Ahhhw, every time is so frustrating..
We have a dedicated server in our hosting company, and everytime i have to write down a new app (or an add to an pre-existing app), i use to 'lose' some time to optimize the code for many behaviors (reducing the db query, optimizing the db structure, reducing the bandwith, etc..) depending on what the app is supposed to do.
Obviously, is the point is not that i write bad code and then rebuild it, its just that after the project is complete, i allways find somethings that could be done better.
And everytime, if my boss catch me doing this, he say 'Your wasting your time! if the application need more resources, we buy more RAM, more CPU, or more bandwith!'.
What is the best (and simplyest) way to explain he that optimization is still important, and that is not so easy or automagically upgrating the hardware of an (production!) server?
EDIT: im not talking just about database optimization, but every aspect of an application
Chances are you are really wasting time. Most of the optimizations that you learn in college or university are sort of irrelevant in the business world. I recommend concentrating on those optimizations where you can quantify WHY your optimization is worth your company's money (your time spent is their money spent).
When trying to optimise code you need to follow the optimisation cycle. There are no short-cuts.
Define specific, detailed, measurable, performance requirements.
Measure your application's performance.
If it meets the requirements, stop, as continuing is a waste of time/money.
Determine where the bottleneck is (which machine? CPU, memory, disk, etc.?).
Fix the bottleneck (either hardware or software fix, whichever is faster/cheaper).
Go to 2.
From your post it doesn't sound like you have done 1 or 2, so you've skipped over 3 and 4 because you don't know enough to do them, and are now stumbling blindly around in 5 attempting to optimise whatever you think needs optimising.
So from what you've said I'd have to agree with your boss. You are wasting your time. Or at least you haven't followed enough process to prove otherwise, which is essentially the same thing.
Keep detailed notes and time sheets the next time you make a server move/upgrade, and protocol every minute of work that arose from it (including things in the weeks after). Then you'll have hard evidence that upgrading is expensive, and optimizations help delay the investment.
In the current situation, you might be able to do something with benchmarks and statistics along the line of "without my optimization, the server will be at 90% capacity with X number of users. With my optimization, we can cater for Y users more, before we have to get a new machine."
On the other hand, the line between optimization and over-optimization is thin. While writing code that doesn't waste resources is good craftsmanship and should be a human right, RAM and disk space are awfully cheap these days. Optimizations also tend to make code more complicated, and harder to maintain for others. When you control your own hardware, code optimization may not always be the top goal.
Do you have performance objectives for the application(s)? Free capacity, response time, bandwidth usage etc. Without them you will find it difficult to justify why an improvement needs to be made. You need to be able to quantify the problem before people will spend money on it.
If the application is already meeting the requirements then your boss is probably right.
To ensure reserve capacity for future customers you (and your boss) could set a threashold such that once performance comes within a percentage of the requirements you need to investigate the problem. This is the time for you to request/push/negociate for performing optimisations as he will be aware of the problem and the risk of doing nothing. If you can recommend some changes to correct the problem that will cost less than the hardware then hopefully he should agree to let you do it. Of course the reverse still applies. If the total cost of the hardware upgrade is cheaper then that is probably the better option.
Wait until buying RAM and CPU becomes more expensive than paying you to optimize the code. Then you have a case.
There's a break even point between improving software vs. improving hardware. For desktop systems, a good threshold is by purchase time:
If that machine fails, can you go to the next mall and buy a replacement within an hour?
As long as you can answer yes, optimization is futile - an investment into the future maybe, but is that really your decision alone?
A similar rule for web sites might be "how long would it take to get a new hosting contract on that?"
This isn't perfect, it covers just a single aspect of scalability. I like it because it's an easy to understand aspect outside of technical concerns.
Of course, you should have more material if that aspect suddenly gets brushed away. It helps to just ask what are the companies/bosses plans for a few key scenarios. - e.g. what contingency plans for DDOS attacks, for "rush events" etc. Having some data to back that up, some key stories ("company foo was mentioned on popular night show, web server broke down under requests, business lost, potential customers angered") helps a lot.
The key to realize is that (assuming from your description alone) it is your responsibility to forsee such events, and provide the technical solutions, but it's not your responsibility to mke these decisions.
Also, getting a written statement from your boss that "five hours downtime isn't a problem, get bck to work on the important things!" is a good CYA.
about the same way one would explain a developer that code/resources optimization is unimportant ...
i guess this problem arises because your way to measure importance is different than his ...
his job is to care about money and make sure he gets the "most" out of it ... and frankly, servers are cheaper than developers ... and managed servers are cheaper than admins ... and the ugly truth is, that many people think that way ... personally, I think PHP isn't really the right choice for performance-critical parts of an app ... neither are RMDBSs ... but nearly every managed server runs PHP and MySQL and business clients prefer to have an app they can run everywhere, based on a technology widely known, which turns out to be PHP and MySQL on the server side ... the world is a cruel place and the truth is ugly ... now when it comes to you, you have only 2 choices:
live with it
work with people who are more like you, and who share the same ambitions
your boss does the job right, from his perspective ... what you do is a waste of company money, because the company's goal is not to write beautiful and highly optimized code ... whatever your company does, in the end you only provide a tool ... and the value of a tool is measured by the use it provides in relation to the money it costs ...
if your optimizations were economically reasonable, then the easiest way would be not to optimize anymore and wait for the moment until your boss comes to you, asking for speedup ...
i suggest, you either look for a new job, or focus on other important things:
good design and use of best practices such as KISS, DRY, SOLID and GRASP
scalable architecture (doesn't mean you have to actually implement it, just have an idea how the whole thing could scale well)
maintainability, including readability and good documentation
flexibility & extensibility
robustness & correctness
do not reinvent the wheel and use the right tools for a job - use adequate technologies, libraries and frameworks
fast developement
these goals are important for the present and the future of a software project (also when it comes to money (especially the bold ones)) ... and they provide the possibility of doing optimization when actually needed ...
this probably isn't the answer you wanted, but I hope it helps ...
