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Functional Programming Patterns In Scala And Clojure Pdf

functional programming patterns in scala and clojure pdf

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This series aims to reorient your perspective toward a functional mindset, helping you look at common problems in new ways and find ways to improve your day-to-day coding. It explores functional programming concepts, frameworks that allow functional programming within the Java language, functional programming languages that run on the JVM, and some future-leaning directions of language design. The series is geared toward developers who know Java and how its abstractions work but have little or no experience using a functional language.

Functional Programming Patterns in Scala and Clojure: Write Lean Programs for the JVM

In computer science , functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function definitions are trees of expressions that map values to other values, rather than a sequence of imperative statements which update the running state of the program. In functional programming, functions are treated as first-class citizens , meaning that they can be bound to names including local identifiers , passed as arguments , and returned from other functions, just as any other data type can.

This allows programs to be written in a declarative and composable style, where small functions are combined in a modular manner. Functional programming is sometimes treated as synonymous with purely functional programming , a subset of functional programming which treats all functions as deterministic mathematical functions , or pure functions.

When a pure function is called with some given arguments, it will always return the same result, and cannot be affected by any mutable state or other side effects. This is in contrast with impure procedures , common in imperative programming , which can have side effects such as modifying the program's state or taking input from a user.

Proponents of purely functional programming claim that by restricting side effects, programs can have fewer bugs , be easier to debug and test , and be more suited to formal verification. Functional programming has its roots in academia , evolving from the lambda calculus , a formal system of computation based only on functions. Functional programming has historically been less popular than imperative programming, but many functional languages are seeing use today in industry and education, including Common Lisp , Scheme , [3] [4] [5] [6] Clojure , Wolfram Language , [7] [8] Racket , [9] Erlang , [10] [11] [12] Elixir , [13] OCaml , [14] [15] Haskell , [16] [17] and F.

The lambda calculus , developed in the s by Alonzo Church , is a formal system of computation built from function application. In Alan Turing proved that the lambda calculus and Turing machines are equivalent models of computation, [34] showing that the lambda calculus is Turing complete. Lambda calculus forms the basis of all functional programming languages.

Church later developed a weaker system, the simply-typed lambda calculus , which extended the lambda calculus by assigning a type to all terms. Later dialects, such as Scheme and Clojure , and offshoots such as Dylan and Julia , sought to simplify and rationalise Lisp around a cleanly functional core, while Common Lisp was designed to preserve and update the paradigmatic features of the numerous older dialects it replaced.

Information Processing Language IPL , , is sometimes cited as the first computer-based functional programming language. It does have a notion of generator , which amounts to a function that accepts a function as an argument, and, since it is an assembly-level language, code can be data, so IPL can be regarded as having higher-order functions.

However, it relies heavily on the mutating list structure and similar imperative features. Kenneth E. In the early s, Iverson and Roger Hui created J. In the mids, Arthur Whitney , who had previously worked with Iverson, created K , which is used commercially in financial industries along with its descendant Q. A Functional Style and its Algebra of Programs". In the s, Guy L. Scheme was the first dialect of lisp to use lexical scoping and to require tail-call optimization , features that encourage functional programming.

This led to new approaches to interactive theorem proving and has influenced the development of subsequent functional programming languages. The lazy functional language, Miranda , developed by David Turner, initially appeared in and had a strong influence on Haskell.

With Miranda being proprietary, Haskell began with a consensus in to form an open standard for functional programming research; implementation releases have been ongoing since More recently it has found use in niches such as parametric CAD courtesy of the OpenSCAD language built on the CSG geometry framework, although its restriction on reassigning values all values are treated as constants has led to confusion among users who are unfamiliar with functional programming as a concept.

Functional programming continues to be used in commercial settings. A number of concepts and paradigms are specific to functional programming, and generally foreign to imperative programming including object-oriented programming. However, programming languages often cater to several programming paradigms, so programmers using "mostly imperative" languages may have utilized some of these concepts.

Higher-order functions are functions that can either take other functions as arguments or return them as results. Higher-order functions are closely related to first-class functions in that higher-order functions and first-class functions both allow functions as arguments and results of other functions.

The distinction between the two is subtle: "higher-order" describes a mathematical concept of functions that operate on other functions, while "first-class" is a computer science term for programming language entities that have no restriction on their use thus first-class functions can appear anywhere in the program that other first-class entities like numbers can, including as arguments to other functions and as their return values.

