2026-05-10 23:12:26 +02:00
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import { describe, expect, it, vi } from 'vitest';
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test: split cli tests from source tree (#216)
* feat(cli): define full warehouse dialect contract
* test(cli): keep dialect edge tests focused
* fix(cli): stabilize dialect contract foundation
* refactor(connectors): own read-only query preparation
* refactor(connectors): resolve dialects through registry
* refactor(connectors): keep concrete dialect classes internal
* chore(workspace): enforce dialect import boundary
* refactor(cli): resolve relationship dialect at scan boundary
* refactor(cli): use dialect display parsing for entity details
* refactor(cli): use dialect display parsing for warehouse catalog
* refactor(cli): use dialect SQL in relationship workflows
* test(cli): verify solid dialect scan workflow closure
* test: split cli tests from source tree
* refactor(cli): standardize BigQuery scope listing
* feat(sqlite): implement connector scope listing
* test(connectors): cover required table listing
* feat(cli): add warehouse driver registry
* refactor(setup): route scope discovery through driver registry
* refactor(cli): route local query execution through driver registry
* refactor(historic-sql): route dialect support through driver registry
* refactor(cli): test warehouse connections through driver registry
* fix(cli): close driver registry type export gaps
* Improve setup daemon diagnostics
* refactor(setup): centralize rail-prefixed diagnostics + query-history fallback
Extract errorMessage, writePrefixedLines, and flushPrefixedBufferedCommandOutput
into clack.ts so the setup wizard, managed daemons, and embedding/agent steps
share one rail-formatted writer. setup-databases.ts also adds a
"disable query history and retry" option when the schema-context build fails
and query history is the likely culprit, surfaced via a new
failed-query-history-unavailable status.
* fix(cli): carry catalog through the picker so BigQuery/Snowflake/SQL Server scope filters match
The setup picker's KtxTableListEntry was a 2-level { schema, name }, so
qualifiedTableId always wrote db.name into enabled_tables. When BigQuery,
Snowflake, or SQL Server later ran fast ingest, their introspect step filtered
the scope set with scopedTableNames(scope, { catalog: projectId|database, db })
— catalog was non-null on the introspect side but null in the scope refs, so
every entry was rejected, the live-database adapter staged zero table files,
and detect() failed with 'Adapter "live-database" did not recognize fetched
source output'.
Align the picker boundary with the canonical 3-level KtxTableRef:
- Add catalog: string | null to KtxTableListEntry.
- BigQuery/Snowflake/SQL Server listTables populate catalog from the
resolved projectId / database; Postgres/MySQL/ClickHouse/SQLite set null.
- qualifiedTableId emits catalog.schema.name when catalog is non-null
(resolveEnabledTables already accepts the 3-part shape) and
schemasFromEnabledTables now goes through parseDottedTableEntry so it
recovers the schema correctly from both 2-part and 3-part entries.
- Export parseDottedTableEntry from enabled-tables.ts (@internal) for picker
reuse.
Update listTables expectations in all seven connector tests and the setup /
picker test fixtures. Add a picker regression test that covers the
catalog-bearing round-trip (save + refine).
* fix(cli): allow debug telemetry under opt-out env
2026-05-26 08:49:05 +02:00
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import { bigQueryConnectionConfigFromConfig, isKtxBigQueryConnectionConfig, type KtxBigQueryClient, KtxBigQueryScanConnector, type KtxBigQueryClientFactory, type KtxBigQueryDataset, type KtxBigQueryQueryJob, type KtxBigQueryTableRef, prepareBigQueryReadOnlyQuery } from '../../../src/connectors/bigquery/connector.js';
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import { createBigQueryLiveDatabaseIntrospection } from '../../../src/connectors/bigquery/live-database-introspection.js';
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import { tableRefSet } from '../../../src/context/scan/table-ref.js';
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2026-05-10 23:12:26 +02:00
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2026-05-24 19:30:06 +02:00
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function fakeClientFactory(options: { primaryKeyError?: Error } = {}): KtxBigQueryClientFactory {
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2026-05-10 23:51:24 +02:00
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const queryResults = vi.fn(async (): ReturnType<KtxBigQueryQueryJob['getQueryResults']> => [
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2026-05-10 23:12:26 +02:00
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[{ id: 1, status: 'paid' }],
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undefined,
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{ schema: { fields: [{ name: 'id', type: 'INT64' }, { name: 'status', type: 'STRING' }] } },
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]);
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2026-05-10 23:51:24 +02:00
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const createQueryJob = vi.fn(async (input: { query: string }): ReturnType<KtxBigQueryClient['createQueryJob']> => {
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2026-05-10 23:12:26 +02:00
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if (input.query.includes('INFORMATION_SCHEMA.TABLE_CONSTRAINTS')) {
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2026-05-24 19:30:06 +02:00
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if (options.primaryKeyError) {
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throw options.primaryKeyError;
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}
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2026-05-10 23:12:26 +02:00
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return [
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{
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2026-05-10 23:51:24 +02:00
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getQueryResults: async (): ReturnType<KtxBigQueryQueryJob['getQueryResults']> => [
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2026-05-10 23:12:26 +02:00
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[{ table_name: 'orders', column_name: 'id' }],
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undefined,
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{ schema: { fields: [{ name: 'table_name', type: 'STRING' }, { name: 'column_name', type: 'STRING' }] } },
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],
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},
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];
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}
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if (input.query.includes('APPROX_COUNT_DISTINCT')) {
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return [
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{
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2026-05-10 23:51:24 +02:00
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getQueryResults: async (): ReturnType<KtxBigQueryQueryJob['getQueryResults']> => [
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2026-05-10 23:12:26 +02:00
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[{ cardinality: 2 }],
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undefined,
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{ schema: { fields: [{ name: 'cardinality', type: 'INT64' }] } },
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],
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},
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];
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}
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if (input.query.includes('SELECT DISTINCT CAST')) {
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return [
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{
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2026-05-10 23:51:24 +02:00
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getQueryResults: async (): ReturnType<KtxBigQueryQueryJob['getQueryResults']> => [
