. After all, I was almost blinded by the infamous Lisp parentheses!
The moment I regained my sight I communicated my frustrations to some members of the Lisp sect. Almost immediately I was bombarded by a standard set of responses: Lisp’s parentheses are only a superficial matter, Lisp has a huge benefit of code and data being expressed in the same manner (which, obviously, is a huge improvement over XML), Lisp has tremendously powerful metaprogramming facilities that allow programs to write code and modify themselves, Lisp allows for creation of mini-languages specific to the problem at hand, Lisp blurs the distinction between run time and compile time, Lisp, Lisp, Lisp… The list was very impressive. Needless to say none of it made sense. Nobody could illustrate the usefulness of these features with specific examples because these techniques are supposedly only useful in large software systems. After many hours of debating that conventional programming languages do the job just fine, I gave up. I wasn’t about to invest months into learning a language with a terrible syntax in order to understand obscure features that had no useful examples. My time has not yet come.
For many months the Lisp advocates pressed on. I was baffled. Many extremely intelligent people I knew and had much respect for were praising Lisp with almost religious dedication. There had to be something there, something I couldn’t afford not to get my hands on! Eventually my thirst for knowledge won me over. I took the plunge, bit the bullet, got my hands dirty, and began months of mind bending exercises. It was a journey on an endless lake of frustration. I turned my mind inside out, rinsed it, and put it back in place. I went through seven rings of hell and came back. And then I got it.
The enlightenment came instantaneously. One moment I understood nothing, and the next moment everything clicked into place. I’ve achieved nirvana. Dozens of times I heard Eric Raymond’s statement quoted by different people: “Lisp is worth learning for the profound enlightenment experience you will have when you finally get it; that experience will make you a better programmer for the rest of your days, even if you never actually use Lisp itself a lot.” I never understood this statement. I never believed it could be true. And finally, after all the pain, it made sense! There was more truth to it than I ever could have imagined. I’ve achieved an almost divine state of mind, an instantaneous enlightenment experience that turned my view of computer science on its head in less than a single second.
That very second I became a member of the Lisp cult. I felt something a ninjitsu master must feel: I had to spread my newfound knowledge to at least ten lost souls in the course of my lifetime. I took the usual path. I was rehashing the same arguments that were given to me for years (only now they actually made sense!), hoping to convert unsuspecting bystanders. It didn’t work. My persistence sparked a few people’s interest but their curiosity dwindled at the mere sight of sample Lisp code. Perhaps years of advocacy would forge a few new Lispers, but I wasn’t satisfied. There had to be a better way.
I gave the matter careful thought. Is there something inherently hard about Lisp that prevents very intelligent, experienced programmers from understanding it? No, there isn’t. After all, I got it, and if I can do it, anybody can. Then what is it that makes Lisp so hard to understand? The answer, as such things usually do, came unexpectedly. Of course! Teaching anybody anything involves building advanced concepts on top of concepts they already understand! If the process is made interesting and the matter is explained properly the new concepts become as intuitive as the original building blocks that aided their understanding. That was the problem! Metaprogramming, code and data in one representation, self-modifying programs, domain specific mini-languages, none of the explanations for these concepts referenced familiar territory. How could I expect anyone to understand them! No wonder people wanted specific examples. I could as well have been speaking in Martian!
I shared my ideas with fellow Lispers. “Well, of course these concepts aren’t explained in terms of familiar territory”, they said. “They are so different, they’re unlike anything these people have learned before.” This was a poor excuse. “I do not believe this to be true”, I said. The response was unanimous: “Why don’t you give it a try?” So I did. This article is a product of my efforts. It is my attempt to explain Lisp in familiar, intuitive concepts. I urge brave souls to read on. Grab your favorite drink. Take a deep breath. Prepare to be blown away. Oh, and may the Force be with you.
XML Reloaded
A thousand mile journey starts with a single step. A journey to enlightenment is no exception and our first step just happens to be XML. What more could possibly be said about XML that hasn’t already been said? It turns out, quite a bit. While there’s nothing particularly interesting about XML itself, its relationship to Lisp is fascinating. XML is the all too familiar concept that Lisp advocates need so much. It is our bridge to conveying understanding to regular programmers. So let’s revive the dead horse, take out the stick, and venture into XML wilderness that no one dared venture into before us. It’s time to see the all too familiar moon from the other side.
Superficially XML is nothing more than a standardized syntax used to express arbitrary hierarchical data in human readable form. To-do lists, web pages, medical records, auto insurance claims, configuration files are all examples of potential XML use. Let’s use a simple to-do list as an example (in a couple of sections you’ll see it in a whole new light):
<todo name=”housework”>
<item priority=”high”>Clean the house.</item>
<item priority=”medium”>Wash the dishes.</item>
<item priority=”medium”>Buy more soap.</item>
</todo>
What happens if we unleash our favorite XML parser on this to-do list? Once the data is parsed, how is it represented in memory? The most natural representation is, of course, a tree – a perfect data structure for hierarchical data. After all is said and done, XML is really just a tree serialized to a human readable form. Anything that can be represented in a tree can be represented in XML and vice versa. I hope you understand this idea. It’s very important for what’s coming next.
