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I remember when I was in DSA I was like wtf O(n) and wondering where would I use it other than in grad school or if you're not a PhD like Bloch. Somehow uses for it does pop up in business analysis, so I was wondering when have you guys had to call up your Big O skills to see how to write an algorithm, which data structure did you use to fit or whether you had to actually create a new ds (like your own implementation of a splay tree or trie).
Understanding Data Structures has been fundamental to many of the projects I've worked on, and that goes beyond the ten minute song 'n dance one does when asked such a question in an interview situation.
Granted that modern environments with all sorts of collection classes can make light work of storing and accessing large amounts of data, but having an understanding that a particular problem is best solved with a particular data structure can be a great timesaver. And by "timesaver" I mean "the difference between something working and not working".
Honestly, being able to answer that stuff is my biggest criterion for taking interviewees seriously in an interview. Knowing how basic data structures work, basic O(n) analysis, and some light theory is really crucial to being able to write large applications successfully.
It's important in the interview because it's important in the job. I've worked with techs in the past that were self taught, without taking the data structures course or reading a data structures book, and their code is occasionally bad in ways they should have seen coming.
If you don't know that n2 is going to run slowly compared to n log n, you've got more to learn.
As far as the later half of the data structures courses, it isn't generally applicable to most tech jobs, but if you ever do wind up needing it, you'll wish you had paid more attention.
Big-O notation is one of the basic notations used when describing algorithms implemented by a particular library. For example, all documentation on STL that I've seen describes various operations in terms of big-O, so naturally you have to e.g. understand the difference between O(1), O(log n) and O(n) to understand the implications of your choice of STL containers and algorithms. MSDN also does that for .NET classes, and IIRC Java documentation does that for standard Java classes. So, I'd say that knowing the notation is pretty much a requirement for understanding documentation of most popular frameworks out there.
Sure (even though I'm a humble MS in EE -- no PhD, no CS, differently from my colleague Joshua Block), I write a lot of stuff that needs to be highly scalable (or components that may need to be reused in highly scalable apps), so big-O considerations are most always at work in my design (and it's not hard to take them into account). The data structures I use are almost always from Python's simple but rich supply (which I did lend a hand developing;-), rarely is a totally custom one needed (rather than building on top of list, dict, etc); but when it does happen (e.g. the bitvectors in my open source project gmpy), no big deal.
I was able to use B-Trees right when I learned about them in algorithm class (that was about 15 years ago when there were much less open source implementations available). And even later the knowledge about the differences of e. g. container classes came in handy...
Absolutely: even though stacks, queues, etc. are pretty straightforward, it helps to have been introduced to them in a disciplined fashion.
B-Tree's and more advanced sorting are a bit more difficult so learning them early was a big benefit and I have indeed had to implement each of them at various points.
Finally, I created an algorithm for single-connected components a few years back that was significantly better than the one our signal-processing team was using but I couldn't convince them that it was better until I could show that it was O(n) complexity rather than O(nlogn).
...just to name a few examples.
Of course, if you are content to remain a CRUD-system hacker with no real desire to do more than collect a paycheck, then it may not be necessary...
I found my knowledge of data structures very useful when I needed to implement a customizable event-driven system about ten years ago. That's the biggie, but I use that sort of knowledge fairly frequently in lesser ways.
For me, knowing the exact algorithms has been... nice as background knowledge. However, the thing that's been the most useful is the more general background of having to pay attention to how different pieces of an algorithm interact. For instance, there can be places in code where moving one piece of code (ie, outside a loop) can make a huge difference in both time and space.
Its less of the specific knowledge the course taught and, rather, more that it acted like several years of experience. The course took something that might take years to encounter (have drilled into you) all the variations of in pure "real world experience" and condensed it.
The title of your question asks about data structures and algorithms, but the body of your question focuses on complexity analysis, so I'll focus on that too:
There are lots of programming jobs where being able to do complexity analysis is at least occasionally useful. See What career can I hope for if I like algorithms? for some examples of these.