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2026-05-10 23:12:26 +02:00
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[{ val: 'open' }, { val: 'paid' }],
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undefined,
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{ schema: { fields: [{ name: 'val', type: 'STRING' }] } },
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],
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},
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];
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}
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if (input.query.includes('SELECT `status`')) {
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return [
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{
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2026-05-10 23:51:24 +02:00
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getQueryResults: async (): ReturnType<KtxBigQueryQueryJob['getQueryResults']> => [
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2026-05-10 23:12:26 +02:00
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[{ status: 'paid' }],
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undefined,
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{ schema: { fields: [{ name: 'status', type: 'STRING' }] } },
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],
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},
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];
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}
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return [{ getQueryResults: queryResults }];
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});
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2026-05-10 23:51:24 +02:00
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const getTable = vi.fn(async (): ReturnType<KtxBigQueryTableRef['get']> => [
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2026-05-10 23:12:26 +02:00
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{
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metadata: {
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type: 'TABLE',
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numRows: '12',
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description: 'Orders table',
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schema: {
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fields: [
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{ name: 'id', type: 'INT64', mode: 'REQUIRED', description: 'Order id' },
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{ name: 'status', type: 'STRING', mode: 'NULLABLE' },
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{ name: 'payload', type: 'RECORD', mode: 'NULLABLE' },
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],
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},
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},
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},
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]);
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2026-05-10 23:51:24 +02:00
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const tableRef: KtxBigQueryTableRef = { id: 'orders', get: getTable };
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2026-05-10 23:12:26 +02:00
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return {
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createClient: vi.fn(() => ({
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2026-05-10 23:51:24 +02:00
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getDatasets: vi.fn(async (): ReturnType<KtxBigQueryClient['getDatasets']> => [[{ id: 'analytics' }, { id: 'staging' }]]),
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2026-05-10 23:12:26 +02:00
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dataset: vi.fn(
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2026-05-10 23:51:24 +02:00
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(datasetId: string): KtxBigQueryDataset => ({
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2026-05-10 23:12:26 +02:00
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get: vi.fn(async () => [{ id: datasetId }]),
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2026-05-10 23:51:24 +02:00
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getTables: vi.fn(async (): ReturnType<KtxBigQueryDataset['getTables']> => [[tableRef]]),
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2026-05-10 23:12:26 +02:00
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}),
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),
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createQueryJob,
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})),
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};
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}
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const connection = {
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driver: 'bigquery',
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dataset_id: 'analytics',
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credentials_json: JSON.stringify({ project_id: 'project-1', client_email: 'reader@example.test' }),
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location: 'US',
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2026-05-15 00:08:11 +02:00
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} as const;
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2026-05-10 23:12:26 +02:00
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2026-05-10 23:51:24 +02:00
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describe('KtxBigQueryScanConnector', () => {
|
test: split cli tests from source tree (#216)
* feat(cli): define full warehouse dialect contract
* test(cli): keep dialect edge tests focused
* fix(cli): stabilize dialect contract foundation
* refactor(connectors): own read-only query preparation
* refactor(connectors): resolve dialects through registry
* refactor(connectors): keep concrete dialect classes internal
* chore(workspace): enforce dialect import boundary
* refactor(cli): resolve relationship dialect at scan boundary
* refactor(cli): use dialect display parsing for entity details
* refactor(cli): use dialect display parsing for warehouse catalog
* refactor(cli): use dialect SQL in relationship workflows
* test(cli): verify solid dialect scan workflow closure
* test: split cli tests from source tree
* refactor(cli): standardize BigQuery scope listing
* feat(sqlite): implement connector scope listing
* test(connectors): cover required table listing
* feat(cli): add warehouse driver registry
* refactor(setup): route scope discovery through driver registry
* refactor(cli): route local query execution through driver registry
* refactor(historic-sql): route dialect support through driver registry
* refactor(cli): test warehouse connections through driver registry
* fix(cli): close driver registry type export gaps
* Improve setup daemon diagnostics
* refactor(setup): centralize rail-prefixed diagnostics + query-history fallback
Extract errorMessage, writePrefixedLines, and flushPrefixedBufferedCommandOutput
into clack.ts so the setup wizard, managed daemons, and embedding/agent steps
share one rail-formatted writer. setup-databases.ts also adds a
"disable query history and retry" option when the schema-context build fails
and query history is the likely culprit, surfaced via a new
failed-query-history-unavailable status.
* fix(cli): carry catalog through the picker so BigQuery/Snowflake/SQL Server scope filters match
The setup picker's KtxTableListEntry was a 2-level { schema, name }, so
qualifiedTableId always wrote db.name into enabled_tables. When BigQuery,
Snowflake, or SQL Server later ran fast ingest, their introspect step filtered
the scope set with scopedTableNames(scope, { catalog: projectId|database, db })
— catalog was non-null on the introspect side but null in the scope refs, so
every entry was rejected, the live-database adapter staged zero table files,
and detect() failed with 'Adapter "live-database" did not recognize fetched
source output'.
Align the picker boundary with the canonical 3-level KtxTableRef:
- Add catalog: string | null to KtxTableListEntry.