Optimizing is often really just a waste of time, what is more important is optimizing for maintainability:
- keep the code simple (see: KISS)
- move duplicate code into functions (see: DRY)
- optimize comments (as few comments as possible, as many as necessary)
- remove unused code
Those changes do not necessarily affect the speed of the program but they reduce the time needed to implement changes and fix bugs so they have real business value as they save expensive developer time.
If you're having to explain why there is a need for optimisation, then you're already on a low-win-rate path. Unfortunately, the best argument is when there is a situation where performance is becoming a noticeable problem (e.g. customers complaining) - this makes people sit up and listen if they're getting constant ear-ache about it. Often, the solution can be that throwing hardware at it is the best/cheapest solution.
However for me, performance is important, when working on highly scalable systems, you need to spend time on optimisations. It should at the very least be on your mind while you're developing and a lot of things can be swallowed up in the dev process (as opposed to after you've finished, then going back to optimise).
e.g.
- What happens if my database has 100,000,000 records instead of 100,000? (you can easily fool SQL Server into thinking there are more records in a table than there actually are to see what the effect on the execution plan would be).
If you're being pro-active about optimisation (which I personally think is good), then you need to try and come with a real-world meaning to your boss. e.g. "if we optimise our site by cutting down the page size, we could save x amount of bandwidth a month, saving us xx much money".
You do have to be careful about micro-optimisations though as you can micro-optimise until the cows come home, for much lower benefit.
You could just wait until your boss says "Why is it taking so long?" Then you could say "Hey, boss, we could buy 10 times as much hardware, or I could spend a couple days and make it 10 times faster". Here's an example.
Added: If your boss says "Well, why did you make it slow in the first place?" I would have no trouble explaining that he could hire God's Right Hand Programmer, and the code as first written would still have room for optimizing.
If you are on a relational database (and your not working with trivial applications).... you cannot afford to write bad database queries. Since you are you using a red-gate tag I assume you are on SQL server. If you design a bad schema (including queries) on SQL Server a database server when you start getting moderate traffic you will be forced to scale in horrendous ways. Once you get a high level of traffic you will be screwed -- and will need to hire a very expensive consultant/DBA to undo the mess -- use this argument with your boss :D.
What I am trying to get at is even though SQL is declarative and it doesn't appear that you have to pay attention to performance -- it doesn't really work that way. You have to design your data model by keeping in mind the way it is going to be used.
If you are writing business logic in application code ... well that doesn't matter much -- you can fix that latter with less trouble if performance becomes an issue. Most business logic these days is stateless (especially if its web-based or web-service based) meaning you can just chuck more machines at the problem -- if it's not than its just a bit harder.
Every time something takes long than it would have if the refactoring had been done ahead of time make sure your boss hears about it.
IMO, It's less a case of convincing that optimizations need to be done (and based on your Boss's responses, you're not going to win this fight), and more a case of always building in refactoring time into your estimates.
Do a cost analysis...
This will take me X hours and will mean every project needs 50% less data storage. Based on us doing 2 projects a year using on average 100Gb space, that will save $Y after a year, $Z after 3 years.
Crude example but it suffices.
Ultimately you and your boss are both wrong... you think code has to be fast (primarily) to satisfy your idealism, he doesn't care at all as he wants to deliver products.
Related
following the text book, I do measure performance whenever I try optimizing my code. Sometimes, however, the performance gain is rather small and I can't decisively decide whether I should implement that optimization.
For example, when a fix shortens an average response time of 100ms to 90ms under some conditions, should I implement that fix? What if it shortens 200ms to 190ms? How many condition should I try before I can conclude that it will be beneficial overall?
I guess it's not possible to give a straight forward answer to this, as it depends on too many things, but is there a good rule of thumb that I should follow? Are there any guideline/best-practices?
EDIT:Thanks for the great answers! I guess the moral of the story is, there is no easy way to tell whether you should, but there ARE guidelines that can aid that process.. Things you should consider, things you shouldn't do etc. This particular time I ended up implementing the fix, even though it made a few line of code into 20-30 lines of code. Because our app. is very performance critical, and it was a consistent 10% gain in various realistic cases.
I think the rule of thumb (at least for me) is two-fold:
"It matters if it matters"--in the business world, this generally means that it matters if the clients care. That is, if the end users will "notice" the difference between 100ms and 90ms (I'm not being facetious here), then it matters.
If "it matters," then you will want to test your code thoroughly against a realistic variety of use cases that are likely to arise or at least may arise. If an optimization speeds up code in 50% of cases, but actually runs slower than what you previously had the other 50% of the time, obviously, it may not be worth implementing.
Regarding point 1 above: by suggesting an end user of your software might "notice" a 10ms difference, I don't mean to suggest that they will actually visibly see a difference. But if your app runs on a server with millions of connections and every little speed increase takes a substantial load off the server, that might matter to the client running the server. Or if your app performs extremely time-critical work, this is another case where the result of a 10ms speedup might be noticeable, even if the speedup itself isn't.
The only sensible approach to your question is something along the lines of "when the benefit is large enough to warrant the time you invest in exploring, implementing and testing the optimization."
