Practical uses of different data structures [closed]
Solution 1:
Found the list in a similar question, previously on StackOverflow:
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
This is the answer originally posted by RV Pradeep
Some other, less useful links:
Applications are only listed for some data structures
Not application focused, by good summary and relevant
Solution 2:
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
mycodeschool
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:
- pros and cons of the common data structures
- why each data structure exist
- how it actually work in the memory
- specific questions/exercises and deciding which structure to use for maximum efficiency
- lucid Big 0 explanation
I also posted notes on github if you are interested.
Solution 3:
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.
- primitive data structures are basic building blocks for all data structures, they have a continuous memory for them: boolean, char, int, float, double, string.
- composite data structures are data structures that are composed of more than one primitive data types.class, structure, union, array/record.
- 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.
I hope this helps you dive in.
Solution 4:
The excellent book "Algorithm Design Manual" 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 NIST Dictionary of Data structures and algorithms.
Solution 5:
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