I can think of several instances in my career where either I or a co-worker have discovered a a piece of code where the (usually time, sometimes space) complexity was higher that it should have been. eg: something that was quadratic or cubic when it could have been linear or nlog(n). Such code would work fine when given small inputs, but on larger inputs would quickly become really slow or consume all available memory. Knowing alternative algorithms and data structures, their complexities, and also how to analyze the complexity to build new algorithms is vital in being able to correct these problems (or avoid them in the first place).
Networking is all I've used it: in an implementation of traveling salesman.
Unfortunately I do a lot of "line of business" and "forms over data" apps, so most problems I work on can be solved by hammering together arrays, linked lists, and hash tables. However, I've had the chance to work my data structures magic here and there:
Due to weird complex business rules, I worked on an application which used a custom thread pool implemented as a leftist-heap.
My dev team struggled to write a complex multithreaded app. It was plagued with race conditions, dead locks, and lousy performance due to very fine-grained locking. We re-worked the code to share state between threads, opting to write a very light-weight wrapper to facilitate message passing. Next, we converting our linked lists and hash tables to immutable stacks and immutable style and immutable red-black trees, we had no more problems with thread safety or performance. The resulting code was immaculate and surprisingly readable.
Frequently, a business rules engine requires you to roll your own state machine, which is very naturally modelled as a graph where vertexes and states and edges are transitions between states.
If for no other reasons, I'm glad I took the time to readable about data structures and algorithms simply to be able picture novel problems a little differently, especially combinatorial problems and graph problems. Graph theory is no longer a synonym for "scary".
When you say "thin data access layer", does this mainly mean you are talking about writing your SQL manually as opposed to relying on an ORM tool to generate it for you?
That probably depends on who says it, but for me a thin data access layer would imply that there is little to no additional logic in the layer (i.e. data storage abstractions), probably no support for targeting multiple RDBMS, no layer-specific caching, no advanced error handling (retry, failover), etc.
Since ORM tools tend to supply many of those things, a solution with an ORM would probably not be considered "thin". Many home-grown data access layers would also not be considered "thin" if they provide features such as the ones listed above.
Depends on how we define the word "thin". It's one of the most abused terms I hear, rivaled only by "lightweight".
That's one way to define it, but perhaps not the best. An ORM layer does a lot besides just generate SQL for you (e.g., caching, marking "dirty" fields, etc.) That "thin" layer written in lovingly crafted SQL can become pretty bloated by the time you implement all the features an ORM is providing.
I think "thin" in this context means:
It is lightweight;
It has a low performance overhead; and
You write minimal code.
Writing SQL certainly fits this bill but there's no reason it couldn't be an ORM either although most ORMs that spring to mind don't strike me as lightweight.
I think it depends on the context.
It could very well mean that, or it may simply mean that your business objects map directly a simple underlying relational table structure: one table per class, one column per class attribute, so that the translation of business object structure to database table structure is "thin" (i.e. not complex). This could still be handled by an ORM of course.
It may mean that there is no or minimal logic employed on the database such as avoiding the use of stored procedures. As other people have mentioned it depends on the statement's context as to the most likely meaning.
I thought data access were always supposed to be thin... DALs aren't really the place to have logic.
Maybe the person you talked to is talking about a combination of a business layer and a data access layer; where the business layer is non-existent (e.g. a very simple app, or perhaps all of the business rules are in the database, etc).
I'm not sure if it's possible to give general advice on this topic, but please try. It's hard to explain my case because it's too complex to explain. And that's exactly the problem.
I seem to constantly stumble on a situation where I try to design some part of my project, but it has so many things to take into consideration that I'm unable to get a grasp of it.
Are there any general tips or advice on how to look at my system in smaller pieces at a time? How to find smaller portions that could be designed separately on their own?
Create a glossary.
In other words, identify the terms that are meaningful to the project domain — not from the programmer's point of view, but from a user's, who is familiar with the subject matter.
Then define the terms as precisely and discretely as you can. A good definition in this form can serve as a kind of pseudocode.
