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149x 147x 147x 16x 16x 10x 10x 567x 567x 567x 1x 566x 567x 224x 263x 263x 263x 263x 263x 12x 12x 12x 12x 12x 251x 251x 251x 566x 101x 108x 108x 108x 108x 108x 23x 23x 23x 23x 23x 85x 85x 85x 566x 567x 567x 108x 567x 68x 75x 1x 74x 75x 566x 101x 101x 7x 101x 108x 80x 566x 533x 533x 533x 532x 533x 196x 236x 236x 236x 236x 236x 86x 533x 566x 566x 1x 566x 566x 51x 1x 50x 565x 17x 1x 16x 564x | /**
* Shared Query Builder
* Contains all SQL building logic that was previously duplicated between executor and multi-cube-builder
* Single source of truth for all SQL generation
*/
import {
sql,
and,
or,
eq,
ne,
gt,
gte,
lt,
lte,
isNull,
isNotNull,
inArray,
notInArray,
count,
sum,
min,
max,
countDistinct,
asc,
desc,
SQL,
type AnyColumn,
arrayContains as drizzleArrayContains,
arrayContained as drizzleArrayContained,
arrayOverlaps as drizzleArrayOverlaps
} from 'drizzle-orm'
import type {
SemanticQuery,
FilterOperator,
Filter,
FilterCondition,
LogicalFilter,
TimeGranularity,
Cube,
QueryContext,
QueryPlan,
JoinKeyInfo
} from './types'
import { resolveSqlExpression } from './cube-utils'
import type { DatabaseAdapter } from './adapters/base-adapter'
import { CalculatedMeasureResolver } from './calculated-measure-resolver'
import { substituteTemplate, getMemberReferences, type ResolvedMeasures } from './template-substitution'
export class QueryBuilder {
constructor(private databaseAdapter: DatabaseAdapter) {}
/**
* Build resolvedMeasures map for a set of measures
* This centralizes the logic for building both regular and calculated measures
* in dependency order, avoiding duplication across main queries and CTEs
*
* @param measureNames - Array of measure names to resolve (e.g., ["Employees.count", "Employees.activePercentage"])
* @param cubeMap - Map of all cubes involved in the query
* @param context - Query context with database and security context
* @param customMeasureBuilder - Optional function to override how individual measures are built
* @returns Map of measure names to SQL builder functions
*/
buildResolvedMeasures(
measureNames: string[],
cubeMap: Map<string, Cube>,
context: QueryContext,
customMeasureBuilder?: (measureName: string, measure: any, cube: Cube) => SQL
): ResolvedMeasures {
const resolvedMeasures: ResolvedMeasures = new Map()
const regularMeasures: string[] = []
const calculatedMeasures: string[] = []
const allMeasuresToResolve = new Set<string>(measureNames)
// Build dependency graph
const resolver = new CalculatedMeasureResolver(cubeMap)
for (const cube of cubeMap.values()) {
resolver.buildGraph(cube)
}
// First pass: classify user-requested measures and collect dependencies
for (const measureName of measureNames) {
const [cubeName, fieldName] = measureName.split('.')
const cube = cubeMap.get(cubeName)
if (cube && cube.measures && cube.measures[fieldName]) {
const measure = cube.measures[fieldName]
if (CalculatedMeasureResolver.isCalculatedMeasure(measure)) {
calculatedMeasures.push(measureName)
// Add all dependencies to measures that need to be resolved
const deps = getMemberReferences(measure.calculatedSql!, cubeName)
deps.forEach(dep => allMeasuresToResolve.add(dep))
// Also add transitive calculated measure dependencies
const calculatedDeps = resolver.getAllDependencies(measureName)
calculatedDeps.forEach(dep => {
const [depCubeName, depFieldName] = dep.split('.')
const depCube = cubeMap.get(depCubeName)
Eif (depCube && depCube.measures[depFieldName]) {
const depMeasure = depCube.measures[depFieldName]
if (CalculatedMeasureResolver.isCalculatedMeasure(depMeasure)) {
const nestedDeps = getMemberReferences(depMeasure.calculatedSql!, depCubeName)
nestedDeps.forEach(nestedDep => allMeasuresToResolve.add(nestedDep))
}
}
})
} else {
regularMeasures.push(measureName)
}
}
}
// Second pass: classify all measures that need to be resolved (including dependencies)
for (const measureName of allMeasuresToResolve) {
const [cubeName, fieldName] = measureName.split('.')
const cube = cubeMap.get(cubeName)
if (cube && cube.measures && cube.measures[fieldName]) {
const measure = cube.measures[fieldName]
if (!CalculatedMeasureResolver.isCalculatedMeasure(measure)) {
if (!regularMeasures.includes(measureName)) {
regularMeasures.push(measureName)
}
} else {
if (!calculatedMeasures.includes(measureName)) {
calculatedMeasures.push(measureName)
}
}
}
}
// Build regular measures first
for (const measureName of regularMeasures) {
const [cubeName, fieldName] = measureName.split('.')
