Code Complete (Developer Best Practices)
by Steve McConnell · 8 Jun 2004 · 1,758pp · 342,766 words
compiler or library might well degrade performance when you change the build environment. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. — Donald Knuth You should optimize as you go—false! One theory is that if you strive to write the
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's code. Would you rather go back and do performance work on five percent of the code or readability work on 100 percent? In short, premature optimization's primary drawback is its lack of perspective. Its victims include final code speed, performance attributes that are more important than code speed, program quality
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you the specific ways in which your software is defective. Testing won't make your program more usable, faster, smaller, more readable, or more extensible. Premature optimization is another kind of process error. In an effective process, you make coarse adjustments at the beginning and fine adjustments at the end. If you
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were a sculptor, you'd rough out the general shape before you started polishing individual features. Premature optimization wastes time because you spend time polishing sections of code that don't need to be polished. You might polish sections that are small enough
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-Development Approaches, Percentage of Defects Detected, Collaborative Construction Provides Mentoring in Corporate Culture and Programming Expertise, Integration, Information About Software Construction importance of, Conquer Complexity premature optimization problem, Pick Your Process quality control, Pick Your Process (see also ) resources for, General Software-Development Approaches sequential approach, Choosing Between Iterative and Sequential Approaches
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considerations, Program Design operations, costs of common, Common Sources of Inefficiency overview of, Recoding in a Low-Level Language paging operations, Common Sources of Inefficiency premature optimization, Pick Your Process program requirements view of, Performance and Code Tuning purpose of, Code-Tuning Strategies quality of code, impact on, Performance Overview resource goals
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Program preconditions, Guidelines for Using Assertions, Design the Routine routine design with, Design the Routine verification, Guidelines for Using Assertions prefixes, standardization of, Standardized Prefixes premature optimization, Pick Your Process preparation, Causes of Incomplete Preparation preprocessors, Plan to Remove Debugging Aids, Plan to Remove Debugging Aids, Plan to Remove Debugging Aids, Code
The Art of UNIX Programming
by Eric S. Raymond · 22 Sep 2003 · 612pp · 187,431 words
. For slightly different reasons, Donald Knuth (author of The Art Of Computer Programming, one of the field's few true classics) popularized the observation that “Premature optimization is the root of all evil”.[11] And he was right. Rushing to optimize before the bottlenecks are known may be the only error to
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use of some resource much less expensive than debugging time. Disturbingly often, premature local optimization actually hinders global optimization (and hence reduces overall performance). A prematurely optimized portion of a design frequently interferes with changes that would have much higher payoffs across the whole design, so you end up with both inferior
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page is maintained by the Postel Center for Experimental Networking. [11] In full: “We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil”. Knuth himself attributes the remark to C. A. R. Hoare. The Unix Philosophy in One Lesson All the philosophy
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corollaries of the SPOT rule: Are you duplicating data because you're caching intermediate results of some computation or lookup? Consider carefully whether this is premature optimization; stale caches (and the layers of code needed to keep caches synchronized) are a fertile source of bugs,[44] and can even slow down overall
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two equivalent expressions is more efficient. This isn't in itself a good reason to avoid using OO techniques on new projects; that would be premature optimization. But it is reason to think twice before transforming non-OO code into a class hierarchy. Unix programmers tend to share an instinctive sense of
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extend or subset cleanly. Transaction economy sometimes pushes in the opposite direction — but we shall see that putting that criterion first is a form of premature optimization that it is often wise to resist. Finally, we must note a difference between data file formats and the run-control files that are often
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in X — for example, in the design of graphics file formats intended to hold very large images. But they are usually just another case of premature-optimization fever. Textual formats don't necessarily have much lower bit density than binary ones; they do after all use seven out of eight bits per
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discussion is not going to be about using concurrency to improve performance. Putting that concern before developing a clean architecture that minimizes global complexity is premature optimization, the root of all evil (see Chapter 12 for further discussion). A closely related red herring is threads (that is, multiple concurrent processes sharing the
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trying alternative methods at runtime until one succeeds. If this strikes you as inelegant or too expensive, ask yourself if you haven't fallen into premature optimization. One of the nicest examples of autodetection I experienced was when Dennis Ritchie and I were porting Unix to the Interdata 8/32. This was
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is on. * * * [111] If your windowing system supports translucent popups that intrude less between the user and the application, use them. Optimization Chapter 12. Optimization Premature optimization is the root of all evil. -- C. A. R. Hoare This is going to be a very short chapter, because the main thing Unix experience
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tend to drag programmers into a swamp of complexity nevertheless. We've examined a rogue's gallery of these in earlier chapters; feature creep and premature optimization are the two most notorious. Traditionally, Unix programmers push back against these tendencies by proclaiming with religious fervor a rhetoric that condemns all complexity as
