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The performance of a particular hash table depends heavily on both the keys and the hash function. Obviously one can improve the performance greatly by trying different hash functions based on the incoming elements, and picking the one resulting into the least collisions. Are there any publications on this subject, exploring the methods of selecting such functions dynamically with or without user guidance?
I doubt there is a formal process to choose the best. There are too many moving parts. Especially when it comes to performance - there is no single "best performance" approach. Is it best latency? throughput? memory usage? cpu usage? More reads? More writes? Concurrent access? etc, etc, etc.
The only sensible way is to run performance tests for your specific code and use cases and choose what works for you.
I am doing a project using CPLEX solver, on Netbeans with Java. We have several optimization problems to solve, I have already solved one of them by coding in Java all the constraints, objective and variables, without using AMPL. However, some people in my team want to use AMPL.
Thus, as I don't want to read all the AMPL book to find the answer, is there an obvious reason to rather use AMPL than coding all the constraints "manually"? Moreover, can AMPL be integrated in Netbeans ? I did not find any documentation about that.
Is AMPL useful when the constraints need to be "flexible" (I mean, we can't guess in advance the exact number of constraints, it depends on the parameters fixed by the user, modularity is a high importance factor...)
I am really curious to hear about that soon !
Thanks for help
AMPL is an algebraic modeling language and quoting from that link:
One advantage of AMPL is the similarity of its syntax to the
mathematical notation of optimization problems.
For example, this can allow you to define groups of constraints without knowing in advance the dimensions of the model. And, perhaps, you can make big changes to your model more quickly. (You'll have to think about how often you will actually do that.)
However, one could argue that the "obvious advantage" of AMPL is that it supports dozens of different solvers. You can create your model and solve it with CPLEX, but then decide that you want to use a different solver (e.g., Gurobi, Xpress, etc.). On the AMPL Solvers web page, they have the following recommendation:
We recommend that you then test alternative solvers to determine which
offers the best tradeoff of price and performance for your needs.
The AMPL API web page says that there is a Java API, so that should allow you to include it in a Netbeans project, but I have no experience with that.
At the end of the day, you could also argue that these "advantages" are a matter of taste. Using the CPLEX Java API directly, as you have already done, is certainly a valid solution if it meets your requirements. It may allow you to build the model more efficiently, use solver-specific/advanced features that might not be supported by AMPL, and to have more fine-grained control over the model formulation.
You have just coded an optimisation model to optimise your company's production of widgets. Your company got a really good deal on $SOLVER1 so that's what you're using.
Over the next ten years, you improve and extend that model as your bosses throw new requirements at you. By the end of that time, you may have tens of thousands of lines of optimisation code as part of a system that, by now, is absolutely critical to your company's operations.
Your company's original licensing deal has expired, and the manufacturers of $SOLVER1 have massively increased the licensing fees, so you're now paying hundreds of thousands a year in licensing costs.
Meanwhile, the boffins at a rival company have just released a new version of $SOLVER2. It has fancy new algorithms that could solve the widget optimisation problem 20% faster and find better solutions than $SOLVER1 is giving you. It doesn't cost any more than $SOLVER1 and the performance is better.
Meanwhile, the open-source community has released $FREESOLVER. It might not be quite as powerful as the top commercial options, but it's as good as $SOLVER1 was ten years ago, and if you weren't paying $100k/year for licensing you could rent an awful lot of server time to make up for it.
...so, did you write your optimisation model on a platform that lets you switch to a new solver and take advantage of these opportunities without having to jettison ten years' worth of code?
There are huge advantages to being able to switch solvers quickly and easily. I know of one company who uses three different solvers for their work: they try two different open-source solvers both running in the cloud, and if neither of those can find an adequate solution then they throw it to an expensive solver with smarter algorithms. The open-source solvers handle 90% of their problems, so they only have to use the commercial solver for the last 10%, which allows them to make significant savings on their licensing costs.
One option we've discussed at my work is to use a commercial solver for mission-critical work, and open-source alternatives for applications like training or small-scale prototyping where we don't have the same requirements. That way we can minimise the number of concurrent users we need to license for the commercial solver.