Since you have not identified even the domain of your problem, I'll choose a random example. In a civilian personnel system, you might have terms like:
billet: a term of service (from start date to end date) at a particular grade and step
employee: a series of billets associated with a particular SSN
grade and step: row and column in the federal general schedule
And so on. This isn't to identify functional units, as it sounds like you are trying to do, but it's a good preparatory step before doing so, so that you can express your functional steps in well-defined terms.
Your key goals are:
High cohesion: Code (methods, fields, classes) within one piece/module/partition should interact intensively; it should make sense for these elements to know about each other. If you find that some of them don't interact much with the rest, they probably belong somwhere else or should form their own partition. If you find code outside interacting intensively with the partition and knowing too much about its inner workings, it probably belongs inside. The typical example is found in OO code written in procedural style, with "dumb" data objects and "manager" code that operates on them but should really be part of the data objects.
Loose coupling: Interaction between pieces/modules/partitions should only happen through narrow, well-defined, well-documented APIs. Try to identify such APIs and see what code is needed to implement them and what code will use them.
It's useful to approach problem decomposition both top-down and bottom-up.
If you're having trouble splitting a big problem into two or more smaller problems, try to think of the smallest possible problems that will need to be solved. Once those are handled, you may start to see ways to combine them into larger problems as you approach your original large problem.
When I find myself copying and pasting chunks of code with minimal adjustments I realize that's a "partition" and then create a class, method, function, or whatever.
Actually, the whole object oriented approach is what it's all about. Try thinking of your application as tangible things that do stuff. Write pseudo code describing what the things are and what they do, I find lots of "partitions" this way.
Here's a try, kind of wild guess.
People usually underestimate how long it will take them to do the work. If your project is large, then most likely you'll need several people to work on it, so you can try planning with that in mind. Now a person can be expected to hold just one area in the head, so you'll need to explain to him exactly what kind of task he's supposed to do.
So I'd say you should try to write a job description that should encompass as much as possible for one person to seriously concentrate on. Repeat, until you have broken your project into parts you wanted to. As a benefit, you're ready to assemble your team. But if you find out the parts are small, maybe you'll still be able to do it yourself.
Obviously, "Hello World" doesn't require a separated, modular front-end and back-end. But any sort of Enterprise-grade project does.
Assuming some sort of spectrum between these points, at which stage should an application be (conceptually, or at a design level) multi-layered? When a database, or some external resource is introduced? When you find that the you're anticipating spaghetti code in your methods/functions?
when a database, or some external resource is introduced.
but also:
always (except for the most trivial of apps) separate AT LEAST presentation tier and application tier
see:
http://en.wikipedia.org/wiki/Multitier_architecture
Layers are a mean to keep a design loosely coupled and highly cohesive.
When you start to have a few classes (either implemented or just sketched with UML), they can be grouped logically, into layers - or more generally packages, or modules. This is called the art of separating the concerns.
The sooner the better: if you do not start layering early enough, then you risk to have never do it as the effort can be too important.
Here are some criteria of when to...
Any time you anticipate the need to
replace one part of it with a
different part.
Any time you find
yourself need to divide work amongst
parallel team.
There is no real answer to this question. It depends largely on your application's needs, and numerous other factors. I'd suggest reading some books on design patterns and enterprise application architecture. These two are invaluable:
Design Patterns: Elements of Reusable Object-Oriented Software
Patterns of Enterprise Application Architecture
Some other books that I highly recommend are:
The Pragmatic Programmer: From Journeyman to Master
Refactoring: Improving the Design of Existing Code
No matter your skill level, reading these will really open your eyes to a world of possibilities.
I'd say in most cases dealing with multiple distinct levels of abstraction in the concepts your code deals with would be a strong signal to mirror this with levels of abstraction in your implementation.
This does not override the scenarios that others have highlighted already though.
I think once you ask yourself "hmm should I layer this" the answer is yes.
I've worked on too many projects that probably started off as proof of concept/prototype that ended up being full projects used in production, which are horribly written and just wreak of "get it done quick, we'll fix it later." Trust me, you wont fix it later.
The Pragmatic Programmer lists this as the Broken Window Theory.
Try and always do it right from the start. Separate your concerns. Build it with modularity in mind.
And of course try and think of the poor maintenance programmer who might take over when you're done!
