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A nullable flag rule in SQL: why (a OR NOT a) is not always true

a OR NOT a is a tautology on the two-valued table, and the engine proves it. The page explains why the same expression over a nullable SQL column is not always true: a third value, NULL, is outside the model.

The formula is the law of excluded middle with a lowercase name, as it would appear in a WHERE clause: a OR NOT a. The engine's reading says always_true is true exactly when a is true or a is false, or both, which is every case the model knows. With one Boolean variable the table has two rows and the output is true on both, Σm(0, 1), with an empty maxterm list. Everything the engine computes about this formula is computed over those two values.

The tautology check holds: always_true is true on every assignment. The contradiction check is violated and satisfiability holds, both with the witness a = FALSE. The simplifier applies the complement law, recorded as A OR NOT A = TRUE, and reduces the expression from two operators to the constant TRUE in one step. Both normal forms are TRUE, the CNF with zero clauses and the DNF with one empty term, by the same law. The Karnaugh map covers both cells with one group, one prime implicant, and derives TRUE. Gate counts for the original: 2 NAND, NAND(NAND(a, a), a), and 5 NOR.

SQL columns can be NULL, and SQL's logic has three values: when a is NULL, NOT a is NULL, a OR NOT a is NULL, and a WHERE clause does not return the row, because only TRUE passes. So a filter that looks like a tautology silently drops every row with a NULL in it. The thing to watch is the gap between the model and the database: the engine is exact within two-valued logic, and the page says so rather than pretending otherwise. The null-is-not-false pitfall page and the SQL language page explain how to model the NULL case as an explicit extra input.

always_true := a OR NOT a · output

Exact checks

Always true (tautology)? Yes · Exhaustively checked and solver cross-checked · 2 rows
Always false (contradiction)? No Counterexample: a = F · Exhaustively checked and solver cross-checked · 1 rows
Can be true? Yes Example assignment: a = F · Exhaustively checked and solver cross-checked · 1 rows

Truth table (2 rows)

A nullable flag rule in SQL: why (a OR NOT a) is not always true
#aalways_true
0FALSETRUE
1TRUETRUE

Plain-language reading

Beginneralways_true is true exactly when a is true or a is false (or both).
In every other case, always_true is false.
About the operators:
- OR is inclusive: it is true when at least one part is true, including when several are.
- NOT reverses a value: true becomes false and false becomes true.
Intermediatealways_true is true exactly when a is true or a is false (or both).
In every other case, always_true is false.
Advancedalways_true is true exactly when a is true or a is false (or both) [inclusive disjunction ∨].
In every other case, always_true is false.
Formal notation: a ∨ ¬a

Logic diagram

A nullable flag rule in SQL: why (a OR NOT a) is not always true A nullable flag rule in SQL: why (a OR NOT a) is not always true a always_true OR NOT
A nullable flag rule in SQL: why (a OR NOT a) is not always true A nullable flag rule in SQL: why (a OR NOT a) is not always true a always_true OR NOT

Simplification, step by step

a OR NOT a
  1. Complement · A OR NOT A = TRUE
    a OR NOT a → TRUEResult: TRUE
TRUE · verified equivalent on every row · simplified, not minimal

Karnaugh map

a
0
1
1m0
1m1
  1. cells m0, m1TRUE
TRUE · Derived TRUE. Exactly equivalent on every assignment.

Minterms and maxterms

always_true(a) = Σm(0, 1)always_true(a) = ΠM()

Variable order: a · 2 rows

Normal forms

Conjunctive normal form (CNF)

TRUE

0 clauses, 0 literals · complete · Complement ×1

Disjunctive normal form (DNF)

TRUE

1 terms, 0 literals · complete · Complement ×1

NAND-only and NOR-only

NAND-only form

NAND(NAND(a, a), a)

2 gates · verified equivalent on every row

NOR-only form

NOR(NOR(a, NOR(a, a)), NOR(a, NOR(a, a)))

5 gates · verified equivalent on every row

The same rule as code

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}

The common slip

Assuming a two-valued proof carries over to SQL unchanged. The engine's verdict is correct for Boolean a; the surprise comes from rows where a is NULL, which the model has no row for. Add a variable such as a_is_null and write the rule over both, and the table will show the dropped rows.

Questions

Does the engine model NULL?

No. The engine is two-valued: every variable is TRUE or FALSE, and every check is exact over those values. To reason about a nullable column, model nullness as its own Boolean input, for example a_is_null, and write the SQL condition in terms of both inputs. The table then has four rows and the NULL case is visible.

How do I write the filter so NULL behaves as intended?

Decide what NULL should mean first. If NULL should count as false, SQL's COALESCE(a, FALSE) or an explicit a IS NULL branch makes the choice visible in the query; the model's corresponding rule then has an extra input, and the truth table documents the decision for the next reader.

Related

Guides

Laws

Operators and gates

Tools

See also

Worked examples

Generated from the Tangibool engine, semantics tangibool-semantics/1, engine 1.0.0, build . Every table, map, simplification and verdict on these pages was computed by the Tangibool engine when the site was built, and every claim was re-checked by the same exact checker the app uses. Limits are stated where they apply. © 2026 Foreranger LTD.