pg_ripple_http-0.115.0 is not a library.

pg_ripple_http

Standalone HTTP service that exposes a W3C SPARQL 1.1 Protocol endpoint for pg_ripple. Any standard SPARQL client — YASGUI, SPARQLWrapper, Jena, or plain curl — can query pg_ripple without a PostgreSQL driver.

Build

cargo build --release -p pg_ripple_http

The binary is placed at target/release/pg_ripple_http.

Requirements: Rust 1.88+, and a running PostgreSQL 18 instance with the pg_ripple extension installed.

Run

./target/release/pg_ripple_http

On startup, the service connects to PostgreSQL, verifies that pg_ripple is available, and logs the connection details:

INFO pg_ripple_http: connected to postgresql://localhost/postgres (port 7878), triple store contains 12345 triples
INFO pg_ripple_http: pg_ripple_http listening on http://0.0.0.0:7878

Configuration

All configuration is via environment variables:

Variable	Default	Description
`PG_RIPPLE_HTTP_PG_URL`	`postgresql://localhost/postgres`	PostgreSQL connection URL
`PG_RIPPLE_HTTP_PORT`	`7878`	HTTP listening port
`PG_RIPPLE_HTTP_POOL_SIZE`	`16`	Database connection pool size
`PG_RIPPLE_HTTP_AUTH_TOKEN`	(unset)	If set, requests must include `Authorization: Bearer <token>`
`PG_RIPPLE_HTTP_METRICS_TOKEN`	(unset)	If set, `GET /metrics` requires `Authorization: Bearer <token>` (M16-22)
`PG_RIPPLE_HTTP_RATE_LIMIT`	`0`	Max requests/sec per client IP (0 = disabled)
`PG_RIPPLE_HTTP_CORS_ORIGINS`	`*`	Comma-separated allowed origins, or `*` for all

Example:

export PG_RIPPLE_HTTP_PG_URL="postgresql://user:password@db-host:5432/mydb"
export PG_RIPPLE_HTTP_PORT=8080
export PG_RIPPLE_HTTP_AUTH_TOKEN="my-secret-token"
./target/release/pg_ripple_http

Endpoints

`GET /health` (liveness)

Returns 200 OK when the process is alive and the database is reachable. Use this for Kubernetes liveness probes — a non-200 response means the container should be restarted.

curl http://localhost:7878/health

`GET /ready` (readiness)

Returns 200 OK once the server has completed start-up and successfully obtained a connection from the pool, meaning it is ready to serve traffic. Use this for Kubernetes readiness probes — a non-200 response removes the pod from the service endpoints until recovery.

curl http://localhost:7878/ready

M16-09 (v0.115.0): Kubernetes livenessProbe should point at /health and readinessProbe at /ready. Using exec: pg_isready for both probes is deprecated from v0.115.0 onward.

`GET /metrics`

Prometheus-compatible metrics.

curl http://localhost:7878/metrics
# With optional bearer-token protection (M16-22, v0.115.0):
curl -H "Authorization: Bearer $PG_RIPPLE_HTTP_METRICS_TOKEN" http://localhost:7878/metrics

Security (M16-22, v0.115.0): Set PG_RIPPLE_HTTP_METRICS_TOKEN to require a bearer token on the /metrics endpoint. Requests without a valid token receive 401 Unauthorized with a WWW-Authenticate: Bearer challenge. Even without this token you should still restrict the metrics port to your Prometheus scraper IP via a reverse-proxy ACL.

See Security → Metrics Port Isolation for details.

`GET /sparql?query=…`

Run a SPARQL query via URL parameter.

curl -G http://localhost:7878/sparql \
  --data-urlencode "query=SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10"

`POST /sparql`

Run a SPARQL query or update via request body.