The "benefit is large enough" is extremely subjective. Can you or your employer sell more units of software if you make this change? Will your user base notice? Will it give you personal gratification to have the fastest-possible code? Which of those or similar questions apply is something only you can know.
By and large, most of the software I have written (in a 20+ year career) has been "fast enough" out of the box, and the code I cared to optimize presented itself as an obvious bottleneck to the end users: Queries taking a long time, scrolling too slow, that sort of thing.
Donald Knuth made the following two statements on optimization:
"We should forget about small
efficiencies, say about 97% of the
time: premature optimization is the
root of all evil" [2]
and
"In established engineering
disciplines a 12 % improvement, easily
obtained, is never considered marginal
and I believe the same viewpoint
should prevail in software
engineering"[5]
src: http://en.wikipedia.org/wiki/Program_optimization
Is the optimization obfuscating your code too much?
Do you really need an optimization? if your app just runs fine then readability of the code is probably more important
Did you work on the general design and algorithms of your application before trying small hacky optimizations?
You should focus optimisation efforts on the parts of code that account for the most runtime. If a particular piece of code takes up 80% of the total runtime, then optimising it to reduce the time is takes by 5% will have as much impact as reducing the time of the rest of the code by 20%.
In general, optimisations make code less readable (not always, but often). Therefore you should avoid optimising until you are sure that there is a problem.
If it speeds up your program at all, why not implement it? You have already done the work by creating the new implementation, so you are not doing extra work by applying the new implementation.
Unless the code is THAT much harder to understand.
Also, 100 ms to 90 ms is a 10% gain in performance. A 10% gain should not be taken lightly.
The real question is, if it only took 100 ms to run in the first place, what was the point in trying to optimize it?
As long as it's fast enough, then you don't need to optimise any more. But then, you wouldn't even bother profiling if that was the case...
If the performance gain is small, consider the other factors: maintainability, risk of making the change, understandability, etc. If it reduces the ability to maintain or understand the code, it probably isn't worth doing. If it improves those attributes, then it's more reason to implement the change.
In most cases, your time is more valuable than the computer's. If you think it'll take you half an hour longer to work out what the code is doing later (say if there's a bug in it), and it's only saved you a few seconds, ever, you're at a net loss.
It depends very much on the usage scenario. I'll assume here that the code in question has been profiled and thus it is known to be the bottleneck--i.e. not just "this could be faster", but "the program would give results/finish running faster if this were faster". In situations where this is not the case--e.g. if you spend 99% of your time waiting for more data to come over an ethernet connection--then you should care about correctness but not optimize for speed.
If you are writing a piece of user interface code, what you care about is perceived speed. Generally anything under ~100 ms is perceived as "instant"--no point speeding it up.
If you are writing a piece of code for a giant server farm, then if the cost of your salary to make the code fast is less than the cost of the extra electricity for the server farm, it's worthwhile. (But be sure to prioritize your time.)
If you are writing a piece of code that is used rarely or when unattended, as long as it completes in a semi-sane duration, don't worry about it. Install scripts tend to be of this sort (unless you start running into many minutes, at which point users might start abandoning the install because it's taking too long).
If you are writing code to automate a task for someone else, then if (your time spent coding + their time spent using the optimized code) is less than (their time spent using the slow code), it's worthwhile. If you're doing this in a commercial setting, weight this by your respective salaries.
If you are writing library code that will be used by many thousands of people, always make it faster if you have time to.
If you are under time pressure to simply have something working e.g. as a demo, don't optimize (except through sensible choice of algorithms from libraries) unless the result would be so slow that it isn't even "working".
One of the biggest annoyances for me personally is finding software which perhaps initially fell into one category and then later fell into another, but for which nobody went back to do needed optimizations. Until recently Javascript performance was a great example of this. Moral of the story is: don't just decide once; revisit the issue as the situation demands.
After my last question, I want to understand when an optimization is really worth the time a developer spends on it.
Is it worth spending 4 hours to have queries that are 20% quicker? Yes, no, maybe, yes if...?
Is 'wasting' 7 hours to switch a task to another language to save about 40% of the CPU usage 'worth it'?
My normal iteration for a new project is:
Understand what the customer wants, and what he needs;
Plan the project: what languages and where, database design;
Develop the project;
Test and bug-fix;
Final analysis of the running project and final optimizations;
If the project requires it, a further analysis on the real-life usage of the resources followed by further optimization;
"Write good, maintainable code" is implied.
Obviously, the big 'optimization' part happens at point #2, but often when reviewing the code after the project is over I find some sections that, even if they do their job well, could be improved. This is the rationale for point #5.
To give a concrete example of the last point, a simple example is when I expect 90% of queries to be SELECT and 10% to be INSERT/UPDATE, so I charge a DB table with indexes. But after 6 months, I see that in real-life there are 10% SELECT queries and 90% INSERT/UPDATEs, so the query speed is not optimized. This is the first example that comes to my mind (and obviously this is more a 'patch' to an initial mis-design than an optimization ;).
Please note that I'm a developer, not a business-man - but I like to have a clear conscience by giving my clients the best, where possible.
I mean, I know that if I lose 50 hours to gain 5% of an application's total speed-up, and the application is used by 10 users, then it maybe isn't worth the time... but what about when it is?
When do you think that an optimization is crucial?
What formula do you usually apply, aware that the time spent optimizing (and the final gain) is not always quantifiable on paper?