- BigQuery/Snowflake/SQL Server listTables populate catalog from the
resolved projectId / database; Postgres/MySQL/ClickHouse/SQLite set null.
- qualifiedTableId emits catalog.schema.name when catalog is non-null
(resolveEnabledTables already accepts the 3-part shape) and
schemasFromEnabledTables now goes through parseDottedTableEntry so it
recovers the schema correctly from both 2-part and 3-part entries.
- Export parseDottedTableEntry from enabled-tables.ts (@internal) for picker
reuse.
Update listTables expectations in all seven connector tests and the setup /
picker test fixtures. Add a picker regression test that covers the
catalog-bearing round-trip (save + refine).
* fix(cli): allow debug telemetry under opt-out env
2026-05-26 08:49:05 +02:00
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it('prepares read-only SQL parameters with BigQuery named placeholders', () => {
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expect(prepareBigQueryReadOnlyQuery('SELECT * FROM orders WHERE id = :id AND id_2 = :id_2', { id: 1, id_2: 2 })).toEqual({
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sql: 'SELECT * FROM orders WHERE id = @id AND id_2 = @id_2',
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params: { id: 1, id_2: 2 },
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});
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expect(prepareBigQueryReadOnlyQuery('SELECT * FROM orders')).toEqual({
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sql: 'SELECT * FROM orders',
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params: undefined,
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});
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});
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2026-05-10 23:12:26 +02:00
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it('resolves configuration safely', () => {
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2026-05-10 23:51:24 +02:00
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expect(isKtxBigQueryConnectionConfig(connection)).toBe(true);
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expect(isKtxBigQueryConnectionConfig({ driver: 'mysql' })).toBe(false);
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2026-05-10 23:12:26 +02:00
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expect(bigQueryConnectionConfigFromConfig({ connectionId: 'warehouse', connection })).toMatchObject({
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projectId: 'project-1',
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datasetIds: ['analytics'],
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location: 'US',
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});
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});
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it('introspects datasets, table metadata, primary keys, and normalized types', async () => {
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2026-05-10 23:51:24 +02:00
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const connector = new KtxBigQueryScanConnector({
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2026-05-10 23:12:26 +02:00
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connectionId: 'warehouse',
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connection,
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clientFactory: fakeClientFactory(),
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now: () => new Date('2026-04-29T17:00:00.000Z'),
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});
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const snapshot = await connector.introspect(
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{ connectionId: 'warehouse', driver: 'bigquery' },
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{ runId: 'scan-run-1' },
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);
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expect(snapshot).toMatchObject({
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connectionId: 'warehouse',
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driver: 'bigquery',
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extractedAt: '2026-04-29T17:00:00.000Z',
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scope: { catalogs: ['project-1'], datasets: ['analytics'] },
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metadata: {
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project_id: 'project-1',
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datasets: ['analytics'],
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table_count: 1,
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total_columns: 3,
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},
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});
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expect(snapshot.tables[0]).toMatchObject({
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catalog: 'project-1',
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db: 'analytics',
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name: 'orders',
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kind: 'table',
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comment: 'Orders table',
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estimatedRows: 12,
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foreignKeys: [],
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});
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expect(snapshot.tables[0]?.columns).toEqual([
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{
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name: 'id',
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nativeType: 'INT64',
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normalizedType: 'BIGINT',
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dimensionType: 'number',
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nullable: false,
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primaryKey: true,
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comment: 'Order id',
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},
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{
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name: 'status',
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nativeType: 'STRING',
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normalizedType: 'VARCHAR',
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dimensionType: 'string',
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nullable: true,
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primaryKey: false,
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comment: null,
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},
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{
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name: 'payload',
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nativeType: 'RECORD',
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normalizedType: 'JSON',
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dimensionType: 'string',
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nullable: true,
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primaryKey: false,
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comment: null,
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},
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]);
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});
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2026-05-24 19:30:06 +02:00
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it.each([
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Object.assign(new Error('Access Denied'), { code: 403 }),
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Object.assign(new Error('Not found'), { errors: [{ reason: 'notFound' }] }),
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])('soft-fails denied BigQuery primary-key discovery with a scan warning', async (primaryKeyError) => {
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const connector = new KtxBigQueryScanConnector({