const cube = cubeMap.get(cubeName)!
const measure = cube.measures[fieldName]
// Use custom builder if provided, otherwise use default
Iif (customMeasureBuilder) {
const builtExpr = customMeasureBuilder(measureName, measure, cube)
resolvedMeasures.set(measureName, () => builtExpr)
} else {
// Store a FUNCTION that builds the SQL expression to avoid mutation issues
resolvedMeasures.set(measureName, () => this.buildMeasureExpression(measure, context))
}
}
// Build calculated measures in dependency order
if (calculatedMeasures.length > 0) {
const sortedCalculated = resolver.topologicalSort(calculatedMeasures)
for (const measureName of sortedCalculated) {
const [cubeName, fieldName] = measureName.split('.')
const cube = cubeMap.get(cubeName)!
const measure = cube.measures[fieldName]
// Store a FUNCTION that builds the calculated measure SQL
resolvedMeasures.set(measureName, () => this.buildCalculatedMeasure(
measure,
cube,
cubeMap,
resolvedMeasures,
context
))
}
}
return resolvedMeasures
}
/**
* Build dynamic selections for measures, dimensions, and time dimensions
* Works for both single and multi-cube queries
* Handles calculated measures with dependency resolution
*/
buildSelections(
cubes: Map<string, Cube> | Cube,
query: SemanticQuery,
context: QueryContext
): Record<string, SQL | AnyColumn> {
const selections: Record<string, SQL | AnyColumn> = {}
// Convert single cube to map for consistent handling
const cubeMap = cubes instanceof Map ? cubes : new Map([[cubes.name, cubes]])
// Add dimensions
if (query.dimensions) {
for (const dimensionName of query.dimensions) {
const [cubeName, fieldName] = dimensionName.split('.')
const cube = cubeMap.get(cubeName)
Eif (cube && cube.dimensions && cube.dimensions[fieldName]) {
const dimension = cube.dimensions[fieldName]
const sqlExpr = resolveSqlExpression(dimension.sql, context)
// Use explicit alias for dimension expressions so they can be referenced in ORDER BY
selections[dimensionName] = sql`${sqlExpr}`.as(dimensionName) as unknown as SQL
}
}
}
// Add measures with aggregations using the centralized helper
if (query.measures) {
const resolvedMeasures = this.buildResolvedMeasures(
query.measures,
cubeMap,
context
)
// Add user-requested measures to selections
for (const measureName of query.measures) {
const measureBuilder = resolvedMeasures.get(measureName)
Eif (measureBuilder) {
const measureExpr = measureBuilder()
selections[measureName] = sql`${measureExpr}`.as(measureName) as unknown as SQL
}
}
}
// Add time dimensions with granularity
if (query.timeDimensions) {
for (const timeDim of query.timeDimensions) {
const [cubeName, fieldName] = timeDim.dimension.split('.')
const cube = cubeMap.get(cubeName)
Eif (cube && cube.dimensions && cube.dimensions[fieldName]) {
const dimension = cube.dimensions[fieldName]
const timeExpr = this.buildTimeDimensionExpression(
dimension.sql,
timeDim.granularity,
context
)
// Use explicit alias for time dimension expressions so they can be referenced in ORDER BY
selections[timeDim.dimension] = sql`${timeExpr}`.as(timeDim.dimension) as unknown as SQL
}
}
}
// Default to COUNT(*) if no selections
Iif (Object.keys(selections).length === 0) {
selections.count = count()
}
return selections
}
/**
* Build calculated measure expression by substituting {member} references
* with resolved SQL expressions
*/
public buildCalculatedMeasure(
measure: any,
cube: Cube,
allCubes: Map<string, Cube>,
resolvedMeasures: ResolvedMeasures,
context: QueryContext
): SQL {
Iif (!measure.calculatedSql) {
throw new Error(
`Calculated measure '${cube.name}.${measure.name}' missing calculatedSql property`
)
}
// Preprocess template for database-specific transformations (e.g., SQLite float division)
const preprocessedSql = this.databaseAdapter.preprocessCalculatedTemplate(measure.calculatedSql)
// Substitute {member} references with resolved SQL
const substitutedSql = substituteTemplate(preprocessedSql, {
cube,
allCubes,
resolvedMeasures,
queryContext: context
})
return substitutedSql
}
/**
* Build resolved measures map for a calculated measure from CTE columns
* This handles re-aggregating pre-aggregated CTE columns for calculated measures
*
* IMPORTANT: For calculated measures in CTEs, we cannot sum/avg pre-computed ratios.
* We must recalculate from the base measures that were pre-aggregated in the CTE.
