ktx/packages/cli/test/context/scan/relationship-discovery.test.ts
Pintouch 2afab61417
feat(connectors): add MongoDB connector (#305) (#310)
* refactor(connectors): split KtxDialect into core and KtxSqlDialect

Separate the dialect contract into a driver-agnostic core (display/ref
formatting and type mapping) and a SQL-only extension (query generators).
The catalog and entity-details paths resolve the core dialect for any
snapshot driver, so it must stay free of SQL generation; this is the
prerequisite refactor for adding non-SQL primary sources.

- KtxDialect keeps type, formatDisplayRef, parseDisplayRef,
  columnDisplayTablePartCount, mapDataType, mapToDimensionType
- KtxSqlDialect extends it with quoteIdentifier, formatTableName, and the
  query/sample/statistics generators; the 7 SQL dialects implement it
- add getSqlDialectForDriver for SQL drivers; the 7 connectors and the
  relationship-benchmark harness consume it
- thread the relationship pipeline (profiling/validation/composite/
  discovery) as KtxSqlDialect | null so a non-SQL source skips coverage SQL
  and its candidates stay in review; local-enrichment builds the SQL
  dialect only when the connector advertises readOnlySql

Pure extraction: no behavior change for the existing 7 drivers.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(connectors): add MongoDB connector for issue #305

Add a read-only MongoDB connector that treats a database as a primary
context source: collections map to tables and inferred top-level fields to
columns. MongoDB is the first non-SQL source (readOnlySql: false), so
ktx sql and metric compilation do not apply, but its collections flow
through ingest, descriptions, and relationship discovery.

- schema-inference: infer a flat column schema from the most recent
  sample_size documents (by _id desc, or order_by for non-ObjectId keys).
  Union BSON types per field, mark multi-type fields mixed (string), keep
  sub-documents/arrays as a single opaque json column, derive nullability
  from presence, treat _id as the primary key
- connector: KtxMongoDbScanConnector behind an injectable client seam;
  strictly read-only (find/listCollections/estimatedDocumentCount only),
  no executeReadOnly; resolves env:/file: via resolveKtxConfigReference
- core-only KtxMongoDbDialect and a live-database introspection adapter
- wire the mongodb driver: driver union, dialect registry, driver
  registration (scopeConfigKey databases), mongodbConnectionSchema,
  connection-drivers, normalizeDriver, the live-database route, and the
  ktx setup picker. ktx sql is refused by the read-only SQL capability gate
- tests: schema inference, connector snapshot via a fake client, dialect,
  driver-schema parsing, and the ktx sql rejection

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs(integrations): document the MongoDB primary source

Add a MongoDB section to the primary-sources reference: connection config
(url, databases, enabled_tables, sample_size, order_by), mongodb+srv/TLS/
Atlas notes, the schema-inference explainer, a features matrix, and the
non-SQL caveat. Update the frontmatter and connection field reference.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(connectors): address review blockers on the MongoDB connector

- introspect: skip estimatedDocumentCount for views. The count command is
  rejected on a MongoDB view (CommandNotSupportedOnView), so counting a view
  aborted introspect for the whole connection; compute estimatedRows only for
  real collections, as ClickHouse does.
- sl: refuse a semantic-layer query against a non-SQL connection instead of
  defaulting it to the Postgres dialect. compileLocalSlQuery (the shared CLI +
  MCP path) now rejects a driver with no SQL dialect via the new
  isSqlQueryableDriver authority, keeping MongoDB context-only per issue #305.
- tests: cover input.tableScope and the empty-scope skip for the Mongo
  connector (the scan layer does not post-filter), the view no-count path, and
  the ktx sl query refusal for a mongodb connection.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* polish(mongodb): compute sampled nullCount and document sampling caveats

Address the non-blocking review notes:

