diff --git a/README.md b/README.md index 804f2d3..a74e013 100644 --- a/README.md +++ b/README.md @@ -556,6 +556,57 @@ flowchart TB T --- E ``` +### Iceberg sources: virtualize the lakehouse + +The system-of-record isn't always an operational database. A lot of +machine-generated relationship data lands in a **lakehouse** — Apache Iceberg +tables in object storage, versioned by snapshot and governed by a catalog. A +virtual edge can resolve straight out of Iceberg, with **DuckDB** as the scan +engine, by setting `source_type="iceberg"`: + +```python +kg_virtual_edge_add( + edge_type="CO_HELD_WITH", + query="SELECT b AS to_id, shared FROM co_held WHERE a = ?", # '?' — DuckDB binds it + source_type="iceberg", + catalog={ # pyiceberg catalog props; any value as + "name": "lake", # "env:VAR" is resolved from the env, so + "type": "rest", # tokens/keys never sit in the graph + "uri": "env:ICEBERG_REST_URI", + "warehouse": "s3://lake/wh", + }, + table="analytics.co_held", # namespace.table — query uses the leaf name + # snapshot_id=..., # optional: pin a version for time-travel + source="id_slug", target_id_template="company:{value}", + prop_cols=["shared"], directions="both", ontology="market", +) +``` + +The split mirrors Iceberg's own architecture: **pyiceberg owns identity and +versioning** — it loads the catalog, maps `namespace.table` to the current +metadata pointer (or a pinned `snapshot_id`), the layer a schema graph actually +cares about; **DuckDB owns the scan** — it reads the resolved table (format +version 2 today; newer versions as the extension gains them) and answers the +binding's parameterized query. The table is exposed as a named DuckDB view, so +the query is written against an ordinary table name — *identical* to the +SQLite/Postgres case, and the resolver's bind path is unchanged. Install with the +`iceberg` extra (`pip install 'knowledge-graph-rdbms[iceberg]'`). + +Any catalog pyiceberg speaks works — swap the `catalog` dict: + +| Catalog | `catalog` config | +| --- | --- | +| **AWS Glue** | `{"type": "glue"}` (region/creds from the AWS chain) | +| **S3 Tables** (managed Iceberg) | `{"type": "rest", "uri": "https://s3tables..amazonaws.com/iceberg", "warehouse": "", "rest.sigv4-enabled": "true", "rest.signing-name": "s3tables", "rest.signing-region": ""}` | +| **Local / SQL catalog** | `{"type": "sql", "uri": "sqlite:///…", "warehouse": "file://…"}` | + +When the resolved metadata lives on `s3://` (S3 Tables, Glue, or a plain S3 +lake), the opener loads DuckDB's `httpfs`/`aws` extensions and a credential-chain +secret automatically — DuckDB reads the managed storage with the host's AWS +identity, sigv4 included. This is proven live against AWS S3 Tables in +`tests/test_iceberg.py::test_s3tables_live_resolve` (opt-in via +`KG_ICEBERG_S3TABLES_ARN`). + Tools: `kg_virtual_edge_add`, `kg_virtual_edges_list`, `kg_virtual_edge_remove`. --- diff --git a/kgrdbms/__init__.py b/kgrdbms/__init__.py index 10504f6..57014c8 100644 --- a/kgrdbms/__init__.py +++ b/kgrdbms/__init__.py @@ -12,7 +12,7 @@ from __future__ import annotations -__version__ = "0.1.5" +__version__ = "0.1.8" from kgrdbms.graph import Edge, Graph, Node, default_graph_path, slug from kgrdbms.federation import Federation, FederatedNode, Located diff --git a/kgrdbms/iceberg.py b/kgrdbms/iceberg.py new file mode 100644 index 0000000..a5f3d3e --- /dev/null +++ b/kgrdbms/iceberg.py @@ -0,0 +1,183 @@ +"""Apache Iceberg as a virtual-edge source. + +A virtual edge normally resolves against an operational SQL store — Postgres or +SQLite, the live system of record. But a great deal of machine-generated +relationship data does not live there; it lands in a lakehouse: Iceberg tables in +object storage, versioned by snapshot and governed by a catalog. This module lets +a binding name a *catalog + table* and resolve edges straight out of Iceberg, with +DuckDB as the query engine — so the graph reads the lake live, copies nothing, and +honors the same parameterized-query contract as every other virtual edge. + +The division of labor mirrors Iceberg's own architecture: + + * **pyiceberg owns identity and versioning.