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Yes—SQL can run a simple game such as Conway’s Game of Life by representing a board as data, calculating each cell’s neighbors, and deriving the next state. Published implementations demonstrate the idea in Microsoft SQL Server and SQLite, while PostgreSQL’s recursive-query documentation explains one way to repeat a computation. These examples are database-specific; they do not establish that one query works unchanged across SQL dialects.
How a game becomes a database query
Conway’s Game of Life is a deterministic cellular automaton: each cell is either alive or dead, and a local rule determines its state in the next generation. In the common B3/S23 rule, a dead cell becomes alive when exactly three neighboring cells are alive. A live cell stays alive with two or three live neighbors; otherwise it dies.
A database can represent the board as rows and apply that rule to calculate a new board. One possible representation stores coordinates and whether each cell is alive. Another stores only the coordinates of living cells, treating absent coordinates as dead. These are implementation choices, not a universal schema. Paul Chang’s SQL Server example and the SQLite pure-SQL project show concrete, dialect-specific approaches.
Count neighbors, then apply the rule
For each candidate cell, the query identifies the surrounding positions and counts how many are alive. A conditional expression can then apply B3/S23. Candidate generation matters: a live cell with no live neighbors still needs to be considered, because it dies in the next generation. Generating candidates only from locations that already have live neighbors can omit cells that must change.
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The board’s boundaries also matter. On a finite board, the implementation must decide what happens at an edge; a sparse-coordinate model can instead represent an unbounded plane, subject to the implementation’s practical limits. The chosen boundary model changes which neighbors exist and therefore can change the result.
How recursive CTEs express generations
A recursive common table expression (CTE) can define an initial state and repeatedly derive later states from the preceding one, where the SQL system supports the required query shape. Conceptually, the initial term supplies generation zero; the recursive term calculates the next generation; a generation indicator or other condition controls how far the calculation proceeds.
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PostgreSQL’s documentation describes recursive WITH evaluation using working and intermediate tables. It notes that, although the syntax uses the word RECURSIVE, the engine evaluates the query iteratively. The documentation’s example sums integers from 1 through 100, illustrating repeated derivation without a game board. Read the PostgreSQL 17 recursive-query documentation for those semantics.
SQL Server’s published Life implementation uses CTEs, and the SQLite project provides a separate pure-SQL version. Those examples are evidence that the idea can be implemented in those systems, not a guarantee of identical syntax or behavior in PostgreSQL or any other database. A program can also orchestrate repeated SQL statements instead of expressing every generation in one recursive query.
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Choose what the query returns and when it stops
An implementation should specify whether it returns every generation or only a selected generation, and what ends the calculation. Possible stopping conditions include reaching a fixed generation count, reaching a stable state, extinction, or an explicit resource cap. Without a condition that eventually stops the recursive term, a query can continue indefinitely.
PostgreSQL’s PostgreSQL 18 documentation warns about recursive cycles and termination. It discusses limiting fetched rows as a way to inspect some potentially looping queries, but that behavior depends on PostgreSQL’s demand-driven execution and should not be treated as a portable production safeguard.
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What the experiment says about computation—and what it does not
Life is computationally universal when configured appropriately: the automaton can simulate an arbitrary Turing machine. A 2025 paper by Ville Salo and Ilkka Törmä discusses the computability of the Game of Life. That is a statement about the automaton’s computational power, not a finding that SQL is an efficient or convenient way to build a game.
The database experiment is useful because it makes several computing ideas concrete: state can be represented as rows, local relationships can be expressed as joins or aggregations, and repeated transformations can be expressed through recursion or orchestrated statements. These are valuable query concepts even if the implementation is best treated as a demonstration.
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When running a game in SQL makes sense
SQL is a reasonable medium for exploring how data transformations work or for demonstrating recursive queries. The examples establish that Life can be implemented in particular database systems; they do not provide a head-to-head performance test, prove a scaling limit, or show that a database query is suitable for an interactive game engine. Without measurements for a specific implementation and workload, claims about speed or capacity are not justified.
- Name the target database and version when giving syntax or describing execution behavior.
- State whether the board is finite or sparse, and define the edge behavior.
- Make the generation count or other termination condition explicit.
- Measure cost and memory use on the actual database and workload before making performance claims.
For broader background on cellular automata and computation, Springer lists Game of Life Cellular Automata, edited by Andrew Adamatzky, ISBN 978-1-84996-216-2. It is background reading rather than a SQL implementation guide: Springer’s book listing.
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