- sampleColumn now counts null/absent values over the sampled window instead of
  returning nullCount: null, since the documents are already in hand
- warn that a custom order_by must be indexed (an unindexed sort hits MongoDB's
  in-memory sort limit on large collections) in the connection schema and docs
- note that sampled values for nested fields are stringified, not faithfully
  serialized, so the json opacity is deliberate

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs(examples): add a MongoDB connector example

A manual, container-backed example mirroring examples/postgres-historic:

- docker-compose.yml + init/seed.js seed a representative dataset (nested
  documents, arrays, a Decimal128, a mixed-type field, a nullable field, an
  ObjectId reference, and a view) on first container start
- scripts/smoke.sh + introspect-smoke.mjs assert the connector's inferred
  schema with no LLM credentials — the same introspection entry point ktx
  ingest's database-schema stage uses, including the view-no-count path
- README.md documents the smoke and a full keyless ktx ingest run
  (claude-code LLM + managed sentence-transformers embeddings)

Works with Docker Compose or podman compose. Verified end to end.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* chore: ignore examples/** in knip to fix dead-code false positives

The MongoDB connector example files (examples/mongodb/init/seed.js and
examples/mongodb/scripts/introspect-smoke.mjs) are used at runtime but were
flagged as unused by knip. Add examples/** to the ignore array, matching the
existing .context/** entry.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0114qQV8fJ5a5ME3XbMVRzbL

* fix(mongodb): refuse non-SQL connections before SQL analysis

`ktx sql` and the MCP sql_execution tool resolved a SQL-analysis dialect
(falling back to Postgres for a non-SQL driver) and ran read-only
validation before the connector capability gate refused the connection.
For a MongoDB connection that spun up the parser/daemon and produced
Postgres parser diagnostics instead of a clean non-SQL refusal.

Route both entry points through a shared assertSqlQueryableConnection
guard before dialect selection, mirroring compileLocalSlQuery. The
federated duckdb path has no driver and is exempted at each call site.
Add CLI and MCP regression tests asserting validation/connector work
never starts for a MongoDB connection.

* fix(mongodb): pass CI gates (dialect boundary, secrets, setup test)

Three latent failures in the connector surfaced once CI ran on the branch:

- connector.ts imported the concrete KtxMongoDbDialect, which the connector
  dialect-import boundary forbids. Route it through getDialectForDriver('mongodb')
  and widen inferKtxMongoCollectionColumns to the base KtxDialect (it only uses
  mapDataType/mapToDimensionType).
- detect-secrets flagged a test ObjectId hex and the mongodb+srv example URL;
  annotate both with allowlist pragmas.
- the "shows every supported database" setup test omitted the new MongoDB option.

---------

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Luca Martial <48870843+luca-martial@users.noreply.github.com>
Co-authored-by: Luca Martial <lucamrtl@gmail.com>
Co-authored-by: Andrey Avtomonov <andreybavt@gmail.com>
2026-06-29 15:17:56 +02:00

