rose-ash/plans/datalog-on-sx.md

Datalog-on-SX: Datalog on the CEK/VM

Datalog is a declarative query language: a restricted subset of Prolog with no function symbols, only relations. Programs are sets of facts and rules; queries ask what follows. Evaluation is bottom-up (fixpoint iteration) rather than Prolog's top-down DFS — which means no infinite loops, guaranteed termination, and efficient incremental updates.

The unique angle: Datalog is a natural companion to the Prolog implementation already in progress (lib/prolog/). The parser and term representation can share infrastructure; the evaluator is an entirely different fixpoint engine rather than a DFS solver.

End-state goal: full core Datalog (facts, rules, stratified negation, aggregation, recursion) with a clean SX query API, and a demonstration of Datalog as a query engine for rose-ash data (e.g. federation graph, content relationships).

Ground rules

• Scope: only touch lib/datalog/** and plans/datalog-on-sx.md. Do not edit spec/, hosts/, shared/, lib/prolog/**, or other lib/<lang>/.
• Shared-file issues go under "Blockers" below with a minimal repro; do not fix here.
• SX files: use sx-tree MCP tools only.
• Architecture: Datalog source → term AST → fixpoint evaluator. No transpiler to SX AST — the evaluator is written in SX and works directly on term structures.
• Reference: Ramakrishnan & Ullman "A Survey of Deductive Database Systems"; Dalmau "Datalog and Constraint Satisfaction".
• Commits: one feature per commit. Keep ## Progress log updated and tick boxes.

Architecture sketch

Datalog source text
    │
    ▼
lib/datalog/tokenizer.sx   — atoms, variables, numbers, strings, punct (?- :- , . ( ) [ ])
    │
    ▼
lib/datalog/parser.sx      — facts: atom(args). rules: head :- body. queries: ?- goal.
    │                        No function symbols (only constants and variables in args).
    ▼
lib/datalog/db.sx          — extensional DB (EDB): ground facts; IDB: derived relations;
    │                        clause index by relation name/arity
    ▼
lib/datalog/eval.sx        — bottom-up fixpoint: semi-naive evaluation with delta sets;
    │                        stratification for negation; incremental update API
    ▼
lib/datalog/query.sx       — query API: (datalog-query db goal) → list of substitutions;
                             SX embedding: define facts/rules as SX data directly

Key differences from Prolog:

• No function symbols — args are atoms, numbers, strings, or variables only. No f(a,b).
• No cuts — no procedural control.
• Bottom-up — derive all consequences of all rules before answering; no search tree.
• Termination guaranteed — no infinite derivation chains (no function symbols → finite Herbrand base).
• Stratified negation — not(P) legal iff P does not recursively depend on its own negation.
• Aggregation — count, sum, min, max over derived tuples (Datalog+).

Roadmap

Phase 1 — tokenizer + parser

• Tokenizer: atoms (lowercase/quoted), variables (uppercase/_), numbers, strings, operators (:- , ?-, ,, .), comments (%, /* */) Note: no function symbol syntax (no nested f(...) in arg position).
• Parser: - Facts: parent(tom, bob). → {:head (parent tom bob) :body ()} - Rules: ancestor(X,Z) :- parent(X,Y), ancestor(Y,Z). → {:head (ancestor X Z) :body ((parent X Y) (ancestor Y Z))} - Queries: ?- ancestor(tom, X). → {:query (ancestor tom X)} - Negation: not(parent(X,Y)) in body position → {:neg (parent X Y)}
• Tests in lib/datalog/tests/parse.sx

Phase 2 — unification + substitution

• Share or port unification from lib/prolog/ — term walk, occurs check off by default
• dl-unify t1 t2 subst → extended subst or nil (no function symbols means simpler)
• dl-ground? term → bool — all variables bound in substitution
• Tests: atom/atom, var/atom, var/var, list args

Phase 3 — extensional DB + naive evaluation

• EDB: {:relation-name → set-of-ground-tuples} using SX sets (Phase 18 of primitives)
• dl-add-fact! db relation args → add ground tuple
• dl-add-rule! db head body → add rule clause
• Naive evaluation: iterate rules until fixpoint For each rule, for each combination of body tuples that unify, derive head tuple. Repeat until no new tuples added.
• dl-query db goal → list of substitutions satisfying goal against derived DB
• Tests: transitive closure (ancestor), sibling, same-generation — classic Datalog programs

Phase 4 — semi-naive evaluation (performance)

• Delta sets: track newly derived tuples per iteration
• Semi-naive rule: only join against delta tuples from last iteration, not full relation
• Significant speedup for recursive rules — avoids re-deriving known tuples
• dl-stratify db → dependency graph + SCC analysis → stratum ordering
• Tests: verify semi-naive produces same results as naive; benchmark on large ancestor chain

Phase 5 — stratified negation

• Dependency graph analysis: which relations depend on which (positively or negatively)
• Stratification check: error if negation is in a cycle (non-stratifiable program)
• Evaluation: process strata in order — lower stratum fully computed before using its complement in a higher stratum
• not(P) in rule body: at evaluation time, check P is NOT in the derived EDB
• Tests: non-member (not(member(X,L))), colored-graph (not(same-color(X,Y))), stratification error detection

Phase 6 — aggregation (Datalog+)

• count(X, Goal) → number of distinct X satisfying Goal
• sum(X, Goal) → sum of X values satisfying Goal
• min(X, Goal) / max(X, Goal) → min/max of X satisfying Goal
• group-by semantics: count(X, sibling(bob, X)) → count of bob's siblings
• Aggregation breaks stratification — evaluate in a separate post-fixpoint pass
• Tests: social network statistics, grade aggregation, inventory sums

Phase 7 — SX embedding API

• (dl-program facts rules) → database from SX data directly (no parsing required) (dl-program '((parent tom bob) (parent tom liz) (parent bob ann)) '((ancestor X Z :- (parent X Y) (ancestor Y Z)) (ancestor X Y :- (parent X Y))))
• (dl-query db '(ancestor tom ?X)) → ((ann) (bob) (liz) (pat))
• (dl-assert! db '(parent ann pat)) → incremental fact addition + re-derive
• (dl-retract! db '(parent tom bob)) → fact removal + re-derive from scratch
• Integration demo: federation graph query — (ancestor actor1 actor2) over rose-ash ActivityPub follow relationships

Phase 8 — Datalog as a query language for rose-ash

• Schema: map SQLAlchemy model relationships to Datalog EDB facts (e.g. (follows user1 user2), (authored user post), (tagged post tag))
• Loader: dl-load-from-db! — query PostgreSQL, populate Datalog EDB
• Query examples: - ?- ancestor(me, X), authored(X, Post), tagged(Post, cooking). → posts about cooking by people I follow (transitively) - ?- popular(Post) :- tagged(Post, T), count(L, (liked(L, Post))) >= 10. → posts with 10+ likes
• Expose as a rose-ash service endpoint: POST /internal/datalog with program + query

Blockers

(none yet)

Progress log

Newest first.

(awaiting phase 1)