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How SQL can represent a game board
Conway’s Game of Life is a deterministic cellular automaton: each cell is either alive or dead, and every new board is derived from the current one by applying the same local rules to every cell. One possible relational representation stores coordinates and an alive/dead value for each cell. Another stores only the coordinates of live cells, treating all other positions as dead. These are implementation choices, not a single required schema.
For each generation, the query needs to identify candidate cells, count their live neighbors, and calculate whether they live in the next generation. A useful way to think about the work is as a data pipeline: represent the current state, derive neighbor counts, apply the rule, then use the result as the next state.
How the Game of Life rules map to SQL
In the common B3/S23 rule, a dead cell is born when exactly three neighbors are alive; a live cell survives with two or three live neighbors. Every other cell is dead in the next generation. A SQL implementation can express the final decision with conditional logic such as a CASE expression, after joins or grouping have produced each candidate cell’s neighbor count.
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With a sparse representation, a query can generate the eight neighboring coordinates around each live cell and count how often each coordinate appears. It must also account for currently live cells that receive no neighbor records: those cells have zero live neighbors and must die. Omitting them from the candidates can leave cells incorrectly alive or absent from the calculated result.
The board’s edges are part of the model. A bounded grid must decide what happens beyond its boundary—often those positions are treated as permanently dead—while an unbounded sparse board can allow coordinates to extend without a fixed edge. The SQL Server and SQLite examples show concrete approaches, but their syntax and assumptions are tied to their respective implementations: Paul Chang’s SQL Server implementation and the pure SQL SQLite project.
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How recursive CTEs can produce generations
A recursive CTE can express a sequence in which one generation feeds the calculation of the next. PostgreSQL’s documentation describes a recursive WITH query as a non-recursive term followed by a recursive term, with working and intermediate tables used during evaluation. Although the syntax uses the word “recursive,” PostgreSQL notes that the query is evaluated iteratively. Its documentation illustrates the pattern with a query that sums integers from 1 through 100. See the PostgreSQL 17 documentation on recursive queries.
For a Life simulation, the initial term supplies the starting board and the recursive term derives a later board from the preceding one. The query must make generations distinguishable and state when to stop—for example, after a chosen number of generations or when no further rows are produced. Alternatively, a program can issue repeated SQL statements and pass each result to the next calculation. Neither approach has universally portable syntax or execution limits.
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Termination deserves particular care. PostgreSQL warns that a recursive query must eventually reach a state where its recursive term returns no rows, or it can continue indefinitely. Its documentation discusses limiting fetched rows as a possible testing aid, but explicitly cautions against relying on that behavior as a production safeguard or assuming other systems behave the same way. Consult the PostgreSQL 18 documentation for its current recursive-query cautions.
What computational universality does—and does not—mean
The Game of Life is computationally universal when suitably configured: its patterns can simulate an arbitrary Turing machine. A 2025 paper by Ville Salo and Ilkka Törmä discusses the automaton’s computability properties in “Structure and computability of preimages in the Game of Life.” This is a statement about what the cellular automaton can compute, not a performance claim about a database running it.
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That distinction matters. SQL can be used to implement a system that performs repeated computation, but universality does not show that a particular SQL query is efficient, easy to scale, or suitable for interactive play. The experiment is more revealing as a way to see how data representation, aggregation, iteration, and termination fit together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When this database experiment is useful
Life in SQL is a good teaching or demonstration project when the goal is to understand how a set of rows can represent state and how queries can transform that state repeatedly. It also makes design decisions concrete: whether the board is dense or sparse, how boundaries work, whether a query returns every generation or only a selected one, and how a run ends.
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For ordinary interactive game development, the examples do not establish SQL as a suitable game engine. They also do not provide a head-to-head performance comparison. Execution cost, memory behavior, recursion limits, and practical scale depend on the database, query design, and board model; claims about speed require measurements for the specific implementation. For broader background on cellular automata and computation, Springer lists Andrew Adamatzky’s edited volume Game of Life Cellular Automata.
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