Learn DATALOG with Real Code Examples

Datalog expresses computation through facts, rules, and queries.

It is a subset of Prolog but disallows complex terms and functions, resulting in predictable and optimizable execution.

Commonly used in databases, compilers, program analysis, security policy engines, and knowledge reasoning.

Facts and predicates

Horn-clause rules

Safety and stratification constraints

Bottom-up fixed-point evaluation

Recursive rule resolution

Facts (base data)

Rules (logical inference)

Predicates (relations)

Stratification and safety

Fixed-point computation

facts/ - base relation data

rules/ - Datalog rule files

output/ - derived relations

lib/ - shared predicates

scripts/ - automation and runners

Define facts in data files

Write rule definitions

Run engine to compute derived relations

Query derived predicates

Iterate until logic matches expectations

Beginner: basic fact and rule queries

Intermediate: recursion and graph reachability

Advanced: stratified negation and modules

Expert: large-scale analysis using Soufflé

Enterprise: reasoning engines and policy computation

More predictable than Prolog due to no backtracking

More expressive for recursion than SQL

Less general-purpose than logic languages like Mercury

More scalable for static analysis than Prolog

Better for graphs than plain relational queries

1970s – Early logic programming roots

1980s – Formalization in database research

2000s – Adoption in static analysis

2010s – Soufflé and modern Datalog optimizations

2020s – Renewed usage in policy engines and graph systems

Predicate: relation used in facts and rules

Horn Clause: logical implication used in rules

Fixed-Point: repeated evaluation until stable

Stratification: ordering to allow negation

Arity: number of arguments to a predicate