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How to Run Doom in SQL: A Practical Guide to Database Game Experiments

“Doom in SQL” can mean SQL-rendered original Doom, a SQLite Doom-like game, or compiled game code running in a database VM. Here’s what each project does and how to start.

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For an original Doom port whose game logic and renderer run in SQL, the clearest documented route is SQLDoom on CedarDB. It needs CedarDB-specific functionality, Python for input and display, and a Doom IWAD. Other projects described as “Doom in SQL” run different things, so choose the one that matches your goal before installing anything.

What does “Doom in SQL” mean?

It can mean several different experiments, not one standard port. SQLDoom represents original Doom’s game logic and renderer in SQL. DOOMQL is a Doom-like raycasting game written around SQL simulation and pixel calculations. Turso’s demonstration runs Doom compiled to bytecode in a SQLite-derived virtual machine. The PostgreSQL project pg_doom exposes a C game core through a database extension.

The key question is what the database actually executes. A project can use SQL for game rules and image generation, or use a database as the runtime for compiled program bytecode, or simply connect a database to code written in C. Those approaches differ in fidelity, setup, and how literally they qualify as “Doom in SQL.”

Project What runs What the database does Practical fit
SQLDoom Original Doom game logic and renderer CedarDB runs SQL logic and rendering; Python handles timing, keyboard input, and display Best documented option here for an original-Doom SQL implementation
DOOMQL (petergpt/doomql) An original Doom-like raycasting game SQLite calculates the simulation and RGB pixel values; Python transports input and terminal output A more approachable SQLite experiment if an original Doom port is not required
Turso VDBE demonstration Unmodified Doom compiled to VDBE bytecode A Turso VM runs the bytecode in a long-lived statement and emits frame rows Interesting as a database-VM experiment, but not ordinary SQL rendering the game
pg_doom A Doom game core written in C A PostgreSQL C extension exposes game input and screen functions Relevant if the goal is integrating a C game core with PostgreSQL, not implementing it in SQL

How SQLDoom turns game state into a picture

SQLDoom separates the game loop from the display loop. Its SQL logic retains Doom’s original 35 Hz game tic—the rate at which game state advances—while rendering can be requested separately. The author describes a complete 320 × 200 frame buffer represented through the SQL renderer. Python supplies timing and keyboard input, then displays the result; it is not where the game’s core logic or renderer live.

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The architecture is unusual because SQL is doing work normally assigned to a game engine: updating state, handling entities, and producing pixels. CedarDB’s author compares the SQLDoom implementation with the original C game logic, reporting about 5,900 lines of SQL for game logic versus about 9,000 lines of original C, plus about 1,300 lines of SQL for the renderer. These are author-reported implementation counts, not a measure of performance or a proof that the implementations contain exactly equivalent code.

Run SQLDoom on CedarDB

SQLDoom’s repository says CedarDB is currently required because some functions use cedarscript. Treat that as a real engine dependency: this is not a drop-in recipe for vanilla PostgreSQL. The project also uses a Python client, with psycopg2 and pygame among its dependencies, and needs a Doom IWAD game-data file.

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  1. Check the project README first. Confirm its current CedarDB requirements, supported setup, Python dependencies, and loader/client instructions. The documented setup depends on CedarDB-specific behavior, so do not substitute another PostgreSQL-compatible service on the assumption that it will work unchanged.
  2. Install and start CedarDB as the README specifies. Ensure the database is reachable by the Python client before loading the game data.
  3. Prepare an IWAD. The SQLDoom author says the freely redistributable shareware doom1.wad is sufficient for episode one. Retail WADs can also be used if you own them; a database project does not grant rights to copyrighted game data.
  4. Load the WAD and start the client using the repository’s current commands. The README documents a WAD-loader invocation and a client command. Follow those exact commands and any version-specific configuration there rather than guessing at filenames, connection settings, or command-line options.

The SQLDoom author reports that the renderer can reach up to 60 Hz. In the author’s account, it typically ran at about 60 FPS and dropped to 35 FPS in very busy scenes on a Ryzen 7 PRO 7840U laptop. The same CedarDB account reports average tic times of 2.15 ms with six awake monsters and 10.45 ms in a slow case with 46 awake monsters. These are implementation-author-reported observations on that hardware, not independent benchmarks or expectations for other systems.

