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56985b7e09
* 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
44 lines
2.1 KiB
TypeScript
44 lines
2.1 KiB
TypeScript
import { describe, expect, it } from 'vitest';
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import { KtxBigQueryDialect } from '../../../src/connectors/bigquery/dialect.js';
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describe('KtxBigQueryDialect', () => {
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const dialect = new KtxBigQueryDialect();
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it('quotes identifiers and formats project.dataset.table names', () => {
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expect(dialect.quoteIdentifier('order`items')).toBe('`order\\`items`');
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expect(dialect.formatTableName({ catalog: 'project-1', db: 'analytics', name: 'orders' })).toBe(
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'`project-1`.`analytics`.`orders`',
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);
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expect(dialect.formatTableName({ db: 'analytics', name: 'orders' })).toBe('`analytics`.`orders`');
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expect(dialect.formatTableName({ name: 'orders' })).toBe('`orders`');
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});
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it('maps native BigQuery types to normalized types and scan dimensions', () => {
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expect(dialect.mapDataType('INT64')).toBe('BIGINT');
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expect(dialect.mapDataType('STRUCT')).toBe('JSON');
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expect(dialect.mapDataType('GEOGRAPHY')).toBe('GEOGRAPHY');
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expect(dialect.mapToDimensionType('TIMESTAMP')).toBe('time');
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expect(dialect.mapToDimensionType('NUMERIC')).toBe('number');
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expect(dialect.mapToDimensionType('BOOL')).toBe('boolean');
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expect(dialect.mapToDimensionType('JSON')).toBe('string');
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});
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it('generates sampling, cardinality, and distinct-value SQL', () => {
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expect(dialect.generateSampleQuery('`p`.`d`.`orders`', 5, ['id', 'status'])).toBe(
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'SELECT `id`, `status` FROM `p`.`d`.`orders` ORDER BY RAND() LIMIT 5',
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);
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expect(dialect.generateColumnSampleQuery('`p`.`d`.`orders`', 'status', 10)).toBe(
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"SELECT `status` FROM `p`.`d`.`orders` WHERE `status` IS NOT NULL AND TRIM(CAST(`status` AS STRING)) != '' ORDER BY RAND() LIMIT 10",
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);
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expect(dialect.generateCardinalitySampleQuery('`p`.`d`.`orders`', '`status`', 100)).toContain(
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'SELECT APPROX_COUNT_DISTINCT(val) AS cardinality',
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);
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expect(dialect.generateDistinctValuesQuery('`p`.`d`.`orders`', '`status`', 20)).toContain(
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'SELECT DISTINCT CAST(`status` AS STRING) AS val',
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);
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});
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it('keeps unsupported statistics explicit', () => {
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expect(dialect.generateColumnStatisticsQuery('analytics', 'orders')).toBeNull();
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});
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});
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