EDIT: sorry, but i cant accept an answer like 'untill people dont complain about id, optimization is not needed'; It can be a business-view (questionable, imho), but not an developer or (imho too) a good-sense answer. I know, this question is really subjective.
I agree with Cheeso, the performance optimization should be deferred, after some analysis about the real-life usage and load of the project, but a small'n'quick optimization can be done immediatly after the project is over.
Thanks to all ;)
YAGNI. Unless people complain, a lot.
EDIT : I built a library that was slightly slower than the alternatives out there. It was still gaining usage and share because it was nicer to use and more powerful. I continued to invest in features and capability, deferring any work on performance.
At some point, there were enough features and performance bubbled to the top of the list I finally spent some time working on perf improvement, but only after considering the effort for a long time.
I think this is the right way to approach it.
There are (at least) two categories of "efficiency" to mention here:
UI applications (and their dependencies), where the most important measure is the response time to the user.
Batch processing, where the main indicator is total running time.
In the first case, there are well-documented rules about response times. If you care about product quality, you need to keep response times short. The shorter the better, of course, but the breaking points are about:
100 ms for an "immediate" response; animation and other "real-time" activities need to happen at least this fast;
1 second for an "uninterrupted" response. Any more than this and users will be frustrated; you also need to start think about showing a progress screen past this point.
10 seconds for retaining user focus. Any worse than this and your users will be pissed off.
If you're finding that several operations are taking more than 10 seconds, and you can fix the performance problems with a sane amount of effort (I don't think there's a hard limit but personally I'd say definitely anything under 1 man-month and probably anything under 3-4 months), then you should definitely put the effort into fixing it.
Similarly, if you find yourself creeping past that 1-second threshold, you should be trying very hard to make it faster. At a minimum, compare the time it would take to improve the performance of your app with the time it would take to redo every slow screen with progress dialogs and background threads that the user can cancel - because it is your responsibility as a designer to provide that if the app is too slow.
But don't make a decision purely on that basis - the user experience matters too. If it'll take you 1 week to stick in some async progress dialogs and 3 weeks to get the running times under 1 second, I would still go with the latter. IMO, anything under a man-month is justifiable if the problem is application-wide; if it's just one report that's run relatively infrequently, I'd probably let it go.
If your application is real-time - graphics-related for example - then I would classify it the same way as the 10-second mark for non-realtime apps. That is, you need to make every effort possible to speed it up. Flickering is unacceptable in a game or in an image editor. Stutters and glitches are unacceptable in audio processing. Even for something as basic as text input, a 500 ms delay between the key being pressed and the character appearing is completely unacceptable unless you're connected via remote desktop or something. No amount of effort is too much for fixing these kinds of problems.
Now for the second case, which I think is mostly self-evident. If you're doing batch processing then you generally have a scalability concern. As long as the batch is able to run in the time allotted, you don't need to improve it. But if your data is growing, if the batch is supposed to run overnight and you start to see it creeping into the wee hours of the morning and interrupting people's work at 9:15 AM, then clearly you need to work on performance.
Actually, you really can't wait that long; once it fails to complete in the required time, you may already be in big trouble. You have to actively monitor the situation and maintain some sort of safety margin - say a maximum running time of 5 hours out of the available 6 before you start to worry.
So the answer for batch processes is obvious. You have a hard requirement that the bast must finish within a certain time. Therefore, if you are getting close to the edge, performance must be improved, regardless of how difficult/costly it is. The question then becomes what is the most economical means of improving the process?
If it costs significantly less to just throw some more hardware at the problem (and you know for a fact that the problem really does scale with hardware), then don't spend any time optimizing, just buy new hardware. Otherwise, figure out what combination of design optimization and hardware upgrades is going to get you the best ROI. It's almost purely a cost decision at this point.
That's about all I have to say on the subject. Shame on the people who respond to this with "YAGNI". It's your professional responsibility to know or at least find out whether or not you "need it." Assuming that anything is acceptable until customers complain is an abdication of this responsibility.
Simply because your customers don't demand it doesn't mean you don't need to consider it. Your customers don't demand unit tests, either, or even reasonably good/maintainable code, but you provide those things anyway because it is part of your profession. And at the end of the day, your customers will be a lot happier with a smooth, fast product than with any of those other developer-centric things.
Optimization is worth it when it is necessary.
If we have promised the client response times on holiday package searches that are 5 seconds or less and that the system will run on a single Oracle server (of whatever spec) and the searches are taking 30 seconds at peak load, then the optimization is definitely worth it because we're not going to get paid otherwise.
When you are initially developing a system, you (if you are a good developer) are designing things to be efficient without wasting time on premature optimization. If the resulting system isn't fast enough, you optimize. But your question seems to be suggesting that there's some hand-wavey additional optimization that you might do if you feel that it's worth it. That's not a good way to think about it because it implies that you haven't got a firm target in mind for what is acceptable to begin with. You need to discuss it with the stakeholders and set some kind of target before you start worrying about what kind of optimizations you need to do.
Like everyone said in the other questions answers is, when it makes monetary sense to change something then it needs changing. In most cases good enough wins the day. If the customers aren't complaining then it is good enough. If they are complaining then fix it enough so they stop complaining. Agile methodologies will give you some guidance on how to know when enough is enough. Who cares if something is using 40% CPU more CPU than you think it needs to be, if it is working and the customers are happy then it is good enough. Really simple, get it working and maintainable and then wait for complaints that probably will never come.
If what you are worried about was really a problem, NO ONE would ever have started using Java to build mission critical server side applications. Or Python or Erlang or anything else that isn't C for that matter. And if they did that, nothing would get done in a time frame to even acquire that first customer that you are so worried about losing. You will know well in advance that you need to change something well before it becomes a problem.