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connectionId: 'warehouse',
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connection,
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clientFactory: fakeClientFactory({ primaryKeyError }),
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now: () => new Date('2026-04-29T17:00:00.000Z'),
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});
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const snapshot = await connector.introspect(
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{ connectionId: 'warehouse', driver: 'bigquery' },
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{ runId: 'scan-run-bigquery-denied-pk' },
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);
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expect(snapshot.warnings).toEqual([
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{
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code: 'constraint_discovery_unauthorized',
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message: 'Skipped primary-key discovery in analytics (insufficient grants on system catalogs)',
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recoverable: true,
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metadata: { schema: 'analytics', kind: 'primary_key' },
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},
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]);
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expect(snapshot.tables[0]?.foreignKeys).toEqual([]);
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expect(snapshot.tables[0]?.columns.every((column) => column.primaryKey === false)).toBe(true);
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});
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|
2026-05-10 23:12:26 +02:00
|
|
|
it('runs samples, read-only SQL, distinct values, dataset listing, row counts, and cleanup', async () => {
|
2026-05-10 23:51:24 +02:00
|
|
|
const connector = new KtxBigQueryScanConnector({
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2026-05-10 23:12:26 +02:00
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|
connectionId: 'warehouse',
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|
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|
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connection,
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|
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|
clientFactory: fakeClientFactory(),
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|
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|
});
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|
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|
await expect(
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|
|
|
|
connector.sampleTable(
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|
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|
{
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|
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|
|
connectionId: 'warehouse',
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|
|
|
|
table: { catalog: 'project-1', db: 'analytics', name: 'orders' },
|
|
|
|
|
columns: ['id', 'status'],
|
|
|
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|
limit: 1,
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|
},
|
|
|
|
|
{ runId: 'scan-run-1' },
|
|
|
|
|
),
|
|
|
|
|
).resolves.toEqual({
|
|
|
|
|
headers: ['id', 'status'],
|
|
|
|
|
headerTypes: ['INT64', 'STRING'],
|
|
|
|
|
rows: [[1, 'paid']],
|
|
|
|
|
totalRows: 1,
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
connector.sampleColumn(
|
|
|
|
|
{
|
|
|
|
|
connectionId: 'warehouse',
|
|
|
|
|
table: { catalog: 'project-1', db: 'analytics', name: 'orders' },
|
|
|
|
|
column: 'status',
|
|
|
|
|
limit: 5,
|
|
|
|
|
},
|
|
|
|
|
{ runId: 'scan-run-1' },
|
|
|
|
|
),
|
|
|
|
|
).resolves.toMatchObject({ values: ['paid'], nullCount: null, distinctCount: null });
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
connector.executeReadOnly(
|
|
|
|
|
{ connectionId: 'warehouse', sql: 'select id, status from `project-1`.`analytics`.`orders`', maxRows: 1 },
|
|
|
|
|
{ runId: 'scan-run-1' },
|
|
|
|
|
),
|
|
|
|
|
).resolves.toMatchObject({ headers: ['id', 'status'], rows: [[1, 'paid']], totalRows: 1, rowCount: 1 });
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
connector.executeReadOnly({ connectionId: 'warehouse', sql: 'delete from orders' }, { runId: 'scan-run-1' }),
|
|
|
|
|
).rejects.toThrow('Only read-only SELECT/WITH queries can be executed locally');
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
connector.getColumnDistinctValues(
|
|
|
|
|
{ catalog: 'project-1', db: 'analytics', name: 'orders' },
|
|
|
|
|
'status',
|
|
|
|
|
{ maxCardinality: 5, limit: 10, sampleSize: 100 },
|
|
|
|
|
),
|
|
|
|
|
).resolves.toEqual({ values: ['open', 'paid'], cardinality: 2 });
|
|
|
|
|
await expect(connector.getTableRowCount('orders')).resolves.toBe(12);
|
test: split cli tests from source tree (#216)
* feat(cli): define full warehouse dialect contract
* test(cli): keep dialect edge tests focused
* fix(cli): stabilize dialect contract foundation
* refactor(connectors): own read-only query preparation
* refactor(connectors): resolve dialects through registry
* refactor(connectors): keep concrete dialect classes internal
* chore(workspace): enforce dialect import boundary
* refactor(cli): resolve relationship dialect at scan boundary
* refactor(cli): use dialect display parsing for entity details
* refactor(cli): use dialect display parsing for warehouse catalog
* refactor(cli): use dialect SQL in relationship workflows
* test(cli): verify solid dialect scan workflow closure
* test: split cli tests from source tree
* refactor(cli): standardize BigQuery scope listing
* feat(sqlite): implement connector scope listing
* test(connectors): cover required table listing
* feat(cli): add warehouse driver registry
* refactor(setup): route scope discovery through driver registry
* refactor(cli): route local query execution through driver registry
* refactor(historic-sql): route dialect support through driver registry
* refactor(cli): test warehouse connections through driver registry
* fix(cli): close driver registry type export gaps
* Improve setup daemon diagnostics
* refactor(setup): centralize rail-prefixed diagnostics + query-history fallback
Extract errorMessage, writePrefixedLines, and flushPrefixedBufferedCommandOutput
into clack.ts so the setup wizard, managed daemons, and embedding/agent steps
share one rail-formatted writer. setup-databases.ts also adds a
"disable query history and retry" option when the schema-context build fails
and query history is the likely culprit, surfaced via a new
failed-query-history-unavailable status.
* fix(cli): carry catalog through the picker so BigQuery/Snowflake/SQL Server scope filters match
The setup picker's KtxTableListEntry was a 2-level { schema, name }, so
qualifiedTableId always wrote db.name into enabled_tables. When BigQuery,
Snowflake, or SQL Server later ran fast ingest, their introspect step filtered
the scope set with scopedTableNames(scope, { catalog: projectId|database, db })
— catalog was non-null on the introspect side but null in the scope refs, so
every entry was rejected, the live-database adapter staged zero table files,
and detect() failed with 'Adapter "live-database" did not recognize fetched
source output'.
Align the picker boundary with the canonical 3-level KtxTableRef:
- Add catalog: string | null to KtxTableListEntry.
- BigQuery/Snowflake/SQL Server listTables populate catalog from the
resolved projectId / database; Postgres/MySQL/ClickHouse/SQLite set null.
- qualifiedTableId emits catalog.schema.name when catalog is non-null
(resolveEnabledTables already accepts the 3-part shape) and
schemasFromEnabledTables now goes through parseDottedTableEntry so it
recovers the schema correctly from both 2-part and 3-part entries.
- Export parseDottedTableEntry from enabled-tables.ts (@internal) for picker
reuse.
Update listTables expectations in all seven connector tests and the setup /
picker test fixtures. Add a picker regression test that covers the
catalog-bearing round-trip (save + refine).