(And, yes, there is still an issue of lock-in with the platform, but platforms like AMPL are significantly cheaper than a high-end commercial solver.)
Totally agree with everything that rkersh says. Also note that you should never write your model in a way that hard-codes details of your problem sizes etc. whether you write in an algebraic modelling language or through one of the more direct APIs.
Also, working with a modelling language gives you an extra level/layer of abstraction which can help, especially in sharing or explaining your model to others, comparing with a range of standard problem types etc., but I prefer the more nuts-and-bolts 'feel' of working with the more direct APIs, and almost never need (or have time & budget) to reformulate my models that deeply.
Even GPL means "general" yet newer and newer GPLs coming to life, so a given GPL is "more general" to somet tasks than others... :-) In theory writing a compiler the most efficiently for Pascal or Perl should not matter, so in fact you could write in whatever language you want and yet you should not lose expressivity or efficiency (e.g. for C# which is in the same league for Java now, MS writes a better compiler than the opensource equivalent).
Humans are specializing - this is why we have gotten this far :-) . No different when it comes to achieve a given task to convert a business problem to a math model (aka modeling). The whole idea of having a given modeling layer is that
A. you have the outmost expressivity for that particular task (aka math modeling)
B. it enforces some best practicies for modeling what in GPL you are not "forced" to do (1. you are free to do 2. it is marketed to you as such = flexibility). E.g. AMPL, GAMS, others are mixing declarative code (aka model code) and procedural code (aka flow-control-like) which is not a good practice. On the other hand e.g. separating data and an abstract model is getting to ALL modeling languages but interestingly enough very slowly...
C. thru no.A you can maintain the code more efficiently than otherwise (contrary to API modeling - I have clients who say they turned to modelinglanguage becuase API modeling is a liability for rapid model revamp)
D. in theory you could be solver independent.
If you look around all modeling languages are trying to maintain no.C except OPL (that's for historical reasons). But even in case of OPL, you get constraint-programming and constraint-based scheduling (beside math-programming) what with AMPL/GAMS you don't, however solverindependent they are...
the $Solver1 and $Solver2 + $Freesolver comparison is a bit broken for 4 reasons
A. opensolvers are still very far away from commercial solvers in term of performance when it comes to large/complex problems (probably LP is getting to the exception) - I have clients - the fastest ever sales in my memory - when they tested commercial solvers after their "free-ride".
B. while indeed the scenario described in relation with $Solver1 and $Solver2 seems plausible ($Solver1, the incumbent is getting more expensive over time), we could witness just the other way around where the $Solver2 (a new comer) actualy increased its pricing 4x in 7 years and in some cases doubled it, while $Solver1 (the incumbent) has had no change.
C. mixing up modeling capabilities and solvers is a mistake. The whole idea is that somebody writes models in APIs IS the way to stick to a solver much more than thru modeling languages. At a minimum, as the Hungarians say "what you gain on the custom you lose it on the ferry", in other words, "freedom (i.e. flexibility) comes with using it responsibly"
D. owning a solver for development is NOT expensive at all, i.e. a company can maintain large # of solvers (for less than 10k$ a company could have +4 solvers for development) to test which is the fastest for any given model and then choose the best suited for deployment.
in addition, solver is just one piece of the puzzle. E.g. I have a client who has disparate data sources and it takes 8hours to create a model and 4hours to solve it. Would this client welcome a more efficient data handling suite or would it insist that the solver should be faster? Modelers are too isolated from the business in most cases and while in their mind a given model is perfect, how it is populated by data is secondary, yet it makes or breaks a good performance.
I witness that API modelers are moving to modeling languages, not the other way around for various reasons...
but as somebody wrote above, there are lots of "tastes in the game", so eventually if you feel more confortable with a given approach then nobody can blame you to choose so... :-) after all it is very difficult to compare the/an other approach since it's almost never there on a given case... so eventually what counts is speed from business problem to a model which solve fast in the given application context :-)
phew, it was long... but I gave all my shots... :-)
To keep it short to illustrate advantage/disadvantage of using AMPL just compare using Java(AMPL) instead of assembly language(CPLEX).