Content-Type	Body
`application/sparql-query`	Raw SPARQL SELECT/ASK/CONSTRUCT/DESCRIBE
`application/sparql-update`	Raw SPARQL INSERT/DELETE
`application/x-www-form-urlencoded`	`query=…` or `update=…`

# SELECT
curl -X POST http://localhost:7878/sparql \
  -H "Content-Type: application/sparql-query" \
  -d "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10"

# Update
curl -X POST http://localhost:7878/sparql \
  -H "Content-Type: application/sparql-update" \
  -d 'INSERT DATA { <http://example.org/alice> <http://example.org/name> "Alice" }'

Content negotiation

Set the Accept header to control the response format:

Accept	Format
`application/sparql-results+json` (default for SELECT/ASK)	SPARQL Results JSON
`application/sparql-results+xml`	SPARQL Results XML
`text/csv`	CSV
`text/tab-separated-values`	TSV
`text/turtle` (default for CONSTRUCT/DESCRIBE)	Turtle
`application/n-triples`	N-Triples
`application/ld+json`	JSON-LD

curl -G http://localhost:7878/sparql \
  -H "Accept: text/csv" \
  --data-urlencode "query=SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 5"

Authentication

If PG_RIPPLE_HTTP_AUTH_TOKEN is set, every request must include the token:

curl -G http://localhost:7878/sparql \
  -H "Authorization: Bearer my-secret-token" \
  --data-urlencode "query=SELECT * WHERE { ?s ?p ?o } LIMIT 5"

Both Authorization: Bearer <token> and Authorization: Basic <token> are accepted.

Docker Compose

The root docker-compose.yml runs both PostgreSQL and pg_ripple_http together:

docker compose up

Services:

Service	Port	Description
`postgres`	5432	PostgreSQL 18 + pg_ripple
`sparql`	7878	SPARQL HTTP endpoint

Datalog API

Since v0.39.0, pg_ripple_http also exposes a /datalog REST namespace that lets any HTTP client manage Datalog rule sets, trigger inference, run goal-directed queries, check integrity constraints, and inspect monitoring statistics — without a PostgreSQL driver.

All Datalog endpoints accept and return application/json (unless specified otherwise). Rule text uses text/x-datalog or text/plain.

Authentication

The same PG_RIPPLE_HTTP_AUTH_TOKEN bearer token covers all /datalog/* endpoints. Optionally, set PG_RIPPLE_HTTP_DATALOG_WRITE_TOKEN to restrict mutating endpoints (POST, PUT, DELETE) to a separate token while keeping read endpoints (inference triggers, monitoring, GET) covered by the main token.

Phase 1 — Rule management

`POST /datalog/rules/{rule_set}`

Load rules from Datalog text. Body: text/x-datalog.

curl -X POST http://localhost:7878/datalog/rules/my-ontology \
  -H "Content-Type: text/x-datalog" \
  -d 'ancestor(?x, ?y) :- parent(?x, ?y).
ancestor(?x, ?z) :- parent(?x, ?y), ancestor(?y, ?z).'
# → {"rule_set": "my-ontology", "rules_loaded": 2}

`POST /datalog/rules/{rule_set}/builtin`

Load a built-in rule set (rdfs, owl-rl).

curl -X POST http://localhost:7878/datalog/rules/rdfs/builtin
# → {"rule_set": "rdfs", "rules_loaded": 13}

`GET /datalog/rules`

List all rule sets and their rules.

curl http://localhost:7878/datalog/rules
# → [{"id": 1, "rule_set": "my-ontology", "rule_text": "…", "active": true}, …]

`DELETE /datalog/rules/{rule_set}`

Delete all rules in a rule set.

curl -X DELETE http://localhost:7878/datalog/rules/my-ontology
# → {"deleted": 2}

`POST /datalog/rules/{rule_set}/add`

Add a single rule to an existing rule set. Body: text/x-datalog.

curl -X POST http://localhost:7878/datalog/rules/my-ontology/add \
  -H "Content-Type: text/x-datalog" \
  -d 'sibling(?x, ?y) :- parent(?p, ?x), parent(?p, ?y).'
# → {"rule_set": "my-ontology", "rule_id": 3}

