Una regla con un indicador NULL en SQL: por qué (a OR NOT a) no siempre es verdadera
a OR NOT a es una tautología en la tabla de dos valores, y el motor lo prueba. La página explica por qué la misma expresión sobre una columna SQL que admite NULL no siempre es verdadera: un tercer valor, NULL, queda fuera del modelo.
La fórmula es la ley del tercero excluido con un nombre en minúscula, tal como aparecería en una cláusula WHERE: a OR NOT a. La lectura del motor dice que always_true es verdadero exactamente cuando a es verdadero o a es falso, o ambas cosas, que es todos los casos que el modelo conoce. Con una variable booleana la tabla tiene dos filas y la salida es verdadera en las dos, Σm(0, 1), con la lista de maxtérminos vacía. Todo lo que el motor calcula sobre esta fórmula se calcula sobre esos dos valores.
La tautología se cumple: always_true es verdadero en todas las asignaciones. La contradicción se viola y la satisfacibilidad se cumple, con el testigo a = FALSO. El simplificador aplica la ley del complemento, registrada como A OR NOT A = TRUE, y reduce la expresión de dos operadores a la constante TRUE en un paso. Las dos formas normales son TRUE, la FNC con cero cláusulas y la FND con un término vacío, por la misma ley. El mapa cubre las dos celdas con un grupo, un implicante primo, y deriva TRUE. Compuertas: 2 NAND, NAND(NAND(a, a), a), y 5 NOR.
Las columnas SQL pueden ser NULL, y la lógica de SQL tiene tres valores: cuando a es NULL, NOT a es NULL, a OR NOT a es NULL, y una cláusula WHERE no devuelve la fila, pues solo pasa TRUE. Así, un filtro que parece una tautología descarta en silencio las filas con NULL. Vigila la distancia entre el modelo y la base de datos: el motor es exacto dentro de la lógica de dos valores, y la página lo dice en vez de fingir lo contrario. La página del error «NULL no es falso» y la del lenguaje SQL explican cómo modelar el caso NULL como una entrada explícita.
always_true := a OR NOT a · salida
Comprobaciones exactas
Tabla de verdad (2 filas)
| # | a | always_true |
|---|---|---|
| 0 | FALSO | VERDADERO |
| 1 | VERDADERO | VERDADERO |
Lectura en lenguaje claro
Principiantealways_true es verdadero exactamente cuando a es verdadero o a es falso (o ambas cosas).
En todos los demás casos, always_true es falso.
Sobre los operadores:
- OR (O) es inclusivo: es verdadero en cuanto al menos una parte es verdadera, también cuando lo son varias.
- NOT (NO) invierte un valor: lo verdadero pasa a ser falso y lo falso, verdadero.
Intermedioalways_true es verdadero exactamente cuando a es verdadero o a es falso (o ambas cosas).
En todos los demás casos, always_true es falso.
Avanzadoalways_true es verdadero exactamente cuando a es verdadero o a es falso (o ambas cosas) [disyunción inclusiva ∨].
En todos los demás casos, always_true es falso.
Notación formal: a ∨ ¬a
Diagrama lógico
Simplificación paso a paso
a OR NOT a- Complemento ·
A OR NOT A = TRUEa OR NOT a→TRUEResultado:TRUE
Mapa de Karnaugh
- celdas m0, m1
TRUE
Mintérminos y maxtérminos
always_true(a) = Σm(0, 1)always_true(a) = ΠM()Orden de variables: a · 2 filas
Formas normales
Forma normal conjuntiva (FNC)
TRUE0 cláusulas, 0 literales · complete · Complemento ×1
Forma normal disyuntiva (FND)
TRUE1 términos, 0 literales · complete · Complemento ×1
Solo NAND, solo NOR
Forma solo con NAND
NAND(NAND(a, a), a)2 compuertas · equivalencia verificada en todas las filas
Forma solo con NOR
NOR(NOR(a, NOR(a, a)), NOR(a, NOR(a, a)))5 compuertas · equivalencia verificada en todas las filas
La misma regla como código
Python
# -*- coding: utf-8 -*-
# Generated by Tangibool (tangibool.python/1). Do not edit by hand; regenerate from the source revision.
# Model: not stated
# Source revision: reference
# Interpretation status: not stated
"""Tangibool evaluator (decision model).