* fix(cli): allow debug telemetry under opt-out env
2026-05-26 08:49:05 +02:00
|
|
|
await expect(connector.listSchemas()).resolves.toEqual(['analytics', 'staging']);
|
2026-05-10 23:12:26 +02:00
|
|
|
await expect(
|
|
|
|
|
connector.columnStats(
|
|
|
|
|
{ connectionId: 'warehouse', table: { catalog: 'project-1', db: 'analytics', name: 'orders' }, column: 'status' },
|
|
|
|
|
{ runId: 'scan-run-1' },
|
|
|
|
|
),
|
|
|
|
|
).resolves.toBeNull();
|
|
|
|
|
await connector.cleanup();
|
|
|
|
|
});
|
|
|
|
|
|
fix(snowflake): unblock multi-schema ingest and relationship discovery (#204)
* feat(setup): drop redundant Snowflake schema prompt; fall back to free-text on listSchemas failure
Snowflake setup previously asked for a single schema as free text, then
ran a multiselect against the discovered schemas — two schema questions
back-to-back, with the first being only a session bootstrap. The SDK's
`schema` is optional, so the bootstrap step is unnecessary.
- Remove the free-text Snowflake schema prompt; only pass `schema` to
snowflake-sdk when one is configured.
- When `listSchemas()` fails (e.g. role lacks SHOW SCHEMAS), prompt the
user for a comma-separated list, persist it as `schema_names`, and use
it as both the table-list filter and the multiselect default. Applies
to every driver with a scope-discovery spec, not just Snowflake.
- Update docs to lead with `schema_names`; keep `schema_name` as a
documented single-schema shorthand.
* fix(snowflake): keep introspecting when primary-key discovery is denied
The PK query joins INFORMATION_SCHEMA.TABLE_CONSTRAINTS and
INFORMATION_SCHEMA.KEY_COLUMN_USAGE, which require grants the
connection role may not have. Previously a 'SQL compilation error:
Object ANALYTICS.INFORMATION_SCHEMA.KEY_COLUMN_USAGE does not exist
or not authorized' aborted the entire introspect — schemas, columns,
and row counts were all discarded over a missing nice-to-have.
Wrap the constraint query in try/catch, log a one-line warning per
schema, and return an empty PK map. Columns end up with
primaryKey=false; relationship inference still has FK and profiling
to fall back on.
* fix(scan): unblock relationship discovery on Snowflake
Two adjacent bugs prevented the scan's relationship pipeline from producing
any joins on a Snowflake warehouse:
- relationship-profiling.ts fell through to a default `GROUP_CONCAT` branch
for unknown drivers. Snowflake has no GROUP_CONCAT, so every per-table
profile query failed with "Unknown function GROUP_CONCAT". Add an explicit
Snowflake branch that uses LISTAGG with a literal '\x1f' delimiter
(Snowflake requires the delimiter to be a constant, so CHR(31) is rejected).
- description-generation.ts destructured `connector.sampleTable` and
`connector.sampleColumn` into bare locals, losing the `this` binding when
the class-method connectors (Snowflake, Postgres, MySQL) were invoked.
Every sample call threw "Cannot read properties of undefined (reading
'assertConnection')" and degraded LLM descriptions to metadata-only
prompts. Call the methods through the connector instead.
Without these, even after the primary-key probe is allowed to fail softly,
the scan ends up with 0 validated relationships and an empty `joins:` block
in every shard YAML.
* test(scan): cover table-ref helpers
* feat(scan): plumb tableScope through live-database introspection port
* feat(scan): apply tableScope during metadata fetch
* feat(scan): enforce table scope at fetch boundary
* feat(scan): pool Snowflake sessions and batch enrichment for faster ingest (#206)
* feat(cli): add RSA key-pair auth option to Snowflake setup wizard
Extends the interactive Snowflake setup flow with an authentication-method
prompt (password vs RSA/JWT key-pair). The RSA branch collects a private-key
path (env/file/absolute) and an optional passphrase; the resulting connection
config records `authMethod: 'rsa'` with `privateKey` and `passphrase` instead
of `password`.
* feat(scan): pool Snowflake sessions
* fix(scan): reuse structural snapshots and cleanup connectors
* feat(scan): parallelize relationship profiling
* feat(scan): batch table description generation
* docs: document Snowflake ingest concurrency knobs
* fix(scan): close Snowflake ingest perf verification gaps
* fix(scan): keep batched description failure bounded
* feat(scan): dispatch query-history probes by connection driver
Extract historic-sql dialect resolution into a shared helper so the
status-project readiness check and the local ingest factory agree on
which connections enable query history and which probe to run. The
status command now picks the postgres/snowflake/bigquery probe based on
the connection's driver instead of always reporting against postgres,
which previously caused snowflake connections with queryHistory.enabled
to surface a misleading "driver is snowflake" failure.
Also drops a noisy console.warn from Snowflake primary-key discovery —
INFORMATION_SCHEMA.KEY_COLUMN_USAGE is commonly ungranted for read-only
roles and the FK + profiling paths handle the empty PK map already.
* fix(llm): allow StructuredOutput tool and raise maxTurns for generateObject
The Claude Code agent SDK announces an internal pseudo-tool named
StructuredOutput in the system/init message whenever outputFormat is set
to { type: 'json_schema' }. The runtime's isolation check built its
allowedToolIds set only from MCP tool ids and treated StructuredOutput
as an unexpected host-injected tool, so every generateObject call threw
"Claude Code runtime isolation failed: tools=StructuredOutput ..." and
the table-descriptions and relationship-LLM-proposal enrichment stages
recorded null output across the board.