Let’s take this a little further. What other type of data is often represented as a tree? At this point the list is as good as infinite so I’ll give you a hint at what I’m getting at – try to remember your old compiler course. If you have a vague recollection that source code is stored in a tree after it’s parsed, you’re on the right track. Any compiler inevitably parses the source code into an abstract syntax tree. This isn’t surprising since source code is hierarchical: functions contain arguments and blocks of code. Blocks of code contain expressions and statements. Expressions contain variables and operators. And so it goes.
Let’s apply our corollary that any tree can easily be serialized into XML to this idea. If all source code is eventually represented as a tree, and any tree can be serialized into XML, then all source code can be converted to XML, right? Let’s illustrate this interesting property by a simple example. Consider the function below:
int add(int arg1, int arg2)
{
return arg1 + arg2;
}
Can you convert this function definition to its XML equivalent? Turns out, it’s reasonably simple. Naturally there are many ways to do this. Here is one way the resulting XML can look like:
<define-function return-type=”int” name=”add”>
<arguments>
<argument type=”int”>arg1</argument>
<argument type=”int”>arg2</argument>
</arguments>
<body>
<return>
<add value1=”arg1″ value2=”arg2″ />
</return>
</body>
</define>
We can go through this relatively simple exercise with any language. We can turn any source code into XML, and we can transform the resulting XML back to original source code. We can write a converter that turns Java into XML and a converter that turns XML back to Java. We could do the same for C++. (In case you’re wondering if anyone is crazy enough to do it, take a look at GCC-XML). Furthermore, for languages that share common features but use different syntax (which to some extent is true about most mainstream languages) we could convert source code from one language to another using XML as an intermediary representation. We could use our Java2XML converter to convert a Java program to XML. We could then run an XML2CPP converter on the resulting XML and turn it into C++ code. With any luck (if we avoid using features of Java that don’t exist in C++) we’ll get a working C++ program. Neat, eh?
All this effectively means that we can use XML for generic storage of source code. We’d be able to create a whole class of programming languages that use uniform syntax, as well as write transformers that convert existing source code to XML. If we were to actually adopt this idea, compilers for different languages wouldn’t need to implement parsers for their specific grammars – they’d simply use an XML parser to turn XML directly into an abstract syntax tree.
By now you’re probably wondering why I’ve embarked on the XML crusade and what it has to do with Lisp (after all, Lisp was created about thirty years before XML). I promise that everything will become clear soon enough. But before we take our second step, let’s go through a small philosophical exercise. Take a good look at the XML version of our “add” function above. How would you classify it? Is it data or code? If you think about it for a moment you’ll realize that there are good reasons to put this XML snippet into both categories. It’s XML and it’s just information encoded in a standardized format. We’ve already determined that it can be generated from a tree data structure in memory (that’s effectively what GCC-XML does). It’s lying around in a file with no apparent way to execute it. We can parse it into a tree of XML nodes and do various transformations on it. It’s data. But wait a moment! When all is said and done it’s the same “add” function written with a different syntax, right? Once parsed, its tree could be fed into a compiler and we could execute it. We could easily write a small interpreter for this XML code and we could execute it directly. Alternatively, we could transform it into Java or C++ code, compile it, and run it. It’s code.
So, where are we? Looks like we’ve just arrived to an interesting point. A concept that has traditionally been so hard to understand is now amazingly simple and intuitive. Code is also always data! Does it mean that data is also always code? As crazy as this sounds this very well might be the case. Remember how I promised that you’ll see our to-do list in a whole new light? Let me reiterate on that promise. But we aren’t ready to discuss this just yet. For now let’s continue walking down our path.
A little earlier I mentioned that we could easily write an interpreter to execute our XML snippet of the add function. Of course this sounds like a purely theoretical exercise. Who in their right mind would want to do that for practical purposes? Well, it turns out quite a few people would disagree. You’ve likely encountered and used their work at least once in your career, too. Do I have you out on the edge of your seat? If so, let’s move on!
Ant Reloaded
Now that we’ve made the trip to the dark side of the moon, let’s not leave quite yet. We may still learn something by exploring it a little more, so let’s take another step. We begin by closing our eyes and remembering a cold rainy night in the winter of 2000. A prominent developer by the name of James Duncan Davidson1 was hacking his way through Tomcat servlet container. As the time came to build the changes he carefully saved all his files and ran make. Errors. Lots of errors. Something was wrong. After careful examination James exclaimed: “Is my command not executing because I have a space in front of my tab?!” Indeed, this was the problem. Again. James has had enough. He could sense the full moon through the clouds and it made him adventurous. He created a fresh Java project and quickly hacked together a simple but surprisingly useful utility. This spark of genius used Java property files for information on how to build the project. James could now write the equivalent of the makefile in a nice format without worrying about the damned spaces ever again. His utility did all the hard work by interpreting the property file and taking appropriate actions to build the project. It was neat. Another Neat Tool. Ant.
After using Ant to build Tomcat for a few months it became clear that Java property files are not sufficient to express complicated build instructions. Files needed to be checked out, copied, compiled, sent to another machine, and unit tested. In case of failure e-mails needed to be sent out to appropriate people. In case of success “Bad to the Bone” needed to be played at the highest possible volume. At the end of the track volume had to be restored to its original level. Yes, Java property files didn’t cut it anymore. James needed a more flexible solution. He didn’t feel like writing his own parser (especially since he wanted an industry standard solution). XML seemed like a reasonable alternative. In a couple of days Ant was ported to XML. It was the best thing since sliced bread.
