For an original Doom port whose game logic and renderer run in SQL, use SQLDoom with CedarDB. You will also need Python, the project’s dependencies, and a Doom IWAD. SQLDoom is not a vanilla PostgreSQL recipe: its README says it currently depends on CedarDB-specific cedarscript features. If you mean a Doom-like game built around SQL, a bytecode demo running on a database VM, or a PostgreSQL extension wrapping C code, those are different experiments with different setup steps.
Choose the kind of “Doom in SQL” you mean
The label covers several architectures. The important distinction is not just which database is used, but what actually runs inside it: SQL game logic, compiled game bytecode, or a conventional game engine exposed through database functions.
| Project | What it runs | What the database executes | Practical fit |
|---|---|---|---|
| SQLDoom | Original Doom logic and renderer | Game logic and rendering represented in SQL on CedarDB; Python handles timing, keyboard input, and display | Best match for an original-Doom experiment centered on SQL |
DOOMQL (petergpt/doomql) |
An original Doom-like raycasting game | SQLite calculates simulation state and RGB pixel values; Python transports terminal input and results | A more self-contained SQLite experiment with a terminal display |
| Turso VDBE demo | Unmodified Doom compiled to bytecode | A Turso VM runs SQLite-derived VDBE bytecode through a long-running statement that emits frame rows | A database-VM and compilation demonstration, not ordinary SQL rendering Doom |
pg_doom |
A Doom game core written in C | A PostgreSQL C extension exposes functions for input and screen data | An extension integration experiment; most game implementation remains C |
Run SQLDoom: the original-Doom SQL route
SQLDoom is the closest fit if you want original Doom’s game logic and renderer to execute in a database. The practical setup boundary matters: the project README says CedarDB is currently required because some functions use cedarscript. Python with psycopg2 and pygame acts as the client for timing, keyboard input, and drawing the returned frame.
What you need before starting
- A CedarDB installation that meets the version and setup requirements in the project’s current README.
- Python and the dependencies specified by that README, including
psycopg2andpygame. - A Doom IWAD. CedarDB author Lukas Vogel says the freely redistributable shareware
doom1.wadis enough to play episode one. A retail WAD can be used if you own it.
Setup sequence
- Open the SQLDoom project README and check its current CedarDB, Python, and dependency requirements before installing anything. The project’s loader and client commands may change, so use the exact invocations in that README rather than substituting commands from a different Doom project.
- Obtain an IWAD you are entitled to use. Follow the README’s WAD-loader invocation to import the game data into the database.
- Start the client using the README’s client command. Python supplies input and timing and displays the frames produced by the database-side game logic and renderer.
The separation explains why the game can be called SQLDoom even though it is not a database-only application: the database computes the game and image, while the client connects those results to a keyboard and screen.
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What its reported performance means
SQLDoom retains Doom’s original 35 Hz game-logic tic rate but requests rendered frames separately. CedarDB’s README describes rendering at up to 60 Hz. In a CedarDB article accessed in 2026, author Lukas Vogel reports about 60 frames per second typically and 35 in very busy scenes on his Ryzen 7 PRO 7840U laptop; those are author-reported results on that hardware, not a general performance guarantee or an independent benchmark.
The article describes a full 320 × 200 frame buffer. Vogel also reports an average 2.15 ms for a typical game tic with six monsters awake, and 10.45 ms in a slow case with 46 awake monsters. These figures describe SQLDoom as reported by its author, not a controlled comparison with other implementations.
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Try DOOMQL if you want a smaller SQLite experiment
DOOMQL is a Doom-like raycasting game, not a port of original Doom. 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 that supports 24-bit color and Unicode upper-half blocks.
- Check that your SQLite build has math functions enabled and that your terminal supports the required color and Unicode output.
- From the project directory, run
make runto start the game. - Optionally, run
make inspectto open a read-only live SQL audit alongside the game.
The project assigns SQL the work of interpreting input, movement, collision, enemy behavior, combat, progression, raycasting, pixel values, and ANSI output. Python carries terminal input and results between the game and the terminal. This makes it a useful way to inspect SQL-owned game state without needing an original Doom WAD.
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Understand the other two architectures before installing
Turso: Doom compiled for a database virtual machine
Turso describes compiling C to LLVM IR, translating that into VDBE bytecode, and loading and running the bytecode on a Turso VM with extensions. The game advances as a long-lived statement emits frame rows. That is a database-VM experiment, but it does not mean ordinary SQL text implements Doom’s game logic or renderer.
pg_doom: a C game core behind PostgreSQL functions
pg_doom is a PostgreSQL extension plus a shell wrapper. C functions bridge input and screen data to the game core; this is not a port of the core into SQL. Its repository says a Doom WAD is required and that WAD media data is not freely distributed, so obtain it legally.
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What is really implemented in SQL?
In SQLDoom, the database does substantially more than store levels or save files: the project describes both original Doom game logic and its renderer as SQL. Python remains responsible for the outside-world tasks of timing, keyboard input, and display. The implementation is also database-specific today because of its use of cedarscript.
Vogel reports about 5,900 lines of SQL for game logic, compared with about 9,000 lines of original C game logic, plus about 1,300 lines of SQL for the renderer. Those are the author’s implementation-size comparisons, not a measure of equivalent complexity across languages.
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By contrast, DOOMQL’s SQL calculates a raycast scene and its pixels for an original Doom-like game; Turso executes compiled VDBE bytecode; and pg_doom calls into C game code. Calling all four “Doom in SQL” without explaining this boundary makes their approaches sound more alike than they are.
Why put a game in a database at all?
For rendering a fast-paced game, using a database is deliberately unconventional. Vogel calls it “obviously a bad idea,” his opinion on the engineering trade-off. His article also discusses a possible counterpoint: relational storage and database concurrency can suit shared game state and multiplayer. SQLDoom’s multiplayer implementation is reported to include about 110 tables, just over 100 functions, and four player roles; these are author-reported implementation details, not evidence that a database is automatically a better game server.
Quick Recap
- Choose SQLDoom when the experiment is specifically to run original Doom logic and rendering in SQL and you are willing to use CedarDB.
- Choose DOOMQL when you want an inspectable, SQL-heavy Doom-like game with a relatively direct SQLite and terminal workflow.
- Choose Turso’s demo when the interesting question is whether a database virtual machine can execute compiled game bytecode.
- Choose
pg_doomwhen you want to explore a PostgreSQL extension wrapping a C game core rather than translating that core into SQL.
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