Whitelist StructuredOutput specifically in generateObject's
allowedToolIds — the check also enforces missing_tools symmetry, so
generateText and runAgentLoop, which do not see StructuredOutput, must
not require it.
generateObject also ran with maxTurns: 1, which the model intermittently
breached when it emitted thinking text before the structured response.
Raised to 5 to give the schema-bound call enough headroom without
allowing unbounded loops. The existing tests now exercise the path with
an init message that announces StructuredOutput so the regression cannot
slip back in.
* chore(scripts): add ktx-reset.sh project-cleanup helper
Convenience script for repeatable ingest testing: takes a project
directory and prunes everything except ktx.yaml and .ktx/secrets/, so
the next ktx setup or ktx ingest run starts from a known-clean state.
2026-05-23 10:41:30 +02:00
|
|
|
it('limits introspection to tables in tableScope', async () => {
|
|
|
|
|
const ordersGet = vi.fn(async (): ReturnType<KtxBigQueryTableRef['get']> => [
|
|
|
|
|
{
|
|
|
|
|
metadata: {
|
|
|
|
|
type: 'TABLE',
|
|
|
|
|
numRows: '12',
|
|
|
|
|
schema: { fields: [{ name: 'id', type: 'INT64', mode: 'REQUIRED' }] },
|
|
|
|
|
},
|
|
|
|
|
},
|
|
|
|
|
]);
|
|
|
|
|
const skippedGet = vi.fn(async (): ReturnType<KtxBigQueryTableRef['get']> => [
|
|
|
|
|
{ metadata: { type: 'TABLE', numRows: '1', schema: { fields: [] } } },
|
|
|
|
|
]);
|
|
|
|
|
const clientFactory: KtxBigQueryClientFactory = {
|
|
|
|
|
createClient: vi.fn(() => ({
|
|
|
|
|
getDatasets: vi.fn(async (): ReturnType<KtxBigQueryClient['getDatasets']> => [[{ id: 'analytics' }]]),
|
|
|
|
|
dataset: vi.fn(
|
|
|
|
|
(): KtxBigQueryDataset => ({
|
|
|
|
|
get: vi.fn(async () => [{ id: 'analytics' }]),
|
|
|
|
|
getTables: vi.fn(async (): ReturnType<KtxBigQueryDataset['getTables']> => [
|
|
|
|
|
[
|
|
|
|
|
{ id: 'orders', get: ordersGet },
|
|
|
|
|
{ id: 'customers', get: skippedGet },
|
|
|
|
|
],
|
|
|
|
|
]),
|
|
|
|
|
}),
|
|
|
|
|
),
|
|
|
|
|
createQueryJob: vi.fn(async (): ReturnType<KtxBigQueryClient['createQueryJob']> => [
|
|
|
|
|
{
|
|
|
|
|
getQueryResults: async (): ReturnType<KtxBigQueryQueryJob['getQueryResults']> => [
|
|
|
|
|
[],
|
|
|
|
|
undefined,
|
|
|
|
|
{ schema: { fields: [{ name: 'table_name', type: 'STRING' }, { name: 'column_name', type: 'STRING' }] } },
|
|
|
|
|
],
|
|
|
|
|
},
|
|
|
|
|
]),
|
|
|
|
|
})),
|
|
|
|
|
};
|
|
|
|
|
const connector = new KtxBigQueryScanConnector({
|
|
|
|
|
connectionId: 'warehouse',
|
|
|
|
|
connection,
|
|
|
|
|
clientFactory,
|
|
|
|
|
});
|
|
|
|
|
const scope = tableRefSet([{ catalog: 'project-1', db: 'analytics', name: 'orders' }]);
|
|
|
|
|
const snapshot = await connector.introspect(
|
|
|
|
|
{ connectionId: 'warehouse', driver: 'bigquery', tableScope: scope },
|
|
|
|
|
{ runId: 'scope-test' },
|
|
|
|
|
);
|
|
|
|
|
expect(snapshot.tables.map((table) => table.name)).toEqual(['orders']);
|
|
|
|
|
expect(ordersGet).toHaveBeenCalledTimes(1);
|
|
|
|
|
expect(skippedGet).not.toHaveBeenCalled();
|
|
|
|
|
});
|
|
|
|
|
|
2026-05-22 14:22:11 +02:00
|
|
|
it('constructs for discovery without dataset scope and lists tables through one region information schema query', async () => {
|
|
|
|
|
const createQueryJob = vi.fn(
|
|
|
|
|
async (
|
|
|
|
|
input: { query: string; params?: Record<string, unknown>; location?: string },
|
|
|
|
|
): ReturnType<KtxBigQueryClient['createQueryJob']> => [
|
|
|
|
|
{
|
|
|
|
|
getQueryResults: async (): ReturnType<KtxBigQueryQueryJob['getQueryResults']> => [
|
|
|
|
|
[
|
|
|
|
|
{ table_schema: 'analytics', table_name: 'orders', table_type: 'BASE TABLE' },
|
|
|
|
|
{ table_schema: 'analytics', table_name: 'order_clone', table_type: 'CLONE' },
|
|
|
|
|
{ table_schema: 'mart', table_name: 'orders_mv', table_type: 'MATERIALIZED VIEW' },
|
|
|
|
|
],
|
|
|
|
|
undefined,
|
|
|
|
|
{
|
|
|
|
|
schema: {
|
|
|
|
|
fields: [
|
|
|
|
|
{ name: 'table_schema', type: 'STRING' },
|
|
|
|
|
{ name: 'table_name', type: 'STRING' },
|
|
|
|
|
{ name: 'table_type', type: 'STRING' },
|
|
|
|
|
],
|
|
|
|
|
},
|
|
|
|
|
},
|
|
|
|
|
],
|
|
|
