general-purpose programming language

Scala in Action

by Nilanjan Raychaudhuri  · 27 Mar 2012

? This chapter covers What Scala is High-level features of the Scala language Why you should pick Scala as your next language Scala is a general-purpose programming language that runs on Java Virtual Machine (JVM) and .NET platforms. But the recent explosion of programming languages on JVM, .NET, and other platforms raises a

…

the fact that Scala runs on the JVM platform. Now let’s explore Scala a bit more. 1.1. What’s Scala? Scala is a general-purpose programming language designed to express common programming patterns in a concise, elegant, and type-safe way. It smoothly integrates features of object-oriented and functional programming languages

Programming Scala

by Unknown  · 2 Jan 2010  · 448pp  · 71,301 words

paradigms is becoming popular, even necessary. We gravitated to Scala from other languages because Scala embodies many of the optimal qualities we want in a general-purpose programming language for the kinds of applications we build today: reliable, high-performance, highly concurrent Internet and enterprise applications. xvii Download at WoweBook.Com Scala is a

…

of view, DSLs are often classified as internal and external. An internal (sometimes called embedded) DSL is an idiomatic way of writing code in a general-purpose programming language, like Scala. No special-purpose parser is necessary for internal DSLs. Instead, they are parsed just like any other code written in the language. In

…

Language A custom programming language that resembles the terms, idioms, and expressions of a particular domain. An internal DSL is an idiomatic form of a general-purpose programming language. That is, no specialpurpose parser is created for the language. Instead, DSL code is written in the generalpurpose language and parsed just like any other

The Productive Programmer

by Neal Ford  · 8 Dec 2008  · 224pp  · 48,804 words

language atop Jaskell. It shows how readable and concise you can make code in Jaskell, using a familiar problem domain. NOTE Dietzler’s Law: even general-purpose programming languages suffer from the “80-10-10” rule. The Law of Demeter The Law of Demeter was developed at Northwestern University in the late ’80s. It

Masterminds of Programming: Conversations With the Creators of Major Programming Languages

by Federico Biancuzzi and Shane Warden  · 21 Mar 2009  · 496pp  · 174,084 words

AWK for many other tasks than we initially thought, it exposed certain aspects of the language where we hadn’t intended it to be a general-purpose programming language. I wouldn’t call these “deficiencies,” but it showed that AWK was a specialized language that was not intended for some of the applications that

…

unnatural for people to say, “Let’s create a language around these notations for solving problems arising in a given area.” You can use a general-purpose programming language to express any algorithm, but on the other hand, it’s often more convenient, more economical, and perhaps even more suggestive of solutions to have

…

started out with is almost always a little language, something very small and simple, not meant to do big things, not meant to be a general-purpose programming language. But if it’s useful, people start to push its limits and they want more. And typically the things they want are the features of

…

general-purpose programming languages that make them programmable rather than just declarative. They want ways to repeat things, they want ways to avoid having to say the same thing

…

automatic memory management, a virtual machine, a large set of bundled libraries, and some degree of “Write Once, Run Anywhere,” it’s grown into a general-purpose programming language. Though Sun Microsystems has released the source code as free software, the company still retains some degree of control over the evolution of the language

…

, and we’ll just talk to a database and then we’ll have it,” but it’s not general enough to merit existence in a general-purpose programming language. You very quickly then become a domain-specific programming language, and you live and die by that domain. You turn your nice 3GL into a

…

or a domain-specific language? Anders: I think the real answer there is “neither.” How I would address that problem is I would create a general-purpose programming language that is great at creating domain-specific languages. Again, the devil that we face with all of these domain-specific languages is that they may

…

can instead create your domain-specific language out of a base that is a general-purpose programming language, then I think you’re much better off than starting out fresh every time. One of the things that is problematic with general-purpose programming languages today is they’re getting better at creating internal DSLs, and you could view

