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* 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
146 lines
5 KiB
TypeScript
146 lines
5 KiB
TypeScript
import { describe, expect, it, vi } from 'vitest';
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import { validateSingleSource } from '../../../../src/context/sl/tools/sl-warehouse-validation.js';
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function makeDeps(opts: { sourceYaml: string; executeQuery: ReturnType<typeof vi.fn> }) {
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return {
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semanticLayerService: {
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readSourceFile: vi.fn().mockResolvedValue({ content: opts.sourceYaml, path: 'x' }),
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isManifestBacked: vi.fn().mockResolvedValue(false),
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listManifestSourceNames: vi.fn().mockResolvedValue([]),
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loadSource: vi.fn().mockResolvedValue(null),
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loadAllSources: vi.fn().mockResolvedValue({ sources: [], loadErrors: [] }),
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validatePhysicalTableReferences: vi.fn().mockResolvedValue([]),
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} as never,
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connections: {
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executeQuery: opts.executeQuery,
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getConnectionById: vi.fn().mockResolvedValue({ id: 'conn-1', name: 'conn-1', connectionType: 'bigquery' }),
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listEnabledConnections: vi.fn().mockResolvedValue([]),
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} as never,
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configService: {} as never,
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gitService: {} as never,
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slSourcesRepository: { deleteByConnectionAndName: vi.fn().mockResolvedValue(undefined) } as never,
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probeRowCount: 1,
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};
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}
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describe('validateSingleSource warehouse dry-run', () => {
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it('surfaces warehouse error when dry-run fails on unknown column', async () => {
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const yaml = `name: fct_arr_delta
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source_type: sql
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sql: |
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SELECT * FROM analytics.fct_arr_delta WHERE date_date < CURRENT_DATE()
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grain: [date_date]
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columns:
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- name: date_date
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type: time
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measures:
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- name: count_delta_events
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expr: count(*)
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joins: []
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`;
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const executeQuery = vi.fn().mockRejectedValue(new Error('Unrecognized name: date_date at [1:42]'));
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const deps = makeDeps({ sourceYaml: yaml, executeQuery });
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const result = await validateSingleSource(deps, 'conn-1', 'fct_arr_delta');
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expect(result.errors.join('\n')).toMatch(/Unrecognized name: date_date/);
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expect(result.errors.join('\n')).toMatch(/embedded sql dry-run failed/);
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});
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it('flags declared columns missing from the dry-run result', async () => {
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const yaml = `name: fct_arr_delta
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source_type: sql
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sql: |
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SELECT date, customer_id FROM analytics.fct_arr_delta
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columns:
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- name: date_date
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type: time
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- name: customer_id
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type: string
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measures:
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- name: count_delta
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expr: count(*)
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joins: []
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grain: [customer_id]
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`;
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const executeQuery = vi.fn().mockResolvedValue({
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headers: ['date', 'customer_id'],
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rows: [],
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totalRows: 0,
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error: null,
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});
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const deps = makeDeps({ sourceYaml: yaml, executeQuery });
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const result = await validateSingleSource(deps, 'conn-1', 'fct_arr_delta');
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expect(result.errors.join('\n')).toMatch(/declared columns absent from sql result — date_date/);
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expect(result.errors.join('\n')).toMatch(/warehouse returned:/);
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});
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it('passes cleanly when dry-run succeeds and declared columns match', async () => {
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const yaml = `name: lab_results
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source_type: sql
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sql: |
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SELECT lab_order_id, admin_user_id FROM analytics.raw_lab_results
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grain: [lab_order_id]
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columns:
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- name: lab_order_id
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type: string
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- name: admin_user_id
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type: string
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measures:
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- name: count_lab_results
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expr: count(lab_order_id)
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joins: []
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`;
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const executeQuery = vi.fn().mockResolvedValue({
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headers: ['lab_order_id', 'admin_user_id'],
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rows: [],
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totalRows: 0,
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error: null,
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});
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const deps = makeDeps({ sourceYaml: yaml, executeQuery });
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const result = await validateSingleSource(deps, 'conn-1', 'lab_results');
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expect(result.errors).toEqual([]);
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});
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it('uses LIMIT 1 (not LIMIT 0) so runtime policies fire', async () => {
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const yaml = `name: foo
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source_type: sql
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sql: |
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SELECT a FROM analytics.bar
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grain: [a]
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columns:
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- {name: a, type: string}
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measures: []
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joins: []
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`;
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const executeQuery = vi.fn().mockResolvedValue({ headers: ['a'], rows: [], totalRows: 0, error: null });
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const deps = makeDeps({ sourceYaml: yaml, executeQuery });
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await validateSingleSource(deps, 'conn-1', 'foo');
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const probeSql = executeQuery.mock.calls[0][1] as string;
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expect(probeSql).toMatch(/LIMIT 1\b/);
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expect(probeSql).not.toMatch(/LIMIT 0\b/);
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});
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it('adds physical manifest errors for table-backed sources', async () => {
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const yaml = `name: int_active_contract_arr
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table: orbit_analytics.int_active_contract_arr
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grain: [contract_id]
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columns:
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- {name: contract_id, type: string}
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- {name: arr_cents, type: number}
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measures:
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- {name: arr, expr: sum(arr_cents)}
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joins: []
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`;
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const executeQuery = vi.fn();
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const deps = makeDeps({ sourceYaml: yaml, executeQuery }) as any;
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deps.semanticLayerService.validatePhysicalTableReferences.mockResolvedValue([
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'int_active_contract_arr.yaml: declared column(s) absent from physical table: arr_cents',
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]);
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const result = await validateSingleSource(deps, 'conn-1', 'int_active_contract_arr');
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expect(result.errors).toContain(
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'int_active_contract_arr.yaml: declared column(s) absent from physical table: arr_cents',
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);
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expect(executeQuery).not.toHaveBeenCalled();
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});
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});
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