`DELETE /datalog/rules/{rule_set}/{rule_id}`

Remove a single rule by ID (triggers DRed retraction).

curl -X DELETE http://localhost:7878/datalog/rules/my-ontology/3
# → {"removed": 1}

`PUT /datalog/rules/{rule_set}/enable`

Activate a rule set.

curl -X PUT http://localhost:7878/datalog/rules/my-ontology/enable
# → {"rule_set": "my-ontology", "enabled": true}

`PUT /datalog/rules/{rule_set}/disable`

Deactivate a rule set.

curl -X PUT http://localhost:7878/datalog/rules/my-ontology/disable
# → {"rule_set": "my-ontology", "enabled": false}

Phase 2 — Inference

`POST /datalog/infer/{rule_set}`

Materialize derived triples (semi-naive evaluation).

curl -X POST http://localhost:7878/datalog/infer/my-ontology
# → {"derived": 42}

`POST /datalog/infer/{rule_set}/stats`

Infer with detailed per-stratum statistics.

curl -X POST http://localhost:7878/datalog/infer/my-ontology/stats
# → {"derived": 42, "iterations": 3, "eliminated_rules": 0, "parallel_groups": 2, …}

`POST /datalog/infer/{rule_set}/agg`

Aggregate-aware inference (Datalog^agg).

curl -X POST http://localhost:7878/datalog/infer/my-ontology/agg
# → {"derived": 12}

`POST /datalog/infer/{rule_set}/wfs`

Well-Founded Semantics inference (three-valued).

curl -X POST http://localhost:7878/datalog/infer/my-ontology/wfs
# → {"derived": 8}

`POST /datalog/infer/{rule_set}/demand`

Demand-transformed (goal-directed) inference. Body: JSON.

curl -X POST http://localhost:7878/datalog/infer/my-ontology/demand \
  -H "Content-Type: application/json" \
  -d '{"demands": [{"predicate": "ancestor", "bound": [0]}]}'
# → {"derived": 12, "iterations": 2, "demand_predicates": ["ancestor_bf"]}

`POST /datalog/infer/{rule_set}/lattice`

Lattice-based inference (Datalog^L). Body: JSON.

curl -X POST http://localhost:7878/datalog/infer/my-ontology/lattice \
  -H "Content-Type: application/json" \
  -d '{"lattice": "min"}'
# → {"derived": 5}

Phase 3 — Query & constraints

`POST /datalog/query/{rule_set}`

Goal-directed query via magic sets. Body: Datalog goal text.

curl -X POST http://localhost:7878/datalog/query/my-ontology \
  -H "Content-Type: text/x-datalog" \
  -d 'ancestor(ex:alice, ?y).'
# → {"derived": 5, "iterations": 2, "matching": [{"y": "http://example.org/bob"}, …]}

`GET /datalog/constraints`

Check all constraint rules; return violations.

curl http://localhost:7878/datalog/constraints
# → [{"rule": "no_self_parent", "violated": false}, …]

`GET /datalog/constraints/{rule_set}`

Check constraints for a specific rule set.

curl http://localhost:7878/datalog/constraints/my-ontology

Phase 4 — Admin & monitoring

`GET /datalog/stats/cache`

Rule plan cache statistics.

curl http://localhost:7878/datalog/stats/cache
# → {"size": 12, "hits": 340, "misses": 8, …}

`GET /datalog/stats/tabling`

Tabling/memoization cache statistics.

curl http://localhost:7878/datalog/stats/tabling
# → {"entries": 100, "hit_rate": 0.82, …}

`GET /datalog/lattices`

List registered lattice types.

curl http://localhost:7878/datalog/lattices
# → [{"name": "min", "join_fn": "LEAST", "bottom": "Infinity"}, …]

`POST /datalog/lattices`

curl -X POST http://localhost:7878/datalog/lattices \
  -H "Content-Type: application/json" \
  -d '{"name": "my_min", "join_fn": "my_schema.my_min", "bottom": "9999"}'
# → {"created": "my_min"}