*
* @param measure - The calculated measure to build
* @param cube - The cube containing this measure
* @param cteInfo - CTE metadata (alias, measures, cube reference)
* @param allCubes - Map of all cubes in the query
* @param context - Query context
* @returns SQL expression for the calculated measure using CTE column references
*/
public buildCTECalculatedMeasure(
measure: any,
cube: Cube,
cteInfo: { cteAlias: string; measures: string[]; cube: Cube },
allCubes: Map<string, Cube>,
context: QueryContext
): SQL {
Iif (!measure.calculatedSql) {
throw new Error(
`Calculated measure '${cube.name}.${measure.name || 'unknown'}' missing calculatedSql property`
)
}
// Build a resolvedMeasures map with CTE column references
const cteResolvedMeasures = new Map<string, () => SQL>()
// Get all dependencies for this calculated measure
const deps = getMemberReferences(measure.calculatedSql, cube.name)
for (const depMeasureName of deps) {
const [depCubeName, depFieldName] = depMeasureName.split('.')
const depCube = allCubes.get(depCubeName)
Eif (depCube && depCube.measures[depFieldName]) {
const depMeasure = depCube.measures[depFieldName]
// Check if this dependency is also in the CTE
Eif (cteInfo.measures.includes(depMeasureName)) {
// Reference the CTE column and apply appropriate aggregation
const cteDepColumn = sql`${sql.identifier(cteInfo.cteAlias)}.${sql.identifier(depFieldName)}`
// Apply aggregation based on the dependency's type
// For pre-aggregated values in CTEs, we need to re-aggregate them properly:
// - count/sum values should be summed
// - avg values should be averaged (though ideally weighted average)
// - min/max values should take min/max
let aggregatedDep: SQL
switch (depMeasure.type) {
case 'count':
case 'countDistinct':
case 'sum':
aggregatedDep = sum(cteDepColumn)
break
case 'avg':
aggregatedDep = this.databaseAdapter.buildAvg(cteDepColumn)
break
case 'min':
aggregatedDep = min(cteDepColumn)
break
case 'max':
aggregatedDep = max(cteDepColumn)
break
case 'number':
aggregatedDep = sum(cteDepColumn)
break
default:
aggregatedDep = sum(cteDepColumn)
}
// Store the aggregated CTE column as a builder function
cteResolvedMeasures.set(depMeasureName, () => aggregatedDep)
}
}
}
// Re-apply the calculated measure template with CTE-based dependencies
return this.buildCalculatedMeasure(
measure,
cube,
allCubes,
cteResolvedMeasures,
context
)
}
/**
* Build measure expression for HAVING clause, handling CTE references correctly
*/
private buildHavingMeasureExpression(
cubeName: string,
fieldKey: string,
measure: any,
context: QueryContext,
queryPlan?: QueryPlan
): SQL {
// Check if this measure is from a CTE cube
if (queryPlan && queryPlan.preAggregationCTEs) {
const cteInfo = queryPlan.preAggregationCTEs.find(cte => cte.cube.name === cubeName)
if (cteInfo && cteInfo.measures.includes(`${cubeName}.${fieldKey}`)) {
// This measure is from a CTE - reference the CTE alias instead of the original table
Iif (measure.type === 'calculated' && measure.calculatedSql) {
// Get the cube for this measure
const cube = queryPlan.primaryCube.name === cubeName
? queryPlan.primaryCube
: queryPlan.joinCubes?.find(jc => jc.cube.name === cubeName)?.cube
if (!cube) {
throw new Error(`Cube ${cubeName} not found in query plan`)
}
// Build a cubeMap for the calculated measure builder
const cubeMap = new Map<string, Cube>([[queryPlan.primaryCube.name, queryPlan.primaryCube]])
if (queryPlan.joinCubes) {
for (const jc of queryPlan.joinCubes) {
cubeMap.set(jc.cube.name, jc.cube)
}
}
// Use the shared helper to build calculated measure from CTE columns
return this.buildCTECalculatedMeasure(
measure,
cube,
cteInfo,
cubeMap,
context
)
} else {
// For non-calculated measures, aggregate the CTE column directly
const cteColumn = sql`${sql.identifier(cteInfo.cteAlias)}.${sql.identifier(fieldKey)}`
// Apply aggregation function based on measure type
switch (measure.type) {
case 'count':
case 'countDistinct':
case 'sum':
return sum(cteColumn)
case 'avg':
// For average of averages, we should use a weighted average, but for now use simple avg
return this.databaseAdapter.buildAvg(cteColumn)
case 'min':
return min(cteColumn)
case 'max':
return max(cteColumn)
case 'number':
// For number type, use sum to combine values
return sum(cteColumn)
default:
return sum(cteColumn)
}
}
}
}
// Not from CTE - use regular measure expression
return this.buildMeasureExpression(measure, context)
}
/**
* Build measure expression with aggregation and filters
* Note: This should NOT be called for calculated measures
*/
buildMeasureExpression(
measure: any,
context: QueryContext
): SQL {
// Calculated measures should be built via buildCalculatedMeasure
Iif (measure.type === 'calculated') {
throw new Error(
`Cannot build calculated measure '${measure.name}' directly. ` +
`Use buildCalculatedMeasure instead.`
)
}
// Non-calculated measures must have sql property
Iif (!measure.sql) {
throw new Error(
`Measure '${measure.name}' of type '${measure.type}' is missing required 'sql' property. ` +
`Only calculated measures can omit 'sql'.`
)
}
// resolveSqlExpression already applies isolation via isolateSqlExpression()
// This protects against Drizzle SQL object mutation during reuse
let baseExpr = resolveSqlExpression(measure.sql, context)
// Apply measure filters if they exist