** It loads the catalog, maps a + ``namespace.table`` name to the current metadata pointer, and — when a + ``snapshot_id`` is bound — pins a specific version for time-travel reads. This + is precisely the layer a schema graph cares about: the stable name and the + snapshot, not the bytes. + + * **DuckDB owns the scan.** Given the resolved metadata location, DuckDB's + ``iceberg`` extension reads the table (format-version 2 today; newer format + versions transparently as the extension gains them) and answers the binding's + SQL. The table is exposed as a named DuckDB VIEW, so the binding's query is + written against an ordinary table name — byte-for-byte identical to the + Postgres/SQLite case, which is why the resolver's bind path is untouched. + +Credentials never live in the graph. Any catalog property may be written as +``env:VAR_NAME`` and is resolved from the environment at query time — the same +discipline as ``dsn_env`` on a SQL virtual edge. +""" + +from __future__ import annotations + +from typing import Any + +# DuckDB's positional placeholder — identical to sqlite's, so resolve()'s +# bind-by-marker logic needs no special case once we hand it this connection. +MARKER = "?" + +_ENV_PREFIX = "env:" + + +def _resolve_env(value: Any) -> Any: + """A catalog property of the form ``env:VAR`` becomes the variable's value. + + Keeps secrets (catalog tokens, S3 keys, signer URIs) out of the stored + binding: the graph holds the *name* of the env var, never the secret. Any + non-string or non-prefixed value passes through unchanged. + """ + if isinstance(value, str) and value.startswith(_ENV_PREFIX): + import os + + name = value[len(_ENV_PREFIX) :] + resolved = os.environ.get(name) + if not resolved: + raise RuntimeError( + f"iceberg catalog property references env var {name!r}, which is " + f"unset — cannot reach the catalog." + ) + return resolved + return value + + +def resolve_catalog_props(catalog: dict[str, Any]) -> dict[str, Any]: + """Catalog config with every ``env:VAR`` value substituted from the env. + + The catalog dict is pyiceberg's own property bag (``uri``, ``warehouse``, + ``type``, ``token``, S3 creds, …) minus the reserved ``name`` key, which + names the catalog rather than configuring it. + """ + return { + k: _resolve_env(v) for k, v in catalog.items() if k != "name" + } + + +def _load_table(catalog: dict[str, Any], table: str): + """Load a pyiceberg table from a catalog config + ``namespace.table`` name.""" + try: + from pyiceberg.catalog import load_catalog # type: ignore + except ImportError as e: # pragma: no cover - env dependent + raise RuntimeError( + "iceberg virtual edge needs the iceberg extra: pip install " + "'knowledge-graph-rdbms[iceberg]'" + ) from e + name = catalog.get("name", "default") + return load_catalog(name, **resolve_catalog_props(catalog)).load_table(table) + + +def _s3_region(catalog: dict[str, Any]) -> str: + """Region for DuckDB's S3 access — catalog hint first, then the AWS env.""" + import os + + for key in ("rest.signing-region", "client.region", "region"): + if catalog.get(key): + return str(_resolve_env(catalog[key])) + return os.environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION") or "us-east-1" + + +def _prepare_s3(conn: Any, catalog: dict[str, Any]) -> None: + """Give DuckDB what it needs to read s3:// metadata + data files. + + Loads the httpfs/aws extensions and registers a credential-chain secret so + DuckDB authenticates with the same AWS identity as the host (env, profile, + instance role). This is what lets ``iceberg_scan`` reach S3 Tables' managed + storage and ordinary S3 warehouses alike — sigv4 signing included. + """ + conn.execute("INSTALL httpfs") + conn.execute("LOAD httpfs") + conn.execute("INSTALL aws") + conn.execute("LOAD