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icybreaker
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icybreaker translating
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How learning data structures and algorithms make you a better developer
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================================================================================
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> "I'm a huge proponent of designing your code around the data, rather than the other way around, and I think it's one of the reasons git has been fairly successful […] I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important."
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-- Linus Torvalds
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---
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> "Smart data structures and dumb code works a lot better than the other way around."
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-- Eric S. Raymond, The Cathedral and The Bazaar
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Learning about data structures and algorithms makes you a stonking good programmer.
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**Data structures and algorithms are patterns for solving problems.** The more of them you have in your utility belt, the greater variety of problems you'll be able to solve. You'll also be able to come up with more elegant solutions to new problems than you would otherwise be able to.
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You'll understand, ***in depth***, how your computer gets things done. This informs any technical decisions you make, regardless of whether or not you're using a given algorithm directly. Everything from memory allocation in the depths of your operating system, to the inner workings of your RDBMS to how your networking stack manages to send data from one corner of Earth to another. All computers rely on fundamental data structures and algorithms, so understanding them better makes you understand the computer better.
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Cultivate a broad and deep knowledge of algorithms and you'll have stock solutions to large classes of problems. Problem spaces that you had difficulty modelling before often slot neatly into well-worn data structures that elegantly handle the known use-cases. Dive deep into the implementation of even the most basic data structures and you'll start seeing applications for them in your day-to-day programming tasks.
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You'll also be able to come up with novel solutions to the somewhat fruitier problems you're faced with. Data structures and algorithms have the habit of proving themselves useful in situations that they weren't originally intended for, and the only way you'll discover these on your own is by having a deep and intuitive knowledge of at least the basics.
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But enough with the theory, have a look at some examples
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###Figuring out the fastest way to get somewhere###
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Let's say we're creating software to figure out the shortest distance from one international airport to another. Assume we're constrained to following routes:
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graph of destinations and the distances between them, how can we find the shortest distance say, from Helsinki to London? **Dijkstra's algorithm** is the algorithm that will definitely get us the right answer in the shortest time.
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In all likelihood, if you ever came across this problem and knew that Dijkstra's algorithm was the solution, you'd probably never have to implement it from scratch. Just ***knowing*** about it would point you to a library implementation that solves the problem for you.
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If you did dive deep into the implementation, you'd be working through one of the most important graph algorithms we know of. You'd know that in practice it's a little resource intensive so an extension called A* is often used in it's place. It gets used everywhere from robot guidance to routing TCP packets to GPS pathfinding.
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###Figuring out the order to do things in###
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Let's say you're trying to model courses on a new Massive Open Online Courses platform (like Udemy or Khan Academy). Some of the courses depend on each other. For example, a user has to have taken Calculus before she's eligible for the course on Newtonian Mechanics. Courses can have multiple dependencies. Here's are some examples of what that might look like written out in YAML:
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# Mapping from course name to requirements
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#
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# If you're a physcist or a mathematicisn and you're reading this, sincere
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# apologies for the completely made-up dependency tree :)
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courses:
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arithmetic: []
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algebra: [arithmetic]
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trigonometry: [algebra]
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calculus: [algebra, trigonometry]
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geometry: [algebra]
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mechanics: [calculus, trigonometry]
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atomic_physics: [mechanics, calculus]
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electromagnetism: [calculus, atomic_physics]
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radioactivity: [algebra, atomic_physics]
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astrophysics: [radioactivity, calculus]
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quantumn_mechanics: [atomic_physics, radioactivity, calculus]
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Given those dependencies, as a user, I want to be able to pick any course and have the system give me an ordered list of courses that I would have to take to be eligible. So if I picked `calculus`, I'd want the system to return the list:
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arithmetic -> algebra -> trigonometry -> calculus
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Two important constraints on this that may not be self-evident:
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- At every stage in the course list, the dependencies of the next course must be met.
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- We don't want any duplicate courses in the list.
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This is an example of resolving dependencies and the algorithm we're looking for to solve this problem is called topological sort (tsort). Tsort works on a dependency graph like we've outlined in the YAML above. Here's what that would look like in a graph (where each arrow means `requires`):
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topological sort does is take a graph like the one above and find an ordering in which all the dependencies are met at each stage. So if we took a sub-graph that only contained `radioactivity` and it's dependencies, then ran tsort on it, we might get the following ordering:
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arithmetic
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algebra
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trigonometry
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calculus
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mechanics
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atomic_physics
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radioactivity
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This meets the requirements set out by the use case we described above. A user just has to pick `radioactivity` and they'll get an ordered list of all the courses they have to work through before they're allowed to.
