Datalog is a declarative logic programming language that syntactically is a subset of Prolog. It is often used as a query language for deductive databases. In recent years, Datalog has found new application in data integration, information extraction, networking, program analysis, security, and cloud computing.
Its origins date back to the beginning of logic programming, but it became prominent as a separate area around 1977 when Hervé Gallaire and Jack Minker organized a workshop on logic and databases. David Maier is credited with coining the term Datalog.
Video Datalog
Features, limitations and extensions
Unlike in Prolog, statements of a Datalog program can be stated in any order. Furthermore, Datalog queries on finite sets are guaranteed to terminate, so Datalog does not have Prolog's cut operator. This makes Datalog a truly declarative language.
In contrast to Prolog, Datalog
- disallows complex terms as arguments of predicates, e.g., p (1, 2) is admissible but not p (f (1), 2),
- imposes certain stratification restrictions on the use of negation and recursion,
- requires that every variable that appears in the head of a clause also appears in a nonarithmetic positive (i.e. not negated) literal in the body of the clause,
- requires that every variable appearing in a negative literal in the body of a clause also appears in some positive literal in the body of the clause
Query evaluation with Datalog is based on first-order logic, and is thus sound and complete. However, Datalog is not Turing complete, and is thus used as a domain-specific language that can take advantage of efficient algorithms developed for query resolution. Indeed, various methods have been proposed to efficiently perform queries, e.g., the Magic Sets algorithm, tabled logic programming or SLG resolution.
Some widely used database systems include ideas and algorithms developed for Datalog. For example, the SQL:1999 standard includes recursive queries, and the Magic Sets algorithm (initially developed for the faster evaluation of Datalog queries) is implemented in IBM's DB2. Moreover, Datalog engines are behind specialised database systems such as Intellidimension's database for the semantic web.
Several extensions have been made to Datalog, e.g., to support aggregate functions, to allow object-oriented programming, or to allow disjunctions as heads of clauses. These extensions have significant impacts on the definition of Datalog's semantics and on the implementation of a corresponding Datalog interpreter.
Maps Datalog
Example
These two lines define two facts, i.e. things that always hold:
This is what they mean: bill is a parent of mary and mary is a parent of john. The names are written in lowercase because uppercase letters stand for variables.
These two lines define rules, which define how new facts can be inferred from known facts.
In plain language they mean the following:
- X is an ancestor of Y if X is a parent of Y.
- X is an ancestor of Y if X is a parent of some Z, and Z is an ancestor of Y.
This line is a query:
It asks the following: Who are all the X that bill is an ancestor of? It would return mary and john when posed against a Datalog system containing the facts and rules described above.
More about rules: a rule has a :- symbol in the middle: the part to the left of this symbol is the head of the rule, the part to the right is the body. A rule reads like this: <head> is known to be true if it is known that <body> is true. Uppercase letters in rules stand for variables: in the example, we don't know who X or Y are, but some X is the ancestor of some Y if that X is the parent of that Y. The ordering of the clauses is irrelevant in Datalog, in contrast to Prolog which depends on the ordering of clauses for computing the result of the query call.
Datalog distinguishes between Extensional predicate symbols (defined by facts) and intensional predicate symbols (defined by rules). In the example above ancestor is an intensional predicate symbol, and parent is extensional. Predicates may also be defined by facts and rules and therefore neither be purely extensional nor intensional, but any Datalog program can be rewritten into an equivalent program without such predicate symbols with duplicate roles.
Systems implementing Datalog
Here is a short list of systems that are either based on Datalog or provide a Datalog interpreter:
Free software/Open source
Non-free software
- Datomic is a distributed database designed to enable scalable, flexible and intelligent applications, running on new cloud architectures. It uses Datalog as the query language.
- DLV is a commercial Datalog extension that supports disjunctive head clauses.
- FoundationDB provides a free-of-charge database binding for pyDatalog, with a tutorial on its use.
- Leapsight Semantic Dataspace (LSD) is a distributed deductive database that offers high availability, fault tolerance, operational simplicity, and scalability. LSD uses Leaplog (a Datalog implementation) for querying and reasoning and was create by Leapsight.
- LogicBlox, a commercial implementation of Datalog used for web-based retail planning and insurance applications.
- Profium Sense is a native RDF compliant graph database written in Java. It provides Datalog evaluation support of user defined rules.
- .QL, a commercial object-oriented variant of Datalog created by Semmle.
- SecPAL a security policy language developed by Microsoft Research.
- Stardog is a graph database, implemented in Java. It provides support for RDF and all OWL 2 profiles providing extensive reasoning capabilities, including datalog evaluation.
- StrixDB: a commercial RDF graph store, SPARQL compliant with Lua API and Datalog inference capabilities. Could be used as httpd (Apache HTTP Server) module or standalone (although beta versions are under the Perl Artistic License 2.0).
See also
- Answer set programming
- SWRL
- D (data language specification)
- D4 (programming language)
- Conjunctive query
References
Bibliography
Further reading
- Grädel, Erich; Kolaitis, Phokion G.; Libkin, Leonid; Maarten, Marx; Spencer, Joel; Vardi, Moshe Y.; Venema, Yde; Weinstein, Scott (2007), Finite model theory and its applications, Texts in Theoretical Computer Science. An EATCS Series, Berlin: Springer-Verlag, ISBN 978-3-540-00428-8, Zbl 1133.03001
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