So how does Ant work? It’s pretty simple. It takes an XML file with specific build instructions (you decide if they’re data or code) and interprets them by running specialized Java code for each XML element. It’s actually much simpler than it sounds. A simple XML instruction like the one below causes a Java class with an equivalent name to be loaded and its code to be executed.
<copy todir=”../new/dir”>
<fileset dir=”src_dir”/>
</copy>
The snippet above copies a source directory to a destination directory. Ant locates a “copy” task (a Java class, really), sets appropriate parameters (todir and fileset) by calling appropriate Java methods and then executes the task. Ant comes with a set of core tasks and anyone can extend it with tasks of their own simply by writing Java classes that follow certain conventions. Ant finds these classes and executes them whenever XML elements with appropriate names are encountered. Pretty simple. Effectively Ant accomplishes what we were talking about in the previous section: it acts as an interpreter for a language that uses XML as its syntax by translating XML elements to appropriate Java instructions. We could write an “add” task and have Ant execute it when it encounters the XML snippet for addition presented in the previous section! Considering that Ant is an extremely popular project, the ideas presented in the previous section start looking more sane. After all, they’re being used every day in what probably amounts to thousands of companies!
So far I’ve said nothing about why Ant actually goes through all the trouble of interpreting XML. Don’t try to look for the answer on its website either – you’ll find nothing of value. Nothing relevant to our discussion, anyway. Let’s take another step. It’s time to find out why.
Why XML?
Sometimes right decisions are made without full conscious understanding of all the issues involved. I’m not sure if James knew why he chose XML – it was likely a subconscious decision. At the very least, the reasons I saw on Ant’s website for using XML are all the wrong reasons. It appears that the main concerns revolved around portability and extensibility. I fail to see how XML helps advance these goals in Ant’s case. What is the advantage of using interpreted XML over simple Java source code? Why not create a set of classes with a nice API for commonly used tasks (copying directories, compiling, etc.) and using those directly from Java source code? This would run on every platform that runs Java (which Ant requires anyway), it’s infinitely extensible, and it has the benefit of having a more pleasant, familiar syntax. So why XML? Can we find a good reason for using it?
It turns out that we can (although as I mentioned earlier I’m not sure if James was consciously aware of it). XML has the property of being far more flexible in terms of introduction of semantic constructs than Java could ever hope to be. Don’t worry, I’m not falling into the trap of using big words to describe incomprehensible concepts. This is actually a relatively simple idea, though it may take some effort to explain. Buckle your seat-belt. We’re about to make a giant leap towards achieving nirvana.
How can we represent ‘copy’ example above in Java code? Here’s one way to do it:
CopyTask copy = new CopyTask();
Fileset fileset = new Fileset();
fileset.setDir(“src_dir”);
copy.setToDir(“../new/dir”);
copy.setFileset(fileset);
copy.execute();
The code is almost the same, albeit a little longer than the original XML. So what’s different? The answer is that the XML snippet introduces a special semantic construct for copying. If we could do it in Java it would look like this:
copy(“../new/dir”)
{
fileset(“src_dir”);
}
Can you see the difference? The code above (if it were possible in Java) is a special operator for copying files – similar to a for loop or a new foreach construct introduced in Java 5. If we had an automatic converter from XML to Java it would likely produce the above gibberish. The reason for this is that Java’s accepted syntax tree grammar is fixed by the language specification – we have no way of modifying it. We can add packages, classes, methods, but we cannot extend Java to make addition of new operators possible. Yet we can do it to our heart’s content in XML – its syntax tree isn’t restricted by anything except our interpreter! If the idea is still unclear, consider introducing a special operator ‘unless’ to Java:
unless(someObject.canFly())
{
someObject.transportByGround();
}
In the previous two examples we extend the Java language to introduce an operator for copying files and a conditional operator unless. We would do this by modifying the abstract syntax tree grammar that Java compiler accepts. Naturally we cannot do it with standard Java facilities, but we can easily do it in XML. Because our XML interpreter parses the abstract syntax tree that results from it, we can extend it to include any operator we like.
For complex operators this ability provides tremendous benefits. Can you imagine writing special operators for checking out source code, compiling files, running unit testing, sending email? Try to come up with some. If you’re dealing with a specialized problem (in our case it’s building projects) these operators can do wonders to decrease the amount of code you have to type and to increase clarity and code reuse. Interpreted XML makes this extremely easy to accomplish because it’s a simple data file that stores hierarchical data. We do not have this option in Java because it’s hierarchical structure is fixed (as you will soon find out, we do have this option in Lisp). Perhaps this is one of the reasons why Ant is so successful?
I urge you to take a look at recent evolution of Java and C# (especially the recently released specification for C# 3.0). The languages are being evolved by abstracting away commonly used functionality and adding it in the form of operators. New C# operators for built-in queries is one example. This is accomplished by relatively traditional means: language creators modify the accepted abstract syntax tree and add implementations of certain features. Imagine the possibilities if the programmer could modify the abstract syntax tree himself! Whole new sub-languages could be built for specialized domains (for example a language for building projects, like Ant). Can you come up with other examples? Think about these concepts for a bit, but don’t worry about them too much. We’ll come back to these issues after introducing a few more ideas. By then things will be a little more clear.