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We don't even need to go into the details of how topological sort works before we put it to good use. In all likelihood, your programming language of choice probably has an implementation of it in the standard library. In the worst case scenario, your Unix probably has the `tsort` utility installed by default, run man `tsort` and have a play with it.
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###Other places tsort get's used###
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- **Tools like** `make` allow you to declare task dependencies. Topological sort is used under the hood to figure out what order the tasks should be executed in.
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- **Any programming language that has a `require` directive**, indicating that the current file requires the code in a different file to be run first. Here topological sort can be used to figure out what order the files should be loaded in so that each is only loaded once and all dependencies are met.
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- **Project management tools with Gantt charts**. A Gantt chart is a graph that outlines all the dependencies of a given task and gives you an estimate of when it will be complete based on those dependencies. I'm not a fan of Gantt charts, but it's highly likely that tsort will be used to draw them.
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###Squeezing data with Huffman coding###
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[Huffman coding](http://en.wikipedia.org/wiki/Huffman_coding) is an algorithm used for lossless data compression. It works by analyzing the data you want to compress and creating a binary code for each character. More frequently occurring characters get smaller codes, so `e` might be encoded as `111` while `x` might be `10010`. The codes are created so that they can be concatenated without a delimeter and still be decoded accurately.
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Huffman coding is used along with LZ77 in the DEFLATE algorithm which is used by gzip to compress things. gzip is used all over the place, in particular for compressing files (typically anything with a `.gz` extension) and for http requests/responses in transit.
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Knowing how to implement and use Huffman coding has a number of benefits:
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- You'll know why a larger compression context results in better compression overall (e.g. the more you compress, the better the compression ratio). This is one of the proposed benefits of SPDY: that you get better compression on multiple HTTP requests/responses.
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- You'll know that if you're compressing your javascript/css in transit anyway, it's completely pointless to run a minifier on them. Sames goes for PNG files, which use DEFLATE internally for compression already.
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- If you ever find yourself trying to forcibly decipher encrypted information , you may realize that since repeating data compresses better, the compression ratio of a given bit of ciphertext will help you determine it's [block cipher mode of operation](http://en.wikipedia.org/wiki/Block_cipher_mode_of_operation).
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###Picking what to learn next is hard###
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Being a programmer involves learning constantly. To operate as a web developer you need to know markup languages, high level languages like ruby/python, regular expressions, SQL and JavaScript. You need to know the fine details of HTTP, how to drive a unix terminal and the subtle art of object oriented programming. It's difficult to navigate that landscape effectively and choose what to learn next.
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I'm not a fast learner so I have to choose what to spend time on very carefully. As much as possible, I want to learn skills and techniques that are evergreen, that is, won't be rendered obsolete in a few years time. That means I'm hesitant to learn the javascript framework of the week or untested programming languages and environments.
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As long as our dominant model of computation stays the same, data structures and algorithms that we use today will be used in some form or another in the future. You can safely spend time on gaining a deep and thorough knowledge of them and know that they will pay dividends for your entire career as a programmer.
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###Sign up to the Happy Bear Software List###
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Find this article useful? For a regular dose of freshly squeezed technical content delivered straight to your inbox, **click on the big green button below to sign up to the Happy Bear Software mailing list.**
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We'll only be in touch a few times per month and you can unsubscribe at any time.
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--------------------------------------------------------------------------------
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via: http://www.happybearsoftware.com/how-learning-data-structures-and-algorithms-makes-you-a-better-developer
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作者：[Happy Bear][a]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创翻译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:http://www.happybearsoftware.com/
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[1]:http://en.wikipedia.org/wiki/Huffman_coding
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[2]:http://en.wikipedia.org/wiki/Block_cipher_mode_of_operation
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@ -0,0 +1,123 @@
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学习数据结构与算法分析如何帮助您成为更优秀的开发人员?
