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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesYes—SQL can simulate a game. A database query can represent a board, count each cell’s live neighbors, and derive the next generation of Conway’s Game of Life. Published examples do this in Microsoft SQL Server and SQLite, though their implementations are specific to those systems. The experiment shows how databases can express repeated computation; it does not make SQL a sensible default for building an interactive game.
How SQL represents a game board
Conway’s Game of Life is a deterministic cellular automaton: each cell is either alive or dead, and its next state depends on the cells around it. One practical representation is a relation containing the coordinates of live cells. Another is a dense grid that records both live and dead cells. These are implementation choices, not a universal schema.
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From a set of live coordinates, a query can derive the neighboring positions for each live cell, group those positions, and count how many live cells touch each candidate location. A conditional expression then applies the transition rule. The SQL Server implementation by Paul Chang and a separate SQLite project show concrete versions of this general approach: SQL Server implementation and SQLite project.
A subtle requirement is to include live cells with no live neighbors among the candidates. They must be evaluated too: under Life’s rules, they die. If candidates are generated only from positions that receive neighbor counts, isolated live cells can disappear from the calculation without being explicitly represented as dead in the next state.
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How the Game of Life rule maps to SQL
The familiar rule is called B3/S23. “B3” means a dead cell is born when it has exactly three live neighbors. “S23” means a live cell survives with two or three live neighbors. In all other cases it is dead in the next generation.
| Current state | Live neighbors | Next state |
|---|---|---|
| Dead | Exactly 3 | Alive |
| Alive | 2 or 3 | Alive |
| Alive or dead | Any other count | Dead |
In SQL terms, the neighbor counts are grouped data and the rule is conditional logic, such as a CASE expression. A query can calculate a new relation of live coordinates from the previous one. That is the core of the simulation; the harder implementation choices concern which positions to consider, how board edges behave, and how to repeat the calculation.
How recursive CTEs express generations
A recursive common table expression (CTE) can express a sequence in which each result feeds the next step. PostgreSQL 17 documents a recursive WITH query as a non-recursive term followed by a recursive term. The engine evaluates this iteratively using working and intermediate tables; as its documentation puts it, “While RECURSIVE allows queries to be specified recursively, internally such queries are evaluated iteratively.” See the PostgreSQL 17 documentation on WITH queries.
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For a Life simulation, the conceptual cycle is:
- Start with the initial board state.
- Calculate neighbor counts and apply B3/S23 to derive a new state.
- Feed that state into the next iteration, stopping under an explicit condition.
The CTE’s recursive structure does not mean every SQL engine accepts the same syntax or executes the same query shape. The published SQL Server and SQLite implementations are evidence that the idea can be realized in those systems, not portable code to paste unchanged into any database.
What “SQL can run games” does—and does not—mean
Life itself is computationally universal when suitably configured: it can simulate an arbitrary Turing machine. A 2025 paper by Ville Salo and Ilkka Törmä discusses the automaton’s computability properties. That is a statement about the computational power of Life, not evidence that an SQL implementation is fast, easy to scale, or suitable for an interactive game engine. See “Structure and computability of preimages in the Game of Life”.
The useful lesson is narrower and more practical: SQL can represent state, aggregate relationships among records, apply rules, and—where supported—repeat a derivation. Those are important database ideas even when the simulation is only a demonstration. For a production game, the article sources establish no performance comparison or recommendation; suitability depends on the particular workload and implementation.
Choices that affect a SQL simulation
- Board representation: A dense grid stores every cell; a sparse representation can store only live coordinates. Neighbor generation and candidate coverage differ between them.
- Edges: A bounded board needs a defined edge rule, such as treating off-board positions as dead or wrapping around. An unbounded sparse board avoids a fixed edge but can still grow as the simulation evolves.
- Iteration and output: Decide whether a query emits every generation or only a selected one, and whether to stop after a fixed number of steps, on extinction, at a stable state, or under a resource cap.
- Termination: PostgreSQL warns that a recursive query must eventually produce no rows if it is to terminate naturally. Its documentation discusses a
LIMIT-based testing technique but cautions against treating demand-driven fetching as a production safeguard or assuming the behavior in other systems. See the PostgreSQL 18 documentation. - Cost and limits: Recursion limits, memory use, and execution cost are database- and query-specific. The published examples do not establish a head-to-head performance result.
Further reading on cellular automata
For broader background beyond SQL, Springer lists Game of Life Cellular Automata, edited by Andrew Adamatzky, with coverage that includes computation: Springer book listing.
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