Generated deterministically from the validated Boolean model; it evaluates exactly that
model. It does not establish that the model is a complete, legal, or safe statement of a
real-world rule, and evaluating examples does not verify the rule.
Source revision: reference
Interpretation status: not stated
Exporter: 1.0.0 (format tangibool.python/1; engine 1.0.0; semantics tangibool-semantics/1)
Logic digest: 9be081b0052570cdb59c417aca08d99038b1e5dbb15e67ebb56cd5c28478c2ca
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
Inputs (dict keys, declared order) and generated parameter names:
'a' -> input_a
Outputs (declared order) and their functions:
'always_true' -> output_always_true(inputs)
Assumptions (the declared scope, checked by in_scope(inputs)):
none; every complete assignment is in scope.
Input contract:
Every required input must be exactly True or False (type(value) is bool). Missing
inputs, None, strings, integers (including 0 and 1), and keys that are not declared
inputs raise InputError before anything is evaluated, including inputs that a
short-circuiting operator would never read. Results are always bool.
Scope:
Decision functions raise OutOfScopeError when the inputs violate an assumption; an
out-of-scope assignment is never turned into a False result. Pass check_scope=False
to evaluate without the scope check, and use in_scope(inputs) to test it separately.
Usage:
import tangibool_model as model
model.evaluate({'a': False})
# -> {'always_true': True}
model.output_always_true({'a': False})
# -> True
model.evaluate({'a': 1})
# raises InputError: 1 is an int, not a bool
"""
import collections.abc as _abc
from typing import Dict, Mapping, Tuple
__all__ = [
"MODEL_INFO",
"IDENTIFIER_MAP",
"INPUT_NAMES",
"OUTPUT_NAMES",
"ASSUMPTIONS",
"InputError",
"OutOfScopeError",
"minterm_index",
"in_scope",
"evaluate",
"output_always_true",
]
MODEL_INFO = {
"format": "tangibool.python/1",
"mode": "decision",
"model": None,
"revision": "reference",
"interpretation": None,
"exporter": "1.0.0",
"engine_version": "1.0.0",
"semantics_version": "tangibool-semantics/1",
"logic_digest": "9be081b0052570cdb59c417aca08d99038b1e5dbb15e67ebb56cd5c28478c2ca",
"assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
"primary_output": "always_true",
"unused_inputs": (),
"subcircuits": (),
}
# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
"v_5747b4a8ab01": {"kind": "input", "name": "a", "identifier": "input_a"},
"f_a942d30f1e08": {"kind": "output", "name": "always_true", "identifier": "output_always_true"},
}
INPUT_NAMES = ("a",)
OUTPUT_NAMES = ("always_true",)
ASSUMPTIONS = ()
_DECLARED_INPUTS = frozenset(INPUT_NAMES)
_SCOPE_INPUTS = ()
_ASSUMPTION_FORMULAS = {entry["name"]: entry["formula"] for entry in ASSUMPTIONS}
class InputError(ValueError):
"""Raised when inputs are missing, undeclared, or not exactly True/False.
Every required input must satisfy type(value) is bool. Missing inputs, None, strings,
integers (including 0 and 1), and keys that are not declared inputs are rejected before
anything is evaluated. A missing input is never treated as False. The individual problems
are listed in the problems attribute.
"""
def __init__(self, problems):
self.problems = tuple(problems)
super().__init__("Invalid inputs: " + "; ".join(self.problems))
class OutOfScopeError(ValueError):
"""Raised when the inputs violate the model's assumptions (its declared scope).