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================================================================================
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> "相较于其它方式，我一直热衷于推崇围绕数据设计代码，我想这也是Git能够如此成功的一大原因[…]在我看来，区别程序员优劣的一大标准就在于他是否认为自己设计的代码或数据结构更为重要。"
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-- Linus Torvalds
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---
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> "优秀的数据结构与简陋的代码组合远比倒过来的组合方式更好。"
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-- Eric S. Raymond, The Cathedral and The Bazaar
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学习数据结构与算法分析会让您成为一名出色的程序员。
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**数据结构与算法分析是一种解决问题的思维模式** 在您的个人知识库中，数据结构与算法分析的相关知识储备越多，您将具备应对并解决越多各类繁杂问题的能力。掌握了这种思维模式，您还将有能力针对新问题提出更多以前想不到的漂亮的解决方案。
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您将***更深入地***了解,计算机如何完成各项操作。无论您是否是直接使用给定的算法，它都影响着您作出的各种技术决定。从计算机操作系统的内存分配到RDBMS的内在工作机制，以及网络堆栈如何实现将数据从地球的一个角落发送至另一个角落这些大大小小的工作的完成，都离不开基础的数据结构与算法，理解并掌握它将会让您更了解计算机的运作机理。
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对算法广泛深入的学习能让为您应对大体系的问题储备解决方案。之前建模困难时遇到的问题如今通常都能融合进经典的数据结构中得到很好地解决。即使是最基础的数据结构，只要对它进行足够深入的钻研，您将会发现在每天的编程任务中都能经常用到这些知识。
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有了这种思维模式，在遇到磨棱两可的问题时，您会具备想出新的解决方案的能力。即使最初并没有打算用数据结构与算法解决相应问题的情况，当真正用它们解决这些问题时您会发现它们将非常有用。要意识到这一点，您至少要对数据结构与算法分析的基础知识有深入直观的认识。
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理论认识就讲到这里，让我们一起看看下面几个例子。
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###最短路径问题###
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我们想要开发一个计算从一个国际机场出发到另一个国际机场的最短距离的软件。假设我们受限于以下路线：
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从这张画出机场各自之间的距离以及目的地的图中，我们如何才能找到最短距离，比方说从赫尔辛基到伦敦？**Dijkstra算法**是能让我们在最短的时间得到正确答案的适用算法。
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在所有可能的解法中，如果您曾经遇到过这类问题，知道可以用Dijkstra算法求解，您大可不必从零开始实现它，只需***知道***该算法能指向固定的代码库帮助您解决相关的实现问题。
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实现了该算法，您将深入理解一项著名的重要图论算法。您会发现实际上该算法太集成化，因此名为A*的扩展包经常会代替该算法使用。这个算法应用广泛，从机器人指引的功能实现到TCP数据包路由，以及GPS寻径问题都能应用到这个算法。
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###先后排序问题###
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您想要在开放式在线课程平台上(如Udemy或Khan学院)学习某课程，有些课程之间彼此依赖。例如，用户学习牛顿力学机制课程前必须先修微积分课程，课程之间可以有多种依赖关系。用YAML表述举例如下：
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# Mapping from course name to requirements
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#
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# If you're a physcist or a mathematicisn and you're reading this, sincere
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# apologies for the completely made-up dependency tree :)
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courses:
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arithmetic: []
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algebra: [arithmetic]
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trigonometry: [algebra]
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calculus: [algebra, trigonometry]
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geometry: [algebra]
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mechanics: [calculus, trigonometry]
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atomic_physics: [mechanics, calculus]
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electromagnetism: [calculus, atomic_physics]
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radioactivity: [algebra, atomic_physics]
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astrophysics: [radioactivity, calculus]
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quantumn_mechanics: [atomic_physics, radioactivity, calculus]
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鉴于以上这些依赖关系，作为一名用户，我希望系统能帮我列出必修课列表，让我在之后可以选择任意一门课程学习。如果我选择了`微积分`课程，我希望系统能返回以下列表：
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arithmetic -> algebra -> trigonometry -> calculus
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这里有两个潜在的重要约束条件：
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- 返回的必修课列表中，每门课都与下一门课存在依赖关系
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- 必修课列表中不能有重复项
