For long student life I was always wondering why there are so many of them yet there seems to be lack of usage at all in many of them. The opinion didn't really change when I got a job.

We have brilliant books on what they are and their complexities, but I never encounter resources which would actually give a good hint of practical usage. I perfectly understand that I have to look at problem , analyse required operations, look for data structure that does them efficiently. However in practice I never do that, not because of human laziness syndrome, but because when it comes to work I acknowledge time priority over self-development.

Over time I thought that when I would be better developer I will automatically use more of them - that didn't happen at all or maybe I just didn't. Then I found that the colleagues usually in the same plate as me - knowing more or less some three of data structures and being totally happy about it and refusing to discuss this matter further with me, coming back to conversations about 'cool new languages' 'libraries that do jobs for you' and the joy to work under scrumban etc.

I am stuck with ArrayLists, Arrays and SortedMap , which no matter what I do always suffice or either I tweak them to be capable of fulfilling my task. Yes, it might be inefficient but do we really have to care if Intel increases performance over years no matter if we improve our skills? Does new Xeon or IBM machines really care what we use? What if I like build things, but I am not particularly excited whether it is n log(n) or just n? Over twenty years the processing power increased enormously, which gives us freedom of not being critical about which one to use? On top of that new more optimized languages appear which support multiple cores more efficiently.

To be more specific: I would like to find motivational material on complex real areas/cases of possible effective usages of data structures. I would be really grateful if you would provide relevant resources. There is similar question ,but in the end the links again mostly describe or do dumb example(vehicles, students or holy grail quest - yes, very relevant) them and people keep referring to the "scenario decides the data structure to use". I want to know these complex scenarios to be able to identify similarities to my scenario and then use them. The complex scenarios where it really matters and not necessarily of quantitive nature. It seems that data structures only concern is efficiency and nothing else? There seems to be no particular convenience for developer in use one over another.

(only when I found scientific resources on why exactly simple carbohydrates are evil I stopped eating sugar and candies completely replacing it with less harmful fruits - I hope you can see the analogy)

  • You missed one: HashMap. If you're into parallel computing, some concurrent data structures (e.g. Blocking queue) are also essential.
    – rwong
    Commented May 14, 2012 at 5:48
  • Data structures are more about memory rather than CPU (Intel) .) Commented May 29, 2012 at 6:39

4 Answers 4


I think there are a few reasons why developers tend to only use a few data structures.

There are general data structures that work well enough most of the time so they become the primary choice of data structures for most problems.

  • I know hash tables and lists are my "goto" data structures. I even know that in some of the cases I used hash tables when Red Black Trees or even AVL Trees would have been better choices. However, the hash table is more familiar to both me and my team. And choosing it doesn't impact the performance of our software very much.

Languages influence the choice by providing certain data structures as builtins leading to greater usage.

  • Whether it's arrays in C or hash tables in Perl and Python I see others (and myself) using these data structures more often because there more familiar and more readily available.

Programmers tend to head into one area during their careers and stay in that area.

  • I know database and filesystem implementers are much more familiar with B-Trees and tend to use that data structure in other things.

Depending on what you work on the choice of data structure may not matter that much within reason. Notice the "within reason". I'm not talking about using a free form text string and full text search to contain numerically indexed data that should in an array. The are reasons why it may not matter.

  • Your dealing with a small amount of data.
  • Your bottleneck is external to the code you control.

To me part of the criteria for journeyman and expert developers is knowing more than a couple of data structures, but more importantly being able to recognize when your "goto" data structures (or even the structures you know) aren't appropriate. And then doing the work and research to find the appropriate structure and use it.


I think you should try mobile or embedded development. Then you will know how using an unnecessary heap object like ArrayList over a primitive array differs in terms of performance and battery time.

Your usage of data structures should change with your development domain. How can you be so ignorant if you are developing a critical real time system or back-end of a web application where that module will be under heavy load?

If you even don't use them, knowing them does not hurt. In your case, if you are developing a front-end application where performance expectations are low, you shouldn't think about using an ArrayList or creating a new data structure which will increase your performance by %20.


Some quick points:

  • When it comes to data structures, their performance characteristics (and modes of usage) are pretty much all that set them apart. It makes sense to describe them on this level, and to let developers choose from them accordingly. Listing possible practical use cases may be illustrative, but always incomplete (with a lot of guessing as to the exact usage scenarios).
  • Yes, hardware has become a lot more powerful, to the point that for most day-to-day applications programming, it is quite difficult to mess up (although we've all seen some notable exceptions, I'm sure). The scenarios where the difference between O(N) and O(log(N)) make a huge difference are not commonly encountered in everyday scenarios anymore. If you ever have to work with really large amounts of data, then you certainly will care.
  • Remember that most software development is "optimised" for testability, readability, maintainability, etc. Premature optimisation is seen as an evil in professional software development. It's a lot easier to work with a hash table that everyone on my team knows than it is for me to write one. Just as importantly, it takes me no time to use what's already there, and I can be pretty sure it's bug free.

On a side note, I think what you are experiencing is our industry becoming ever more mature in the classical sense. Your everyday programmer is not there to reinvent the most efficient algorithms, just like car mechanics are not supposed to come up with a new gearbox for your car (yep, it's bad analogy time). We're still way off from that level of maturity, of course, but it's no surprise if your average "mechanic" is surprised that they aren't using all those theories they learnt at university. However, if you are the guy at the factory designing the gearbox, you certainly will care if whether it's O(N) or O(log(N)).


I think learning datastructures for most of us developpers is not that important any more.

you have either an (un-)ordered list or a kind of dictionary (hashmap).

Instead you implement against a list interface and your code doesn-t care if the list is an array list, a linked list or b+-tree. All that counts is the interface api.

Of course there are rare situations where performance/memoryconsumtion really matters. Only then you need to know about the different implementations and their performance/memory characteristics. I assume that these cases are less than 0.1% of all.

At scool you learn algorithms and datastructures by implementing them. On the job you finde these in ready made libraries and just uses them.

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