683 lines
23 KiB
TypeScript

import Database from 'better-sqlite3';
import { afterEach, describe, expect, it, vi } from 'vitest';
import type { KtxLlmRuntimePort } from '../../../src/context/llm/runtime-port.js';
import { getSqlDialectForDriver } from '../../../src/context/connections/dialects.js';
import { buildDefaultKtxProjectConfig } from '../../../src/context/project/config.js';
import { snapshotToKtxEnrichedSchema } from '../../../src/context/scan/local-enrichment.js';
import {
loadKtxRelationshipBenchmarkFixture,
maskKtxRelationshipBenchmarkSnapshot,
} from '../../../src/context/scan/relationship-benchmarks.js';
import { discoverKtxRelationships } from '../../../src/context/scan/relationship-discovery.js';
import { createKtxConnectorCapabilities } from '../../../src/context/scan/types.js';
import type { KtxQueryResult, KtxReadOnlyQueryInput, KtxScanConnector, KtxScanContext, KtxSchemaSnapshot } from '../../../src/context/scan/types.js';
class InMemorySqliteExecutor {
readonly db = new Database(':memory:');
queryCount = 0;
executeReadOnly(input: KtxReadOnlyQueryInput, _ctx: KtxScanContext): Promise<KtxQueryResult> {
this.queryCount += 1;
const rows = this.db.prepare(input.sql).all() as Record<string, unknown>[];
const headers = Object.keys(rows[0] ?? {});
return Promise.resolve({
headers,
rows: rows.map((row) => headers.map((header) => row[header])),
totalRows: rows.length,
rowCount: rows.length,
});
}
close(): void {
this.db.close();
}
}
function snapshot(): KtxSchemaSnapshot {
return {
connectionId: 'warehouse',
driver: 'sqlite',
extractedAt: '2026-05-07T00:00:00.000Z',
scope: {},
metadata: {},
tables: [
{
catalog: null,
db: null,
name: 'accounts',
kind: 'table',
comment: null,
estimatedRows: 2,
foreignKeys: [],
columns: [
{
name: 'id',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
{
name: 'name',
nativeType: 'TEXT',
normalizedType: 'text',
dimensionType: 'string',
nullable: false,
primaryKey: false,
comment: null,
},
],
},
{
catalog: null,
db: null,
name: 'orders',
kind: 'table',
comment: null,
estimatedRows: 3,
foreignKeys: [],
columns: [
{
name: 'id',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
{
name: 'account_id',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
],
},
],
};
}
function declaredForeignKeySnapshot(): KtxSchemaSnapshot {
const source = snapshot();
return {
...source,
tables: source.tables.map((table) =>
table.name === 'accounts'
? {
...table,
columns: table.columns.map((column) => (column.name === 'id' ? { ...column, primaryKey: true } : column)),
}
: table.name === 'orders'
? {
...table,
foreignKeys: [
{
fromColumn: 'account_id',
toCatalog: null,
toDb: null,
toTable: 'accounts',
toColumn: 'id',
constraintName: 'orders_account_id_fkey',
},
],
}
: table,
),
};
}
function naturalKeySnapshot(): KtxSchemaSnapshot {
return {
connectionId: 'warehouse',
driver: 'sqlite',
extractedAt: '2026-05-07T00:00:00.000Z',
scope: {},
metadata: {},
tables: [
{
catalog: null,
db: null,
name: 'dim_countries',
kind: 'table',
comment: null,
estimatedRows: 3,
foreignKeys: [],
columns: [
{
name: 'iso_code',
nativeType: 'TEXT',
normalizedType: 'text',
dimensionType: 'string',
nullable: false,
primaryKey: false,
comment: null,
},
{
name: 'name',
nativeType: 'TEXT',
normalizedType: 'text',
dimensionType: 'string',
nullable: false,
primaryKey: false,
comment: null,
},
],
},
{
catalog: null,
db: null,
name: 'fct_accounts',
kind: 'table',
comment: null,
estimatedRows: 4,
foreignKeys: [],
columns: [
{
name: 'id',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
{
name: 'country_code',
nativeType: 'TEXT',
normalizedType: 'text',
dimensionType: 'string',
nullable: false,