if (measure.filters && measure.filters.length > 0) {
const filterConditions = measure.filters.map((filter: (ctx: QueryContext) => SQL) => {
const filterResult = filter(context)
// Single wrap is OK here - we're creating fresh SQL for grouping in parentheses
// The filter function itself should handle isolation if needed
return filterResult ? sql`(${filterResult})` : undefined
}).filter(Boolean) // Remove any undefined conditions
Eif (filterConditions.length > 0) {
// Use CASE WHEN for conditional aggregation via adapter
const andCondition = filterConditions.length === 1 ? filterConditions[0] : and(...filterConditions)
const caseExpr = this.databaseAdapter.buildCaseWhen([
{ when: andCondition!, then: baseExpr }
])
baseExpr = caseExpr
}
}
// Apply aggregation function based on measure type
switch (measure.type) {
case 'count':
return count(baseExpr)
case 'countDistinct':
return countDistinct(baseExpr)
case 'sum':
return sum(baseExpr)
case 'avg':
return this.databaseAdapter.buildAvg(baseExpr)
case 'min':
return min(baseExpr)
case 'max':
return max(baseExpr)
case 'number':
return baseExpr as SQL
default:
return count(baseExpr)
}
}
/**
* Build time dimension expression with granularity using database adapter
*/
buildTimeDimensionExpression(
dimensionSql: any,
granularity: string | undefined,
context: QueryContext
): SQL {
const baseExpr = resolveSqlExpression(dimensionSql, context)
if (!granularity) {
// Ensure we return SQL even when no granularity is applied
return baseExpr instanceof SQL ? baseExpr : sql`${baseExpr}`
}
// Use database adapter for database-specific time dimension building
return this.databaseAdapter.buildTimeDimension(granularity as TimeGranularity, baseExpr)
}
/**
* Build WHERE conditions from semantic query filters (dimensions only)
* Works for both single and multi-cube queries
*/
buildWhereConditions(
cubes: Map<string, Cube> | Cube,
query: SemanticQuery,
context: QueryContext,
queryPlan?: QueryPlan
): SQL[] {
const conditions: SQL[] = []
// Convert single cube to map for consistent handling
const cubeMap = cubes instanceof Map ? cubes : new Map([[cubes.name, cubes]])
// Process regular filters (dimensions only for WHERE clause)
if (query.filters && query.filters.length > 0) {
for (const filter of query.filters) {
const condition = this.processFilter(filter, cubeMap, context, 'where', queryPlan)
if (condition) {
conditions.push(condition)
}
}
}
// Process time dimension date range filters
if (query.timeDimensions) {
for (const timeDim of query.timeDimensions) {
const [cubeName, fieldName] = timeDim.dimension.split('.')
const cube = cubeMap.get(cubeName)
if (cube && cube.dimensions[fieldName] && timeDim.dateRange) {
// Check if this cube is in a pre-aggregation CTE - if so, skip this filter in WHERE clause
// The time dimension filter will be applied within the CTE itself during pre-aggregation
if (queryPlan?.preAggregationCTEs) {
const isInCTE = queryPlan.preAggregationCTEs.some((cte: any) => cte.cube.name === cubeName)
if (isInCTE) {
continue // Skip this filter - it's handled in the CTE
}
}
const dimension = cube.dimensions[fieldName]
// Use the raw field expression for date filtering (not the truncated version)
// This ensures we filter on the actual timestamp values before aggregation
const fieldExpr = resolveSqlExpression(dimension.sql, context)
const dateCondition = this.buildDateRangeCondition(fieldExpr, timeDim.dateRange)
if (dateCondition) {
conditions.push(dateCondition)
}
}
}
}
return conditions
}
/**
* Build HAVING conditions from semantic query filters (measures only)
* Works for both single and multi-cube queries
*/
buildHavingConditions(
cubes: Map<string, Cube> | Cube,
query: SemanticQuery,
context: QueryContext,
queryPlan?: QueryPlan
): SQL[] {
const conditions: SQL[] = []
// Convert single cube to map for consistent handling
const cubeMap = cubes instanceof Map ? cubes : new Map([[cubes.name, cubes]])
// Process regular filters (measures only for HAVING clause)
if (query.filters && query.filters.length > 0) {
for (const filter of query.filters) {
const condition = this.processFilter(filter, cubeMap, context, 'having', queryPlan)
if (condition) {
conditions.push(condition)
}
}
}
return conditions
}
/**
* Process a single filter (basic or logical)
* @param filterType - 'where' for dimension filters, 'having' for measure filters
*/
private processFilter(
filter: Filter,
cubes: Map<string, Cube>,
context: QueryContext,
filterType: 'where' | 'having',
queryPlan?: QueryPlan
): SQL | null {
// Handle logical filters (AND/OR)
if ('and' in filter || 'or' in filter) {
const logicalFilter = filter as LogicalFilter
if (logicalFilter.and) {
const conditions = logicalFilter.and
.map(f => this.processFilter(f, cubes, context, filterType, queryPlan))
.filter((condition): condition is SQL => condition !== null)
return conditions.length > 0 ? and(...conditions) as SQL : null
}
Eif (logicalFilter.or) {
const conditions = logicalFilter.or
.map(f => this.processFilter(f, cubes, context, filterType, queryPlan))
.filter((condition): condition is SQL => condition !== null)
return conditions.length > 0 ? or(...conditions) as SQL : null
}
}
// Handle simple filter condition
const filterCondition = filter as FilterCondition
const [cubeName, fieldKey] = filterCondition.member.split('.')