Almost Lisp
Let’s forget about the operator business for the moment and try to expand our horizons beyond the constraints of Ant’s design. I mentioned earlier that Ant can be extended by writing conventional Java classes. Ant interpreter then attempts to match XML elements to appropriately named Java classes and if the match is found the task is executed. An interesting question begs to be asked. Why not extend Ant in Ant itself? After all, core tasks contain a lot of conventional programming language constructs (‘if’ being a perfect example). If Ant provided constructs to develop tasks in Ant itself we’d reach a higher degree of portability. We’d be dependent on a core set of tasks (a standard library, if you will) and we wouldn’t care if Java runtime is present: the core set could be implemented in anything. The rest of the tasks would be built on top of the core using Ant-XML itself. Ant would then become a generic, extensible, XML-based programming language. Consider the possibilities:
<task name=”Test”>
<echo message=”Hello World!”/>
</task>
<Test />
If ant supported the “task” construct, the example above would print “Hello World!”. In fact, we could write a “task” task in Java and make Ant able to extend itself using Ant-XML! Ant would then be able to build more complicated primitives on top of simple ones, just like any other programming language! This is an example of “XML” based programming language we were talking about in the beginning of this tutorial. Not very useful (can you tell why?) but pretty damn cool.
By the way, take a look at our ‘Test’ task once again. Congratulations. You’re looking at Lisp code. What on Earth am I talking about? It doesn’t look anything like Lisp? Don’t worry, we’ll fix that in a bit. Confused? Good. Let’s clear it all up!
A Better XML
I mentioned in the previous section that self-extending Ant wouldn’t be very useful. The reason for that is XML’s verbosity. It’s not too bad for data files but the moment you try writing reasonably complex code the amount of typing you have to do quickly starts to get in the way and progresses to becoming unusable for any real project. Have you ever tried writing Ant build scripts? I have, and once they get complex enough having to do it in XML becomes really annoying. Imagine having to type almost everything in Java twice because you have to close every element. Wouldn’t that drive you nuts?
The solution to this problem involves using a less verbose alternative to XML. Remember, XML is just a format for representing hierarchical data. We don’t have to use XML’s angle brackets to serialize trees. We could come up with many other formats. One such format (incidentally, the one Lisp uses) is called an s-expression. S-expressions accomplish the same goals as XML. They’re just a lot less verbose, which makes them much better suited for typing code. I will explain s-expressions in a little while, but before I do I have to clear up a few things about XML. Let’s consider our XML example for copying files:
<copy todir=”../new/dir”>
<fileset dir=”src_dir”/>
</copy>
Think of what the parse tree of this snippet would look like in memory. We’d have a ‘copy’ node that contains a fileset node. But what about attributes? How do they fit into our picture? If you’ve ever used XML to describe data and wondered whether you should use an element or an attribute, you’re not alone. Nobody can really figure this out and doing it right tends to be black magic rather than science. The reason for that is that attributes are really subsets of elements. Anything attributes can do, elements can do as well. The reason attributes were introduced is to curb XML’s verbosity. Take a look at another version of our ‘copy’ snippet:
<copy>
<todir>../new/dir</todir>
<fileset>
<dir>src_dir</dir>
</fileset>
</copy>
The two snippets hold exactly the same information. However, we use attributes to avoid typing the same thing more than once. Imagine if attributes weren’t part of XML specification. Writing anything in XML would drive us nuts!
Now that we got attributes out of the way, let’s look at s-expressions. The reason we took this detour is that s-expressions do not have attributes. Because they’re a lot less verbose, attributes are simply unnecessary. This is one thing we need to keep in mind when transforming XML to s-expressions. Let’s take a look at an example. We could translate above snippet to s-expressions like this:
(copy
(todir “../new/dir”)
(fileset (dir “src_dir”)))
Take a good look at this representation. What’s different? Angle brackets seem to be replaced by parentheses. Instead of enclosing each element into a pair of parentheses and then closing each element with a “(/element)” we simply skip the second parenthesis in “(element” and proceed. The element is then closed like this: “)”. That’s it! The translation is natural and very simple. It’s also a lot easier to type. Do parentheses blind first time users? Maybe, but now that we’re understand the reasoning behind them they’re a lot easier to handle. At the very least they’re better than arthritis inducing verbosity of XML. After you get used to s-expressions writing code in them is not only doable but very pleasant. And they provide all the benefits of writing code in XML (many of which we’re yet to explore). Let’s take a look at our ‘task’ code in something that looks a lot more like lisp:
(task (name “Test”)
(echo (message “Hello World!”)))
(Test)
S-expressions are called lists in Lisp lingo. Consider our ‘task’ element above. If we rewrite it without a line break and with comas instead of spaces it’s starting to look surprisingly like a list of elements and other lists (the formatting is added to make it easier to see nested lists):
(task, (name, “test”), (echo, (message, “Hello World!”)))
We could do the same with XML. Of course the line above isn’t really a list, it’s a tree, just like its XML-alternative. Don’t let references to lists confuse you, it’s just that lists that contain other lists and trees are effectively the same thing. Lisp may stand for List Processing, but it’s really tree processing – no different than processing XML nodes.