…

of those internal DSLs. In some ways, when you create internal DSLs you actually want to limit the things that you can do with the general-purpose programming language. You want to be able to shut off the general-purposeness of the language, and you want to only reveal it in certain spots in

…

your DSL. That’s one thing that general-purpose programming languages are not very good at right now. That might be something that would be useful to look at. Brian Kernighan said that if you want

…

adding a language feature that make everyone a bit more productive or one that makes just a few developers much more productive? Anders: For a general-purpose programming language, it’s not a good idea to add features that only help a few because you end up being a grab bag of strange things

Exploring Python

by Timothy Budd  · 17 Feb 2009  · 263pp  · 20,730 words

. Exploring Python – Preface 3 What is Python? For those looking for buzzwords, Python is a high-level, interpreted, reflective, dynamically-typed, open-source, multi-paradigm, general-purpose programming language. I could explain each of those terms in detail, but in the end the result would still not convey what makes Python programming different from

Numpy Beginner's Guide - Third Edition

by Ivan Idris  · 23 Jun 2015  · 681pp  · 64,159 words

too complex by many. The common commercial alternatves, such as MATLAB, Maple, and Mathematca, provide powerful scriptng languages that are even more limited than any general-purpose programming language. Other open source tools similar to MATLAB exist, such as R, GNU Octave, and Scilab. Obviously, they too lack the power of a language such

…

as Python. Python is a popular general-purpose programming language that is widely used in the scientfc community. You can access legacy C, Fortran, or R code easily from Python. It is object-oriented and

Successful Lisp - About

by Unknown  · 304pp  · 125,363 words

the Lisp compiler; this, together with advances in compiler and garbage collector technology, was a first step toward making Lisp competitive in the arena of general-purpose programming languages. The long road to standardization The committee produced the first public edition of the Common LISP specification in 1984. In a shining example of computer

Real World Haskell

by Bryan O'Sullivan, John Goerzen, Donald Stewart and Donald Bruce Stewart  · 2 Dec 2008  · 1,065pp  · 229,099 words

, as we move from the basics of the language to increasingly powerful and productive features and techniques. What to Expect from Haskell Haskell is a general-purpose programming language. It was designed without any application niche in mind. Although it takes a strong stand on how programs should be written, it does not favor

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

by Martin Kleppmann  · 16 Mar 2017  · 1,237pp  · 227,370 words

stored procedures (Oracle has PL/SQL, SQL Server has T-SQL, PostgreSQL has PL/pgSQL, etc.). These languages haven’t kept up with developments in general-purpose programming languages, so they look quite ugly and archaic from today’s point of view, and they lack the ecosystem of libraries that you find with most

…

badly written code in an application server. However, those issues can be overcome. Modern implementations of stored procedures have abandoned PL/SQL and use existing general-purpose programming languages instead: VoltDB uses Java or Groovy, Datomic uses Java or Clojure, and Redis uses Lua. With stored procedures and in-memory data, executing all transactions

Dawn of the New Everything: Encounters With Reality and Virtual Reality

by Jaron Lanier  · 21 Nov 2017  · 480pp  · 123,979 words

night, drawing sketches of how people might invent digital worlds to connect with one another. One of my weird little projects was a purely sonic general purpose programming language with no connection to vision at all, operated entirely by singing. An odd development was that by around 1982 I had money. Video game royalty

The Art of R Programming

by Norman Matloff  · 404pp  · 43,442 words

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig  · 14 Jul 2019  · 2,466pp  · 668,761 words

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

by Martin Kleppmann  · 17 Apr 2017

Gnuplot Cookbook

by Lee Phillips  · 15 Feb 2012  · 199pp  · 47,154 words

Terraform: Up and Running: Writing Infrastructure as Code

by Yevgeniy Brikman  · 13 Mar 2017

Elixir in Action

by Saša Jurić  · 30 Jan 2019

Programming Python

by Mark Lutz  · 5 Jan 2011

Practical OCaml

by Joshua B. Smith  · 30 Sep 2006

Ruby by example: concepts and code

by Kevin C. Baird  · 1 Jun 2007  · 309pp  · 65,118 words

Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems

by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski and Adam Stubblefield  · 29 Mar 2020  · 1,380pp  · 190,710 words