Good posting everyone.
Have you looked at the unnecessary usage of transactions for simple SELECT(s)? I got burned on that one a few times... I also did some code cleanup and found MANY graphs being returned for maybe 10 records needed.... on and on... sometimes it's not YOUR code per se, but someone cutting corners.... Good luck!
If the client doesn't see a need to do performance optimization, then there's no reason to do it.
Defining a measurable performance requirements SLA with the client (i.e., 95% of queries complete in under 2 seconds) near the beginning of the project lets you know if you're meeting that goal, or if you have more optimization to do. Performance testing at current and estimated future loads gives you the data that you need to see if you're meeting the SLA.
Optimization is rarely worth it before you know what needs to be optimized. Always remember that if I/O is basically idle and CPU is low, you're not getting anything out of the computer. Obviously you don't want the CPU pegged all the time and you don't want to be running out of I/O bandwidth, but realize that trying to have the computer basically idle all day while it performs intense operations is unrealistic.
Wait until you start to reach a predefined threshold (80% utilization is the mark I usually use, others think that's too high/low) and then optimize at that point if necessary. Keep in mind that the best solution may be scaling up or scaling out and not actually optimizing the software.
Use Amdal's law. It shows you which is the overall improvement when optimizing a certain part of a system.
Also: If it ain't broke, don't fix it.
Optimization is worth the time spent on it when you get good speedups for spending little time in optimizing (obviously). To get that, you need tools/techniques that lead you very quickly to the code whose optimization yields the most benefit.
It is common to think that the way to find that code is by measuring the time spent by functions, but to my mind that only provides clues - you still have to play detective. What takes me straight to the code is stackshots. Here is an example of a 40-times speedup, achieved by finding and fixing several problems. Others on SO have reported speedup factors from 7 to 60, achieved with little effort.*
*(7x: Comment 1. 60x: Comment 30.)
Since I am a Lone Developer, I have to think about every aspect of the systems I am working on. Lately I've been thinking about performance of my two websites, and ways to improve it. Sites like StackOverflow proclaim, "performance is a feature." However, "premature optimization is the root of all evil," and none of my customers have complained yet about the sites' performance.
My question is, is performance always important? Should performance always be a feature?
Note: I don't think this question is the same as this one, as that poster is asking when to consider performance and I am asking if the answer to that question is always, and if so, why. I also don't think this question should be CW, as I believe there is an answer and reasoning for that answer.
Adequate performance is always important.
Absolute fastest possible performance is almost never important.
It's always worth keeping an eye on performance and being aware of anything outrageously non-optimal that you're doing (particularly at a design/architecture level) but that's not the same as micro-optimising every line of code.
Performance != Optimization.
Performance is a feature indeed, but premature optimization will cost you time and will not yield the same result as when you optimize the parts that need optimization. And you can't really know which parts need optimization until you can actually profile something.
Performance is the feature that your clients will not tell you about if it's missing, unless it's really painfully slow and they're forced to use your product. Existing customers may report it in the end, but new customers will simply not bother if the performance is required.
You need to know what performance you need, and formulate it as a requirement. Then, you have to meet your own requirement.
That 'root of all evil' quote is almost always misused and misunderstood.
Designing your application to perform well can be mostly be done with just good design. Good design != premature optimization, and it's utterly ridiculous to go off writing crap code and blowing off doing a better job on the design as an 'evil' waste. Now, I'm not specifically talking about you here... but I see people do this a lot.
It usually saves you time to do a good job on the design. If you emphasize that, you'll get better at it... and get faster and faster at writing systems that perform well from the start.
Understanding what kinds of structures and access methods work best in certain situations is key here.
Sure, if you're app becomes truly massive or has insane speed requirements you may find yourself doing tricked out optimizations that make your code uglier or harder to maintain... and it would be wrong to do those things before you need to.
But that is absolutely NOT the same thing as making an effort to understand and use the right algorithms or data patterns or whatever in the first place.
Your users are probably not going to complain about bad performance if it's bearable. They possibly wouldn't even know it could be faster. Reacting to complaints as a primary driver is a bad way to operate. Sure, you need to address complaints you receive... but a lack of them does not mean there isn't a problem. The fact that you are considering improving performance is a bit of an indicator right there. Was it just a whim, or is some part of you telling you it should be better? Why did you consider improving it?
Just don't go crazy doing unnecessary stuff.
Keep performance in mind but given your situation it would be unwise to spend too much time up front on it.
Performance is important but it's often hard to know where your bottleneck will be. Therefore I'd suggest planning to dedicate some time to this feature once you've got something to work with.
Thus you need to set up metrics that are important to your clients and you. Keep and analyse these measurements. Then estimate how long and how much each would take to implement. Now you can aim on getting as much bang for you buck/time.
If it's web it would be wise to note your page size and performance using Firebug + yslow and/or google page speed. Again, know what applies to a small site like yours and things that only apply to yahoo and google.
Jackson’s Rules of Optimization:
Rule 1. Don’t do it.
Rule 2 (for experts only). Don’t do it
yet— that is, not until you have a
perfectly clear and unoptimized
solution.
—M. A. Jackson
Extracted from Code Complete 2nd edition.
To give a generalized answer to a general question:
First make it work, then make it right, then make it fast.
http://c2.com/cgi/wiki?MakeItWorkMakeItRightMakeItFast
This puts a more constructive perspective on "premature optimization is the root of all evil".