Whew. After much rambling we finally got to something that looks like Lisp (and is Lisp, really). By now the mysterious Lisp parentheses as well as some claims made by Lisp advocates should become more clear. But we still have a lot of ground to cover. Ready? Let’s move on!
C Macros Reloaded
By now you must be tired of all the XML talk. I’m tired of it as well. It’s time to take a break from all the trees, s-expressions, and Ant business. Instead, let’s go back to every programmer’s roots. It’s time to talk about C preprocessor. What’s C got to do with anything, I hear you ask? Well, we now know enough to get into metaprogramming and discuss code that writes other code. Understanding this tends to be hard since all tutorials discuss it in terms of languages that you don’t know. But there is nothing hard about the concept. I believe that a metaprogramming discussion based on C will make the whole thing much easier to understand. So, let’s see (pun intended).
Why would anyone want to write a program that writes programs? How can we use something like this in the real world? What on Earth is metaprogramming, anyway? You already know all the answers, you just don’t know it yet. In order to unlock the hidden vault of divine knowledge let’s consider a rather mundane task of simple database access from code. We’ve all been there. Writing SQL queries all over the code to modify data within tables turns into repetitive hell soon enough. Even with the new C# 3.0 LINQ stuff this is a huge pain. Writing a full SQL query (albeit with a nice built in syntax) to get someone’s name or to modify someone’s address isn’t exactly a programmer’s idea of comfort. What do we do to solve these problems? Enter data access layers.
The idea is simple enough. You abstract database access (at least trivial queries, anyway) by creating a set of classes that mirror the tables in the database and use accessor methods to execute actual queries. This simplifies development tremendously – instead of writing SQL queries we make simple method calls (or property assignments, depending on your language of choice). Anyone who has ever used even the simplest of data access layers knows how much time it can save. Of course anyone who has ever written one knows how much time it can kill – writing a set of classes that mirror tables and convert accessors to SQL queries takes a considerable chunk of time. This seems especially silly since most of the work is manual: once you figure out the design and develop a template for your typical data access class you don’t need to do any thinking. You just write code based on the same template over and over and over and over again. Many people figured out that there is a better way – there are plenty of tools that connect to the database, grab the schema, and write code for you based on a predefined (or a custom) template.
Anyone who has ever used such a tool knows what an amazing time saver it can be. In a few clicks you connect the tool to the database, get it to generate the data access layer source code, add the files to your project and voilà – ten minutes worth of work do a better job than hundreds of man-hours that were required previously. What happens if your database schema changes? Well, you just have to go through this short process again. Of course some of the best tools let you automate this – you simply add them as a part of your build step and every time you compile your project everything is done for you automatically. This is perfect! You barely have to do anything at all. If the schema ever changes your data access layer code updates automatically at compile time and any obsolete access in your code will result in compiler errors!
Data access layers are one good example, but there are plenty of others. From boilerplate GUI code, to web code, to COM and CORBA stubs, to MFC and ATL, – there are plenty of examples where the same code is written over and over again. Since writing this code is a task that can be automated completely and a programmer’s time is far more expensive than CPU time, plenty of tools have been created that generate this boilerplate code automatically. What are these tools, exactly? Well, they are programs that write programs. They perform a simple task that has a mysterious name of metaprogramming. That’s all there is to it.
We could create and use such tools in millions of scenarios but more often than not we don’t. What it boils down to is a subconscious calculation – is it worth it for me to create a separate project, write a whole tool to generate something, and then use it, if I only have to write these very similar pieces about seven times? Of course not. Data access layers and COM stubs are written hundreds, thousands of times. This is why there are tools for them. For similar pieces of code that repeat only a few times, or even a few dozen times, writing code generation tools isn’t even considered. The trouble to create such a tool more often than not far outweighs the benefit of using one. If only creating such tools was much easier, we could use them more often, and perhaps save many hours of our time. Let’s see if we can accomplish this in a reasonable manner.
Surprisingly C preprocessor comes to the rescue. We’ve all used it in C and C++. On occasion we all wish Java had it. We use it to execute simple instructions at compile time to make small changes to our code (like selectively removing debug statements). Let’s look at a quick example:
define triple(X) X + X + X
What does this line do? It’s a simple instruction written in the preprocessor language that instructs it to replace all instances of triple(X) with X + X + X. For example all instances of ‘triple(5)’ will be replaced with ‘5 + 5 + 5’ and the resulting code will be compiled by the C compiler. We’re really doing a very primitive version of code generation here. If only C preprocessor was a little more powerful and included ways to connect to the database and a few more simple constructs, we could use it to develop our data access layer right there, from within our program! Consider the following example that uses an imaginary extension of the C preprocessor:
get-db-schema(“127.0.0.1, un, pwd”);
iterate-through-tables
for-each-table
class #table-name
{
};
end-for-each
We’ve just connected to the database schema, iterated through all the tables, and created an empty class for each. All in a couple of lines right within our source code! Now every time we recompile the file where above code appears we’ll get a freshly built set of classes that automatically update based on the schema. With a little imagination you can see how we could build a full data access layer straight from within our program, without the use of any external tools! Of course this has a certain disadvantage (aside from the fact that such an advanced version of C preprocessor doesn’t exist) – we’d have to learn a whole new “compile-time language” to do this sort of work. For complex code generation this language would have to be very complex as well, it would have to support many libraries and language constructs. For example, if our generated code depended on some file located at some ftp server the preprocessor would have to be able to connect to ftp. It’s a shame to create and learn a new language just to do this. Especially since there are so many nice languages already out there. Of course if we add a little creativity we can easily avoid this pitfall.