const cube = cubes.get(cubeName)
if (!cube) return null
// Find the field in dimensions or measures
const dimension = cube.dimensions[fieldKey]
const measure = cube.measures[fieldKey]
const field = dimension || measure
Iif (!field) return null
// Apply filter based on type and what we're looking for
if (filterType === 'where' && dimension) {
// Check if this cube is in a pre-aggregation CTE - if so, skip this filter in WHERE clause
// The filter will be applied within the CTE itself
if (queryPlan?.preAggregationCTEs) {
const isInCTE = queryPlan.preAggregationCTEs.some((cte: any) => cte.cube.name === cubeName)
if (isInCTE) {
return null // Skip this filter - it's handled in the CTE
}
}
// For array operators, we need the raw column (not isolated SQL)
// because Drizzle's array functions need column type metadata for proper encoding
const isArrayOperator = ['arrayContains', 'arrayOverlaps', 'arrayContained'].includes(filterCondition.operator)
const fieldExpr = isArrayOperator
? (typeof dimension.sql === 'function' ? dimension.sql(context) : dimension.sql)
: resolveSqlExpression(dimension.sql, context)
return this.buildFilterCondition(
fieldExpr,
filterCondition.operator,
filterCondition.values,
field,
filterCondition.dateRange
)
} else if (filterType === 'where' && measure) {
// NEVER apply measure filters in WHERE clause - they should only be in HAVING
// This prevents incorrect behavior where measure filters are applied before aggregation
return null
} else if (filterType === 'having' && measure) {
// HAVING clause: use aggregated measure expression
// Check if this measure is from a CTE cube
const measureExpr = this.buildHavingMeasureExpression(cubeName, fieldKey, measure, context, queryPlan)
return this.buildFilterCondition(
measureExpr,
filterCondition.operator,
filterCondition.values,
field,
filterCondition.dateRange
)
}
// Skip if this filter doesn't match the type we're processing
return null
}
/**
* Build filter condition using Drizzle operators
*/
private buildFilterCondition(
fieldExpr: AnyColumn | SQL,
operator: FilterOperator,
values: any[],
field?: any,
dateRange?: string | string[]
): SQL | null {
// Handle dateRange for date filters
if (dateRange !== undefined) {
// Validate: dateRange only works with inDateRange operator
if (operator !== 'inDateRange') {
throw new Error(
`dateRange can only be used with 'inDateRange' operator, but got '${operator}'. ` +
`Use explicit date values in the 'values' array for other date operators.`
)
}
// Validate: field must be a time dimension
if (field && field.type !== 'time') {
throw new Error(
`dateRange can only be used on time dimensions, but field '${field.name || 'unknown'}' has type '${field.type}'`
)
}
// Use existing buildDateRangeCondition logic - dateRange takes precedence over values
return this.buildDateRangeCondition(fieldExpr, dateRange)
}
// Handle empty values
if (!values || values.length === 0) {
// For empty equals filter, return condition that matches nothing
if (operator === 'equals') {
return this.databaseAdapter.buildBooleanLiteral(false)
}
return null
}
// Filter out empty/null values and values containing null bytes for security
// For date operators, don't convert values yet - we'll normalize to Date first
const filteredValues = values.filter(v => {
if (v === null || v === undefined || v === '') return false
// Reject values containing null bytes for security
if (typeof v === 'string' && v.includes('\x00')) return false
return true
}).map(this.databaseAdapter.convertFilterValue)
// For certain operators, we need at least one non-empty value
if (filteredValues.length === 0 && !['set', 'notSet'].includes(operator)) {
// For empty equals filter, return condition that matches nothing
if (operator === 'equals') {
return this.databaseAdapter.buildBooleanLiteral(false)
}
return null
}
const value = filteredValues[0]
switch (operator) {
case 'equals':
if (filteredValues.length > 1) {
// For time-type fields, normalize all values
Iif (field?.type === 'time') {