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这是解决数据间依赖关系的例子，解决该问题的排序算法称作拓扑排序算法(tsort)。它适用于解决上述我们用YAML列出的依赖关系图的情况，以下是在图中显示的相关结果(其中箭头代表`需要先修的课程`):
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拓扑排序算法的实现就是从如上所示的图中找到满足各层次要求的依赖关系。因此如果我们只列出包含`radioactivity`和与它有依赖关系的子图，运行tsort排序，会得到如下的顺序表：
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arithmetic
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algebra
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trigonometry
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calculus
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mechanics
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atomic_physics
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radioactivity
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这符合我们上面描述的需求，用户只需选出`radioactivity`，就能得到在此之前所有必修课程的有序列表。
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在运用该排序算法之前，我们甚至不需要深入了解算法的实现细节。一般来说，选择不同的编程语言在其标准库中都会有相应的算法实现。即使最坏的情况，Unix也会默认安装`tsort`程序，运行`tsort`程序，您就可以实现该算法。
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###其它拓扑排序适用场合###
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- **工具** 使用诸如`make`的工具您可以声明任务之间的依赖关系，这里拓扑排序算法将从底层实现具有依赖关系的任务顺序执行的功能。
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- **有`require`指令的编程语言**,适用于要运行当前文件需先运行另一个文件的情况。这里拓扑排序用于识别文件运行顺序以保证每个文件只加载一次，且满足所有文件间的依赖关系要求。
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- **包含甘特图的项目管理工具**.甘特图能直观列出给定任务的所有依赖关系，在这些依赖关系之上能提供给用户任务完成的预估时间。我不常用到甘特图，但这些绘制甘特图的工具很可能会用到拓扑排序算法。
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###霍夫曼编码实现数据压缩###
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[霍夫曼编码](http://en.wikipedia.org/wiki/Huffman_coding)是一种用于无损数据压缩的编码算法。它的工作原理是先分析要压缩的数据，再为每个字符创建一个二进制编码。字符出现的越频繁，编码赋值越小。因此在一个数据集中`e`可能会编码为`111`，而`x`会编码为`10010`。创建了这种编码模式，就可以串联无定界符，也能正确地进行解码。
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在gzip中使用的DEFLATE算法就结合了霍夫曼编码与LZ77一同用于实现数据压缩功能。gzip应用领域很广，特别适用于文件压缩(以`.gz`为扩展名的文件)以及用于数据传输中的http请求与应答。
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学会实现并使用霍夫曼编码有如下益处：
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- 您会理解为什么较大的压缩文件会获得较好的整体压缩效果(如压缩的越多，压缩率也越高)。这也是SPDY协议得以推崇的原因之一：在复杂的HTTP请求/响应过程数据有更好的压缩效果。
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- 您会了解数据传输过程中如果想要压缩JavaScript/CSS文件，运行压缩软件是完全没有意义的。PNG文件也是类似，因为它们已经使用DEFLATE算法完成了压缩。
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- 如果您试图强行破译加密的信息，您可能会发现重复数据压缩质量越好，给定的密文单位bit的数据压缩将帮助您确定相关的[分组密码模式](http://en.wikipedia.org/wiki/Block_cipher_mode_of_operation).
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###下一步选择学习什么是困难的###
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作为一名程序员应当做好持续学习的准备。为成为一名web开发人员，您需要了解标记语言以及Ruby/Python，正则表达式，SQL，JavaScript等高级编程语言，还需要了解HTTP的工作原理，如何运行UNIX终端以及面向对象的编程艺术。您很难有效地预览到未来的职业全景，因此选择下一步要学习哪些知识是困难的。
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我没有快速学习的能力，因此我不得不在时间花费上非常谨慎。我希望尽可能地学习到有持久生命力的技能，即不会在几年内就过时的技术。这意味着我也会犹豫这周是要学习JavaScript框架还是那些新的编程语言。
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只要占主导地位的计算模型体系不变，我们如今使用的数据结构与算法在未来也必定会以另外的形式继续适用。您可以放心地将时间投入到深入掌握数据结构与算法知识中，它们将会成为您作为一名程序员的职业生涯中一笔长期巨大的财富。
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--------------------------------------------------------------------------------
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via: http://www.happybearsoftware.com/how-learning-data-structures-and-algorithms-makes-you-a-better-developer
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作者：[Happy Bear][a]
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译者：[icybreaker](https://github.com/icybreaker)
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校对：[校对者ID](https://github.com/校对者ID)
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||||
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||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创翻译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:http://www.happybearsoftware.com/
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[1]:http://en.wikipedia.org/wiki/Huffman_coding
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[2]:http://en.wikipedia.org/wiki/Block_cipher_mode_of_operation
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