|
|
},
|
|
|
|
|
],
|
|
|
|
|
);
|
|
|
|
|
const clientFactory: KtxBigQueryClientFactory = {
|
|
|
|
|
createClient: vi.fn(() => ({
|
|
|
|
|
getDatasets: vi.fn(async () => [[{ id: 'analytics' }, { id: 'mart' }]] as [{ id: string }[]]),
|
|
|
|
|
dataset: vi.fn((datasetId: string) => ({
|
|
|
|
|
get: vi.fn(async () => [{ id: datasetId }]),
|
|
|
|
|
getTables: vi.fn(async () => [[]] as [never[]]),
|
|
|
|
|
})),
|
|
|
|
|
createQueryJob,
|
|
|
|
|
})),
|
|
|
|
|
};
|
|
|
|
|
const connector = new KtxBigQueryScanConnector({
|
|
|
|
|
connectionId: 'warehouse',
|
|
|
|
|
connection: {
|
|
|
|
|
driver: 'bigquery',
|
|
|
|
|
credentials_json: JSON.stringify({ project_id: 'project-1' }),
|
|
|
|
|
location: 'US',
|
|
|
|
|
},
|
|
|
|
|
clientFactory,
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(connector.listTables(['analytics', 'mart'])).resolves.toEqual([
|
test: split cli tests from source tree (#216)
* feat(cli): define full warehouse dialect contract
* test(cli): keep dialect edge tests focused
* fix(cli): stabilize dialect contract foundation
* refactor(connectors): own read-only query preparation
* refactor(connectors): resolve dialects through registry
* refactor(connectors): keep concrete dialect classes internal
* chore(workspace): enforce dialect import boundary
* refactor(cli): resolve relationship dialect at scan boundary
* refactor(cli): use dialect display parsing for entity details
* refactor(cli): use dialect display parsing for warehouse catalog
* refactor(cli): use dialect SQL in relationship workflows
* test(cli): verify solid dialect scan workflow closure
* test: split cli tests from source tree
* refactor(cli): standardize BigQuery scope listing
* feat(sqlite): implement connector scope listing
* test(connectors): cover required table listing
* feat(cli): add warehouse driver registry
* refactor(setup): route scope discovery through driver registry
* refactor(cli): route local query execution through driver registry
* refactor(historic-sql): route dialect support through driver registry
* refactor(cli): test warehouse connections through driver registry
* fix(cli): close driver registry type export gaps
* Improve setup daemon diagnostics
* refactor(setup): centralize rail-prefixed diagnostics + query-history fallback
Extract errorMessage, writePrefixedLines, and flushPrefixedBufferedCommandOutput
into clack.ts so the setup wizard, managed daemons, and embedding/agent steps
share one rail-formatted writer. setup-databases.ts also adds a
"disable query history and retry" option when the schema-context build fails
and query history is the likely culprit, surfaced via a new
failed-query-history-unavailable status.
* fix(cli): carry catalog through the picker so BigQuery/Snowflake/SQL Server scope filters match
The setup picker's KtxTableListEntry was a 2-level { schema, name }, so
qualifiedTableId always wrote db.name into enabled_tables. When BigQuery,
Snowflake, or SQL Server later ran fast ingest, their introspect step filtered
the scope set with scopedTableNames(scope, { catalog: projectId|database, db })
— catalog was non-null on the introspect side but null in the scope refs, so
every entry was rejected, the live-database adapter staged zero table files,
and detect() failed with 'Adapter "live-database" did not recognize fetched
source output'.
Align the picker boundary with the canonical 3-level KtxTableRef:
- Add catalog: string | null to KtxTableListEntry.
- BigQuery/Snowflake/SQL Server listTables populate catalog from the
resolved projectId / database; Postgres/MySQL/ClickHouse/SQLite set null.
- qualifiedTableId emits catalog.schema.name when catalog is non-null
(resolveEnabledTables already accepts the 3-part shape) and
schemasFromEnabledTables now goes through parseDottedTableEntry so it
recovers the schema correctly from both 2-part and 3-part entries.
- Export parseDottedTableEntry from enabled-tables.ts (@internal) for picker
reuse.
Update listTables expectations in all seven connector tests and the setup /
picker test fixtures. Add a picker regression test that covers the
catalog-bearing round-trip (save + refine).