aws") + conn.execute("INSTALL iceberg") + conn.execute("LOAD iceberg") + region = _s3_region(catalog) + conn.execute( + f"CREATE SECRET (TYPE s3, PROVIDER credential_chain, REGION '{region}')" + ) + + +def _view_name(table: str) -> str: + """The DuckDB view name a binding's query references — the table's leaf name. + + ``analytics.co_held`` → ``co_held``. The binding author writes plain + ``FROM co_held``, exactly as they would against a Postgres relation. + """ + return table.rsplit(".", 1)[-1] + + +def open_source( + catalog: dict[str, Any], + tables: str | list[str], + snapshot_id: int | None = None, +) -> tuple[Any, str]: + """Open one or more Iceberg tables as a single queryable DuckDB connection. + + Resolves each table in ``tables`` against ``catalog`` via pyiceberg and + registers it as a DuckDB VIEW named after its leaf segment, so the binding's + ``query`` may JOIN across them by plain table name. Returns + ``(connection, marker)`` shaped exactly like :func:`kgrdbms.virtual._connect`, + so :func:`kgrdbms.virtual.resolve` runs the parameterized query unchanged. + + ``snapshot_id`` pins a time-travel version; a snapshot id is table-specific, + so it is only valid when mounting a single table. + """ + try: + import duckdb # type: ignore + except ImportError as e: # pragma: no cover - env dependent + raise RuntimeError( + "iceberg virtual edge needs the iceberg extra: pip install " + "'knowledge-graph-rdbms[iceberg]'" + ) from e + + names = [tables] if isinstance(tables, str) else list(tables) + if not names: + raise RuntimeError("iceberg open_source: no tables to mount.") + if snapshot_id is not None and len(names) > 1: + raise RuntimeError( + "iceberg virtual edge: `snapshot_id` pins a single table's version " + "and cannot be used when mounting multiple tables." + ) + + resolved = [(_view_name(n), _load_table(catalog, n).metadata_location) for n in names] + + conn = duckdb.connect() + # INSTALL is idempotent and cached; kept here (not at import) so importing + # this module never reaches the network. An s3:// warehouse (S3 Tables, Glue, + # or a plain S3 lake) needs httpfs/aws + a credential secret; a local file + # warehouse needs only the iceberg extension. One credential setup serves + # every view on the connection. + if any(str(loc).startswith("s3://") for _, loc in resolved): + _prepare_s3(conn, catalog) + else: + conn.execute("INSTALL iceberg") + conn.execute("LOAD iceberg") + + for view, metadata_location in resolved: + # The metadata location is operator-resolved (from the catalog), not user + # input, so formatting it into the DDL is safe — DuckDB's scan function + # takes a path literal, not a bound parameter, for the table source. + scan = f"iceberg_scan('{metadata_location}'" + if snapshot_id is not None: + scan += f", snapshot_from_id => {int(snapshot_id)}" + scan += ")" + conn.execute(f'CREATE VIEW "{view}" AS SELECT * FROM {scan}') + return conn, MARKER diff --git a/kgrdbms/mcp_server.py b/kgrdbms/mcp_server.py index 5d94e56..69b7111 100644 --- a/kgrdbms/mcp_server.py +++ b/kgrdbms/mcp_server.py @@ -332,16 +332,22 @@ def kg_virtual_edge_add( name_col: str | None = None, prop_cols: list[str] | None = None, directions: str = "out", + source_type: str = "sql", + catalog: dict | None = None, + table: str | None = None, + tables: list[str] | None = None, + snapshot_id: int | None = None, ontology: str | None = None, ) -> dict: - """Bind an edge TYPE to a SQL query that resolves its instances live from an + """Bind an edge TYPE to a query that resolves its instances live from an external source — no rows are copied into the graph (Ontology-Based Data Access). At traversal time kg_edges runs `query`, parameterized by the node you're standing on, and synthesizes the edges; results carry `_virtual: true`. `query` must hold exactly one placeholder bound to the source value — the - driver's native marker ('?' for sqlite, '%s' for postgres). The value is - always *bound*, never string-formatted, so the query is injection-safe. + driver's native marker ('?' for sqlite *and* iceberg/duckdb, '%s' for + postgres). The value is always *bound*, never string-formatted, so the query + is injection-safe. Source of the bound value (`source`): "id" (default), "id_slug" (after the last ':'), or "prop:" (a node property, e.g. "prop:ticker"). Each result @@ -349,18 +355,35 @@ def kg_virtual_edge_add( (e.g. "company:{value}") builds its node id, `name_col` names it, `prop_cols` (or every other column) become edge properties. `directions` is out|in|both. - Credentials: prefer `dsn_env` (an env-var *name*, resolved at query time) so - secrets stay out of the graph; `dsn` is the literal form for non-secret paths. + SOURCE_TYPE selects where `query` runs: + * "sql" (default) — a direct SQL store. Prefer `dsn_env` (an env-var *name*, + resolved at query time) so secrets stay out of the graph; `dsn` is the + literal form for non-secret paths. + * "iceberg" — an Apache Iceberg table in a lakehouse. Give `catalog` (a + pyiceberg catalog property dict, e.g. {"name","type","uri","warehouse"}; + any value written "env:VAR" is resolved from the environment so creds stay + out of the graph) and `table` ("namespace.table"). pyiceberg resolves the + current metadata (or `snapshot_id` for a time-travel read); DuckDB scans it. + Write `query` against the table's leaf name, e.g. + "SELECT b AS to_id FROM co_held WHERE a = ?". To JOIN across tables in one + edge, pass `tables` (a list of "namespace.table"); each is mounted as a + view by its leaf name and the query may join them freely (`snapshot_id` + then doesn't apply — it pins a single table's version). + The binding is stored as a reserved `_VirtualEdge` node in the ontology. """ ve = virtual.VirtualEdge( edge_type=edge_type, query=query, dsn=dsn, dsn_env=dsn_env, source=source, target_col=target_col, target_id_template=target_id_template, target_kind=target_kind, name_col=name_col, prop_cols=list(prop_cols or []), - directions=directions, + directions=directions, source_type=source_type, catalog=catalog, + table=table, tables=list(tables or []), snapshot_id=snapshot_id, ) virtual.register(_bundle(ontology).backend, ve) - return {"edge_type": edge_type, "directions": directions, "bound": True} + return { + "edge_type": edge_type, "directions": directions, + "source_type": source_type, "bound": True, + } @mcp.tool() @@ -372,7 +395,9 @@ def kg_virtual_edges_list(ontology: str | None = None) -> list[dict]: { "edge_type": ve.edge_type, "directions": ve.directions, "source": ve.source, "target_kind": ve.target_kind, "target_id_template": ve.target_id_template, - "dsn_env": ve.dsn_env, "dsn": ve.dsn, "query": ve.query, + "source_type": ve.source_type, "dsn_env": ve.dsn_env, "dsn": ve.dsn, + "catalog": ve.catalog, "table": ve.table, "tables": list(ve.tables), + "snapshot_id": ve.snapshot_id, "query": ve.query, } for ve in virtual.list_bindings(_bundle(ontology).backend) ] diff --git a/kgrdbms/virtual.py b/kgrdbms/virtual.py index 1d7e5c5..611ff4b 100644 --- a/kgrdbms/virtual.py +++ b/kgrdbms/virtual.py @@ -34,6 +34,7 @@ from __future__ import annotations import os +from collections.abc import Mapping from dataclasses import dataclass, field from typing import Any @@ -88,6 +89,27 @@ class VirtualEdge: name_col: str | None = None prop_cols: list[str] = field(default_factory=list) directions: str = "out" + # ---- source kind ---- + # "sql" (default) resolves against dsn/dsn_env via _connect. "iceberg" resolves + # against an Apache Iceberg table named by `catalog` + `table` (see iceberg.py); + # the binding's query then references the table by its leaf name like any SQL + # relation. `snapshot_id` optionally pins a version for time-travel reads. + source_type: str = "sql" + catalog: dict[str, Any] | None = None + table: str | None = None + tables: list[str] = field(default_factory=list) + snapshot_id: int | None = None + + def