blocks|key|3084799|text|各种数据结构的任何排名都将至少部分地与问题上下文相关联。这将有助于学习如何分析算法的时间和空间性能。通常，使用“大O表示法”，例如，二进制搜索的时间是O(log+)时间，这意味着搜索元素的时间是元素数量的对数(以2为底，隐式地)。直观地说，由于每一步都会丢弃一半不相关的剩余数据，因此将元素数量加倍将使时间增加1步。(二进制搜索的伸缩性相当好。)空间性能关系到较大数据集的内存量如何增长。此外，请注意，大O表示法忽略了常数因子-对于较小的数据集，O(n%5E2)算法可能仍然比具有较高常数因子的O(n+*+log+)算法更快。复杂的算法在启动时通常有更多的工作要做。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3084800|除了时间和空间之外，其他特征包括数据结构是否排序(树和跳过列表排序，哈希表不排序)，持久性(二叉树可以重用旧版本的指针，而哈希表被就地修改)等。|3084801|虽然您需要学习几种数据结构的行为才能对它们进行比较，但了解它们性能差异的原因的一种方法是仔细研究几种数据结构。我建议比较一下单链表、二叉树和skip+lists，它们都相对简单，但具有非常不同的特征。想一想查找一个值、添加一个新值、按顺序查找所有值等需要做多少工作。|offset|length|3084802|人们推荐了各种关于分析算法/数据结构性能的文章，但对我来说真正有意义的是学习OCaml。处理复杂数据结构是ML的强项，当您可以像在C中那样避免指针和内存管理时，它们的行为就会清晰得多。(不过，学习OCaml只是为了理解数据结构几乎肯定是一条漫长的道路。:)|3084803|entityMap|0|LINK|mutability|MUTABLE|url|ftp://ftp.cs.umd.edu/pub/skipLists/skiplists.pdf^0|0|0|1Y|A|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$F|U|G|V|1|W]]|A|$]]|$1|H|3|I|5|6|7|X|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

Any ranking of various data structures will be at least partially tied to problem context. It would help to learn how to analyze time and space performance of algorithms. Typically, "big O notation" is used, e.g. binary search is in O(log n) time, which means that the time to search for an element is the log (in base 2, implicitly) of the number of elements. Intuitively, since every step discards half of the remaining data as irrelevant, doubling the number of elements will increases the time by 1 step. (Binary search scales rather well.) Space performance concerns how the amount of memory grows for larger data sets. Also, note that big-O notation ignores constant factors - for smaller data sets, an O(n^2) algorithm may still be faster than an O(n * log n) algorithm that has a higher constant factor. Complex algorithms often have more work to do on startup.

Besides time and space, other characteristics include whether a data structure is sorted (trees and skiplists are sorted, hash tables are not), persistence (binary trees can reuse pointers from older versions, while hash tables are modified in place), etc.

While you'll need to learn the behavior of several data structures to be able to compare them, one way to develop a sense for why they differ in performance is to closely study a few. I'd suggest comparing singly-linked lists, binary search trees, and <a href="ftp://ftp.cs.umd.edu/pub/skipLists/skiplists.pdf" rel="nofollow noreferrer">skip lists</a>, all of which are relatively simple, but have very different characteristics. Think about how much work it takes to find a value, add a new value, find all values in order, etc.

There are various texts on analyzing algorithms / data structure performance that people recommend, but what really made them make sense to me was learning OCaml. Dealing with complex data structures is ML's strong suit, and their behavior is much clearer when you can avoid pointers and memory management as in C. (Learning OCaml just to understand data structures is almost certainly the long way around, though. :) )