Thinking of it in terms of layers is a little limiting. It's what you see in whitepapers about a product, but it's not how products really work. They have "boxes" that depend on each other in various ways, and you can make it look like they fit into layers but you can do this in several different configurations, depending on what information you're leaving out of the diagram.
And in a really well-designed application, the boxes get very small. They are down to the level of individual interfaces and classes.
This is important because whenever you change a line of code, you need to have some understanding of the impact your change will have, which means you have to understand exactly what the code currently does, what its responsibilities are, which means it has to be a small chunk that has a single responsibility, implementing an interface that doesn't cause clients to be dependent on things they don't need (the S and the I of SOLID).
You may find that your application can look like it has two or three simple layers, if you narrow your eyes, but it may not. That isn't really a problem. Of course, a disastrously badly designed application can look like it has layers tiers if you squint as hard as you can. So those "high level" diagrams of an "architecture" can hide a multitude of sins.
My generic rule of thumb is to at least to separate the problem into a model and view layer, and throw in a controller if there is a possibility of more than one ways of handling the model or piping data to the view.
(Or as the first answer, at least the presentation tier and the application tier).
Loose coupling is all about minimising dependencies, so I would say 'layer' when a dependency is introduced. i.e. a database, third party application, etc.
Although 'layer' is probably the wrong term these days. Most of the time I use Dependency Injection (DI) through an Inversion of Control container such as Castle Windsor. This means that I can code on one part of my system without worrying about the rest. It has the side effect of ensuring loose coupling.
I would recommend DI as a general programming principle all of the time so that you have the choice on how to 'layer' your application later.
Give it a look.
R
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One thing I struggle with is planning an application's architecture before writing any code.
I don't mean gathering requirements to narrow in on what the application needs to do, but rather effectively thinking about a good way to lay out the overall class, data and flow structures, and iterating those thoughts so that I have a credible plan of action in mind before even opening the IDE. At the moment it is all to easy to just open the IDE, create a blank project, start writing bits and bobs and let the design 'grow out' from there.
I gather UML is one way to do this but I have no experience with it so it seems kind of nebulous.
How do you plan an application's architecture before writing any code? If UML is the way to go, can you recommend a concise and practical introduction for a developer of smallish applications?
I appreciate your input.
I consider the following:
what the system is supposed to do, that is, what is the problem that the system is trying to solve
who is the customer and what are their wishes
what the system has to integrate with
are there any legacy aspects that need to be considered
what are the user interractions
etc...
Then I start looking at the system as a black box and:
what are the interactions that need to happen with that black box
what are the behaviours that need to happen inside the black box, i.e. what needs to happen to those interactions for the black box to exhibit the desired behaviour at a higher level, e.g. receive and process incoming messages from a reservation system, update a database etc.
Then this will start to give you a view of the system that consists of various internal black boxes, each of which can be broken down further in the same manner.
UML is very good to represent such behaviour. You can describe most systems just using two of the many components of UML, namely:
class diagrams, and
sequence diagrams.
You may need activity diagrams as well if there is any parallelism in the behaviour that needs to be described.
A good resource for learning UML is Martin Fowler's excellent book "UML Distilled" (Amazon link - sanitised for the script kiddie link nazis out there (-: ). This book gives you a quick look at the essential parts of each of the components of UML.
Oh. What I've described is pretty much Ivar Jacobson's approach. Jacobson is one of the Three Amigos of OO. In fact UML was initially developed by the other two persons that form the Three Amigos, Grady Booch and Jim Rumbaugh
I really find that a first-off of writing on paper or whiteboard is really crucial. Then move to UML if you want, but nothing beats the flexibility of just drawing it by hand at first.
You should definitely take a look at Steve McConnell's Code Complete-
and especially at his giveaway chapter on "Design in Construction"
You can download it from his website:
http://cc2e.com/File.ashx?cid=336
If you're developing for .NET, Microsoft have just published (as a free e-book!) the Application Architecture Guide 2.0b1. It provides loads of really good information about planning your architecture before writing any code.
If you were desperate I expect you could use large chunks of it for non-.NET-based architectures.