const normalizedValues = filteredValues.map(v => this.normalizeDate(v) || v)
return inArray(fieldExpr as AnyColumn, normalizedValues)
}
return inArray(fieldExpr as AnyColumn, filteredValues)
E} else if (filteredValues.length === 1) {
// For time-type fields, normalize the single value
const finalValue = field?.type === 'time' ? this.normalizeDate(value) || value : value
return eq(fieldExpr as AnyColumn, finalValue)
}
return this.databaseAdapter.buildBooleanLiteral(false)
case 'notEquals':
Iif (filteredValues.length > 1) {
return notInArray(fieldExpr as AnyColumn, filteredValues)
E} else if (filteredValues.length === 1) {
return ne(fieldExpr as AnyColumn, value)
}
return null
case 'contains':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'contains', value)
case 'notContains':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'notContains', value)
case 'startsWith':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'startsWith', value)
case 'endsWith':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'endsWith', value)
case 'gt':
return gt(fieldExpr as AnyColumn, value)
case 'gte':
return gte(fieldExpr as AnyColumn, value)
case 'lt':
return lt(fieldExpr as AnyColumn, value)
case 'lte':
return lte(fieldExpr as AnyColumn, value)
case 'set':
return isNotNull(fieldExpr as AnyColumn)
case 'notSet':
return isNull(fieldExpr as AnyColumn)
case 'inDateRange':
if (filteredValues.length >= 2) {
const startDate = this.normalizeDate(filteredValues[0])
let endDate = this.normalizeDate(filteredValues[1])
if (startDate && endDate) {
// For date-only strings in original values, treat end date as end-of-day
// Check original values array (before filtering/conversion)
const originalEndValue = values[1]
if (typeof originalEndValue === 'string' && /^\d{4}-\d{2}-\d{2}$/.test(originalEndValue.trim())) {
const endDateObj = typeof endDate === 'number'
? new Date(endDate * (this.databaseAdapter.getEngineType() === 'sqlite' ? 1000 : 1))
: new Date(endDate)
const endOfDay = new Date(endDateObj)
endOfDay.setUTCHours(23, 59, 59, 999)
if (this.databaseAdapter.isTimestampInteger()) {
endDate = this.databaseAdapter.getEngineType() === 'sqlite'
? Math.floor(endOfDay.getTime() / 1000)
: endOfDay.getTime()
} else E{
// PostgreSQL and MySQL need ISO strings
endDate = endOfDay.toISOString()
}
}
return and(
gte(fieldExpr as AnyColumn, startDate),
lte(fieldExpr as AnyColumn, endDate)
) as SQL
}
}
return null
case 'beforeDate': {
const beforeValue = this.normalizeDate(value)
Eif (beforeValue) {
return lt(fieldExpr as AnyColumn, beforeValue)
}
return null
}
case 'afterDate': {
const afterValue = this.normalizeDate(value)
if (afterValue) {
return gt(fieldExpr as AnyColumn, afterValue)
}
return null
}
case 'between':
if (filteredValues.length >= 2) {
return and(
gte(fieldExpr as AnyColumn, filteredValues[0]),
lte(fieldExpr as AnyColumn, filteredValues[1])
) as SQL
}
return null
case 'notBetween':
Eif (filteredValues.length >= 2) {
return or(
lt(fieldExpr as AnyColumn, filteredValues[0]),
gt(fieldExpr as AnyColumn, filteredValues[1])
) as SQL
}
return null
case 'in':
Eif (filteredValues.length > 0) {
return inArray(fieldExpr as AnyColumn, filteredValues)
}
return null
case 'notIn':
Eif (filteredValues.length > 0) {
return notInArray(fieldExpr as AnyColumn, filteredValues)
}
return null
case 'like':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'like', value)
case 'notLike':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'notLike', value)
case 'ilike':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'ilike', value)
case 'regex':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'regex', value)
case 'notRegex':
return this.databaseAdapter.buildStringCondition(fieldExpr, 'notRegex', value)
case 'isEmpty':
return or(
isNull(fieldExpr as AnyColumn),
eq(fieldExpr as AnyColumn, '')
) as SQL
case 'isNotEmpty':
return and(
isNotNull(fieldExpr as AnyColumn),
ne(fieldExpr as AnyColumn, '')
) as SQL
// PostgreSQL array operators - silent no-op for other databases