iceberg_tables(self) -> list[str]: + """The full set of Iceberg tables to mount as DuckDB views for this + binding — the primary `table` plus any `tables`, de-duplicated, order + preserved. A binding's `query` may JOIN freely across all of them + (referenced by leaf name). Multiple tables let one edge stitch together, + say, a holdings fact table with a lookup dimension in a single query.""" + seen: dict[str, None] = {} + for t in ([self.table] if self.table else []) + list(self.tables): + seen.setdefault(t, None) + return list(seen) # ---- (de)serialization to a reserved-kind node ---- @@ -104,6 +126,11 @@ def to_node_properties(self) -> dict[str, Any]: "name_col": self.name_col, "prop_cols": list(self.prop_cols), "directions": self.directions, + "source_type": self.source_type, + "catalog": self.catalog, + "table": self.table, + "tables": list(self.tables), + "snapshot_id": self.snapshot_id, } @classmethod @@ -121,6 +148,11 @@ def from_node(cls, node: Node) -> "VirtualEdge": name_col=p.get("name_col"), prop_cols=list(p.get("prop_cols", [])), directions=p.get("directions", "out"), + source_type=p.get("source_type", "sql"), + catalog=p.get("catalog"), + table=p.get("table"), + tables=list(p.get("tables", [])), + snapshot_id=p.get("snapshot_id"), ) def resolved_dsn(self) -> str: @@ -204,6 +236,27 @@ def _connect(dsn: str): return conn, "?" +def _open_source(ve: VirtualEdge) -> tuple[Any, str]: + """Open the binding's external source, returning ``(connection, marker)``. + + Dispatches on ``source_type``: an Iceberg binding goes through the lakehouse + opener (catalog → DuckDB view); everything else is a direct SQL connection to + ``dsn``/``dsn_env``. Both return a DB-API connection plus its positional + placeholder, so the rest of :func:`resolve` is source-agnostic. + """ + if ve.source_type == "iceberg": + tables = ve.iceberg_tables() + if not ve.catalog or not tables: + raise RuntimeError( + f"virtual edge {ve.edge_type!r}: source_type='iceberg' requires " + f"`catalog` and at least one of `table` / `tables`." + ) + from kgrdbms import iceberg + + return iceberg.open_source(ve.catalog, tables, ve.snapshot_id) + return _connect(ve.resolved_dsn()) + + def _source_value(ve: VirtualEdge, node_id: str, node: Node | None) -> Any: if ve.source == "id": return node_id @@ -237,11 +290,19 @@ def resolve( value = _source_value(ve, node_id, node) if value is None: return [] - conn, marker = _connect(ve.resolved_dsn()) + conn, marker = _open_source(ve) try: n_params = ve.query.count(marker) cur = conn.execute(ve.query, tuple([value] * max(1, n_params))) - rows = [dict(r) for r in cur.fetchall()] + fetched = cur.fetchall() + # Normalize to dict rows across drivers: sqlite Row and psycopg dict_row + # are already mappings; duckdb yields positional tuples, so zip them with + # the cursor's column names. + cols = [d[0] for d in cur.description] if cur.description else [] + rows = [ + dict(r) if isinstance(r, Mapping) else dict(zip(cols, r)) + for r in fetched + ] finally: conn.close() diff --git a/pyproject.toml b/pyproject.toml index f3cc149..c18f988 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "knowledge-graph-rdbms" -version = "0.1.7" +version = "0.1.8" description = "A label property graph on an RDBMS (SQLite): nodes, typed edges, an append-only event log, and an optional MCP server." readme = "README.md" requires-python = ">=3.10" @@ -28,8 +28,9 @@ Issues = "https://github.com/cunicopia-dev/knowledge-graph-rdbms/issues" mcp = ["mcp>=1.0"] charts = ["matplotlib>=3.7"] postgres = ["psycopg[binary]>=3.1"] +iceberg = ["duckdb>=1.1", "pyiceberg[sql-sqlite,glue,s3fs]>=0.7", "pyarrow>=14", "boto3>=1.34"] rdf = ["rdflib>=7.0"] # only needed for parsing/SPARQL on import; export is dep-free -dev = ["pytest>=7.0", "mcp>=1.0", "psycopg[binary]>=3.1", "rdflib>=7.0"] +dev = ["pytest>=7.0", "mcp>=1.0", "psycopg[binary]>=3.1", "rdflib>=7.0", "duckdb>=1.1", "pyiceberg[sql-sqlite]>=0.7", "pyarrow>=14"] [project.scripts] kg = "kgrdbms.cli:main" diff --git a/tests/test_iceberg.py b/tests/test_iceberg.py new file mode 100644 index 0000000..edb8d6b --- /dev/null +++ b/tests/test_iceberg.py @@ -0,0 +1,289 @@ +"""Iceberg virtual edges: relationships resolved live from an Apache Iceberg +table in a lakehouse, with DuckDB as the scan engine. + +These exercise the real stack — a pyiceberg ``SqlCatalog`` writes an actual +format-version-2 table to a temp warehouse, and the graph resolves edges out of +it through DuckDB on traversal, copying nothing. Skipped cleanly if the optional +``iceberg`` extra (duckdb + pyiceberg + pyarrow) isn't installed. +""" + +import pytest + +pytest.importorskip("duckdb") +pytest.importorskip("pyiceberg") +pytest.importorskip("pyarrow") + +import pyarrow as pa # noqa: E402 +from pyiceberg.catalog.sql import SqlCatalog # noqa: E402 + +from kgrdbms import iceberg, virtual # noqa: E402 +from kgrdbms.graph import Graph # noqa: E402 +from kgrdbms.virtual import VirtualEdge # noqa: E402 + + +def _warehouse(tmp_path, fmt="2"): + """A local Iceberg lakehouse with one co-holdings table, returning the + catalog props dict a binding would carry plus the live table handle.""" + wh = tmp_path / "lake" + wh.mkdir() + props = { + "name": "test", + "uri": f"sqlite:///{wh}/catalog.db", + "warehouse": f"file://{wh}", + } + cat = SqlCatalog(props["name"], **{k: v for k, v in props.items() if k != "name"}) + cat.create_namespace("analytics") + data = pa.table( + { + "a": ["NVDA", "NVDA", "AAPL"], + "b": ["AMD", "TSM", "MSFT"], + "shared": [12, 9, 20], + } + ) + tbl = cat.create_table( + "analytics.co_held", schema=data.schema, properties={"format-version": fmt} + ) + tbl.append(data) + return props, tbl + + +def _binding(props): + return VirtualEdge( + edge_type="CO_HELD_WITH", + query="SELECT b AS to_id, shared FROM co_held WHERE a = ?", + source_type="iceberg", + catalog=props, + table="analytics.co_held", + source="id_slug", # company:NVDA -> "NVDA" + target_id_template="company:{value}", + target_kind="company", + prop_cols=["shared"], + directions="both", + ) + + +def test_resolves_edges_from_iceberg_without_storing(tmp_path): + props, _ = _warehouse(tmp_path) + g = Graph(path=tmp_path / "kg.db") + virtual.register(g, _binding(props)) + + # Nothing copied into the graph — the lake stays the single source of truth. + assert g.total_edges() == 0 + + triples = virtual.augment(g, "company:NVDA", "out", None) + targets = sorted((e.to_node, e.properties["shared"]) for _, e, _ in triples) + assert targets == [("company:AMD", 12), ("company:TSM", 9)] + assert all(far.kind == "company" for _, _, far in triples) + assert all(e.properties["_virtual"] is True for _, e, _ in triples) + g.close() + + +def test_multi_table_join(tmp_path): + """A binding can mount several Iceberg tables and JOIN across them in one edge: + here a holdings table against a name-lookup dimension.""" + props, _ = _warehouse(tmp_path) + # Add a lookup table to the same catalog. + cat = SqlCatalog(props["name"], **{k: v for k, v in props.items() if k != "name"}) + names = pa.table({"b": ["AMD", "TSM", "MSFT"], "long_name": ["Advanced Micro", "Taiwan Semi", "Microsoft"]}) + lk = cat.create_table("analytics.names", schema=names.schema, properties={"format-version": "2"}) + lk.append(names) + + g = Graph(path=tmp_path / "kg.db") + ve = VirtualEdge( + edge_type="CO_HELD_WITH", + query=( + "SELECT c.b AS to_id, n.long_name, c.shared " + "FROM co_held c JOIN names n ON c.b = n.b WHERE c.a = ?" + ), + source_type="iceberg", catalog=props, + table="analytics.co_held", tables=["analytics.names"], + source="id_slug", target_id_template="company:{value}", + target_kind="company", name_col="long_name", prop_cols=["shared"], + ) + virtual.register(g, ve) + out = virtual.augment(g, "company:NVDA", "out", None) + by_target = {e.to_node: far.name for _, e, far in out} + assert by_target == {"company:AMD": "Advanced Micro", "company:TSM": "Taiwan