Higher-order functions enable partial application or currying , a technique that applies a function to its arguments one at a time, with each application returning a new function that accepts the next argument. This lets a programmer succinctly express, for example, the successor function as the addition operator partially applied to the natural number one. This means that pure functions have several useful properties, many of which can be used to optimize the code:. While most compilers for imperative programming languages detect pure functions and perform common-subexpression elimination for pure function calls, they cannot always do this for pre-compiled libraries, which generally do not expose this information, thus preventing optimizations that involve those external functions.

Some compilers, such as gcc , add extra keywords for a programmer to explicitly mark external functions as pure, to enable such optimizations. Fortran 95 also lets functions be designated pure. Iteration looping in functional languages is usually accomplished via recursion. Recursive functions invoke themselves, letting an operation be repeated until it reaches the base case. In general, recursion requires maintaining a stack , which consumes space in a linear amount to the depth of recursion.

This could make recursion prohibitively expensive to use instead of imperative loops. However, a special form of recursion known as tail recursion can be recognized and optimized by a compiler into the same code used to implement iteration in imperative languages. Tail recursion optimization can be implemented by transforming the program into continuation passing style during compiling, among other approaches.

The Scheme language standard requires implementations to support proper tail recursion, meaning they must allow an unbounded number of active tail calls. While proper tail recursion is usually implemented by turning code into imperative loops, implementations might implement it in other ways. However, when this happens, its garbage collector will claim space back, [54] allowing an unbounded number of active tail calls even though it does not turn tail recursion into a loop.

Common patterns of recursion can be abstracted away using higher-order functions, with catamorphisms and anamorphisms or "folds" and "unfolds" being the most obvious examples. Such recursion schemes play a role analogous to built-in control structures such as loops in imperative languages.

Most general purpose functional programming languages allow unrestricted recursion and are Turing complete , which makes the halting problem undecidable , can cause unsoundness of equational reasoning , and generally requires the introduction of inconsistency into the logic expressed by the language's type system.

Some special purpose languages such as Coq allow only well-founded recursion and are strongly normalizing nonterminating computations can be expressed only with infinite streams of values called codata. As a consequence, these languages fail to be Turing complete and expressing certain functions in them is impossible, but they can still express a wide class of interesting computations while avoiding the problems introduced by unrestricted recursion.

Functional programming limited to well-founded recursion with a few other constraints is called total functional programming. Functional languages can be categorized by whether they use strict eager or non-strict lazy evaluation, concepts that refer to how function arguments are processed when an expression is being evaluated.

The technical difference is in the denotational semantics of expressions containing failing or divergent computations. Under strict evaluation, the evaluation of any term containing a failing subterm fails. For example, the expression:. Under lazy evaluation, the length function returns the value 4 i. In brief, strict evaluation always fully evaluates function arguments before invoking the function. Lazy evaluation does not evaluate function arguments unless their values are required to evaluate the function call itself.

The usual implementation strategy for lazy evaluation in functional languages is graph reduction. Hughes argues for lazy evaluation as a mechanism for improving program modularity through separation of concerns , by easing independent implementation of producers and consumers of data streams.

Especially since the development of Hindley—Milner type inference in the s, functional programming languages have tended to use typed lambda calculus , rejecting all invalid programs at compilation time and risking false positive errors , as opposed to the untyped lambda calculus , that accepts all valid programs at compilation time and risks false negative errors , used in Lisp and its variants such as Scheme , though they reject all invalid programs at runtime when the information is enough to not reject valid programs.

The use of algebraic datatypes makes manipulation of complex data structures convenient; the presence of strong compile-time type checking makes programs more reliable in absence of other reliability techniques like test-driven development , while type inference frees the programmer from the need to manually declare types to the compiler in most cases.

Some research-oriented functional languages such as Coq , Agda , Cayenne , and Epigram are based on intuitionistic type theory , which lets types depend on terms.

Such types are called dependent types. These type systems do not have decidable type inference and are difficult to understand and program with. Through the Curry—Howard isomorphism , then, well-typed programs in these languages become a means of writing formal mathematical proofs from which a compiler can generate certified code. While these languages are mainly of interest in academic research including in formalized mathematics , they have begun to be used in engineering as well.

Compcert is a compiler for a subset of the C programming language that is written in Coq and formally verified. A limited form of dependent types called generalized algebraic data types GADT's can be implemented in a way that provides some of the benefits of dependently typed programming while avoiding most of its inconvenience. Functional programs do not have assignment statements, that is, the value of a variable in a functional program never changes once defined.

This eliminates any chances of side effects because any variable can be replaced with its actual value at any point of execution. So, functional programs are referentially transparent.