// These use Drizzle's built-in array operator functions
case 'arrayContains':
Iif (this.databaseAdapter.getEngineType() === 'postgres') {
return drizzleArrayContains(fieldExpr as AnyColumn, filteredValues)
}
return null
case 'arrayOverlaps':
Iif (this.databaseAdapter.getEngineType() === 'postgres') {
return drizzleArrayOverlaps(fieldExpr as AnyColumn, filteredValues)
}
return null
case 'arrayContained':
Iif (this.databaseAdapter.getEngineType() === 'postgres') {
return drizzleArrayContained(fieldExpr as AnyColumn, filteredValues)
}
return null
default:
return null
}
}
/**
* Build date range condition for time dimensions
*/
buildDateRangeCondition(
fieldExpr: AnyColumn | SQL,
dateRange: string | string[]
): SQL | null {
Iif (!dateRange) return null
// Handle array date range first
if (Array.isArray(dateRange) && dateRange.length >= 2) {
const startDate = this.normalizeDate(dateRange[0])
let endDate = this.normalizeDate(dateRange[1])
Iif (!startDate || !endDate) return null
// For date-only strings, treat end date as end-of-day (23:59:59.999)
// to include all records on that day
Eif (typeof dateRange[1] === 'string' && /^\d{4}-\d{2}-\d{2}$/.test(dateRange[1].trim())) {
const endDateObj = typeof endDate === 'number'
? new Date(endDate * (this.databaseAdapter.getEngineType() === 'sqlite' ? 1000 : 1))
: new Date(endDate)
const endOfDay = new Date(endDateObj)
endOfDay.setUTCHours(23, 59, 59, 999)
if (this.databaseAdapter.isTimestampInteger()) {
endDate = this.databaseAdapter.getEngineType() === 'sqlite'
? Math.floor(endOfDay.getTime() / 1000)
: endOfDay.getTime()
} else E{
// PostgreSQL and MySQL need ISO strings
endDate = endOfDay.toISOString()
}
}
return and(
gte(fieldExpr as AnyColumn, startDate),
lte(fieldExpr as AnyColumn, endDate)
) as SQL
}
// Handle string date range
Eif (typeof dateRange === 'string') {
// Handle relative date expressions
const relativeDates = this.parseRelativeDateRange(dateRange)
if (relativeDates) {
// Convert Date objects to appropriate format for the database
let start: string | number
let end: string | number
if (this.databaseAdapter.isTimestampInteger()) {
if (this.databaseAdapter.getEngineType() === 'sqlite') {
start = Math.floor(relativeDates.start.getTime() / 1000)
end = Math.floor(relativeDates.end.getTime() / 1000)
} else E{
start = relativeDates.start.getTime()
end = relativeDates.end.getTime()
}
} else E{
// PostgreSQL and MySQL need ISO strings
start = relativeDates.start.toISOString()
end = relativeDates.end.toISOString()
}
return and(
gte(fieldExpr as AnyColumn, start),
lte(fieldExpr as AnyColumn, end)
) as SQL
}
// Handle absolute date (single date)
const normalizedDate = this.normalizeDate(dateRange)
if (!normalizedDate) return null
// For single date, create range for the whole day
// normalizedDate might be a timestamp (number) or ISO string depending on database
const dateObj = typeof normalizedDate === 'number'
? new Date(normalizedDate * (this.databaseAdapter.getEngineType() === 'sqlite' ? 1000 : 1))
: new Date(normalizedDate)
const startOfDay = new Date(dateObj)
startOfDay.setUTCHours(0, 0, 0, 0) // Ensure we start at midnight UTC
const endOfDay = new Date(dateObj)
endOfDay.setUTCHours(23, 59, 59, 999) // Ensure we end at 11:59:59.999 UTC
// Convert to appropriate format for the database
let startValue: string | number
let endValue: string | number
if (this.databaseAdapter.isTimestampInteger()) {
if (this.databaseAdapter.getEngineType() === 'sqlite') {
startValue = Math.floor(startOfDay.getTime() / 1000)
endValue = Math.floor(endOfDay.getTime() / 1000)
} else E{
startValue = startOfDay.getTime()
endValue = endOfDay.getTime()
}
} else E{
// PostgreSQL and MySQL need ISO strings
startValue = startOfDay.toISOString()
endValue = endOfDay.toISOString()
}
return and(
gte(fieldExpr as AnyColumn, startValue),
lte(fieldExpr as AnyColumn, endValue)
) as SQL
}
return null
}
/**
* Parse relative date range expressions like "today", "yesterday", "last 7 days", "this month", etc.