So to parallel Jon Skeet's answer, adequate performance (as part of making something work, and making it right) is always important. Even then it can often be addressed after other functionality.
Jon Skeets 'adequate' nails it, with the additional provision that for a library you don't know yet what's adequate, so it's better to err on the safe side.
It is one of the many stakes you must not get wrong, but the quality of your app is largely determined by the weakest link.
Performance is definitely always important in a certain sense - maybe not the one you mean: namely in all phases of development.
In Big O notation, what's inside the parantheses is largely decided by design - both components isolation and data storage. Choice of algorithm will usually only best/worst case behavior (unless you start with decidedly substandard algorithms). Code optimizations will mostly affect the constant factor - which shouldn't be neglected, either.
But that's true for all aspects of code: in any stage, you have a good chance to fail any aspect - stability, maintainability, compatibility etc. Performance needs to be balanced, so that no aspect is left behind.
In most applications 90% or more of execution time is spend in 10% or less of the code. Usually there is little use in optimizing other code than these 10%.
performance is only important to the extent that developing the performance improvement takes less time than the total amount of time that will be saved for the user(s).
the result is that if you're developing something for millions... yeah it's important to save them time. if you're coding up a tool for your own use... it might be more trouble than it's worth to save a minute or even an hour or more.
(this is clearly not a rule set in stone... there are times when performance is truly critical no matter how much development time it takes)
There should be a balance to everything. Cost (or time to develop) vs Performance for instance. More performance = more cost. If a requirement of the system being built is high performance then the cost should not matter, but if cost is a factor then you optimize within reason. After a while, your return on investment suffers in that more performance does not bring in more returns.
The importance of performance is IMHO highly correlated to your problem set. If you are creating a site with an expectation of a heavy load and lot of server side processing, then you might want to put some more time into performance (otherwise your site might end up being unusable). However, for most applications the the time put into optimizing your perfomance on a website is not going to pay off - users won't notice the difference.
So I guess it breaks down to this:
Will users notice the improvements?
How does this improvement compare to competing sites?
If users will notice AND the improvement would be enough to differentiate you from the competition - performance is an important feature - otherwise not so much. (To a point - I don't recommend ignoring it entirely - you don't want your site to turtle along after all).
No. Fast enough is generally good enough.
It's not necessarily true, however, that your client's ideas about "fast enough" should trump your own. If you think it's fast enough and your client doesn't then yes, you need to accommodate your ideas to theirs. But if you're client thinks it's fast enough and you don't you should seriously consider going with your opinion, no theirs (since you may be more knowledgeable about performance standards in the wider world).
How important performance is depends largely and foremost on what you do.
For example, if you write a library that can be used in any environment, this can hardly ever have too much performance. In some environments, a 10% performance advantage can be a major feature for a library.
If you, OTOH, write an application, there's always a point where it is fast enough. Users won't neither realize nor care whether a button pressed reacts within 0.05 or 0.2 seconds - even though that's a factor of 4.
However, it is always easier to get working code faster, than it is to get fast code working.
No. Performance is not important.
Lack of performance is important.
Performance is something to be designed in from the outset, not tacked on at the end. For the past 15 years I have been working in the performance engineering space and the cause of most project failures that I work on is a lack of requirements on performance. A couple of posts have noted "fast enough" as an observation and whether your expectation matches that of your clients, but what about when you have a situation of your client, your architectural team, your platform engineering team, your functional test team, your performance test team and your operations team all have different expectations on performance, none of which have been committed to stone and measured against. Bad Magic to be certain.
Capture those expectations on the part of your clients. Commit them to a specific, objective, measurable requirement that you can evaluate at each stage of production of your software. Expectations may not be uniform, with one section of your app/code needing to be faster than others, nor will each customer have the same expectations on what is considered acceptable. Having this information will force you to confront decisions in the design and implementation that you may have overlooked in the past and it will result in a product which is a better match to your clients expectations.
Let's say you have a project that will involve two web applications (that will share DAL/DAO/BO assemblies and some OSS libraries):
a semi complex management application that uses Windows Live ID for authentication and is also capable of communicating with various notifier services (email, sms, twitter etc.), targeted notifiers being about 10% of functionality
a low to semi complex user application with less functionality but more robustness that also uses Windows Live ID for authentication
There are two of us with medium estimation capabilities and we won't be able to do it in two days even if we wanted/have to. At least it would be a far off estimate.
Questions
How long would/does it normally take you to make a reliable/valuable estimate?
What would you suggest to speed-up estimation without sacrificing accuracy?
How much slack (in terms of cost/time) would you add depending on estimation speed (when you would say: I could estimate it a bit more, because I think it's still quite off)
Since we use Agile methods (Scrum, specifically) it takes us about an hour longer than it takes the users to prioritize.
More time doesn't lead to more accuracy.
The hard part, then, is getting the users to prioritize. We hear this discussion all the time "if the whole thing isn't completed on time, it's all worthless." "Except for the XYZZY component, which does have some value." That argument can go on for hours until it's resolved that XYZZY should be first.
Generally, we try to create 4-week sprints. The first few are complicated because there's always something new. After the first two (or three) we seem to set a steady pace.
Each use case has a relatively simple, subjective valuation of how the effort it will take to finish it. Anything over one full sprint in duration has to be decomposed. Most times a few use cases are bundled into a single sprint.
The are formal ways of scoring each use case to better handle the cost and schedule issues. We don't use them because the extra effort doesn't help.
After the first two sprints,
There's new and different functionality,
The priorities have all changed,
The details of each use case have been dramatically revised.