Why not replace the preprocessor language with C/C++ itself? We’d have full power of the language at compile time and we’d only need to learn a few simple directives to differentiate between compile time and runtime code!
<%
cout << “Enter a number: “;
cin >> n;
%>
for(int i = 0; i < <%= n %>; i++)
{
cout << “hello” << endl;
}
Can you see what happens here? Everything that’s between <% and %> tags runs when the program is compiled. Anything outside of these tags is normal code. In the example above you’d start compiling your program in the development environment. The code between the tags would be compiled and then ran. You’d get a prompt to enter a number. You’d enter one and it would be placed inside the for loop. The for loop would then be compiled as usual and you’d be able to execute it. For example, if you’d enter 5 during the compilation of your program, the resulting code would look like this:
for(int i = 0; i < 5; i++)
{
cout << “hello” << endl;
}
Simple and effective. No need for a special preprocessor language. We get full power of our host language (in this case C/C++) at compile time. We could easily connect to a database and generate our data access layer source code at compile time in the same way JSP or ASP generate HTML! Creating such tools would also be tremendously quick and simple. We’d never have to create new projects with specialized GUIs. We could inline our tools right into our programs. We wouldn’t have to worry about whether writing such tools is worth it because writing them would be so fast – we could save tremendous amounts of time by creating simple bits of code that do mundane code generation for us!
Hello, Lisp!
Everything we’ve learned about Lisp so far can be summarized by a single statement: Lisp is executable XML with a friendlier syntax. We haven’t said a single word about how Lisp actually operates. It’s time to fill this gap2.
Lisp has a number of built in data types. Integers and strings, for example, aren’t much different from what you’re used to. The meaning of 71 or “hello” is roughly the same in Lisp as in C++ or Java. What is of more interest to us are symbols, lists, and functions. I will spend the rest of this section describing these data types as well as how a Lisp environment compiles and executes the source code you type into it (this is called evaluation in Lisp lingo). Getting through this section in one piece is important for understanding true potential of Lisp’s metaprogramming, the unity of code and data, and the notion of domain specific languages. Don’t think of this section as a chore though, I’ll try to make it fun and accessible. Hopefully you can pick up a few interesting ideas on the way. Ok. Let’s start with Lisp’s symbols.
A symbol in Lisp is roughly equivalent to C++ or Java’s notion of an identifier. It’s a name you can use to access a variable (like currentTime, arrayCount, n, etc.) The difference is that a symbol in Lisp is a lot more liberal than its mainstream identifier alternative. In C++ or Java you’re limited to alphanumeric characters and an underscore. In Lisp, you are not. For example + is a valid symbol. So is -, =, hello-world, hello+world, *, etc. (you can find the exact definition of valid Lisp symbols online). You can assign to these symbols any data-type you like. Let’s ignore Lisp syntax and use pseudo-code for now. Assume that a function set assigns some value to a symbol (like = does in Java or C++). The following are all valid examples:
set(test, 5) // symbol ‘test’ will equal an integer 5
set(=, 5) // symbol ‘=’ will equal an integer 5
set(test, “hello”) // symbol ‘test’ will equal a string “hello”
set(test, =) // at this point symbol ‘=’ is equal to 5
// therefore symbol ‘test’ will equal to 5
set(, “hello”) // symbol ‘‘ will equal a string “hello”
At this point something must smell wrong. If we can assign strings and integers to symbols like *, how does Lisp do multiplication? After all, * means multiply, right? The answer is pretty simple. Functions in Lisp aren’t special. There is a data-type, function, just like integer and string, that you assign to symbols. A multiplication function is built into Lisp and is assigned to a symbol *. You can reassign a different value to * and you’d lose the multiplication function. Or you can store the value of the function in some other variable. Again, using pseudo-code:
(3, 4) // multiplies 3 by 4, resulting in 12
set(temp, ) // symbol ‘‘ is equal to the multiply function
// so temp will equal to the multiply function
set(, 3) // sets symbol ‘‘ to equal to 3
(3, 4) // error, symbol ‘‘ no longer equals to a function
// it’s equal to 3
temp(3, 4) // temp equals to a multiply function
// so Lisp multiplies 3 by 4 resulting in 12
set(, temp) // symbol ‘*’ equals multiply function again
*(3, 4) // multiplies 3 by 4, resulting in 12
You can even do wacky stuff like reassigning plus to minus:
set(+, -) // the value of ‘-‘ is a built in minus function
// so now symbol ‘+’ equals to a minus function
+(5, 4) // since symbol ‘+’ is equal to the minus function
// this results in 1
I’ve used functions quite liberally in these examples but I didn’t describe them yet. A function in Lisp is just a data-type like an integer, a string, or a symbol. A function doesn’t have a notion of a name like in Java or C++. Instead, it stands on its own. Effectively it is a pointer to a block of code along with some information (like a number of parameters it accepts). You only give the function a name by assigning it to a symbol, just like you assign an integer or a string. You can create a function by using a built in function for creating functions, assigned to a symbol ‘fn’. Using pseudo-code:
fn [a]
{
return *(a, 2);
}
This returns a function that takes a single parameter named ‘a’ and doubles it. Note that the function has no name but you can assign it to a symbol:
set(times-two, fn [a] { return *(a, 2); })
We can now call this function:
times-two(5) // returns 10
Now that we went over symbols and functions, what about lists? Well, you already know a lot about them. Lists are simply pieces of XML written in s-expression form. A list is specified by parentheses and contains Lisp data-types (including other lists) separated by a space. For example (this is real Lisp, note that we use semicolons for comments now):
() ; an empty list
(1) ; a list with a single element, 1
(1 “test”) ; a list with two elements
; an integer 1 and a string “test”
(test “hello”) ; a list with two elements
; a symbol test and a string “hello”
(test (1 2) “hello”) ; a list with three elements, a symbol test
; a list of two integers 1 and 2
; and a string “hello”
When a Lisp system encounters lists in the source code it acts exactly like Ant does when it encounters XML – it attempts to execute them. In fact, Lisp source code is only specified using lists, just like Ant source code is only specified using XML. Lisp executes lists in the following manner. The first element of the list is treated as the name of a function. The rest of the elements are treated as functions parameters. If one of the parameters is another list it is executed using the same principles and the result is passed as a parameter to the original function. That’s it. We can write real code now:
(* 3 4) ; equivalent to pseudo-code (3, 4).