Has anyone successfully used Drools as a kind of "rating engine" before? What are your experiences?
I'm trying to process a couple of millions of records (of slightly different types) and apply rating/pricing to these records.
Rating would be based of tables or database lookups as well as chains of if/then/else/else/else/else conditions using the lookup data.
Traditional rating engines don't employ rule mechanisms in ways that I'm comfortable with...
thanks for your help
To provide a slightly more informative response (although your question can't be answered based on the very vague description you've given), your "rating" is just one of the many names for what I use to call "classification problem". It has been solved many times using Drools.
However, this doesn't mean to say that your problem, with its particular environmental flavour and expected performance (how fast do you want to have the 2M records processed?) can be solved best using Drools - especially when the measure for deciding the quality isn't settled. (For instance: Is ease of maintenance more important than top efficiency?)
Go ahead and rig up a prototype and run a test to see how it goes. That will give you a more reliable answer than anything else. If someone says that something similar couldn't be done, it could be due to bad rule coding. If someone says that something similar was done successfully, it may not have had one of the quirks of your setup. And so on.
I just started working for a large company. in a recent internal audit, measuring metrics such as Cyclomatic complexity and file sizes it turned out that several modules including the one owned by my team have a very high index. so in the last week we have been all concentrating on lowering these indexes for our code. by removing decision points and splitting files.
maybe I am missing something being the new guy but, how will this make our software better?, I know that software metrics can measure how good your code is, but dose it work the other way around? will our code become better just because for example we are making a 10000 lines file into 4 2500 lines files?
The purpose of metrics is to have more control over your project. They are not a goal on their own, but can help to increase the overall quality and/or to spot design disharmonies. Cyclomatic complexity is just one of them.
Test coverage is another one. It is however well-known that you can get high test coverage and still have a poor test suite, or the opposite, a great test suite that focus on one part of the code. The same happens for cyclomatic complexity. Consider the context of each metrics, and whether there is something to improve.
You should try to avoid accidental complexity, but if the processing has essential complexity, you code will anyway be more complicated. Try then to write mainteanble code with a fair balance between the number of methods and their size.
A great book to look at is "Object-oriented metrics in practice".
It depends how you define "better". Smaller files and less cyclomatic complexity generally makes it easier to maintain. Of course the code itself could still be wrong, and unit tests and other test methods will help with that. It's just a part of making code more maintainable.
Code is easier to understand and manage in smaller chunks.
It is a good idea to group related bits of code in their own functional areas for improved readability and cohesiveness.
Having a whole large program all in a single file will make your project very difficult to debug, extend, and maintain. I think this is quite obvious.
The particular metric is really only a rule of thumb and should not be followed religiously, but it may indicate something is not as nice as it could be.
Whether legacy working code should be touched and refactored is something that needs to be evaluated. If you decide to do so, you should consider writing tests for it first, that way you'll quickly know whether your changes broke any required behavior.
Never ever opened one of your own projects after several months again? The larger and more complex the single components are the more one asks oneself, what genious wrote that code and why the heck he wrote it that way.
And, there's never too much or even enough documentation. So if the components themself are lesser complex and smaller, its easier to re-understand 'em
This is bit Subjective. The idea of assigning a maximim Cyclomatic complexity index is to improve the maintainability and the readability of the code.
As an example in the perspective of the unit testing, it is really convenient to have smaller "units". And avoiding the long codes will help the reader to understand the code. You cannot ensure that the original developer works on the code forever so in the company's perspective it is fair to assign such a criteria to keep the code "simple"
It is easy to write a code that can undertand by a computer. It is more harder to write a code that can understood by a human.
how will this make our software better?
Excerpt from the articles Fighting Fabricated Complexity related to the tool for .NET developers NDepend. NDepend is good at helping team to manage large and complex code base. The idea is that code metrics are good are reducing fabricated complexity in the code implementation:
During my interview on Code Metrics by Scott Hanselman’s on Software Metrics, Scott had a particularly relevant remark.
