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Looking for how else such programming languages could be modified to avoid some common pitfalls, software designers can find inspiration, curiously enough, from their counterparts on the hardware side. But programmers do this kind of sharing all the time in their software.
Programmers write code that calls a function, expecting it to perform a single task. But many functions have side effects that change the shared global state, giving rise to unexpected consequences. Of course, hardware engineers can mess up, but not like you can with software, where just too many things are possible, for better or worse.
Another complexity monster lurking in the software quagmire is called a null reference , meaning that a reference to a place in memory points to nothing at all. If you try to use this reference, an error ensues. So programmers have to remember to check whether something is null before trying to read or change what it references. Nearly every popular language today has this flaw. The pioneering computer scientist Tony Hoare introduced null references in the ALGOL language back in , and it was later incorporated into numerous other languages.
The architects of structured programming knew this to be true for GOTO statements and left developers no escape hatch. History is proof that removing a dangerous feature can greatly improve the quality of code. Today, we have a slew of dangerous practices that compromise the robustness and maintainability of software.
Nearly all modern programming languages have some form of null references, shared global state, and functions with side effects—things that are far worse than the GOTO ever was. How can those flaws be eliminated? It turns out that the answer has been around for decades : purely functional programming languages. Of the top dozen functional-programming languages, Haskell is by far the most popular, judging by the number of GitHub repositories that use these languages.
The first purely functional language to become popular, called Haskell , was created in So by the mids, the world of software development really had the solution to the vexing problems it still faces. Indeed, software based on pure functions is particularly well suited to modern multicore CPUs.
This allows the compiler to be optimized to produce code that runs on multiple cores efficiently and easily. As the name suggests, with purely functional programming, the developer can write only pure functions, which, by definition, cannot have side effects. But what if a function needs to know or needs to manipulate the state of the system?
In that case, the state is passed through a long chain of what are called composed functions—functions that pass their outputs to the inputs of the next function in the chain. Avoiding Null-Reference Surprises A comparison of Javascript and Purescript shows how the latter can help programmers avoid bugs. It addresses that problem by disallowing nulls. Instead, there is a construct usually called Maybe or Option in some languages.
A Maybe can be Nothing or Just some value. Working with Maybes forces developers to always consider both cases. They have no choice in the matter. They must handle the Nothing case every single time they encounter a Maybe. Doing so eliminates the many bugs that null references can spawn.
Functional programming also requires that data be immutable, meaning that once you set a variable to some value, it is forever that value. Variables are more like variables in math. So, the same value for x is used when computing x2 as is used when computing 2x. In most programming languages, there is no such restriction. You can compute x2 with one value, then change the value of x before computing 2x. By disallowing developers from changing mutating values, they can use the same reasoning they did in middle-school algebra class.
Unlike most languages, functional programming languages are deeply rooted in mathematics. Why is that? Most programming paradigms, such as object-oriented programming, have at most half a dozen decades of work behind them. They are crude and immature by comparison. In software, this kind of thing goes on all the time. Let me share an example of how programming is sloppy compared with mathematics. In math, this equation has zero solutions. It is a statement that commands the computer to take the value of x, add one to it, and put it back into a variable called x.
In functional programming, there are no statements, only expressions. Mathematical thinking that we learned in middle school can now be employed when writing code in a functional language. Thanks to functional purity, you can reason about code using algebraic substitution to help reduce code complexity in the same way you reduced the complexity of equations back in algebra class.
In non-functional languages imperative languages , there is no equivalent mechanism for reasoning about how the code works. The biggest problem with this hybrid approach is that it still allows developers to ignore the functional aspects of the language. Had we left GOTO as an option 50 years ago, we might still be struggling with spaghetti code today. You need to use languages that were designed with these principles from the start.
It comes at a cost. Learning to program according to this functional paradigm is almost like learning to program again from the beginning. More important, developers need to learn a new way of thinking. The digital gold rush has come with a catch: Massive electricity consumption. That usage exceeds the entire annual electricity consumption of Norway. Meanwhile, the blockchain technology that underlies it could be the key to a greener future. All you have to do is point and click or tap on your smartphone to buy and sell the cryptocurrency.
To verify transactions, Bitcoin requires computers to solve ever more complex math problems. This proof of work consensus mechanism is drastically more energy-intensive than many people realize. The miner who completes the mathematical equation the fastest not only certifies the transaction but also gets a small reward for their trouble in the form of a Bitcoin payment. Multiple miners are using electricity in competition for rewards. Even though there may be hundreds of thousands of computers racing to solve the same problem, only one can ultimately receive the Bitcoin honorarium.
While this process produces a fair and secure result, it also creates a ton of carbon emissions. This process also takes an immense amount of time: Upwards of 10 minutes per Bitcoin transaction. Other digital transactions, like those powered by Visa, are faster and rely on less energy. In terms of crypto mining, the U. Switch to Renewable Energy Bitcoin mining powered by renewable energy fell when China took measures to eliminate Bitcoin mining within its borders, forcing mining in that country to go underground.
Take LiquidStack, which aims to more efficiently lower the temperature of mining rigs, or Genesis Mining, which exclusively uses clean energy sources.
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