What does "accuracy" mean when the thing you're trying to estimate changes at the end of each sprint?
One lesson learned. Parts of my company spend a long time fully defining exactly what will be delivered, and then measuring that they are delivering precisely what they want.
Customers notice this, and one said we "spend a lot of time delivering what the contract says, but it isn't what we needed."
The problem with firm up-front estimates is that they take on a life of their own. The more you "invest" in the estimating, the more the estimates seem to be a useful deliverable. They aren't useful because they're generally totally wrong. They're based on up-front assumptions that are totally wrong.
It's a bad policy to invest more time in estimating. The "accurate" answers aren't more accurate, but they are more treasured by every layer of management. As you and the customer learn, you invalidate numerous assumptions and you absolutely must re-estimate constantly.
Don't do it up front. If your contract requires you to do it up front, then make sure you have a change control provision and tell the customer that you absolutely will make changes as you go forward. As both you and the customer learn, you both must make changes.
Interesting question. I'm afraid the answer is "it really depends!" I know that's not terribly useful (although it IS true) so here are some factors:
1) Quality and completeness of requirements and their specification. This is, to me, most often the project-estimate killer. If you don't have quality requirements, you have no reasonable basis for an estimate. We use a "RUP-lite" style of product development here, so as the engineering manager I won't give anything but the coarsest-grain estimate until we've completed our "elaboration" phase and gotten sign-off from product management that the core 80% of the product features are in fact accurately covered.
2) The scope & nature of the product. Bigger/more expensive/more complicated = substantially longer to estimate. I've spent years working in telco-carrier land delivering solutions which have the normal robust carrier requirements ("5 9's" of uptime required by SLA mean you must really do a good job of solution design and failure recovery!). In that sort of environment with all the moving parts across functional areas of the business, the estimation of work is going to hinge on getting the whole picture...specifically, cross-functional dependencies and external dependencies can be a REAL killer here. That said, I've also built lots of shrink-wrapped and enterprise software, too. In those environments it's much easier as the scope is typically substantially smaller, so thus easier to estimate.
3) How "new" is this project? How "new" is the team to this product or technology set? The newer the product or team, the longer and more buffer you should allocate.
4) How specific do we need to be? If this is a "rough guess" then I'll lean on my engineering leads to provide a conservative estimate, then I'll pad that. If we need a "real" estimate (e.g., one which is used by my boss and which I'll be responsible for hitting), I'd need the input from a number of different managers and team members, who will need time to analyze the requirements and confer amongst themselves.
That can take as little as a couple days, or weeks..it all depends on the size. "Two or three days" is, frankly, not long enough for sizing anything but the most trivial of projects.
The best thing you can do to improve the quality of your estimates to to improve the quality of your requirements, and be ruthless in identifying hidden dependencies.
One final thing: FWIW, I've been doing this since '81 and I regard accurately estimating a project's duration/cost as the single most difficult/fraught with peril part of engineering management.
To make a reliable estimate you really would need to create a list of tasks to be done. Break it down into stories/tasks (even if you don't use agile) and evaluate them. This can take a lot of time already - especially the amount of research (to look for libraries to do this notifier stuff to reduce the cost). I would take at least 3 days - however 1-2 week(s) sound more reasonable for me. This doesn't seem to be a small project.
I wouldn't dare to speed-up the estimation process if you wan't to have somewhat reasonable results. Estimations are never accurate and you will just make things worse.
An option would be to make a totally rough guess within a few hours and then start to work on it already (for a week or so). At the end of the week you might be able to give a better estimation based on your current progress. However it's important to create a good prototype (which looks like crap but has a little bit of code in all of the regions).
Well, a common quote is "The price will be between 50% and 400% of our initial estimates". The reason that this quote has grown large is that it's true. Estimation accuracy largely depends on your domain-knowledge. If this is the 100th time you sold a given type of blog-engine than you're pretty sure about the estimates. However, more often than not, you don't have that indepth knowledge of the domain (If the app already exists, why create a new one?).
Agile development has become popular because people largely recognize that the traditional "waterfall" type of thinking just doesn't work for most real-world projects. You should apply the same way of thinking to your estimates. Obviously, you need some kind of selling point, but be sure to tell your customers that this information is very vague (and that no matter what any competitor will tell them, their estimates are vague as well).
You need to sell iterations, and therefore also estimate iterations. I'm sure some marketing-guy in any company will know how to handle the paperwork and the legal stuff for this, so it shouldn't be that big a deal.
Consider a 5 iterations project:
Start out by putting up milestones. These are subject to change, but will provide your vargue first estimates for the final product.
Plan iteration #1.
Estimate iteration #1, adjust total estimates accordingly.
Do iteration #1
Rinse / repeat till iteration #5
You're done :)
Reflect over your project. How did your estimates evolve? Reasons? Learn by doing :)
Most customers would rather have part-estimates and part-deliverances than some unrealistic goal that some suit sold them :)
Straying slightly from your original question but it's also important to learn from the estimates that you have produced in the past by keeping information about how long it actually took to do the things that you estimated.
We estimated 5 days to produce these pages, it actually took 10 days, and etc.
In the long term information like this will (hopefully!) enable you to produce more accurate estimates.
The IT department I work in as a programmer revolves around a 30+ year old code base (Fortran and C). The code is in a poor condition partially as a result of 30+ years of ad-hoc poorly thought out changes but I also suspect a lot of it has to do with the capabilities of the programmers who made the changes (and who incidentally are still around).