primaryKey: false,
comment: null,
},
],
},
],
};
}
function connector(executor: InMemorySqliteExecutor | null): KtxScanConnector {
return {
id: 'sqlite:test',
driver: 'sqlite',
capabilities: createKtxConnectorCapabilities({
readOnlySql: executor !== null,
columnStats: executor !== null,
tableSampling: false,
columnSampling: false,
}),
introspect: async () => snapshot(),
listSchemas: async () => [],
listTables: async () => [],
executeReadOnly: executor ? executor.executeReadOnly.bind(executor) : undefined,
};
}
function llmRuntime(output: unknown): KtxLlmRuntimePort {
return {
generateText: vi.fn(),
generateObject: vi.fn(async () => output) as KtxLlmRuntimePort['generateObject'],
runAgentLoop: vi.fn(),
};
}
function relationshipSettings() {
return buildDefaultKtxProjectConfig().scan.relationships;
}
function llmOnlyRelationshipSnapshot(): KtxSchemaSnapshot {
return {
connectionId: 'warehouse',
driver: 'sqlite',
extractedAt: '2026-05-07T00:00:00.000Z',
scope: {},
metadata: {},
tables: [
{
catalog: null,
db: null,
name: 'customers',
kind: 'table',
comment: null,
estimatedRows: 2,
foreignKeys: [],
columns: [
{
name: 'id',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
],
},
{
catalog: null,
db: null,
name: 'orders',
kind: 'table',
comment: null,
estimatedRows: 2,
foreignKeys: [],
columns: [
{
name: 'id',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
{
name: 'buyer_ref',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
],
},
],
};
}
describe('production relationship discovery', () => {
let executor: InMemorySqliteExecutor | null = null;
afterEach(() => {
executor?.close();
executor = null;
});
it('accepts a validated relationship without declared PK or FK metadata', async () => {
executor = new InMemorySqliteExecutor();
executor.db.exec(`
CREATE TABLE accounts (id INTEGER NOT NULL, name TEXT NOT NULL);
CREATE TABLE orders (id INTEGER NOT NULL, account_id INTEGER NOT NULL);
INSERT INTO accounts (id, name) VALUES (1, 'Acme'), (2, 'Globex');
INSERT INTO orders (id, account_id) VALUES (10, 1), (11, 1), (12, 2);
`);
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: connector(executor),
schema: snapshotToKtxEnrichedSchema(snapshot()),
context: { runId: 'relationship-run-1' },
settings: relationshipSettings(),
});
expect(result.relationships).toEqual({ accepted: 1, review: 0, rejected: 0, skipped: 0 });
expect(result.statisticalValidation).toBe('completed');
expect(result.profile.sqlAvailable).toBe(true);
expect(result.profile.queryCount).toBeGreaterThan(0);
expect(result.relationshipUpdate.accepted).toEqual([
expect.objectContaining({
from: expect.objectContaining({ table: expect.objectContaining({ name: 'orders' }), columns: ['account_id'] }),
to: expect.objectContaining({ table: expect.objectContaining({ name: 'accounts' }), columns: ['id'] }),
relationshipType: 'many_to_one',
source: 'inferred',
isPrimaryKeyReference: true,
}),
]);
expect(result.resolvedRelationships[0]).toMatchObject({
status: 'accepted',
validation: expect.objectContaining({ reasons: expect.arrayContaining(['validation_passed']) }),
graph: expect.objectContaining({ reasons: expect.arrayContaining(['fk_score_passed']) }),
});
});
it('accepts a profile-driven natural-key relationship without declared metadata', async () => {
executor = new InMemorySqliteExecutor();
executor.db.exec(`
CREATE TABLE dim_countries (iso_code TEXT NOT NULL, name TEXT NOT NULL);
CREATE TABLE fct_accounts (id INTEGER NOT NULL, country_code TEXT NOT NULL);
INSERT INTO dim_countries (iso_code, name) VALUES ('US', 'United States'), ('FR', 'France'), ('DE', 'Germany');
INSERT INTO fct_accounts (id, country_code) VALUES (1, 'US'), (2, 'FR'), (3, 'US'), (4, 'DE');