I'll preface this by saying that I do mostly web development where much of the architecture is already decided in advance (WebForms, now MVC) and most of my projects are reasonably small, one-person efforts that take less than a year. I also know going in that I'll have an ORM and DAL to handle my business object and data interaction, respectively. Recently, I've switched to using LINQ for this, so much of the "design" becomes database design and mapping via the DBML designer.
Typically, I work in a TDD (test driven development) manner. I don't spend a lot of time up front working on architectural or design details. I do gather the overall interaction of the user with the application via stories. I use the stories to work out the interaction design and discover the major components of the application. I do a lot of whiteboarding during this process with the customer -- sometimes capturing details with a digital camera if they seem important enough to keep in diagram form. Mainly my stories get captured in story form in a wiki. Eventually, the stories get organized into releases and iterations.
By this time I usually have a pretty good idea of the architecture. If it's complicated or there are unusual bits -- things that differ from my normal practices -- or I'm working with someone else (not typical), I'll diagram things (again on a whiteboard). The same is true of complicated interactions -- I may design the page layout and flow on a whiteboard, keeping it (or capturing via camera) until I'm done with that section. Once I have a general idea of where I'm going and what needs to be done first, I'll start writing tests for the first stories. Usually, this goes like: "Okay, to do that I'll need these classes. I'll start with this one and it needs to do this." Then I start merrily TDDing along and the architecture/design grows from the needs of the application.
Periodically, I'll find myself wanting to write some bits of code over again or think "this really smells" and I'll refactor my design to remove duplication or replace the smelly bits with something more elegant. Mostly, I'm concerned with getting the functionality down while following good design principles. I find that using known patterns and paying attention to good principles as you go along works out pretty well.
http://dn.codegear.com/article/31863
I use UML, and find that guide pretty useful and easy to read. Let me know if you need something different.
UML is a notation. It is a way of recording your design, but not (in my opinion) of doing a design. If you need to write things down, I would recommend UML, though, not because it's the "best" but because it is a standard which others probably already know how to read, and it beats inventing your own "standard".
I think the best introduction to UML is still UML Distilled, by Martin Fowler, because it's concise, gives pratical guidance on where to use it, and makes it clear you don't have to buy into the whole UML/RUP story for it to be useful
Doing design is hard.It can't really be captured in one StackOverflow answer. Unfortunately, my design skills, such as they are, have evolved over the years and so I don't have one source I can refer you to.
However, one model I have found useful is robustness analysis (google for it, but there's an intro here). If you have your use-cases for what the system should do, a domain model of what things are involved, then I've found robustness analysis a useful tool in connecting the two and working out what the key components of the system need to be.
But the best advice is read widely, think hard, and practice. It's not a purely teachable skill, you've got to actually do it.
I'm not smart enough to plan ahead more than a little. When I do plan ahead, my plans always come out wrong, but now I've spend n days on bad plans. My limit seems to be about 15 minutes on the whiteboard.
Basically, I do as little work as I can to find out whether I'm headed in the right direction.
I look at my design for critical questions: when A does B to C, will it be fast enough for D? If not, we need a different design. Each of these questions can be answer with a spike. If the spikes look good, then we have the design and it's time to expand on it.
I code in the direction of getting some real customer value as soon as possible, so a customer can tell me where I should be going.
Because I always get things wrong, I rely on refactoring to help me get them right. Refactoring is risky, so I have to write unit tests as I go. Writing unit tests after the fact is hard because of coupling, so I write my tests first. Staying disciplined about this stuff is hard, and a different brain sees things differently, so I like to have a buddy coding with me. My coding buddy has a nose, so I shower regularly.
Let's call it "Extreme Programming".
"White boards, sketches and Post-it notes are excellent design
tools. Complicated modeling tools have a tendency to be more
distracting than illuminating." From Practices of an Agile Developer
by Venkat Subramaniam and Andy Hunt.