Outside its assumptions the rule has no decision: this error is never a False result.
The names of the violated assumptions are in the violated attribute.
"""
def __init__(self, violated):
self.violated = tuple(violated)
details = ", ".join(name + " (" + _ASSUMPTION_FORMULAS.get(name, "?") + ")" for name in self.violated)
super().__init__(
"Out of scope: the inputs violate "
+ ("assumption " if len(self.violated) == 1 else "assumptions ")
+ details
+ ". The rule has no decision outside its assumptions; this is not a False result."
)
def _short_repr(value: object) -> str:
text = repr(value)
return text if len(text) <= 60 else text[:57] + "..."
def _checked_inputs(inputs: Mapping[str, bool], required: Tuple[str, ...], check_scope: bool) -> Dict[str, bool]:
"""Validate inputs strictly before anything is evaluated and return a plain copy."""
if type(check_scope) is not bool:
raise TypeError("check_scope must be True or False")
if not isinstance(inputs, _abc.Mapping):
raise InputError(["inputs must be a mapping from input names to True/False, got " + type(inputs).__name__])
problems = []
for key in inputs:
if type(key) is not str or key not in _DECLARED_INPUTS:
declared = ", ".join(repr(name) for name in INPUT_NAMES) or "none"
problems.append("undeclared input " + _short_repr(key) + " (declared inputs: " + declared + ")")
needed = tuple(required) + (_SCOPE_INPUTS if check_scope else ())
for name in needed:
if name not in inputs:
problems.append("missing required input " + repr(name))
for key, value in inputs.items():
if type(key) is str and key in _DECLARED_INPUTS and type(value) is not bool:
problems.append(
"input " + repr(key) + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
)
if problems:
raise InputError(list(dict.fromkeys(problems)))
return dict(inputs.items())
def _require_bool_args(function: str, pairs: Tuple[Tuple[str, object], ...]) -> None:
problems = [
"argument " + repr(name) + " of " + function + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
for name, value in pairs
if type(value) is not bool
]
if problems:
raise InputError(problems)
def minterm_index(*values: bool) -> int:
"""Row index of an assignment over a declared variable order.
The first value is the most significant bit, so all-False is row 0. Used for indexed
minterm (sum) and maxterm (product) forms.
"""
index = 0
for value in values:
if type(value) is not bool:
raise InputError(["minterm_index() takes True/False values, got " + type(value).__name__ + " " + _short_repr(value)])
index = index * 2 + (1 if value else 0)
return index
def _scope_values(values: Dict[str, bool]) -> Tuple[Tuple[str, bool], ...]:
"""Value of every enabled, confirmed assumption for validated inputs, in declared order."""
return ()
def _require_in_scope(values: Dict[str, bool]) -> None:
violated = tuple(name for name, holds in _scope_values(values) if not holds)
if violated:
raise OutOfScopeError(violated)
def in_scope(inputs: Mapping[str, bool]) -> bool:
"""True when the inputs satisfy every assumption (the model's declared scope).
Requires the inputs the assumptions read; raises InputError for invalid inputs.
"""
values = _checked_inputs(inputs, (), True)
return all(holds for _, holds in _scope_values(values))
# ---- Formula bodies (inputs are validated by the public functions below) ----
def _compute_output_always_true(input_a: bool) -> bool:
"""Decision output 'always_true': a OR NOT a"""
return input_a or (not input_a)
# ---- Public API ----
def output_always_true(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'always_true' (stable ID f_a942d30f1e08).
'always_true' := a OR NOT a
Required inputs: 'a'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("a",), check_scope)
if check_scope:
_require_in_scope(values)
output_always_true_value = _compute_output_always_true(values["a"])
return output_always_true_value
def evaluate(inputs: Mapping[str, bool], *, check_scope: bool = True) -> Dict[str, bool]:
"""Evaluate every output (declared order); each output is computed once.
