mirror of
https://github.com/Kaelio/ktx.git
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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
430 lines
14 KiB
TypeScript
430 lines
14 KiB
TypeScript
import { readFile } from 'node:fs/promises';
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import { join } from 'node:path';
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import Database from 'better-sqlite3';
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import { afterEach, describe, expect, it, vi } from 'vitest';
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import { getDialectForDriver } from '../../../src/context/connections/dialects.js';
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import type { KtxEnrichedColumn, KtxEnrichedSchema, KtxEnrichedTable } from '../../../src/context/scan/enrichment-types.js';
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import { snapshotToKtxEnrichedSchema } from '../../../src/context/scan/local-enrichment.js';
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import { loadKtxRelationshipBenchmarkFixture, maskKtxRelationshipBenchmarkSnapshot } from '../../../src/context/scan/relationship-benchmarks.js';
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import { createKtxRelationshipProfileCache, profileKtxRelationshipSchema } from '../../../src/context/scan/relationship-profiling.js';
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import type { KtxQueryResult, KtxReadOnlyQueryInput, KtxScanContext } from '../../../src/context/scan/types.js';
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class InMemorySqliteExecutor {
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readonly db = new Database(':memory:');
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queryCount = 0;
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executeReadOnly(input: KtxReadOnlyQueryInput, _ctx: KtxScanContext): Promise<KtxQueryResult> {
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this.queryCount += 1;
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const rows = this.db.prepare(input.sql).all() as Record<string, unknown>[];
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const headers = Object.keys(rows[0] ?? {});
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return Promise.resolve({
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headers,
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rows: rows.map((row) => headers.map((header) => row[header])),
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totalRows: rows.length,
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rowCount: rows.length,
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});
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}
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close(): void {
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this.db.close();
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}
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}
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class FileSqliteExecutor {
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readonly db: Database.Database;
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queryCount = 0;
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constructor(dataPath: string) {
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this.db = new Database(dataPath, { readonly: true, fileMustExist: true });
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}
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executeReadOnly(input: KtxReadOnlyQueryInput, _ctx: KtxScanContext): Promise<KtxQueryResult> {
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this.queryCount += 1;
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const rows = this.db.prepare(input.sql).all() as Record<string, unknown>[];
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const headers = Object.keys(rows[0] ?? {});
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return Promise.resolve({
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headers,
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rows: rows.map((row) => headers.map((header) => row[header])),
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totalRows: rows.length,
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rowCount: rows.length,
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});
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}
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close(): void {
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this.db.close();
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}
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}
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function column(tableId: string, name: string, overrides: Partial<KtxEnrichedColumn> = {}): KtxEnrichedColumn {
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const tableRef = overrides.tableRef ?? { catalog: null, db: null, name: tableId };
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return {
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id: `${tableId}.${name}`,
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tableId,
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tableRef,
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name,
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nativeType: overrides.nativeType ?? 'INTEGER',
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normalizedType: overrides.normalizedType ?? 'integer',
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dimensionType: overrides.dimensionType ?? 'number',
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nullable: overrides.nullable ?? true,
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primaryKey: overrides.primaryKey ?? false,
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parentColumnId: null,
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descriptions: {},
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embedding: null,
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sampleValues: null,
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cardinality: null,
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...overrides,
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};
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}
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function table(name: string, columns: KtxEnrichedColumn[]): KtxEnrichedTable {
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const ref = { catalog: null, db: null, name };
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return {
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id: name,
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ref,
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enabled: true,
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descriptions: {},
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columns: columns.map((item) => ({ ...item, tableId: name, tableRef: ref })),
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};
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}
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function schema(tables: KtxEnrichedTable[]): KtxEnrichedSchema {
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return { connectionId: 'warehouse', tables, relationships: [] };
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}
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describe('relationship profiling', () => {
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let executor: InMemorySqliteExecutor | null = null;
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afterEach(() => {
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executor?.close();
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executor = null;
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});
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it('keeps profiling on the batched table path', async () => {
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const source = await readFile(new URL('../../../src/context/scan/relationship-profiling.ts', import.meta.url), 'utf-8');
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expect(source).not.toMatch(new RegExp('queryColumn' + 'Profile'));
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expect(source).not.toMatch(/for \(const column of table\.columns\)[\s\S]*executeReadOnly/);
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expect(source).toMatch(/queryTableProfile/);
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expect(source).toMatch(/UNION ALL/);
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});
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it('profiles row count, null rate, uniqueness, sample values, and text lengths', async () => {
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executor = new InMemorySqliteExecutor();
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executor.db.exec(`
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CREATE TABLE accounts (id INTEGER, code TEXT, parent_id INTEGER);
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INSERT INTO accounts (id, code, parent_id) VALUES
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(1, 'A-1', NULL),
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(2, 'B-2', 1),
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(3, 'C-3', 1),
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(4, 'C-3', 2);
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`);
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const result = await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: schema([
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table('accounts', [
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column('accounts', 'id', { primaryKey: false, nullable: false }),
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column('accounts', 'code', { nativeType: 'TEXT', normalizedType: 'text', dimensionType: 'string' }),
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column('accounts', 'parent_id'),
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]),
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]),
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executor,
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ctx: { runId: 'profile-test' },
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sampleValuesPerColumn: 3,
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});
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expect(result.sqlAvailable).toBe(true);