; Symbol ‘‘ is a function
; 3 and 4 are its parameters.
; Returns 12.
(times-two 5) ; returns 10
(3 4) ; error: 3 is not a function
(times-two) ; error, times-two expects one parameter
(times-two 3 4) ; error, times-two expects one parameter
(set + -) ; sets symbol ‘+’ to be equal to whatever symbol ‘-‘
; equals to, which is a minus function
(+ 5 4) ; returns 1 since symbol ‘+’ is now equal
; to the minus function
(* 3 (* 2 2)) ; multiplies 3 by the second parameter
; (which is a function call that returns 4).
; Returns 12.
Note that so far every list we’ve specified was treated by a Lisp system as code. But how can we treat a list as data? Again, imagine an Ant task that accepts XML as one of its parameters. In Lisp we do this using a quote operator ‘ like so:
(set test ‘(1 2)) ; test is equal to a list of two integers, 1 and 2
(set test (1 2)) ; error, 1 is not a function
(set test ‘(* 3 4)) ; sets test to a list of three elements,
; a symbol *, an integer 3, and an integer 4
We can use a built in function head to return the first element of the list, and a built in function tail to return the rest of the list’s elements:
(head ‘(* 3 4)) ; returns a symbol ‘‘
(tail ‘( 3 4)) ; returns a list (3 4)
(head (tail ‘( * 3 4))) ; (tail ‘(* 3 4)) returns a list (3 4)
; and (head ‘(3 4)) returns 3.
(head test) ; test was set to a list in previous example
; returns a symbol ‘*’
You can think of built in Lisp functions as you think of Ant tasks. The difference is that we don’t have to extend Lisp in another language (although we can), we can extend it in Lisp itself as we did with the times-two example. Lisp comes with a very compact set of built in functions – the necessary minimum. The rest of the language is implemented as a standard library in Lisp itself.
Lisp Macros
So far we’ve looked at metaprogramming in terms of a simple templating engine similar to JSP. We’ve done code generation using simple string manipulations. This is generally how most code generation tools go about doing this task. But we can do much better. To get on the right track, let’s start off with a question. How would we write a tool that automatically generates Ant build scripts by looking at source files in the directory structure?
We could take the easy way out and generate Ant XML by manipulating strings. Of course a much more abstract, expressive and extensible way is to work with XML processing libraries to generate XML nodes directly in memory. The nodes can then be serialized to strings automatically. Furthermore, our tool would be able to analyze and transform existing Ant build scripts by loading them and dealing with the XML nodes directly. We would abstract ourselves from strings and deal with higher level concepts which let us get the job done faster and easier.
Of course we could write Ant tasks that allow dealing with XML transformations and write our generation tool in Ant itself. Or we could just use Lisp. As we saw earlier, a list is a built in Lisp data structure and Lisp has a number of facilities for processing lists quickly and effectively (head and tail being the simplest ones). Additionally Lisp has no semantic constraints – you can have your code (and data) have any structure you want.
Metaprogramming in Lisp is done using a construct called a “macro”. Let’s try to develop a set of macros that transform data like, say, a to-do list (surprised?), into a language for dealing with to-do lists.
Let’s recall our to-do list example. The XML looks like this:
<todo name=”housework”>
<item priority=”high”>Clean the house.</item>
<item priority=”medium”>Wash the dishes.</item>
<item priority=”medium”>Buy more soap.</item>
</todo>
The corresponding s-expression version looks like this:
(todo “housework”
(item (priority high) “Clean the house.”)
(item (priority medium) “Wash the dishes.”)
(item (priority medium) “Buy more soap.”))
Suppose we’re writing a to-do manager application. We keep our to-do items serialized in a set of files and when the program starts up we want to read them and display them to the user. How would we do this with XML and some other language (say, Java)? We’d parse our XML files with the to-do lists using some XML parser, write the code that walks the XML tree and converts it to a Java data structure (because frankly, processing DOM in Java is a pain in the neck), and then use this data structure to display the data. Now, how would we do the same thing in Lisp?