The Art of UNIX Programming

by Eric S. Raymond  · 22 Sep 2003  · 612pp  · 187,431 words

Mastering Blockchain, Second Edition

by Imran Bashir  · 28 Mar 2018

ANSI Common LISP

by Paul Graham  · 12 Nov 1995  · 450pp  · 569 words

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data

by Dipanjan Sarkar  · 1 Dec 2016

Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement

by Eric Redmond, Jim Wilson and Jim R. Wilson  · 7 May 2012  · 713pp  · 93,944 words

The Pragmatic Programmer

by Andrew Hunt and Dave Thomas  · 19 Oct 1999  · 509pp  · 92,141 words

Gnuplot in Action: Understanding Data With Graphs

by Philipp Janert  · 2 Jan 2010  · 398pp  · 31,161 words

97 Things Every Programmer Should Know

by Kevlin Henney  · 5 Feb 2010  · 292pp  · 62,575 words

The C Programming Language

by Brian W. Kernighan and Dennis M. Ritchie  · 15 Feb 1988  · 238pp  · 93,680 words

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

by Wes McKinney  · 25 Sep 2017  · 1,829pp  · 135,521 words

The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze

by Laura Shin  · 22 Feb 2022  · 506pp  · 151,753 words

Concepts, Techniques, and Models of Computer Programming

by Peter Van-Roy and Seif Haridi  · 15 Feb 2004  · 931pp  · 79,142 words

Managing Projects With GNU Make

by Robert Mecklenburg and Andrew Oram  · 19 Nov 2004  · 471pp  · 94,519 words

Programming Clojure

by Stuart Halloway and Aaron Bedra  · 17 Apr 2012  · 536pp  · 73,482 words

Clojure Programming

by Chas Emerick, Brian Carper and Christophe Grand  · 15 Aug 2011  · 999pp  · 194,942 words

The C++ Programming Language

by Bjarne Stroustrup  · 2 Jan 1986  · 923pp  · 516,602 words

Ajax: The Definitive Guide

by Anthony T. Holdener  · 25 Jan 2008  · 982pp  · 221,145 words

The Founders: The Story of Paypal and the Entrepreneurs Who Shaped Silicon Valley

by Jimmy Soni  · 22 Feb 2022  · 505pp  · 161,581 words

When Computers Can Think: The Artificial Intelligence Singularity

by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann and Michelle Estes  · 28 Feb 2015

Beautiful Architecture: Leading Thinkers Reveal the Hidden Beauty in Software Design

by Diomidis Spinellis and Georgios Gousios  · 30 Dec 2008  · 680pp  · 157,865 words

Quantitative Trading: How to Build Your Own Algorithmic Trading Business

by Ernie Chan  · 17 Nov 2008

What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry

by John Markoff  · 1 Jan 2005  · 394pp  · 108,215 words

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

by Michael Kearns and Aaron Roth  · 3 Oct 2019

Advanced Software Testing—Vol. 3, 2nd Edition

by Jamie L. Mitchell and Rex Black  · 15 Feb 2015

This Is for Everyone: The Captivating Memoir From the Inventor of the World Wide Web

by Tim Berners-Lee  · 8 Sep 2025  · 347pp  · 100,038 words

Ansible: Up and Running: Automating Configuration Management and Deployment the Easy Way

by Lorin Hochstein  · 8 Dec 2014  · 761pp  · 80,914 words

Network Security Through Data Analysis: Building Situational Awareness

by Michael S Collins  · 23 Feb 2014  · 446pp  · 102,421 words

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines

by Thomas H. Davenport and Julia Kirby  · 23 May 2016  · 347pp  · 97,721 words

The End of Work

by Jeremy Rifkin  · 28 Dec 1994  · 372pp  · 152 words