blocks|key|3084922|text|根据我的理解，数据结构是驻留在任何电子系统的内存中的任何数据，都可以被有效地管理。很多时候，这是一个内存或更快的数据访问的游戏。同样在内存方面，基于最终产品公司的成本对数据的管理进行了权衡。Efficiently告诉我们如何根据最终产品的主要要求以最佳方式访问数据。这是一个非常高层次的解释，但数据结构是一个庞大的主题。大多数面试官都会深入研究他们有能力在面试中讨论的数据结构，这取决于他们有多少时间，这些数据结构是链表和相关主题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3084923|现在，根据它们在逻辑上构造和访问的方式，这些数据类型可以分为原始、抽象和复合。|3084924|3084925|primitive数据结构是所有数据结构的基本构建块，它们有一个连续的存储器:+boolean，char，int，string.|unordered-list-item|offset|length|style|BOLD|3084926|composite，string.|3084927|composite数据结构types.class是由多个原始数据类型组成的数据结构，结构，并集，|3084928|primitive数据类型是复合数据类型，可以有效地访问它们，称为算法。根据访问数据的方式，数据结构分为线性数据类型和非线性数据类型。链表、堆栈、队列等都是线性数据类型。堆、二叉树和哈希表等都是非线性数据类型。|3084929|3084930|3084931|我希望这能帮助你深入了解。|3084932|entityMap^0|0|0|0|0|1S|0|0|H|0|0|1C|1|0|2X|1|0|0|0|D|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Y|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|E|3|F|5|G|7|10|8|@$H|11|I|12|J|K]]|9|@]|A|$]]|$1|L|3|M|5|G|7|13|8|@$H|14|I|15|J|K]]|9|@]|A|$]]|$1|N|3|O|5|G|7|16|8|@$H|17|I|18|J|K]]|9|@]|A|$]]|$1|P|3|Q|5|G|7|19|8|@$H|1A|I|1B|J|K]]|9|@]|A|$]]|$1|R|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1D|8|@]|9|@]|A|$]]|$1|T|3|U|5|6|7|1E|8|@$H|1F|I|1G|J|K]]|9|@]|A|$]]|$1|V|3|-4|5|6|7|1H|8|@]|9|@]|A|$]]]|W|$]]

As per my understanding data structure is any data residing in memory of any electronic system that can be efficiently managed. Many times it is a game of memory or faster accessibility of data. In terms of memory again, there are tradeoffs done with the management of data based on cost to the company of that end product. Efficiently managed tells us how best the data can be accessed based on the primary requirement of the end product. This is a very high level explanation but data structures is a vast subjects. Most of the interviewers dive into data structures that they can afford to discuss in the interviews depending on the time they have, which are linked lists and related subjects.

Now, these data types can be divided into primitive, abstract, composite, based on the way they are logically constructed and accessed. 

<ul>
<li>primitive data structures are basic building blocks for all data structures, they have a continuous memory for them: boolean, char, int, float, double, string.</li>
<li>composite data structures are data structures that are composed of more than one primitive data types.class, structure, union, array/record.</li>
<li>abstract datatypes are composite datatypes that have way to access them efficiently which is called as an algorithm. Depending on the way the data is accessed data structures are divided into linear and non linear datatypes. Linked lists, stacks, queues, etc are linear data types. heaps, binary trees and hash tables etc are non linear data types.</li>
</ul>

I hope this helps you dive in.

blocks|key|3085009|text|斯基耶纳的优秀著作"Algorithm+Design+Manual"“包含了大量的算法和数据结构。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3085010|针对大量问题，对数据结构和算法进行了描述、比较，并讨论了实际应用。作者还提供了实现和原始研究论文的参考资料。|3085011|如果你为你要解决的问题寻找最好的数据结构，那么这本书放在你的桌子上是很棒的。这对面试准备也很有帮助。|3085012|另一个很棒的资源是NIST+Dictionary+of+Data+structures+and+algorithms。|3085013|entityMap|0|LINK|mutability|MUTABLE|url|http://www.algorist.com/|1|http://xlinux.nist.gov/dads//^0|A|O|0|0|0|0|9|1D|1|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|D|3|E|5|6|7|X|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|Y|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|J|3|-4|5|6|7|13|8|@]|9|@]|C|$]]]|K|$L|$5|M|N|O|C|$P|Q]]|R|$5|M|N|O|C|$P|S]]]]

The excellent book "<a href="http://www.algorist.com/" rel="noreferrer">Algorithm Design Manual"</a> by Skienna contains a huge repository of Algorithms and Data structure.

For tons of problems, data structures and algorithm are described, compared, and discusses the practical usage. The author also provides references to implementations and the original research papers.

The book is great to have it on your desk if you search the best data structure for your problem to solve. It is also very helpful for interview preparation.