I'm not convinced anything can be planned in advance before implementation. I've got 10 years experience, but that's only been at 4 companies (including 2 sites at one company, that were almost polar opposites), and almost all of my experience has been in terms of watching colossal cluster********s occur. I'm starting to think that stuff like refactoring is really the best way to do things, but at the same time I realize that my experience is limited, and I might just be reacting to what I've seen. What I'd really like to know is how to gain the best experience so I'm able to arrive at proper conclusions, but it seems like there's no shortcut and it just involves a lot of time seeing people doing things wrong :(. I'd really like to give a go at working at a company where people do things right (as evidenced by successful product deployments), to know whether I'm a just a contrarian bastard, or if I'm really as smart as I think I am.
I beg to differ: UML can be used for application architecture, but is more often used for technical architecture (frameworks, class or sequence diagrams, ...), because this is where those diagrams can most easily been kept in sync with the development.
Application Architecture occurs when you take some functional specifications (which describe the nature and flows of operations without making any assumptions about a future implementation), and you transform them into technical specifications.
Those specifications represent the applications you need for implementing some business and functional needs.
So if you need to process several large financial portfolios (functional specification), you may determine that you need to divide that large specification into:
a dispatcher to assign those heavy calculations to different servers
a launcher to make sure all calculation servers are up and running before starting to process those portfolios.
a GUI to be able to show what is going on.
a "common" component to develop the specific portfolio algorithms, independently of the rest of the application architecture, in order to facilitate unit testing, but also some functional and regression testing.
So basically, to think about application architecture is to decide what "group of files" you need to develop in a coherent way (you can not develop in the same group of files a launcher, a GUI, a dispatcher, ...: they would not be able to evolve at the same pace)
When an application architecture is well defined, each of its components is usually a good candidate for a configuration component, that is a group of file which can be versionned as a all into a VCS (Version Control System), meaning all its files will be labeled together every time you need to record a snapshot of that application (again, it would be hard to label all your system, each of its application can not be in a stable state at the same time)
I have been doing architecture for a while. I use BPML to first refine the business process and then use UML to capture various details! Third step generally is ERD! By the time you are done with BPML and UML your ERD will be fairly stable! No plan is perfect and no abstraction is going to be 100%. Plan on refactoring, goal is to minimize refactoring as much as possible!
I try to break my thinking down into two areas: a representation of the things I'm trying to manipulate, and what I intend to do with them.
When I'm trying to model the stuff I'm trying to manipulate, I come up with a series of discrete item definitions- an ecommerce site will have a SKU, a product, a customer, and so forth. I'll also have some non-material things that I'm working with- an order, or a category. Once I have all of the "nouns" in the system, I'll make a domain model that shows how these objects are related to each other- an order has a customer and multiple SKUs, many skus are grouped into a product, and so on.
These domain models can be represented as UML domain models, class diagrams, and SQL ERD's.
Once I have the nouns of the system figured out, I move on to the verbs- for instance, the operations that each of these items go through to commit an order. These usually map pretty well to use cases from my functional requirements- the easiest way to express these that I've found is UML sequence, activity, or collaboration diagrams or swimlane diagrams.
It's important to think of this as an iterative process; I'll do a little corner of the domain, and then work on the actions, and then go back. Ideally I'll have time to write code to try stuff out as I'm going along- you never want the design to get too far ahead of the application. This process is usually terrible if you think that you are building the complete and final architecture for everything; really, all you're trying to do is establish the basic foundations that the team will be sharing in common as they move through development. You're mostly creating a shared vocabulary for team members to use as they describe the system, not laying down the law for how it's gotta be done.
I find myself having trouble fully thinking a system out before coding it. It's just too easy to only bring a cursory glance to some components which you only later realize are much more complicated than you thought they were.
One solution is to just try really hard. Write UML everywhere. Go through every class. Think how it will interact with your other classes. This is difficult to do.
What I like doing is to make a general overview at first. I don't like UML, but I do like drawing diagrams which get the point across. Then I begin to implement it. Even while I'm just writing out the class structure with empty methods, I often see things that I missed earlier, so then I update my design. As I'm coding, I'll realize I need to do something differently, so I'll update my design. It's an iterative process. The concept of "design everything first, and then implement it all" is known as the waterfall model, and I think others have shown it's a bad way of doing software.
Try Archimate.