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expect(result.queryCount).toBe(1);
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expect(executor.queryCount).toBe(1);
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expect(result.tables).toHaveLength(1);
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expect(result.tables[0]).toMatchObject({ table: { name: 'accounts' }, rowCount: 4 });
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expect(result.columns['accounts.id']).toMatchObject({
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table: { name: 'accounts' },
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column: 'id',
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rowCount: 4,
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nullCount: 0,
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distinctCount: 4,
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uniquenessRatio: 1,
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nullRate: 0,
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minTextLength: 1,
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maxTextLength: 1,
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});
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expect(result.columns['accounts.code']).toMatchObject({
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distinctCount: 3,
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uniquenessRatio: 0.75,
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sampleValues: ['C-3', 'A-1', 'B-2'],
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minTextLength: 3,
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maxTextLength: 3,
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});
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expect(result.columns['accounts.parent_id']).toMatchObject({
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nullCount: 1,
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distinctCount: 2,
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uniquenessRatio: 0.5,
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nullRate: 0.25,
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});
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});
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it('profiles each enabled table with one read-only SQL query', async () => {
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executor = new InMemorySqliteExecutor();
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executor.db.exec(`
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CREATE TABLE accounts (id INTEGER, code TEXT, parent_id INTEGER);
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CREATE TABLE users (id INTEGER, account_id INTEGER);
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INSERT INTO accounts (id, code, parent_id) VALUES
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(1, 'A-1', NULL),
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(2, 'B-2', 1),
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(3, 'C-3', 1),
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(4, 'C-3', 2);
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INSERT INTO users (id, account_id) VALUES
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(10, 1),
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(11, 1),
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(12, 2);
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`);
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const result = await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: schema([
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table('accounts', [
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column('accounts', 'id', { nullable: false }),
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column('accounts', 'code', { nativeType: 'TEXT', normalizedType: 'text', dimensionType: 'string' }),
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column('accounts', 'parent_id'),
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]),
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table('users', [column('users', 'id', { nullable: false }), column('users', 'account_id')]),
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]),
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executor,
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ctx: { runId: 'profile-batched-query-count' },
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sampleValuesPerColumn: 3,
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});
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expect(result.sqlAvailable).toBe(true);
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expect(result.queryCount).toBe(2);
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expect(executor.queryCount).toBe(2);
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expect(result.tables).toEqual([
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{ table: { catalog: null, db: null, name: 'accounts' }, rowCount: 4 },
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{ table: { catalog: null, db: null, name: 'users' }, rowCount: 3 },
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]);
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expect(result.columns['accounts.code']).toMatchObject({
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distinctCount: 3,
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uniquenessRatio: 0.75,
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sampleValues: ['C-3', 'A-1', 'B-2'],
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});
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expect(result.columns['users.account_id']).toMatchObject({
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rowCount: 3,
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nullCount: 0,
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distinctCount: 2,
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uniquenessRatio: 2 / 3,
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});
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});
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it('bounds column profile statistics with profileSampleRows', async () => {
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const executor = new InMemorySqliteExecutor();
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executor.db.exec(`
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CREATE TABLE accounts (id INTEGER NOT NULL, account_code TEXT NOT NULL);
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INSERT INTO accounts VALUES (1, 'a1'), (2, 'a2'), (3, 'a3'), (4, 'a4');
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`);
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const profiles = await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: schema([
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table('accounts', [
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column('accounts', 'id', { nullable: false }),
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column('accounts', 'account_code', {
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nativeType: 'TEXT',
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normalizedType: 'text',
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dimensionType: 'string',
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nullable: false,
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}),
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]),
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]),
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executor,
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ctx: { runId: 'profile-sample-rows' },
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profileSampleRows: 2,
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});
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expect(profiles.queryCount).toBe(1);
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expect(executor.queryCount).toBe(1);
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expect(profiles.tables).toEqual([{ table: { catalog: null, db: null, name: 'accounts' }, rowCount: 4 }]);
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expect(profiles.columns['accounts.id']).toMatchObject({
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rowCount: 2,
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distinctCount: 2,
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uniquenessRatio: 1,
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});
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expect(profiles.columns['accounts.account_code']?.sampleValues).toEqual(['a1', 'a2']);
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executor.close();
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});
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it('reuses a profile cache inside one scan run but re-queries with a fresh cache', async () => {
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executor = new InMemorySqliteExecutor();
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executor.db.exec(`
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CREATE TABLE accounts (id INTEGER NOT NULL, account_code TEXT NOT NULL);
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INSERT INTO accounts VALUES (1, 'a1'), (2, 'a2'), (3, 'a2');
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`);
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const relationshipSchema = schema([
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table('accounts', [
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column('accounts', 'id', { nullable: false }),
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column('accounts', 'account_code', {
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nativeType: 'TEXT',
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normalizedType: 'text',
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dimensionType: 'string',
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nullable: false,
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}),
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]),
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]);
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const cache = createKtxRelationshipProfileCache();
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const first = await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: relationshipSchema,
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executor,