Try DOOMQL with SQLite instead

DOOMQL is not an original Doom port. It is an original raycasting game inspired by Doom, which makes a simpler project for seeing SQL participate in a game simulation and image pipeline. Its README specifies a Unix-like environment or WSL, Python 3.11 or newer, SQLite 3.45 or newer with math functions enabled, and a terminal with 24-bit color and Unicode upper-half-block support.

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DOOM: The Dark Ages – PlayStation 5
  • Developed by id Software, DOOM: The Dark Ages is the prequel to the critically acclaimed DOOM (2016) and DOOM Eternal that tells the epic cinematic origin story of the DOOM Slayer’s rage.
  • In this third installment of the modern DOOM series, players will step into the blood-stained boots of the DOOM Slayer, in this never-before-seen dark and sinister medieval war against Hell.
  • A dark fantasy/sci-fi single-player experience that delivers the searing combat and over-the-top visuals of the incomparable DOOM franchise, powered by the latest idTech engine. With a customizable difficulty system, it’s the perfect entry point whether you’re new to the franchise or a long time fan.
  • As the super weapon of gods and kings, shred enemies with devastating favorites like the Super Shotgun while also wielding a variety of new bone-chewing weapons, including the versatile Shield Saw.
  • Experience the origin story of the DOOM Slayer’s rage in this epic, cinematic, and action-packed story.
  1. Check the runtime prerequisites: use Python 3.11+, SQLite 3.45+ with math functions enabled, and a compatible terminal.
  2. From the project directory, run make run. This starts the game using the project’s documented target.
  3. Optionally run make inspect. The project describes this as opening a read-only live SQL audit alongside the game, useful for examining the database’s role while it runs.

According to the project README, SQL handles input interpretation, movement, collision, enemy behavior, combat, progression, raycasting, pixel values, and ANSI output. Python transports terminal input and results. The terminal requirement matters: this project paints its output as colored text rather than opening a conventional graphical game window.

How the Turso demonstration differs

Turso’s demonstration is about executing a compiled program in a database-derived virtual machine, not expressing Doom’s renderer as ordinary SQL statements. Turso describes compiling C to LLVM IR, translating that into VDBE bytecode, then loading and running the result on a Turso VM with extensions. The game advances as a long-running statement emits rows containing frames.

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That distinction is useful when evaluating claims that a database “runs Doom.” The demonstration’s subject is the database VM and its ability to execute compiled bytecode; it is not evidence that a standard SQLite query can render the original game’s graphics. Setup involves the demonstration’s compiler and VM approach, rather than the CedarDB SQLDoom loader/client path.

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What pg_doom does—and does not do

pg_doom uses a PostgreSQL extension to bridge database calls to a game core written in C. Its functions expose input and screen data, while a shell wrapper handles I/O. This can make Doom playable through a PostgreSQL-backed experiment, but the substantial game implementation remains C rather than SQL.

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The repository says a Doom WAD is required and that WAD media data is not freely distributed, so users must obtain it legally. That is a separate acquisition constraint from SQLDoom’s author’s statement about the shareware doom1.wad.

Which project should you choose?

  • Choose SQLDoom if you want the most directly documented route in this set for original Doom game logic and rendering represented in SQL, and you are prepared to use CedarDB.
  • Choose DOOMQL if your priority is to inspect SQL-driven simulation and pixel generation with SQLite, and a Doom-like game is enough.
  • Study Turso’s VDBE demo if you are interested in virtual-machine execution of compiled code inside a database-derived runtime, rather than game rules authored as SQL.
  • Look at pg_doom if you want to explore a PostgreSQL extension around a C game core; it is not the route for keeping the implementation in SQL.

SQLDoom’s author calls rendering Doom in a database “obviously a bad idea.” That is the author’s characterization of the engineering choice, not a claim that the experiment has no value: the same discussion points to relational game state and multiplayer as areas where database capabilities may be useful. The right evaluation depends on whether the goal is a faithful game, a SQL programming experiment, or a demonstration of what a database runtime can execute.

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