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. Codebase size, interface complexity and implementation complexity may all rise together. That is the usual result of feature creep, and why programmers especially dread it. Premature optimization doesn't tend to raise interface complexity, but it has bad effects (often severely bad) on implementation complexity and codebase size. But those sorts of
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each does exactly one thing. Accidental code complexity (making code more complicated than it needs to be to get the job done) often results from premature optimization. Accidental codebase bloat often results from violating the SPOT rule, duplicating code or organizing it poorly so that opportunities for reuse aren't recognized. Essential
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really valuable effect is that not optimizing that 90% holds down global complexity and reduces bugs. You may recall that we quoted Donald Knuth observing “Premature optimization is the root of all evil” in Chapter 1, and that Rob Pike and Ken Thompson had a few pungent observations on the topic as
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later. Profilers help you do this. If you get in the good habit of using them, you can get rid of the bad habit of premature optimization. Profilers don't just change the way you work; they change how you think. Profilers for compiled languages rely on instrumenting object code, so they
EuroTragedy: A Drama in Nine Acts
by Ashoka Mody · 7 May 2018
had been set so low that it was easy for officials and commentators to hail the recovery as a significant step forward. The dangers of premature optimism were once again great, as was made clear with the unhelpful reaction of financial markets to comments by ECB President Mario Draghi. In June 2017
The Singularity Is Near: When Humans Transcend Biology
by Ray Kurzweil · 14 Jul 2005 · 761pp · 231,902 words
the e-commerce and telecommunications busts of several years ago, which helped fuel a recession from which we are now recovering. AI experienced a similar premature optimism in the wake of programs such as the 1957 General Problem Solver created by Allen Newell, J. C. Shaw, and Herbert Simon, which was able
Building Microservices
by Sam Newman · 25 Dec 2014 · 540pp · 103,101 words
if necessary. Remember that we have a large amount of help out of the box by using HTTP, which we discussed earlier. The evils of premature optimization have been well documented before, so I don’t need to expand upon them here. Also note that a lot of these approaches were developed
Coders at Work
by Peter Seibel · 22 Jun 2009 · 1,201pp · 233,519 words
it better. Seibel: When you publish a literate program, it's the final form of the program, typically. And you are often credited with saying, “Premature optimization is the root of all evil.” But by the time you get to the final form it's not premature—you may have optimized some
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won't have to think—there'll be no subtlety at all so I won't be outsmarting myself. There I'm not doing any premature optimization. Then I can change that into something else and see if I get something that agrees with my brute-force way. Then I can scale
Write Great Code, Volume 2
by Randall Hyde · 6 Aug 2012 · 828pp · 205,338 words
it can rarely deliver a miracle. Although the quote is often attributed to Donald Knuth, who popularized it, it was Tony Hoare who originally said, “Premature optimization is the root of all evil.” This statement has long been the rallying cry of software engineers who avoid any thought of application performance until
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did not say, “Concern about application performance during the early stages of an application’s development is the root of all evil.” He specifically said premature optimization, which, back then, meant counting cycles and instructions in assembly language code—not the type of coding you want to do during initial program design
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problem domain to what was intended. The full version of the quote is “We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.” and I agree with this. It’s usually not worth spending a lot of time micro-optimizing code before
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choose the most appropriate data types, constants, variables, and control structures to produce efficient applications. While you read, keep Dr. Hoare’s quote in mind: “Premature optimization is the root of all evil.” It is certainly possible to misapply the information in this book and produce code that is difficult to read
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann · 17 Apr 2017
for scale that you don’t need is wasted effort and may lock you into an inflexible design. In effect, it is a form of premature optimization. However, it’s also important to choose the right tool for the job, and different technologies each have their own strengths and weaknesses. As we
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for scale that you don’t need is wasted effort and may lock you into an inflexible design. In effect, it is a form of premature optimization. The goal of unbundling is not to compete with individual databases on performance for particular workloads; the goal is to allow you to combine several
System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich, Mehran Sahami and Jeremy M. Weinstein · 6 Sep 2021
them to think critically about the company or its societal impacts when looking for a job. The pioneering computer scientist Donald Knuth famously said that “premature optimization is the root of all evil.” We could interpret this quote any number of ways, and indeed it has been widely misunderstood. Knuth uses optimization
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code faster and writes the program in a way that doesn’t sacrifice code readability. Overall, the program becomes more efficient. Has the technologist avoided premature optimization? Not necessarily. Suppose the program is used for an undesirable end: suppose it is used successfully by a malicious hacker or to guide an economy
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann · 16 Mar 2017 · 1,237pp · 227,370 words
for scale that you don’t need is wasted effort and may lock you into an inflexible design. In effect, it is a form of premature optimization. However, it’s also important to choose the right tool for the job, and different technologies each have their own strengths and weaknesses. As we
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for scale that you don’t need is wasted effort and may lock you into an inflexible design. In effect, it is a form of premature optimization. The goal of unbundling is not to compete with individual databases on performance for particular workloads; the goal is to allow you to combine several
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