Another great resource is the <a href="http://xlinux.nist.gov/dads//" rel="noreferrer">NIST Dictionary of Data structures and algorithms</a>.

blocks|key|328106|text|之前在StackOverflow上，我在一个类似的问题中找到了这个列表：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|328107|328108|哈希表-用于快速数据查找-用于编译器的符号表，数据库索引，缓存，唯一的数据表示。|blockquote|328109|328110|Trie字典，例如在移动电话上找到的用于自动补全和拼写检查的字典。|328111|后缀树-在大多数文字处理程序中使用的快速全文搜索。|328112|堆栈-撤消\重做操作在字处理器、表达式求值和语法分析中，许多虚拟机(如JVM+)都是面向堆栈的。|328113|队列-传输和操作研究，各种实体被存储并保存以供以后处理，即队列执行缓冲区的功能。|328114|Priority+queues+-内核中的进程调度|328115|树-解析器，文件系统|328116|基数树-+IP路由表|328117|BSP树-+3D计算机图形学|328118|图表-社交网站、路由、通信网络、数据组织等中的连接/关系。|328119|堆-+lisp中的动态内存分配|328120|328121|这是最初由RV+Pradeep发布的答案|offset|length|328122|其他一些不太有用的链接：|328123|Applications+are+only+listed+for+some+data+structures|328124|Not+application+focused,+by+good+summary+and+relevant|328125|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/users/750940/r-v-pradeep|1|http://www.cs.fsu.edu/~jestes/cop3330/notes/datastruct.html|2|http://www.idevelopment.info/data/Programming/data_structures/overview/Data_Structures_Algorithms_Introduction.shtml^0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|5|A|0|0|0|0|1H|1|0|0|1H|2|0^^$0|@$1|2|3|4|5|6|7|1N|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|1O|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|1P|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|1Q|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|1R|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|1S|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1T|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|1U|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1V|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1W|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1X|8|@]|9|@]|A|$]]|$1|U|3|V|5|6|7|1Y|8|@]|9|@]|A|$]]|$1|W|3|X|5|6|7|1Z|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|6|7|20|8|@]|9|@]|A|$]]|$1|10|3|-4|5|6|7|21|8|@]|9|@]|A|$]]|$1|11|3|12|5|6|7|22|8|@]|9|@$13|23|14|24|1|25]]|A|$]]|$1|15|3|16|5|6|7|26|8|@]|9|@]|A|$]]|$1|17|3|18|5|6|7|27|8|@]|9|@$13|28|14|29|1|2A]]|A|$]]|$1|19|3|1A|5|6|7|2B|8|@]|9|@$13|2C|14|2D|1|2E]]|A|$]]|$1|1B|3|-4|5|6|7|2F|8|@]|9|@]|A|$]]]|1C|$1D|$5|1E|1F|1G|A|$1H|1I]]|1J|$5|1E|1F|1G|A|$1H|1K]]|1L|$5|1E|1F|1G|A|$1H|1M]]]]

Found the list in a similar question, previously on StackOverflow:

<blockquote>
 Hash Table - used for fast data lookup - symbol table for compilers,
 database indexing, caches,Unique data representation.
 
 Trie - dictionary, such as one found on a mobile telephone for
 autocompletion and spell-checking.
 
 Suffix tree - fast full text searches used in most word processors.
 
 Stack - undo\redo operation in word processors, Expression evaluation
 and syntax parsing, many virtual machines like JVM are stack oriented.
 
 Queues - Transport and operations research where various entities are
 stored and held to be processed later ie the queue performs the
 function of a buffer.
 
 Priority queues - process scheduling in the kernel
 
 Trees - Parsers, Filesystem
 
 Radix tree - IP routing table
 
 BSP tree - 3D computer graphics
 
 Graphs - Connections/relations in social networking sites, Routing
 ,networks of communication, data organization etc.
 
 Heap - Dynamic memory allocation in lisp
</blockquote>

This is the answer originally posted by <a href="https://stackoverflow.com/users/750940/r-v-pradeep">RV Pradeep</a>

Some other, less useful links:

<a href="http://www.cs.fsu.edu/~jestes/cop3330/notes/datastruct.html" rel="noreferrer">Applications are only listed for some data structures</a>