`);
const schema = naturalKeySnapshot();
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: {
...connector(executor),
introspect: async () => schema,
},
schema: snapshotToKtxEnrichedSchema(schema),
context: { runId: 'natural-key-relationship-run' },
settings: relationshipSettings(),
});
expect(result.relationships).toEqual({ accepted: 1, review: 0, rejected: 0, skipped: 0 });
expect(result.relationshipUpdate.accepted).toEqual([
expect.objectContaining({
from: expect.objectContaining({ table: expect.objectContaining({ name: 'fct_accounts' }), columns: ['country_code'] }),
to: expect.objectContaining({ table: expect.objectContaining({ name: 'dim_countries' }), columns: ['iso_code'] }),
relationshipType: 'many_to_one',
source: 'inferred',
isPrimaryKeyReference: true,
}),
]);
expect(result.resolvedRelationships[0]).toMatchObject({
source: 'profile_match',
status: 'accepted',
validation: expect.objectContaining({ reasons: expect.arrayContaining(['validation_passed']) }),
graph: expect.objectContaining({ reasons: expect.arrayContaining(['fk_score_passed']) }),
});
});
it('accepts an embedding-driven relationship without declared metadata or LLM proposals', async () => {
executor = new InMemorySqliteExecutor();
executor.db.exec(`
CREATE TABLE customers (id INTEGER NOT NULL, name TEXT NOT NULL);
CREATE TABLE orders (id INTEGER NOT NULL, buyer_ref INTEGER NOT NULL);
INSERT INTO customers (id, name) VALUES (1, 'Acme'), (2, 'Orbit'), (3, 'Globex');
INSERT INTO orders (id, buyer_ref) VALUES (10, 1), (11, 2), (12, 2), (13, 3);
`);
const sourceSnapshot = llmOnlyRelationshipSnapshot();
const schema = snapshotToKtxEnrichedSchema(
sourceSnapshot,
new Map([
['customers.id', [1, 0, 0]],
['customers.name', [0, 1, 0]],
['orders.id', [0, 0, 1]],
['orders.buyer_ref', [0.995, 0.005, 0]],
]),
);
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: {
...connector(executor),
introspect: async () => sourceSnapshot,
},
schema,
context: { runId: 'embedding-relationship-run' },
settings: {
...relationshipSettings(),
llmProposals: false,
},
});
expect(result.llmRelationshipValidation).toBe('skipped');
expect(result.relationships).toEqual({ accepted: 1, review: 0, rejected: 0, skipped: 0 });
expect(result.relationshipUpdate.accepted[0]).toMatchObject({
from: { table: { name: 'orders' }, columns: ['buyer_ref'] },
to: { table: { name: 'customers' }, columns: ['id'] },
});
expect(result.resolvedRelationships[0]).toMatchObject({
source: 'embedding_similarity',
status: 'accepted',
validation: expect.objectContaining({ reasons: expect.arrayContaining(['validation_passed']) }),
evidence: expect.objectContaining({
reasons: expect.arrayContaining(['embedding_similarity', 'target_key_like']),
embeddingSimilarity: expect.any(Number),
}),
});
});
it('keeps candidates review-only when read-only SQL is unavailable', async () => {
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: connector(null),
schema: snapshotToKtxEnrichedSchema(snapshot()),
context: { runId: 'relationship-run-no-sql' },
settings: relationshipSettings(),
});
expect(result.relationships).toEqual({ accepted: 0, review: 1, rejected: 0, skipped: 0 });
expect(result.statisticalValidation).toBe('skipped');
expect(result.relationshipUpdate.accepted).toEqual([]);
expect(result.resolvedRelationships[0]).toMatchObject({
status: 'review',
validation: expect.objectContaining({ reasons: expect.arrayContaining(['validation_unavailable']) }),
});
expect(result.warnings).toContainEqual({
code: 'connector_capability_missing',
message: 'ktx scan connector cannot run read-only SQL relationship validation',
recoverable: true,
metadata: { capability: 'readOnlySql' },