Semi"} + # both tables resolved through iceberg_tables() + assert ve.iceberg_tables() == ["analytics.co_held", "analytics.names"] + g.close() + + +def test_snapshot_pin_rejects_multiple_tables(tmp_path): + props, _ = _warehouse(tmp_path) + with pytest.raises(RuntimeError, match="single table"): + iceberg.open_source(props, ["analytics.co_held", "analytics.other"], snapshot_id=123) + + +def test_format_version_2(tmp_path): + _, tbl = _warehouse(tmp_path, fmt="2") + assert tbl.metadata.format_version == 2 + + +def test_catalog_env_indirection(tmp_path, monkeypatch): + """A catalog value written `env:VAR` is resolved from the environment, so the + warehouse URI (and any creds) never sit in the stored binding.""" + props, _ = _warehouse(tmp_path) + real_uri = props["uri"] + props["uri"] = "env:KG_ICEBERG_URI" + + monkeypatch.delenv("KG_ICEBERG_URI", raising=False) + with pytest.raises(RuntimeError, match="KG_ICEBERG_URI"): + iceberg.resolve_catalog_props(props) + + monkeypatch.setenv("KG_ICEBERG_URI", real_uri) + resolved = iceberg.resolve_catalog_props(props) + assert resolved["uri"] == real_uri + assert "name" not in resolved # name configures nothing; it's the catalog id + + # And the full resolve path works through the env indirection. + g = Graph(path=tmp_path / "kg.db") + virtual.register(g, _binding(props)) + out = virtual.augment(g, "company:NVDA", "out", None) + assert sorted(e.to_node for _, e, _ in out) == ["company:AMD", "company:TSM"] + g.close() + + +def test_snapshot_pinning_time_travels(tmp_path): + """A pinned `snapshot_id` reads the table as of that version. The first append + holds 3 rows; a second append adds a row. Pinning the first snapshot must not + see the later write.""" + props, tbl = _warehouse(tmp_path) + first_snapshot = tbl.metadata.snapshots[-1].snapshot_id + + tbl.append(pa.table({"a": ["NVDA"], "b": ["INTC"], "shared": [3]})) + + # Unpinned: sees the new INTC edge. + live = _binding(props) + g = Graph(path=tmp_path / "kg.db") + virtual.register(g, live) + live_targets = sorted(e.to_node for _, e, _ in virtual.augment(g, "company:NVDA", "out", None)) + assert "company:INTC" in live_targets + + # Pinned to the first snapshot: INTC didn't exist yet. + pinned = _binding(props) + pinned.snapshot_id = first_snapshot + pairs = virtual.resolve(pinned, "company:NVDA", None, "out") + assert "company:INTC" not in [e.to_node for e, _ in pairs] + assert sorted(e.to_node for e, _ in pairs) == ["company:AMD", "company:TSM"] + g.close() + + +def test_binding_roundtrips_through_ontology(tmp_path): + props, _ = _warehouse(tmp_path) + g = Graph(path=tmp_path / "kg.db") + virtual.register(g, _binding(props)) + + listed = virtual.list_bindings(g) + assert len(listed) == 1 + ve = listed[0] + assert ve.source_type == "iceberg" + assert ve.table == "analytics.co_held" + assert ve.catalog["uri"] == props["uri"] + g.close() + + +# --- live AWS integration ------------------------------------------------- +# Opt-in: set KG_ICEBERG_S3TABLES_ARN to a table-bucket ARN holding the SEC +# form13f namespace (and have AWS creds in the environment). Proves the s3:// +# path — DuckDB reading S3 Tables' managed storage via httpfs + credential_chain. +import os # noqa: E402 + +_S3TABLES_ARN = os.environ.get("KG_ICEBERG_S3TABLES_ARN") + + +@pytest.mark.skipif(not _S3TABLES_ARN, reason="set KG_ICEBERG_S3TABLES_ARN to run") +def test_s3tables_live_resolve(): + region = os.environ.get("AWS_REGION", "us-east-1") + catalog = { + "name": "s3tables", + "type": "rest", + "uri": f"https://s3tables.