ktx/packages/cli/test/connectors/mongodb/schema-inference.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

103 lines
3.8 KiB
TypeScript

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