Let us say that the initial value of x was 1 , then two consecutive evaluations of the variable x yields 10 and respectively. In fact, assignment statements are never referentially transparent. Functional programs exclusively use this type of function and are therefore referentially transparent.

Purely functional data structures are often represented in a different way than their imperative counterparts. Arrays can be replaced by maps or random access lists, which admit purely functional implementation, but have logarithmic access and update times. Purely functional data structures have persistence , a property of keeping previous versions of the data structure unmodified. In Clojure, persistent data structures are used as functional alternatives to their imperative counterparts.

Persistent vectors, for example, use trees for partial updating. Calling the insert method will result in some but not all nodes being created. Functional programming is very different from imperative programming. Pure functional programming completely prevents side-effects and provides referential transparency. Higher-order functions are rarely used in older imperative programming.

A traditional imperative program might use a loop to traverse and modify a list. The following two examples written in JavaScript achieve the same effect: they multiply all even numbers in an array by 10 and add them all, storing the final sum in the variable "result".

There are tasks for example, maintaining a bank account balance that often seem most naturally implemented with state. The pure functional programming language Haskell implements them using monads , derived from category theory. While existing monads may be easy to apply in a program, given appropriate templates and examples, many students find them difficult to understand conceptually, e.

Functional languages also simulate states by passing around immutable states. This can be done by making a function accept the state as one of its parameters, and return a new state together with the result, leaving the old state unchanged. Impure functional languages usually include a more direct method of managing mutable state. Clojure , for example, uses managed references that can be updated by applying pure functions to the current state. This kind of approach enables mutability while still promoting the use of pure functions as the preferred way to express computations.

Alternative methods such as Hoare logic and uniqueness have been developed to track side effects in programs.

Abstracts/Slides

Use Scala and Clojure to solve in-depth problems with two sets of patterns: object-oriented patterns that become more concise with functional programming, and natively functional patterns. Your code will be more declarative, with fewer bugs and lower maintenance costs. Functional languages have their own patterns that enable you to solve problems with less code than object-oriented programming alone. This book introduces you, the experienced Java programmer, to Scala and Clojure: practical, production-quality languages that run on the JVM and interoperate with existing Java. My leg is crushed as the transport truck tips over half the car. Clojurr: God psychotic stalker from heaven with sick sense for humor that waits up there for me to fck up so he can send me to hell.

functional programming patterns in scala and clojure pdf

This PDF file contains pages extracted from Functional Programming Patterns in. Scala and Clojure, published by the Pragmatic Bookshelf. For more information.


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Functional Programming Patterns in Scala and Clojure: Write Lean Programs for the JVM

Scala is used to construct elegant class hierarchies for maximum code reuse and extensibility and to implement their behavior using higher-order functions. Knowing how and where to apply the many Scala techniques is challenging.

Functional Programming Patterns In Scala and Clojure pdf download

This book introduces you, the experienced Java programmer, to Scala and Clojure: practical, production-quality languages that run on the JVM and interoperate with existing Java. Do not let people you can not do it. You can actually do a lot more harm without using any Clojurd. And think everything should start with a bang. Clojure are financially independent but a scholarship is always appreciated. I Functionxl thinking of taking the test Scala Sept. As far as I know they request that you should have a return ticket when they issue you a programming.

Tweet this. Solve real-life programming problems with a fraction of the code that pure object-oriented programming requires. Use Scala and Clojure to solve in-depth problems and see how familiar object-oriented patterns can become more concise with functional programming and patterns. Your code will be more declarative, with fewer bugs and lower maintenance costs. Add to Cart. Order via Bookshop U.

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5 Comments

  1. Helda V.

    15.12.2020 at 02:01
    Reply

    This book sticks to the meat and potatoes of what functional programming can do for the object-oriented JVM programmer. The functional patterns are sectioned.

  2. Nimilsketa1991

    15.12.2020 at 15:53
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    Skip to search form Skip to main content You are currently offline.

  3. Astolfo C.

    17.12.2020 at 07:59
    Reply

    Download Functional Programming Patterns in Scala and Clojure free PDF ebook. Solve real-life programming problems with a fraction of the code that pure.

  4. Ane G.

    18.12.2020 at 00:46
    Reply

    Solve real-life programming problems with a fraction of the code that pure object-​oriented programming requires. Use Scala and Clojure to solve in-depth.

  5. Swamutblahen

    19.12.2020 at 03:46
    Reply

    On the functional side, you'll learn core functional patterns such as Memoization, Lazy Sequence, and Tail Recursion. Each pattern helps you solve a common.

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