{region}.amazonaws.com/iceberg", + "warehouse": _S3TABLES_ARN, + "rest.sigv4-enabled": "true", + "rest.signing-name": "s3tables", + "rest.signing-region": region, + } + g = Graph(path="/tmp/kg_s3tables_it.db") + ve = VirtualEdge( + edge_type="HOLDS", + query=( + "SELECT matched_symbol AS to_id, name_of_issuer, value_normalized " + "FROM infotable WHERE manager_cik = ? AND matched_symbol IS NOT NULL " + "ORDER BY value_normalized DESC LIMIT 5" + ), + source_type="iceberg", catalog=catalog, table="form13f.infotable", + source="id_slug", target_id_template="issuer:{value}", target_kind="issuer", + name_col="name_of_issuer", prop_cols=["value_normalized"], + ) + virtual.register(g, ve) + assert g.total_edges() == 0 # nothing copied — resolved live + # CIK 0000895421 is a large 13F filer with deep holdings. + out = virtual.augment(g, "manager:0000895421", "out", None) + assert out, "expected live holdings resolved from S3 Tables" + assert all(e.properties["_virtual"] is True for _, e, _ in out) + assert all(far.id.startswith("issuer:") for _, _, far in out) + g.close() + + +def test_core_stays_zero_dependency_without_iceberg_extra(tmp_path): + """The zero-dependency promise: with duckdb/pyiceberg/pyarrow absent, the + package still imports and every non-iceberg path works; only an iceberg + binding fails — and with a clear 'install the extra' hint, not a raw + ImportError. Run in a subprocess with those modules blocked at the finder.""" + import subprocess + import sys + import textwrap + + script = textwrap.dedent( + """ + import sys, importlib.abc + BLOCK = {"duckdb", "pyiceberg", "pyarrow"} + class Blocker(importlib.abc.MetaPathFinder): + def find_spec(self, name, path, target=None): + if name.split(".")[0] in BLOCK: + raise ImportError("blocked " + name) + return None + sys.meta_path.insert(0, Blocker()) + for m in [m for m in sys.modules if m.split(".")[0] in BLOCK]: + del sys.modules[m] + + import tempfile, sqlite3 + import kgrdbms + from kgrdbms.graph import Graph + from kgrdbms import virtual + from kgrdbms.virtual import VirtualEdge + + # SQL virtual edge resolves with the iceberg libs gone. + p = tempfile.mktemp(suffix=".db"); c = sqlite3.connect(p) + c.execute("CREATE TABLE co(a TEXT, b TEXT)") + c.execute("INSERT INTO co VALUES('NVDA','AMD')"); c.commit(); c.close() + g = Graph(path=tempfile.mktemp(suffix=".db")) + ve = VirtualEdge(edge_type="X", query="SELECT b AS to_id FROM co WHERE a=?", + dsn=p, source="id_slug", target_id_template="c:{value}") + assert [e.to_node for e, _ in virtual.resolve(ve, "c:NVDA", None, "out")] == ["c:AMD"] + + # An iceberg binding fails with the install hint, not a raw ImportError. + ice = VirtualEdge(edge_type="Y", query="SELECT b AS to_id FROM t WHERE a=?", + source_type="iceberg", catalog={"type": "sql"}, table="ns.t") + try: + virtual.resolve(ice, "c:NVDA", None, "out") + raise SystemExit("expected failure") + except RuntimeError as e: + assert "iceberg extra" in str(e), str(e) + print("OK") + """ + ) + out = subprocess.run([sys.executable, "-c", script], capture_output=True, text=True) + assert out.returncode == 0 and "OK" in out.stdout, out.stderr + + +def test_iceberg_requires_catalog_and_table(tmp_path): + g = Graph(path=tmp_path / "kg.db") + ve = VirtualEdge( + edge_type="X", query="SELECT b AS to_id FROM co_held WHERE a = ?", + source_type="iceberg", catalog=None, table=None, + ) + with pytest.raises(RuntimeError, match="requires"): + virtual.resolve(ve, "company:NVDA", None, "out") + g.close() diff --git a/uv.lock b/uv.lock index 7e08a4f..d570ace 100644 --- a/uv.lock +++ b/uv.lock @@ -11,6 +11,193 @@ resolution-markers = [ "python_full_version < '3.11'", ] +[[package]] +name = "aiobotocore" +version = "3.7.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "aiohttp" }, + { name = "aioitertools" }, + { name = "botocore" }, + { name = "jmespath" }, + { name = "multidict" }, + { name = "python-dateutil" }, + { name = "typing-extensions", marker = "python_full_version < '3.11'" }, + { name = "wrapt" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/e7/75/42cce839c2ec263ff74b10b650fe36b066fbb124cbee6f247eac0983e1ab/aiobotocore-3.7.0.tar.gz", hash = "sha256:c64d871ed5491a6571948dd48eabd185b46c6c23b64e3afd0c059fc7593ada30", size = 127054, upload-time = "2026-05-09T10:02:52.332Z" } +wheels = [ 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