If we were to adopt the same approach we’d parse the files using Lisp libraries responsible for parsing XML. The XML would then be presented to us as a Lisp list (an s-expression) and we’d walk the list and present relevant data to the user. Of course if we used Lisp it would make sense to persist the data as s-expressions directly as there’s no reason to do an XML conversion. We wouldn’t need special parsing libraries since data persisted as a set of s-expressions is valid Lisp and we could use Lisp compiler to parse it and store it in memory as a Lisp list. Note that Lisp compiler (much like .NET compiler) is available to a Lisp program at runtime.
But we can do better. Instead of writing code to walk the s-expression that stores our data we could write a macro that allows us to treat data as code! How do macros work? Pretty simple, really. Recall that a Lisp function is called like this:
(function-name arg1 arg2 arg3)
Where each argument is a valid Lisp expression that’s evaluated and passed to the function. For example if we replace arg1 above with (+ 4 5), it will be evaluated and 9 would be passed to the function. A macro works the same way as a function, except its arguments are not evaluated.
(macro-name (+ 4 5))
In this case, (+ 4 5) is not evaluated and is passed to the macro as a list. The macro is then free to do what it likes with it, including evaluating it. The return value of a macro is a Lisp list that’s treated as code. The original place with the macro is replaced with this code. For example, we could define a macro plus that takes two arguments and puts in the code that adds them.
What does it have to do with metaprogramming and our to-do list problem? Well, for one, macros are little bits of code that generate code using a list abstraction. Also, we could create macros named to-do and item that replace our data with whatever code we like, for instance code that displays the item to the user.
What benefits does this approach offer? We don’t have to walk the list. The compiler will do it for us and will invoke appropriate macros. All we need to do is create the macros that convert our data to appropriate code!
For example, a macro similar to our triple C macro we showed earlier looks like this:
(defmacro triple (x)
‘(+ ~x ~x ~x))
The quote prevents evaluation while the tilde allows it. Now every time triple is encountered in lisp code:
(triple 4)
it is replaced with the following code:
(+ 4 4 4)
We can create macros for our to-do list items that will get called by lisp compiler and will transform the to-do list into code. Now our to-do list will be treated as code and will be executed. Suppose all we want to do is print it to standard output for the user to read:
(defmacro item (priority note)
‘(block
(print stdout tab “Priority: “
~(head (tail priority)) endl)
(print stdout tab “Note: ” ~note endl endl)))
We’ve just created a very small and limited language for managing to-do lists embedded in Lisp. Such languages are very specific to a particular problem domain and are often referred to as domain specific languages or DSLs.
Domain Specific Languages
In this article we’ve already encountered two domain specific languages: Ant (specific to dealing with project builds) and our unnamed mini-language for dealing with to-do lists. The difference is that Ant was written from scratch using XML, an XML parser, and Java while our language is embedded into Lisp and is easily created within a couple of minutes.
We’ve already discussed the benefits of DSLs, mainly why Ant is using XML, not Java source code. Lisp lets us create as many DSLs as we need for our problem. We can create domain specific languages for creating web applications, writing massively multiplayer games, doing fixed income trading, solving the protein folding problem, dealing with transactions, etc. We can layer these languages on top of each other and create a language for writing web-based trading applications by taking advantage of our web application language and bond trading language. Every day we’d reap the benefits of this approach, much like we reap the benefits of Ant.
Using DSLs to solve problems results in much more compact, maintainable, flexible programs. In a way we create them in Java by creating classes that help us solve the problem. The difference is that Lisp allows us to take this abstraction to the next level: we’re not limited by Java’s parser. Think of writing build scripts in Java itself using some supporting library. Compare it to using Ant. Now apply this same comparison to every single problem you’ve ever worked on and you’ll begin to glimpse a small share of the benefits offered by Lisp.
What’s next?
Learning Lisp is an uphill battle. Even though in Computer Science terms Lisp is an ancient language, few people to date figured out how to teach it well enough to make it accessible. Despite great efforts by many Lisp advocates, learning Lisp today is still hard. The good news is that this won’t remain the case forever since the amount of Lisp-related resources is rapidly increasing. Time is on Lisp’s side.
Lisp is a way to escape mediocrity and to get ahead of the pack. Learning Lisp means you can get a better job today, because you can impress any reasonably intelligent interviewer with fresh insight into most aspects of software engineering. It also means you’re likely to get fired tomorrow because everyone is tired of you constantly mentioning how much better the company could be doing if only its software was written in Lisp. Is it worth the effort? Everyone who has ever learned Lisp says yes. The choice, of course, remains yours.
Comments?
Whew. That’s enough. I’ve been writing this article, on and off, for months. If you find it interesting, have any questions, comments, or suggestions, please drop a note at coffeemug@gmail.com. I’ll be glad to hear your feedback.
1I have never met James, nor does he know about my existence. The story is entirely fictional and is based on a few postings about Ant’s history I found on the internet.
2Lisp has many different dialects (the most popular of which are Common Lisp and Scheme). Each dialect deals with intricate details differently yet shares the same set of basic principles. Since the goal of this article is to give you an understanding of Lisp’s principles I will use Blaise for examples (which at the time of this writing is vaporware). With some minor modifications these examples can be translated to other Lisp dialects.