* fix(cli): allow debug telemetry under opt-out env
2026-05-26 08:49:05 +02:00
|
|
|
{ catalog: 'project-1', schema: 'analytics', name: 'orders', kind: 'table' },
|
|
|
|
|
{ catalog: 'project-1', schema: 'analytics', name: 'order_clone', kind: 'table' },
|
|
|
|
|
{ catalog: 'project-1', schema: 'mart', name: 'orders_mv', kind: 'view' },
|
2026-05-22 14:22:11 +02:00
|
|
|
]);
|
|
|
|
|
|
|
|
|
|
expect(createQueryJob).toHaveBeenCalledTimes(1);
|
|
|
|
|
expect(createQueryJob).toHaveBeenCalledWith(
|
|
|
|
|
expect.objectContaining({
|
|
|
|
|
location: 'US',
|
|
|
|
|
params: { dataset_ids: ['analytics', 'mart'] },
|
|
|
|
|
}),
|
|
|
|
|
);
|
|
|
|
|
expect(createQueryJob.mock.calls[0]?.[0].query).toContain('`project-1`.`region-us`.INFORMATION_SCHEMA.TABLES');
|
|
|
|
|
expect(createQueryJob.mock.calls[0]?.[0].query).toContain("'CLONE'");
|
|
|
|
|
expect(createQueryJob.mock.calls[0]?.[0].query).toContain("'SNAPSHOT'");
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
it('keeps scan paths requiring dataset scope', async () => {
|
|
|
|
|
const connector = new KtxBigQueryScanConnector({
|
|
|
|
|
connectionId: 'warehouse',
|
|
|
|
|
connection: {
|
|
|
|
|
driver: 'bigquery',
|
|
|
|
|
credentials_json: JSON.stringify({ project_id: 'project-1' }),
|
|
|
|
|
location: 'US',
|
|
|
|
|
},
|
|
|
|
|
clientFactory: fakeClientFactory(),
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
connector.introspect(
|
|
|
|
|
{ connectionId: 'warehouse', driver: 'bigquery' },
|
|
|
|
|
{ runId: 'scan-run-1' },
|
|
|
|
|
),
|
|
|
|
|
).rejects.toThrow('Native BigQuery scan requires connections.warehouse.dataset_ids or dataset_id');
|
|
|
|
|
});
|
|
|
|
|
|
2026-05-10 23:12:26 +02:00
|
|
|
it('applies maximumBytesBilled to read-only queries when configured', async () => {
|
|
|
|
|
const clientFactory = fakeClientFactory();
|
2026-05-10 23:51:24 +02:00
|
|
|
const connector = new KtxBigQueryScanConnector({
|
2026-05-10 23:12:26 +02:00
|
|
|
connectionId: 'warehouse',
|
|
|
|
|
connection,
|
|
|
|
|
clientFactory,
|
|
|
|
|
maxBytesBilled: 123456789,
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
connector.executeReadOnly(
|
|
|
|
|
{ connectionId: 'warehouse', sql: 'select id, status from `project-1`.`analytics`.`orders`', maxRows: 1 },
|
|
|
|
|
{ runId: 'scan-run-1' },
|
|
|
|
|
),
|
|
|
|
|
).resolves.toMatchObject({ rows: [[1, 'paid']], rowCount: 1 });
|
|
|
|
|
|
2026-05-10 23:51:24 +02:00
|
|
|
const client = vi.mocked(clientFactory.createClient).mock.results[0]?.value as KtxBigQueryClient;
|
2026-05-10 23:12:26 +02:00
|
|
|
expect(client.createQueryJob).toHaveBeenLastCalledWith(
|
|
|
|
|
expect.objectContaining({
|
|
|
|
|
maximumBytesBilled: '123456789',
|
|
|
|
|
}),
|
|
|
|
|
);
|
|
|
|
|
});
|
|
|
|
|
|
2026-05-14 01:27:31 +02:00
|
|
|
it('applies canonical BigQuery YAML scan limits to query jobs', async () => {
|
|
|
|
|
const clientFactory = fakeClientFactory();
|
|
|
|
|
const connector = new KtxBigQueryScanConnector({
|
|
|
|
|
connectionId: 'warehouse',
|
|
|
|
|
connection: { ...connection, max_bytes_billed: '987654321', job_timeout_ms: 30_000 },
|
|
|
|
|
clientFactory,
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
connector.executeReadOnly(
|
|
|
|
|
{ connectionId: 'warehouse', sql: 'select id, status from `project-1`.`analytics`.`orders`', maxRows: 1 },
|
|
|
|
|
{ runId: 'scan-run-1' },
|
|
|
|
|
),
|
|
|
|
|
).resolves.toMatchObject({ rows: [[1, 'paid']], rowCount: 1 });
|
|
|
|
|
|
|
|
|
|
const client = vi.mocked(clientFactory.createClient).mock.results[0]?.value as KtxBigQueryClient;
|
|
|
|
|
expect(client.createQueryJob).toHaveBeenLastCalledWith(
|
|
|
|
|
expect.objectContaining({
|
|
|
|
|
maximumBytesBilled: '987654321',
|
|
|
|
|
jobTimeoutMs: 30_000,
|
|
|
|
|
}),
|
|
|
|
|
);
|
|
|
|
|
});
|
|
|
|
|
|
2026-05-10 23:12:26 +02:00
|
|
|
it('adapts native snapshots to live-database introspection snapshots', async () => {
|
|
|
|
|
const introspection = createBigQueryLiveDatabaseIntrospection({
|
|
|
|
|
connections: { warehouse: connection },
|
|
|
|
|
clientFactory: fakeClientFactory(),
|
|
|
|
|
now: () => new Date('2026-04-29T17:00:00.000Z'),
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(introspection.extractSchema('warehouse')).resolves.toMatchObject({
|
|
|
|
|
connectionId: 'warehouse',
|
|
|
|
|
metadata: { project_id: 'project-1' },
|
|
|
|
|
tables: expect.arrayContaining([
|
|
|
|
|
expect.objectContaining({
|
|
|
|
|
catalog: 'project-1',
|
|
|
|
|
db: 'analytics',
|
|
|
|
|
name: 'orders',
|
|
|
|
|
columns: expect.arrayContaining([
|
|
|
|
|
{
|
|
|
|
|
name: 'id',
|
|
|
|
|
nativeType: 'INT64',
|
|
|
|
|
normalizedType: 'BIGINT',
|
|
|
|
|
dimensionType: 'number',
|
|
|
|
|
nullable: false,
|
|
|
|
|
primaryKey: true,
|
|
|
|
|
comment: 'Order id',
|
|
|
|
|
},
|
|
|
|
|
]),
|
|
|
|
|
}),
|
|
|
|
|
]),
|
|
|
|
|
});
|
|
|
|
|
});
|
|
|
|
|
});
|