<a href="http://www.idevelopment.info/data/Programming/data_structures/overview/Data_Structures_Algorithms_Introduction.shtml" rel="noreferrer">Not application focused, by good summary and relevant</a>

blocks|key|3085075|text|我和你在同一条船上。我需要为技术面试做准备，但是死记硬背并没有多大用处。如果你有3-4个小时的空闲时间，并且想要做一次更深的潜水，我建议你去看看|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3085076|mycodeschool|offset|length|3085077|我看过Coursera和其他资源，如博客和教科书，但我发现它们要么不够全面，要么在光谱的另一端，过于密集的必备计算机科学术语。|3085078|视频中的那个家伙有一大堆关于数据结构的讲座。一点也不介意那些愚蠢的图画，或者轻微的口音。您不仅需要了解选择哪种数据结构，还需要了解人们在考虑数据结构时需要考虑的其他一些要点：|3085079|3085080|常用数据结构的优缺点和每个数据结构存在的原因以及它在内存中的实际工作原理和具体问题/练习以及决定使用哪种结构以实现最高效率|unordered-list-item|3085081|清晰的Big0说明|3085082|3085083|I+also+posted+notes+on+github+if+you+are+interested.|3085084|entityMap|0|LINK|mutability|MUTABLE|url|https://www.youtube.com/watch?v=92S4zgXN17o&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=2|1|https://github.com/anhkim/devbootcamp/tree/master/data_structures_notes^0|0|0|C|0|0|0|0|0|0|0|0|0|1G|1|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|13|8|@]|9|@$D|14|E|15|1|16]]|A|$]]|$1|F|3|G|5|6|7|17|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|18|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|19|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|1A|8|@]|9|@]|A|$]]|$1|N|3|O|5|M|7|1B|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1D|8|@]|9|@$D|1E|E|1F|1|1G]]|A|$]]|$1|S|3|-4|5|6|7|1H|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]|10|$5|V|W|X|A|$Y|11]]]]

I am in the same boat as you do. I need to study for tech interviews, but memorizing a list is not really helpful. If you have 3-4 hours to spare, and want to do a deeper dive, I recommend checking out

<a href="https://www.youtube.com/watch?v=92S4zgXN17o&amp;list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&amp;index=2">mycodeschool</a> 
I’ve looked on Coursera and other resources such as blogs and textbooks, 
but I find them either not comprehensive enough or at the other end of the spectrum, too dense with prerequisite computer science terminologies.

The dude in the video have a bunch of lectures on data structures. Don’t mind the silly drawings, or the slight accent at all. You need to understand not just which data structure to select, but some other points to consider when people think about data structures: 

<ul>
<li>pros and cons of the common data structures </li>
<li>why each data structure exist </li>
<li>how it actually work in the memory </li>
<li>specific questions/exercises and deciding which structure to use for maximum efficiency </li>
<li>lucid Big 0 explanation </li>
</ul>

<a href="https://github.com/anhkim/devbootcamp/tree/master/data_structures_notes">I also posted notes on github if you are interested.</a>

blocks|key|331445|text|数据结构的更多实际应用|type|unstyled|depth|inlineStyleRanges|entityRanges|data|331446|红-黑树(用于频繁插入/删除和较少搜索时)-使用红黑树的K-均值聚类，数据库，头脑简单的数据库，在字典中搜索单词，在web上搜索|331447|AVL树(搜索次数多，插入/删除次数少)--数据分析、数据挖掘及其应用|331448|最小堆聚类算法|331449|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

Few more Practical Application of data structures

Red-Black Trees (Used when there is frequent Insertion/Deletion 
and few searches) - K-mean Clustering using red black tree, Databases, Simple-minded database, searching words inside dictionaries, searching on web

AVL Trees (More Search and less of Insertion/Deletion) - Data Analysis and Data Mining and the applications which involves more searches

Min Heap - Clustering Algorithms

There's a lot of talk about data structures, but I can't find a simple list of data structures and their practical use out there. I'm trying to study for an interview and I think this would help me out, along with many others. I'm looking for something like this:

Data structure - Example/Used for

Hash table - fast data lookup ...then give an example

Array - ...

Binary tree - ...

If there is a resource like this somewhere, please let me know.

Thanks!

EDIT: I mean wikipedia is good and all, but on most of the pages they don't actually list practical uses. I'm looking for something more than that.

Practical uses of different data structures

数据分析

数据挖掘

数据结构

文件系统

内存管理

数据库

大数据

虚拟机

编译器

路由

有很多关于数据结构的讨论，但我找不到数据结构及其实际用途的简单列表。我正在为面试做准备，我想这对我和其他许多人都会有帮助。我在找这样的东西：用于的数据结构-示例/哈希表-快速数据查找...then举例说明数组- ...二叉树- ...如果有这样的资源，请让我知道。谢谢!编辑:我的意思是维基百科是好的，但在大多数页面上，...

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