Required inputs: 'a'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("a",), check_scope)
if check_scope:
_require_in_scope(values)
output_always_true_value = _compute_output_always_true(values["a"])
return {
"always_true": output_always_true_value,
}
PostgreSQL
-- Tangibool PostgreSQL export (tangibool.postgresql/1)
-- Target: PostgreSQL 16 or later with typed boolean inputs; tested with PostgreSQL 18.3 (PGlite 0.5.8).
-- Not a claim of compatibility with other SQL dialects.
-- Model: not stated
-- Source revision: reference
-- Interpretation status: not stated
-- Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
-- Logic digest: 9be081b0052570cdb59c417aca08d99038b1e5dbb15e67ebb56cd5c28478c2ca
-- Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
-- Mode: decision
--
-- Read-only: this file contains only WITH / VALUES / SELECT statements. No DDL, DML,
-- procedures, credentials, network access, or database changes. The expressions are pure
-- Boolean expressions, so their result does not depend on SQL evaluation order.
--
-- NON-NULL INPUT PRECONDITION: NULL is not a truth value of this rule. Every fragment below
-- is defined only when every input column it reads is a non-NULL boolean. The guard template
-- (section 2) marks any NULL input as 'invalid_input' and any row that violates the
-- assumptions as 'out_of_scope', and fills output columns only for 'ok' rows; a NULL in an
-- output column of another row is an envelope marker, not a third truth value. Never wrap
-- the fragments in COALESCE(..., FALSE) or otherwise choose a meaning for unknown inputs.
-- This evaluates the supplied model; it does not show that the model is a complete, legal,
-- or safe statement of a real-world rule.
--
-- Input columns (stable ID: name -> column):
-- v_5747b4a8ab01: a -> "in_a"
-- Output columns:
-- f_a942d30f1e08: always_true -> "out_always_true"
-- Assumptions (scope K; exported separately and never folded into an output):
-- none; every complete input row is in scope.
-- ----------------------------------------------------------------------------------------------
-- 1. Expression fragments (parenthesised; non-NULL boolean inputs required)
-- ----------------------------------------------------------------------------------------------
--
-- [out_always_true] output always_true (stable ID f_a942d30f1e08)
-- always_true := a OR NOT a
-- PRECONDITION: "in_a" is a non-NULL boolean.
-- ("in_a" OR (NOT "in_a"))
--
-- ----------------------------------------------------------------------------------------------
-- 2. Guard template (read-only SELECT over an inputs list)
-- ----------------------------------------------------------------------------------------------
-- Replace the example rows between the markers with your own rows, or replace the inputs
-- CTE with a SELECT that provides the boolean columns listed in the header. status is
-- 'invalid_input' when any input is NULL, 'out_of_scope' when the assumptions are false, and
-- 'ok' otherwise; output columns are filled only for 'ok' rows (NULL elsewhere is an
-- envelope marker). Assumption columns are shown for rows with complete inputs.
