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* 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>
103 lines
3.8 KiB
TypeScript
103 lines
3.8 KiB
TypeScript
import { describe, expect, it } from 'vitest';
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import { KtxMongoDbDialect } from '../../../src/connectors/mongodb/dialect.js';
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import {
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bsonTypeOf,
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inferKtxMongoCollectionColumns,
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type KtxMongoDocument,
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} from '../../../src/connectors/mongodb/schema-inference.js';
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const dialect = new KtxMongoDbDialect();
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function objectId(): unknown {
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return { _bsontype: 'ObjectId', toString: () => '64b7f0c2a1b2c3d4e5f60718' }; // pragma: allowlist secret
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}
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function decimal128(value: string): unknown {
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return { _bsontype: 'Decimal128', toString: () => value };
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}
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function infer(documents: KtxMongoDocument[]) {
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const columns = inferKtxMongoCollectionColumns(documents, dialect);
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return new Map(columns.map((column) => [column.name, column]));
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}
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describe('bsonTypeOf', () => {
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it('maps JS and BSON runtime values to canonical type names', () => {
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expect(bsonTypeOf(objectId())).toBe('objectId');
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expect(bsonTypeOf('hi')).toBe('string');
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expect(bsonTypeOf(7)).toBe('int');
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expect(bsonTypeOf(7.5)).toBe('double');
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expect(bsonTypeOf(true)).toBe('bool');
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expect(bsonTypeOf(new Date())).toBe('date');
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expect(bsonTypeOf(decimal128('1.5'))).toBe('decimal');
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expect(bsonTypeOf({ city: 'NY' })).toBe('object');
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expect(bsonTypeOf([1, 2])).toBe('array');
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expect(bsonTypeOf(null)).toBe('null');
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});
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});
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describe('inferKtxMongoCollectionColumns', () => {
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it('treats _id as the non-nullable primary key', () => {
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const columns = infer([{ _id: objectId(), name: 'a' }]);
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const id = columns.get('_id')!;
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expect(id.primaryKey).toBe(true);
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expect(id.nullable).toBe(false);
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expect(id.dimensionType).toBe('string');
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expect(id.normalizedType).toBe('objectid');
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});
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it('derives nullability from field presence and observed nulls', () => {
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const columns = infer([
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{ _id: objectId(), email: 'a@x.com', deleted_at: null },
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{ _id: objectId(), email: 'b@x.com' },
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]);
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// present in every document, never null -> not nullable
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expect(columns.get('email')!.nullable).toBe(false);
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// missing in one document and null in another -> nullable
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expect(columns.get('deleted_at')!.nullable).toBe(true);
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});
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it('maps scalar BSON types to dimension types', () => {
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const columns = infer([
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{ _id: objectId(), age: 30, score: 9.5, active: true, created: new Date(), balance: decimal128('10.00') },
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]);
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expect(columns.get('age')!.dimensionType).toBe('number');
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expect(columns.get('score')!.dimensionType).toBe('number');
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expect(columns.get('active')!.dimensionType).toBe('boolean');
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expect(columns.get('created')!.dimensionType).toBe('time');
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expect(columns.get('balance')!.dimensionType).toBe('number');
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});
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it('marks a field seen with more than one type as mixed and treats it as a string', () => {
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const columns = infer([
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{ _id: objectId(), ref: 'abc' },
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{ _id: objectId(), ref: 123 },
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]);
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const ref = columns.get('ref')!;
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expect(ref.nativeType).toBe('mixed');
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expect(ref.normalizedType).toBe('mixed');
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expect(ref.dimensionType).toBe('string');
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});
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it('keeps sub-documents and arrays as a single opaque json column', () => {
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const columns = infer([
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{ _id: objectId(), address: { city: 'NY', zip: '10001' }, tags: ['a', 'b'] },
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]);
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const address = columns.get('address')!;
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expect(address.nativeType).toBe('object');
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expect(address.normalizedType).toBe('json');
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expect(address.dimensionType).toBe('string');
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const tags = columns.get('tags')!;
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expect(tags.nativeType).toBe('array');
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expect(tags.normalizedType).toBe('json');
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});
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it('preserves first-seen field order', () => {
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const columns = inferKtxMongoCollectionColumns(
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[{ _id: objectId(), b: 1, a: 2 }],
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dialect,
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);
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expect(columns.map((column) => column.name)).toEqual(['_id', 'b', 'a']);
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});
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});
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