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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
193 lines
5.6 KiB
TypeScript
193 lines
5.6 KiB
TypeScript
import type { KtxDialect } from '../../context/connections/dialects.js';
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import {
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columnDisplayPartCount,
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formatDialectDisplayRef,
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formatDialectTableName,
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limitOffsetClause,
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parseDialectDisplayRef,
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} from '../../context/connections/dialect-helpers.js';
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import type { KtxSchemaDimensionType, KtxTableRef } from '../../context/scan/types.js';
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type BigQueryTableNameRef = Pick<KtxTableRef, 'name'> & Partial<Pick<KtxTableRef, 'catalog' | 'db'>>;
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/** @internal */
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export class KtxBigQueryDialect implements KtxDialect {
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readonly type = 'bigquery' as const;
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private readonly typeMappings: Record<string, KtxSchemaDimensionType> = {
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TIMESTAMP: 'time',
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DATETIME: 'time',
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DATE: 'time',
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TIME: 'time',
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INT64: 'number',
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INTEGER: 'number',
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FLOAT64: 'number',
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FLOAT: 'number',
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NUMERIC: 'number',
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BIGNUMERIC: 'number',
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STRING: 'string',
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BYTES: 'string',
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BOOL: 'boolean',
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BOOLEAN: 'boolean',
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};
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quoteIdentifier(identifier: string): string {
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return `\`${identifier.replace(/`/g, '\\`')}\``;
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}
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formatTableName(table: BigQueryTableNameRef): string {
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return formatDialectTableName(table, this.quoteIdentifier.bind(this), 'three-part');
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}
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formatDisplayRef(table: BigQueryTableNameRef): string {
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return formatDialectDisplayRef(table, 'three-part');
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}
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parseDisplayRef(display: string): KtxTableRef | null {
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return parseDialectDisplayRef(display, 'three-part');
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}
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columnDisplayTablePartCount(): 1 | 2 | 3 {
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return columnDisplayPartCount('three-part');
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}
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mapDataType(nativeType: string): string {
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const fieldType = nativeType.toUpperCase().trim();
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if (fieldType === 'RECORD' || fieldType === 'STRUCT') {
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return 'JSON';
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}
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const typeMapping: Record<string, string> = {
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STRING: 'VARCHAR',
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BYTES: 'VARBINARY',
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INTEGER: 'BIGINT',
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INT64: 'BIGINT',
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FLOAT: 'DOUBLE',
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FLOAT64: 'DOUBLE',
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NUMERIC: 'DECIMAL',
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BIGNUMERIC: 'DECIMAL',
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BOOLEAN: 'BOOLEAN',
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BOOL: 'BOOLEAN',
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TIMESTAMP: 'TIMESTAMP',
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DATE: 'DATE',
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TIME: 'TIME',
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DATETIME: 'DATETIME',
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GEOGRAPHY: 'GEOGRAPHY',
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JSON: 'JSON',
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};
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return typeMapping[fieldType] || fieldType;
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}
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mapToDimensionType(nativeType: string): KtxSchemaDimensionType {
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if (!nativeType) {
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return 'string';
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}
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const normalizedType = nativeType.toUpperCase().trim();
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if (this.typeMappings[normalizedType]) {
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return this.typeMappings[normalizedType];
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}
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if (normalizedType.includes('TIME') || normalizedType.includes('DATE')) {
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return 'time';
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}
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if (normalizedType.includes('INT') || normalizedType.includes('NUM') || normalizedType.includes('FLOAT')) {
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return 'number';
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}
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if (normalizedType.includes('BOOL')) {
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return 'boolean';
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}
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return 'string';
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}
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generateSampleQuery(tableName: string, limit: number, columns?: string[]): string {
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const columnList =
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columns && columns.length > 0 ? columns.map((column) => this.quoteIdentifier(column)).join(', ') : '*';
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return `SELECT ${columnList} FROM ${tableName} ORDER BY RAND() LIMIT ${limit}`;
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}
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generateColumnSampleQuery(tableName: string, columnName: string, limit: number): string {
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const quotedColumn = this.quoteIdentifier(columnName);
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return `SELECT ${quotedColumn} FROM ${tableName} WHERE ${quotedColumn} IS NOT NULL AND TRIM(CAST(${quotedColumn} AS STRING)) != '' ORDER BY RAND() LIMIT ${limit}`;
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}
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getRandomSampleFilter(samplePct: number): string {
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if (samplePct <= 0 || samplePct >= 1) {
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return '';
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}
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return `RAND() < ${samplePct}`;
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}
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getTableSampleClause(samplePct: number): string {
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if (samplePct <= 0 || samplePct >= 1) {
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return '';
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}
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return `TABLESAMPLE SYSTEM (${samplePct * 100} PERCENT)`;
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}
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getLimitOffsetClause(limit: number, offset?: number): string {
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return limitOffsetClause(limit, offset);
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}
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getTopClause(_limit: number): string {
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return '';
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}
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getNullCountExpression(column: string): string {
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return `COUNTIF(${column} IS NULL)`;
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}
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getDistinctCountExpression(column: string): string {
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return `APPROX_COUNT_DISTINCT(${column})`;
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}
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textLengthExpression(columnSql: string): string {
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return `LENGTH(CAST(${columnSql} AS STRING))`;
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}
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castToText(columnSql: string): string {
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return `CAST(${columnSql} AS STRING)`;
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}
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getSampleValueAggregation(innerSql: string): string {
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return `(SELECT STRING_AGG(CAST(value AS STRING), '\\u001F') FROM (${innerSql}) AS relationship_profile_values)`;
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}
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generateCardinalitySampleQuery(tableName: string, columnName: string, sampleSize: number): string {
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return `
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WITH sampled AS (
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SELECT ${columnName} AS val
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FROM ${tableName}
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WHERE ${columnName} IS NOT NULL
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LIMIT ${sampleSize}
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)
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SELECT APPROX_COUNT_DISTINCT(val) AS cardinality
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FROM sampled
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`;
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}
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generateDistinctValuesQuery(tableName: string, columnName: string, limit: number): string {
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return `
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SELECT DISTINCT CAST(${columnName} AS STRING) AS val
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FROM ${tableName}
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WHERE ${columnName} IS NOT NULL
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ORDER BY val
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LIMIT ${limit}
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`;
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}
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generateColumnStatisticsQuery(_schemaName: string, _tableName: string): string | null {
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return null;
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}
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generateRandomizedCardinalitySampleQuery(tableName: string, columnName: string, sampleSize: number): string {
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return `
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WITH sampled AS (
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SELECT ${columnName} AS val
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FROM ${tableName}
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WHERE ${columnName} IS NOT NULL
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ORDER BY RAND()
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LIMIT ${sampleSize}
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)
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SELECT APPROX_COUNT_DISTINCT(val) AS cardinality
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FROM sampled
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`;
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}
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}
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