Basically, while I was explaining that long and complex methods are killing quality and should be split into smaller methods, Scott asked me:
looking at this big too complicated
method and I break it up into smaller
methods, the complexity of the
business problem is still there,
looking at my application I can say,
this is no longer complex from the
method perspective, but the software
itself, the way it is coupled with
other bits of code, may indicate other
problem…
Software complexity is a subjective measure relative to the human cognition capacity. Something is complex when it requires effort to be understood by a human. The fact is that software complexity is a 2 dimensional measure. To understand a piece of code one must understand both:
what this piece of code is supposed to do at run-time, the behavior of the code, this is the business problem complexity
how the actual implementation does achieve the business problem, what was the developer mental state while she wrote the code, this is the implementation complexity.
Business problem complexity lies into the specification of the program and reducing it means working on the behavior of the code itself. On the other hand, we are talking of fabricated complexity when it comes to the complexity of the implementation: it is fabricated in the sense that it can be reduced without altering the behavior of the code.
how will this make our software better?
It can be a trigger for a refactoring, but following one metric doesn't guarantee that all other quality metrics stay the same. And tools are only able to follow very few metrics. You can't measure to which degree code is understandable.
Will our code become better just
because for example we are making a
10 000 lines file into 4 2500 lines
files?
Not necessarily. Sometimes the larger one can be more understandable, better structured and have lesser bugs.
Most design patterns for example "improve" your code by making it more general and maintenable, but often with the cost of added source lines.
I am very eager to know the real cause though earned some knowledge from googling.
Thanks in adavnce
Because SQL is a really poor language for writing procedural code, and because the SQL engine, storage, and optimizer are designed to make it efficient to assemble and join sets of records.
(Note that this isn't just applicable to SQL Server, but I'll leave your tags as they are)
Because, in general, the hundreds of man-years of development time that have gone into the database engine and optimizer, and the fact that it has access to real-time statistics about the data, have resulted in it being better than the user in working out the best way to process the data, for a given request.
Therefore by saying what we want to achieve (with a set-based approach), and letting it decide how to do it, we generally achieve better results than by spelling out exactly how to provess the data, line by line.
For example, suppose we have a simple inner join from table A to table B. At design time, we generally don't know 'which way round' will be most efficient to process: keep a list of all the values on the A side, and go through B matching them, or vice versa. But the query optimizer will know at runtime both the numbers of rows in the tables, and also the most recent statistics may provide more information about the values themselves. So this decision is obviously better made at runtime, by the optimizer.
Finally, note that I have put a number of 'generally's in this post - there will always be times when we know better than the optimizer will, and for such times we can provide hints (NOLOCK etc).
Set based approaches are declarative, so you don't describe the way the work will be done, only what you want the result to look like. The server can decide between several strategies how to complay with your request, and hopefully choose one that is efficient.
If you write procedural code, that code will at best be less then optimal in some situation.
Because using a set-based approach to SQL development conforms to the design of the data model. SQL is a very set-based language, used to build sets, subsets, unions, etc, from data. Keeping that in mind while developing in TSQL will generally lead to more natural algorithms. TSQL makes many procedural commands available that don't exist in plain SQL, but don't let that switch you to a procedural methodology.
This makes me think of one of my favorite quotes from Rob Pike in Notes on Programming C:
Data dominates. If you have chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
SQL databases and the way we query them are largely set-based. Thus, so should our algorithms be.
From an even more tangible standpoint, SQL servers are optimized with set-based approaches in mind. Indexing, storage systems, query optimizers, and other optimizations made by various SQL database implmentations will do a much better job if you simply tell them the data you need, through a set-based approach, rather than dictating how you want to get it procedurally. Let the SQL engine worry about the best way to get you the data, you just worry about telling it what data you want.
As each one has explained, let the SQL engine help you, believe, it is very smart.
If you do not use to write set based solution and use to develop procedural code, you will have to spend some time until write well formed set based solutions. This is a barrier for most people. A tip if you wish to start coding set base solutions is, stop thinking what you can do with rows, and start thinking what you can do with collumns, and do practice functional languages.