The business that depends on the software operates 363 days a year and 20 hours a day. Unfortunately there are numerous outages. This is the first place I have worked where there are developers on call to apply operational code fixes to production systems. When I was first, there was actually a copy of the source code and development tools on the production servers so that on the fly changes could be applied; thankfully that practice has now been stopped.
I have hinted a couple of times to management that the costs of the downtime, having developers on call, extra operational staff, unsatisifed customers etc. are costing the business a lot more in the medium, and possibly even short term, than it would to launch a whole hearted effort to re-write/refactor/replace the whole thing (the code base is about 300k lines).
Ideally they'd be some external consultancy that could come in and run the rule over the quality of the code and the costs involved to keep it running vs rewrite/refactor/replace it. The question I have is how should a business go about doing that kind of cost analysis on software AND be able to have confidence in that analysis? The first IT consultants down the street may claim to be able to do the analysis but how could management be made to feel comfortable with it over what they are being told by internal staff?
We recently decided to completely rewrite large portions of our business code from scratch, and it has not gone as well as we had hoped. I've seen a lot of quotes saying you should never try to rewrite anything from scratch, and now I see why. I would recommend starting small - don't try to rewrite the whole thing at once. Identify the large problem areas and focus on refactoring small portions of the system at a time. Since there is 30+ years worth of work in the system, it will take a long time to get it back to a reasonable state. We had about 5-8 years worth of work to rewrite, and it has been difficult. I can't imagine 30+ years of work!
First, the profile of the consultant you need is very specific. Unless you can find someone who worked in a similar domain with the same languages, don't hire him.
Second, there's a 99% probability (I like dramatic numbers) the analysis will go as follow:
Consultant explores the application
Consultant does understand 10% of the application
Time's up, time for the report
Consultant advices a complete rewrite (no refactoring, plain rewrite)
So you may as well make the economy of what the consultant will cost.
You have only two solutions here:
Keep with the actual source code but determine proper methods to fix problems so that you have a very long run refactoring that is progressly made by those who know the application
Get a secondary team to make a new application to replace the old one
If I talk about a secondary team, it's because you cannot bring just one architect to make the new application and have the old team working with him:
They're too busy on the old application
There will be frictions because the newcomer will undoubtedly underestimate the task at hand
I talk from experience, believe me.
If you go the "new application" way don't put your hopes too high. You'll end up with an application that has less than half the functionalities of the current one, simply because you cannot cram 30+ years of special case and exceptional situation fixes into a freshly design software.
Oh, also, if your developers happen to tell you they have a plan, by all means, hear them out. They most probably know what they are talking about.
The first thing that comes to mind is that you are prematurely addressing the rewrite/refactor/replace argument. The first step two steps I would recommend would be:
Unit tests
QA
It's well within engineering scope to implement these. Unit tests are an essential preliminary step before any reasonable refactor or rewrite could possibly take place. By 'unit test' I mean wrap each function call with corresponding code that proves the code works for all known conditions. In complex retrofits this may not actually happen at the most granular level but any automated tests will help immensely.
And QA - have an independent (and aggressive) quality assurance team that rigorously tests beta releases before production. Their test plans and test procedures become essential for any kind of replacement effort.
Once you've got the code under control, then you are in a position where the business can reasonably consider massive changes.
Just a note about your comment about external consultants - no consultancy will ever care enough about the code to provide realistic quality assurance. QA ends up being married to the hip of business defending the company bottom line. It's an internal function ultimately and an external consultant can't provide much more than getting you started really.
I think that your description provides all of the necessary information on code quality (lack thereof). The fact that so many support resources are required also indicates the high costs involved with maintaining the existing system.
As I answered here, a good approach to consider is refactoring one piece of the system at a time until everything works at an acceptable level. I agree with Joel re not throwing away existing code (see Things You Should Never Do. Parts of your code work, so you should leave those in place whenever possible, and focus on the sections that lead to downtime.
Andy also makes a great point about starting small as well.
Another thing to try, is reviewing the processes around the system. When you do this, you should try to determine what failure situations are caused directly or indirectly by user action?, are there configuration or environment problems? If you are having trouble fixing the code directly, then you can still prop it up by dealing with external issues more effectively.
Read the book Working Effectively with Legacy Code (also see the short PDF version) and surround the code with automated tests, as instructed in that book.
Refactor the system little by little. If you rewrite some parts of the code, do it a small subsystem at a time. Don't try to make a Grand Redesign.
The code has been around for 30 years?
Development paradigms have shifted substantially in the last three decades in many ways, and most relevant to your predicament, I feel, is in terms of the amount of time (in man days) required to create something to input->process->output something.
300,000 lines of code 30 years ago, could probably fit into 100,000 lines or less today, and expending fewer man hours(?) This could seem optimistic/ridiculous to some, but on the other hand is achievable, depending on the type of application in question. You have given no indication as to the classification of system - is it a real-time manufacturing process control system of sorts with sensors and actuators tied to it? An airline booking system ? Does it post-process some backlog of data? In other words could it be rebuilt in something like Java and quickly with an agressive, smallish team? Have the requirements been documented, and if so do they need updating or redeveloping from scratch? Is human safety a factor?
Just a quick sanity check, I think whether or not you should rebuild depends on (any order means the same thing):
Number of code dudes required.
Level of expertise of said dudes.
Which languages do not fit.
Which languages do fit.
How much it costs to use chosen language(s) them in terms of hardware and software.
How much does the business depend on this to stay alive.
Is it really too much downtime, or are you just nitpicking? (maybe they really don't care, but pretend to).
Good luck with that!