`GET /datalog/views`

List all Datalog materialized views.

curl http://localhost:7878/datalog/views
# → [{"name": "ancestor_view", "goal": "ancestor(?x, ?y).", …}, …]

`POST /datalog/views`

Create a Datalog materialized view. Body: JSON.

curl -X POST http://localhost:7878/datalog/views \
  -H "Content-Type: application/json" \
  -d '{"name": "ancestor_view", "goal": "ancestor(?x, ?y).", "rule_set": "my-ontology"}'
# → {"created": "ancestor_view"}

`DELETE /datalog/views/{name}`

Drop a Datalog materialized view.

curl -X DELETE http://localhost:7878/datalog/views/ancestor_view
# → {"dropped": "ancestor_view"}

Error codes

HTTP Status	`error` field	Trigger
`400`	`datalog_parse_error`	Malformed Datalog rule text
`400`	`datalog_goal_error`	Invalid goal pattern
`400`	`invalid_request`	Missing body, wrong content-type, non-numeric `rule_id`
`404`	`rule_set_not_found`	Infer/drop on a nonexistent rule set
`401`	—	Missing or invalid `Authorization` token
`503`	`service_unavailable`	Connection pool exhausted

All error responses include a trace_id field for log correlation.

pg_ripple_http 0.115.0

pg_ripple_http

Build

Run

Configuration

Endpoints

GET /health (liveness)

GET /ready (readiness)

GET /metrics

GET /sparql?query=…

POST /sparql

Content negotiation

Authentication

Docker Compose

Datalog API

Authentication

Phase 1 — Rule management

POST /datalog/rules/{rule_set}

POST /datalog/rules/{rule_set}/builtin

GET /datalog/rules

DELETE /datalog/rules/{rule_set}

POST /datalog/rules/{rule_set}/add

DELETE /datalog/rules/{rule_set}/{rule_id}

PUT /datalog/rules/{rule_set}/enable

PUT /datalog/rules/{rule_set}/disable

Phase 2 — Inference

POST /datalog/infer/{rule_set}

POST /datalog/infer/{rule_set}/stats

POST /datalog/infer/{rule_set}/agg

POST /datalog/infer/{rule_set}/wfs

POST /datalog/infer/{rule_set}/demand

POST /datalog/infer/{rule_set}/lattice

Phase 3 — Query & constraints

POST /datalog/query/{rule_set}

GET /datalog/constraints

GET /datalog/constraints/{rule_set}

Phase 4 — Admin & monitoring

GET /datalog/stats/cache

GET /datalog/stats/tabling

GET /datalog/lattices

POST /datalog/lattices

GET /datalog/views

POST /datalog/views

DELETE /datalog/views/{name}

Error codes

`GET /health` (liveness)

`GET /ready` (readiness)

`GET /metrics`

`GET /sparql?query=…`

`POST /sparql`

`POST /datalog/rules/{rule_set}`

`POST /datalog/rules/{rule_set}/builtin`

`GET /datalog/rules`

`DELETE /datalog/rules/{rule_set}`

`POST /datalog/rules/{rule_set}/add`

`DELETE /datalog/rules/{rule_set}/{rule_id}`

`PUT /datalog/rules/{rule_set}/enable`

`PUT /datalog/rules/{rule_set}/disable`

`POST /datalog/infer/{rule_set}`

`POST /datalog/infer/{rule_set}/stats`

`POST /datalog/infer/{rule_set}/agg`

`POST /datalog/infer/{rule_set}/wfs`

`POST /datalog/infer/{rule_set}/demand`

`POST /datalog/infer/{rule_set}/lattice`

`POST /datalog/query/{rule_set}`

`GET /datalog/constraints`

`GET /datalog/constraints/{rule_set}`

`GET /datalog/stats/cache`

`GET /datalog/stats/tabling`

`GET /datalog/lattices`

`POST /datalog/lattices`

`GET /datalog/views`

`POST /datalog/views`

`DELETE /datalog/views/{name}`