data-oriented programming

Brian Will makes the case for procedural programming over object-oriented programming, arguing that OOP's promised encapsulation and decoupling are largely illusory. He analyzes real code examples and the problems of tying data to behavior in classes.

Andrew Kelley, creator of Zig, presents practical data-oriented design techniques for shrinking memory footprint and making programs cache friendly. He ends with a case study of how these ideas sped up the Zig compiler.

Brian Will critiques object-oriented programming and rewrites a roughly 3800-line OO program in a procedural style, cutting code and complexity. It is part of a series of OOP critiques.

Brian Will follows up his earlier anti-OOP video by walking through four short code examples, including Java code in the style of Uncle Bob, to show how object-oriented design complicates simple programs. It argues that OOP hides and scatters state and adds needless indirection.

Mark Bastian contrasts top-down object-oriented design with bottom-up, data-oriented design in Clojure. He models a board game as plain data plus small composable functions and compares it with a roughly 22-class OOP version.

Michael Arntzenius's Strange Loop talk on Datafun, a functional query language that brings database-style querying into a general-purpose language. It discusses bridging database theory and programming languages.

Richard Feldman's elm-conf talk on designing models so that bugs from impossible application states cannot occur, using Elm's compiler and types. It demonstrates techniques for making invalid states unrepresentable.

Jafar Husain's ACM keynote presents Netflix's JSON Graph and Falcor, which let web developers treat all cloud data as one virtual JSON model. It combines reactive programming with REST principles.

John Oliver's Last Week Tonight segment explains how data brokers collect and sell personal information, including a stunt in which his team bought data to identify members of Congress.

Database pioneer Michael Stonebraker argues that 'big data' is really at least four different problems: big volumes with small analytics, big analytics, velocity, and variety. He comments on where each market is heading.

Felienne Hermans argues that Excel is the world's most popular programming language and shows how spreadsheets work as a pure functional language. She implements a few mathematical problems in spreadsheet formulas to illustrate why they are so powerful and approachable.

Lee Byron of Facebook explains how persistent immutable data structures work and how to use them in React apps with Immutable.js. He covers memoization benefits and avoiding coupling through shared mutable state.

Benn Jordan investigates infrasound and low-frequency noise from data centers, including xAI's Colossus and a crypto mine, and reports his own experiments on the effects. The video covers the health and community impacts on nearby residents.

Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference. It argues that more data often beats cleverer models.

A Business Insider investigation into the US boom in AI data centers, mapping where they are, who owns them, and how much power and water they consume. It looks at the costs borne by communities, with a focus on Virginia.

Scott Manley examines why SpaceX, Google and others are looking at orbital data centers. He walks through the economics, heat rejection and solar power arguments, with links to the Lumen/Starcloud and Google Suncatcher papers.

Scott Manley explains the thermal balance of spacecraft and how a satellite could be configured to radiate away the heat from datacenter-scale computing in space. His rough numbers suggest it is feasible.

Raymond Hettinger explains Python's dataclasses module, showing the problem it solves and its key design decisions. He places it in a progression from tuples and dicts through named tuples and attrs.

Felienne Hermans presents Hedy, a gradual programming language for teaching kids that moves through levels from almost no syntax toward full Python. The talk covers why error messages and syntax are scary for beginners.

A guest lecture Jon Gjengset gave at Two Sigma on using Rust for large, high-performance systems. It covers why Rust suits such projects, the Noria research database, a concurrent data structure used in it, and practical experience.

Jon Gjengset's 2020 PhD dissertation presentation at MIT on partial state in dataflow-based materialized views, the idea behind the Noria database. The Q&A is cut out.

Ahmon Dancy of Franz Inc explains low-level programming in Common Lisp, including off-heap data structures used in AllegroGraph to manage memory outside the garbage collector.

Linus Tech Tips documents a major RAID failure on its Whonnock server and the attempt to recover the lost data with a data recovery firm.

James Hamilton of AWS gives a Velocity 2010 keynote on datacenter infrastructure innovation, focused on power and cooling costs and a breakdown of where datacenter spending goes.

A 1987 talk in which John Backus, creator of Fortran, presents function-level programming and the FL language, a successor to his FP work. It is an archival recording with links to the related paper.

Sandi Metz takes an ugly section of conditional code and refactors it into a few simple objects, bridging object-oriented theory and practice. The talk includes her take on the Gilded Rose refactoring kata and the idea that duplication is cheaper than the wrong abstraction.

Rich Hickey's JaxConf 2012 keynote examines what values are and why immutable values matter as information systems grow more complex and data gets bigger. It contrasts places with values and builds the case for immutability.

An Alan Kay talk on programming and scaling, covering theoretical limits of code size, engineering versus tinkering, and biological cells as inspiration for object-oriented design.

Casey Muratori shows how typical 'clean code' object-oriented patterns can cost large factors in performance. He benchmarks them against simpler data-oriented code.

Brian Bucklew of Freehold Games discusses design decisions behind Caves of Qud and Sproggiwood at IRDC 2015. The talk centers on data-driven, event-driven engine design.

Douglas Crockford's code::dive 2022 talk on what the next programming language should look like. He presents ideas behind his Misty language, including message-passing actors.

Chris Granger, creator of Light Table and the Eve language, gives a Berkeley data science lecture on what computation and programming might look like in ten years. He covers the path that led to Eve and ends with demos.

Richard Feldman argues that functional programming is not the norm largely because of historical accidents, such as C++ popularity, platform exclusivity, marketing spend and network effects, rather than merit. He traces the history of how languages become mainstream.

Bret Victor's DBX 2013 talk, delivered in the guise of a 1973 IBM engineer with an overhead projector. It surveys forgotten ideas from 1960s and early 70s computing and warns against assuming you know what you're doing.

Felienne Hermans's Strange Loop keynote on the history and pedagogy of teaching programming. She questions exploration-only approaches like Logo and discusses research supporting explicit instruction and rote practice.

Timothy Daly, an original author of the Axiom computer algebra system, talks about turning a large Common Lisp codebase into a literate program. He argues code should be written as readable manuals, and the talk was given at Write the Docs 2013.

Jonathan Blow talks at Gamelab 2018 about the design decisions behind Jai, the programming language he is building for game programmers. He covers low-friction development, quality of life and the effects of compile times.

Lukas Eder's Devoxx talk on how modern cost-based SQL optimizers rewrite and plan queries in ways developers would not expect. It argues that declarative SQL, used well, can run complex queries very fast.

First lecture of Stanford's CS106A Programming Methodology by Mehran Sahami, giving a course overview and starting on programming. The course teaches software engineering principles using Java.

Aaron Hsu explains why programmers often find APL harder to learn than people with no programming background. He identifies eight patterns that contrast traditional software engineering practice with APL practice, starting from Ken Iverson's language design principles.