});
});
it('accepts formal metadata relationships when read-only SQL is unavailable', async () => {
const sourceSnapshot = declaredForeignKeySnapshot();
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: connector(null),
schema: snapshotToKtxEnrichedSchema(sourceSnapshot),
context: { runId: 'formal-metadata-no-sql' },
settings: relationshipSettings(),
});
expect(result.statisticalValidation).toBe('skipped');
expect(result.relationships).toEqual({ accepted: 1, review: 0, rejected: 0, skipped: 0 });
expect(result.resolvedRelationships).toEqual([]);
expect(result.relationshipUpdate.accepted).toEqual([
expect.objectContaining({
id: 'orders:(orders.account_id)->accounts:(accounts.id)',
source: 'formal',
confidence: 1,
from: expect.objectContaining({ table: expect.objectContaining({ name: 'orders' }), columns: ['account_id'] }),
to: expect.objectContaining({ table: expect.objectContaining({ name: 'accounts' }), columns: ['id'] }),
}),
]);
expect(result.relationshipUpdate.rejected).toEqual([]);
expect(result.relationshipUpdate.skipped).toEqual([]);
});
it('accepts LLM-only relationship proposals only after SQL validation and graph resolution pass', async () => {
executor = new InMemorySqliteExecutor();
executor.db.exec(`
CREATE TABLE customers (id INTEGER);
CREATE TABLE orders (id INTEGER, buyer_ref INTEGER);
INSERT INTO customers (id) VALUES (1), (2);
INSERT INTO orders (id, buyer_ref) VALUES (10, 1), (11, 2);
`);
const llmOutput = {
pkCandidates: [{ table: 'customers', column: 'id', confidence: 0.91, rationale: 'Unique customer key.' }],
fkCandidates: [
{
fromTable: 'orders',
fromColumn: 'buyer_ref',
toTable: 'customers',
toColumn: 'id',
confidence: 0.89,
rationale: 'Buyer reference values align with customer identifiers.',
},
],
};
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: connector(executor),
schema: snapshotToKtxEnrichedSchema(llmOnlyRelationshipSnapshot()),
context: { runId: 'llm-relationship-orchestrator' },
settings: relationshipSettings(),
llmRuntime: llmRuntime(llmOutput),
});
expect(result.llmRelationshipValidation).toBe('completed');
expect(result.relationships).toEqual({ accepted: 1, review: 0, rejected: 0, skipped: 0 });
expect(result.resolvedRelationships[0]).toMatchObject({
source: 'llm_proposal',
status: 'accepted',
evidence: {
llmRationale: 'Buyer reference values align with customer identifiers.',
},
});
expect(result.relationshipUpdate.accepted[0]).toMatchObject({
from: { table: { name: 'orders' }, columns: ['buyer_ref'] },
to: { table: { name: 'customers' }, columns: ['id'] },
});
});
it('uses configured acceptance thresholds when resolving graph relationships', async () => {
const executor = new InMemorySqliteExecutor();
executor.db.exec(`
CREATE TABLE accounts (id INTEGER NOT NULL, name TEXT NOT NULL);
CREATE TABLE orders (id INTEGER NOT NULL, account_id INTEGER NOT NULL);
INSERT INTO accounts VALUES (1, 'Acme'), (2, 'Orbit');
INSERT INTO orders VALUES (10, 1), (11, 1), (12, 2);
`);
const settings = {
...buildDefaultKtxProjectConfig().scan.relationships,
acceptThreshold: 0.99,
reviewThreshold: 0.55,
};
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: connector(executor),
schema: snapshotToKtxEnrichedSchema(snapshot()),
context: { runId: 'configured-thresholds' },
settings,
});
expect(result.relationships).toEqual({ accepted: 0, review: 1, rejected: 0, skipped: 0 });
expect(result.relationshipUpdate.accepted).toEqual([]);
expect(result.resolvedRelationships[0]).toMatchObject({
status: 'review',
graph: { reasons: expect.arrayContaining(['fk_score_review']) },
});
executor.close();
});