WITH inputs ("row_id", "in_a") AS (
-- tangibool:example-rows:begin
VALUES
(1, FALSE),
(2, TRUE),
(3, NULL::boolean)
-- tangibool:example-rows:end
),
checked AS (
SELECT
inputs.*,
CASE
WHEN "in_a" IS NULL THEN 'invalid_input'
ELSE 'ok'
END AS "status"
FROM inputs
)
SELECT
"row_id",
"in_a",
"status",
CASE WHEN "status" = 'ok' THEN ("in_a" OR (NOT "in_a")) END AS "out_always_true"
FROM checked
ORDER BY "row_id";
SMT-LIB
; Tangibool SMT-LIB export (tangibool.smtlib/1) ; Profile: SMT-LIB 2.6 Boolean subset (QF_UF): Bool constants, define-fun, let, push/pop, assert, check-sat, echo. ; Tested with Z3 5.1.0 (z3-solver 5.2.0, WebAssembly build). ; Model: not stated ; Source revision: reference ; Interpretation status: not stated ; Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1) ; Logic digest: 9be081b0052570cdb59c417aca08d99038b1e5dbb15e67ebb56cd5c28478c2ca ; Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78 ; Mode: decision ; ; Reading results: each query block states what sat and unsat mean for its question. A sat ; answer comes with a witness or counterexample (ask for it with (get-model) after sat only); ; unsat settles that scoped query and is solver-checked, not an independently checked proof ; certificate; unknown settles nothing. Blocks are scoped with push/pop, so assertions never ; accumulate across questions. This checks the supplied model, not whether it is a complete, ; legal, or safe statement of a real-world rule. ; ; Symbols (stable ID: name -> symbol): ; v_5747b4a8ab01: a -> in_a ; f_a942d30f1e08: always_true -> out_always_true (set-option :produce-models true) (set-info :smt-lib-version 2.6) (set-logic QF_UF) ; ---- Inputs (declared order) (declare-const in_a Bool) ; a ; ---- Outputs (dependencies first) ; output always_true (stable ID f_a942d30f1e08): a OR NOT a (define-fun out_always_true () Bool (or in_a (not in_a))) ; ---- Assumptions (the scope K; never folded into an output) ; K: conjunction of the enabled, confirmed assumptions (true when there are none) (define-fun K () Bool true) ; ---- Definitions only: nothing is asserted and no check is run. ; Add (assert ...) and (check-sat) inside (push 1) ... (pop 1) to ask a question.
LaTeX
always_true = a + \overline{a}El error habitual
Suponer que una prueba de dos valores se traslada a SQL sin cambios. El veredicto del motor es correcto para una a booleana; la sorpresa viene de las filas en que a es NULL, para las que el modelo no tiene fila. Añade una variable como a_is_null, escribe la regla sobre las dos, y la tabla mostrará las filas descartadas.
Preguntas
¿El motor modela NULL?
No. El motor es de dos valores: toda variable es VERDADERO o FALSO, y las comprobaciones son exactas sobre ellos. Para una columna que admite NULL, modela la nulidad como una entrada booleana propia, por ejemplo a_is_null, y escribe la condición SQL con las dos entradas. La tabla pasa a cuatro filas y el caso NULL queda visible.
¿Cómo escribo el filtro para que NULL se comporte como quiero?
Decide primero qué debe significar NULL. Si NULL debe contar como falso, el COALESCE(a, FALSE) de SQL o una rama explícita a IS NULL hacen visible la decisión en la consulta; la regla correspondiente del modelo tiene entonces una entrada más, y la tabla de verdad documenta la decisión para quien lea después.
Relacionado
Leyes
Operadores y compuertas
- Compuerta NOT (inversor): tabla de verdad, símbolo y el operador NOT
- Compuerta OR (puerta O): tabla de verdad de 2, 3 y 4 entradas
Herramientas
- Generador de tablas de verdad: cualquier expresión booleana, hasta 12 variables, con columnas intermedias
- Calculadora y simplificador de álgebra de Boole paso a paso (cada ley nombrada y verificada)
- Solucionador de mapas de Karnaugh: de 2 a 4 variables, agrupaciones, SOP y POS
Véase también
- NULL no es FALSO: la lógica trivaluada de SQL
- Lógica booleana en SQL: AND, OR, NOT y la trampa de NULL (lógica trivaluada)
- Una tautología: A OR NOT A, comprobada en todas las filas
- Tautología: definición, ejemplo y equivalente en inglés
- Ley del complemento: A·A' = 0, A+A' = 1
- De expresión booleana a Python, SQL, SMT-LIB y LaTeX
Ejemplos resueltos
Generado a partir del motor de Tangibool, semántica tangibool-semantics/1, motor 1.0.0, compilación . Cada tabla, mapa, simplificación y veredicto de estas páginas lo calculó el motor de Tangibool al compilar el sitio, y cada afirmación la volvió a comprobar el mismo verificador exacto que usa la app. Los límites se indican donde aplican. © 2026 Foreranger LTD.