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ctx: { runId: 'profile-cache-run' },
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cache,
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});
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const second = await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: relationshipSchema,
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executor,
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ctx: { runId: 'profile-cache-run' },
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cache,
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});
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const third = await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: relationshipSchema,
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executor,
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ctx: { runId: 'profile-cache-fresh-run' },
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cache: createKtxRelationshipProfileCache(),
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});
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expect(first.queryCount).toBe(1);
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expect(second.queryCount).toBe(0);
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expect(third.queryCount).toBe(1);
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expect(executor.queryCount).toBe(2);
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expect(second.tables).toEqual(first.tables);
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expect(second.columns).toEqual(first.columns);
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});
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it('profiles the checked-in scale stress fixture with one query per table', async () => {
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const fixtureRoot = new URL('../../fixtures/relationship-benchmarks', import.meta.url);
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const fixture = await loadKtxRelationshipBenchmarkFixture(join(fixtureRoot.pathname, 'scale_stress_no_declared_constraints'));
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if (!fixture.dataPath) {
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throw new Error('scale_stress_no_declared_constraints is missing data.sqlite');
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}
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const maskedSnapshot = maskKtxRelationshipBenchmarkSnapshot(
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fixture.snapshot,
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'declared_pks_and_declared_fks_removed',
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);
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const scaleExecutor = new FileSqliteExecutor(fixture.dataPath);
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try {
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const result = await profileKtxRelationshipSchema({
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connectionId: fixture.snapshot.connectionId,
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dialect: getDialectForDriver(fixture.snapshot.driver),
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schema: snapshotToKtxEnrichedSchema(maskedSnapshot, new Map()),
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executor: scaleExecutor,
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ctx: { runId: 'scale-stress-profile-query-count' },
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profileSampleRows: 3,
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});
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expect(fixture.snapshot.tables).toHaveLength(400);
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expect(result.queryCount).toBe(400);
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expect(result.queryCount).toBeLessThanOrEqual(2 * fixture.snapshot.tables.length);
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expect(scaleExecutor.queryCount).toBe(400);
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} finally {
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scaleExecutor.close();
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}
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});
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it('profiles tables concurrently up to profileConcurrency', async () => {
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let inFlight = 0;
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let maxInFlight = 0;
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const executor = {
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executeReadOnly: vi.fn(async (input: KtxReadOnlyQueryInput) => {
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inFlight += 1;
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maxInFlight = Math.max(maxInFlight, inFlight);
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await new Promise((resolve) => setTimeout(resolve, 10));
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inFlight -= 1;
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return {
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headers: [
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'column_name',
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'table_row_count',
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'row_count',
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'null_count',
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'distinct_count',
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'min_text_length',
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'max_text_length',
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'sample_values',
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],
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rows: [[input.sql.includes('accounts') ? 'id' : 'account_id', 2, 2, 0, 2, 1, 2, '1\u001f2']],
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totalRows: 1,
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rowCount: 1,
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};
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}),
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};
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await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: schemaWithTables(['accounts', 'orders', 'payments', 'refunds']),
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executor,
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ctx: { runId: 'profile-concurrency' },
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profileConcurrency: 4,
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});
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expect(maxInFlight).toBe(4);
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});
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it('keeps profiling other tables when one table profile fails', async () => {
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const executor = {
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executeReadOnly: vi.fn(async (input: KtxReadOnlyQueryInput) => {
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if (input.sql.includes('"orders"')) {
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throw new Error('orders unavailable');
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}
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return {
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headers: [
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'column_name',
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'table_row_count',
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'row_count',
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'null_count',
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'distinct_count',
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'min_text_length',
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'max_text_length',
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'sample_values',
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],
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rows: [['id', 2, 2, 0, 2, 1, 2, '1\u001f2']],
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totalRows: 1,
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rowCount: 1,
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};
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}),
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};
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const result = await profileKtxRelationshipSchema({
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connectionId: 'warehouse',
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dialect: getDialectForDriver('sqlite'),
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schema: schemaWithTables(['accounts', 'orders']),
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executor,
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ctx: { runId: 'profile-error-isolated' },
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profileConcurrency: 2,
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});
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expect(result.warnings).toContain('profile_failed:orders:orders unavailable');
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expect(result.tables).toHaveLength(2);
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expect(Object.keys(result.columns)).toContain('accounts.id');
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});
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});
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function schemaWithTables(names: string[]): KtxEnrichedSchema {
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return schema(
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names.map((name) =>
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table(name, [
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column(name, name === 'orders' ? 'account_id' : 'id', {
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nullable: false,
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primaryKey: name !== 'orders',
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}),
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]),
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),
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);
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}
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