it('passes maxCandidatesPerColumn into broad deterministic candidate generation', async () => {
const executor = new InMemorySqliteExecutor();
executor.db.exec(`
CREATE TABLE accounts (id INTEGER NOT NULL, name TEXT NOT NULL);
CREATE TABLE account_archive (id INTEGER NOT NULL, name TEXT NOT NULL);
CREATE TABLE orders (id INTEGER NOT NULL, account_id INTEGER NOT NULL);
INSERT INTO accounts VALUES (1, 'Acme'), (2, 'Orbit');
INSERT INTO account_archive VALUES (99, 'Archive');
INSERT INTO orders VALUES (10, 1), (11, 1), (12, 2);
`);
const richSnapshot = snapshot();
richSnapshot.tables.splice(1, 0, {
catalog: null,
db: null,
name: 'account_archive',
kind: 'table',
comment: null,
estimatedRows: 1,
foreignKeys: [],
columns: [
{
name: 'id',
nativeType: 'INTEGER',
normalizedType: 'integer',
dimensionType: 'number',
nullable: false,
primaryKey: false,
comment: null,
},
{
name: 'name',
nativeType: 'TEXT',
normalizedType: 'text',
dimensionType: 'string',
nullable: false,
primaryKey: false,
comment: null,
},
],
});
const result = await discoverKtxRelationships({
connectionId: 'warehouse',
dialect: getSqlDialectForDriver('sqlite'),
connector: {
...connector(executor),
introspect: async () => richSnapshot,
},
schema: snapshotToKtxEnrichedSchema(richSnapshot),
context: { runId: 'candidate-cap' },
settings: {
...buildDefaultKtxProjectConfig().scan.relationships,
maxCandidatesPerColumn: 1,
},
});
const sourceTargets = result.resolvedRelationships
.filter((relationship) => relationship.from.columns[0] === 'account_id')
.map((relationship) => `${relationship.to.table.name}.${relationship.to.columns[0]}`);
expect(sourceTargets).toHaveLength(1);
expect(sourceTargets).toEqual(['accounts.id']);
executor.close();
});
it('accepts SQL-validated composite relationships in production relationship-discovery detection', async () => {
const fixtureRoot = new URL(
'../../fixtures/relationship-benchmarks/composite_keys_no_declared_constraints',
import.meta.url,
);
const fixture = await loadKtxRelationshipBenchmarkFixture(fixtureRoot.pathname);
const maskedSnapshot = maskKtxRelationshipBenchmarkSnapshot(fixture.snapshot, 'declared_pks_and_declared_fks_removed');
const database = new Database(fixture.dataPath ?? '', { readonly: true, fileMustExist: true });
const testConnector: KtxScanConnector = {
id: 'sqlite:composite',
driver: 'sqlite',
capabilities: createKtxConnectorCapabilities({
readOnlySql: true,
columnStats: true,
tableSampling: false,
columnSampling: false,
}),
introspect: async () => maskedSnapshot,
listSchemas: async () => [],
listTables: async () => [],
executeReadOnly: async (input) => {
const rows = database.prepare(input.sql).all() as Record<string, unknown>[];
const headers = Object.keys(rows[0] ?? {});
return {
headers,
rows: rows.map((row) => headers.map((header) => row[header])),
totalRows: rows.length,
rowCount: rows.length,
};
},
};
const result = await discoverKtxRelationships({
connectionId: maskedSnapshot.connectionId,
dialect: getSqlDialectForDriver(maskedSnapshot.driver),
connector: testConnector,
schema: snapshotToKtxEnrichedSchema(maskedSnapshot, new Map()),
context: { runId: 'test:production-composite' },
settings: relationshipSettings(),
});
database.close();
expect(
result.relationshipUpdate.accepted.map(
(relationship) =>
`${relationship.from.table.name}.(${relationship.from.columns.join(',')})->${relationship.to.table.name}.(${relationship.to.columns.join(',')})`,
),
).toContain('order_line_allocations.(order_id,line_number)->order_lines.(order_id,line_number)');
expect(result.relationships.accepted).toBeGreaterThanOrEqual(1);
expect(result.compositeRelationships.map((relationship) => relationship.status)).toContain('accepted');
});
});