* Handles all 14 DATE_RANGE_OPTIONS from the client
*/
private parseRelativeDateRange(dateRange: string): { start: Date; end: Date } | null {
const now = new Date()
const lowerRange = dateRange.toLowerCase().trim()
// Extract UTC date components for consistent calculations
const utcYear = now.getUTCFullYear()
const utcMonth = now.getUTCMonth()
const utcDate = now.getUTCDate()
const utcDay = now.getUTCDay()
// Handle "today"
if (lowerRange === 'today') {
const start = new Date(now)
start.setUTCHours(0, 0, 0, 0)
const end = new Date(now)
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "yesterday"
if (lowerRange === 'yesterday') {
const start = new Date(now)
start.setUTCDate(utcDate - 1)
start.setUTCHours(0, 0, 0, 0)
const end = new Date(now)
end.setUTCDate(utcDate - 1)
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "this week" (Monday to Sunday)
if (lowerRange === 'this week') {
const mondayOffset = utcDay === 0 ? -6 : 1 - utcDay // If Sunday, go back 6 days, otherwise go to Monday
const start = new Date(now)
start.setUTCDate(utcDate + mondayOffset)
start.setUTCHours(0, 0, 0, 0)
const end = new Date(start)
end.setUTCDate(start.getUTCDate() + 6) // Sunday
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "this month"
if (lowerRange === 'this month') {
const start = new Date(Date.UTC(utcYear, utcMonth, 1, 0, 0, 0, 0))
const end = new Date(Date.UTC(utcYear, utcMonth + 1, 0, 23, 59, 59, 999))
return { start, end }
}
// Handle "this quarter"
if (lowerRange === 'this quarter') {
const quarter = Math.floor(utcMonth / 3)
const start = new Date(Date.UTC(utcYear, quarter * 3, 1, 0, 0, 0, 0))
const end = new Date(Date.UTC(utcYear, quarter * 3 + 3, 0, 23, 59, 59, 999))
return { start, end }
}
// Handle "this year"
if (lowerRange === 'this year') {
const start = new Date(Date.UTC(utcYear, 0, 1, 0, 0, 0, 0))
const end = new Date(Date.UTC(utcYear, 11, 31, 23, 59, 59, 999))
return { start, end }
}
// Handle "last N days" pattern
const lastDaysMatch = lowerRange.match(/^last\s+(\d+)\s+days?$/)
if (lastDaysMatch) {
const days = parseInt(lastDaysMatch[1], 10)
const start = new Date(now)
start.setUTCDate(utcDate - days + 1) // Include today in the count
start.setUTCHours(0, 0, 0, 0)
const end = new Date(now)
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "last N weeks" pattern
const lastWeeksMatch = lowerRange.match(/^last\s+(\d+)\s+weeks?$/)
if (lastWeeksMatch) {
const weeks = parseInt(lastWeeksMatch[1], 10)
const days = weeks * 7
const start = new Date(now)
start.setUTCDate(utcDate - days + 1) // Include today in the count
start.setUTCHours(0, 0, 0, 0)
const end = new Date(now)
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "last week" (previous Monday to Sunday)
if (lowerRange === 'last week') {
const lastMondayOffset = utcDay === 0 ? -13 : -6 - utcDay // Go to previous Monday
const start = new Date(now)
start.setUTCDate(utcDate + lastMondayOffset)
start.setUTCHours(0, 0, 0, 0)
const end = new Date(start)
end.setUTCDate(start.getUTCDate() + 6) // Previous Sunday
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "last month"
if (lowerRange === 'last month') {
const start = new Date(Date.UTC(utcYear, utcMonth - 1, 1, 0, 0, 0, 0))
const end = new Date(Date.UTC(utcYear, utcMonth, 0, 23, 59, 59, 999))
return { start, end }
}
// Handle "last quarter"
if (lowerRange === 'last quarter') {
const currentQuarter = Math.floor(utcMonth / 3)
const lastQuarter = currentQuarter === 0 ? 3 : currentQuarter - 1
const year = currentQuarter === 0 ? utcYear - 1 : utcYear
const start = new Date(Date.UTC(year, lastQuarter * 3, 1, 0, 0, 0, 0))
const end = new Date(Date.UTC(year, lastQuarter * 3 + 3, 0, 23, 59, 59, 999))
return { start, end }
}
// Handle "last year"
if (lowerRange === 'last year') {
const start = new Date(Date.UTC(utcYear - 1, 0, 1, 0, 0, 0, 0))
const end = new Date(Date.UTC(utcYear - 1, 11, 31, 23, 59, 59, 999))
return { start, end }
}
// Handle "last 12 months" (rolling 12 months)
if (lowerRange === 'last 12 months') {
const start = new Date(Date.UTC(utcYear, utcMonth - 11, 1, 0, 0, 0, 0))
const end = new Date(now)
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "last N months" pattern (legacy support)
const lastMonthsMatch = lowerRange.match(/^last\s+(\d+)\s+months?$/)
if (lastMonthsMatch) {
const months = parseInt(lastMonthsMatch[1], 10)
const start = new Date(Date.UTC(utcYear, utcMonth - months + 1, 1, 0, 0, 0, 0))
const end = new Date(now)
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
// Handle "last N years" pattern (legacy support)
const lastYearsMatch = lowerRange.match(/^last\s+(\d+)\s+years?$/)
if (lastYearsMatch) {
const years = parseInt(lastYearsMatch[1], 10)
const start = new Date(Date.UTC(utcYear - years, 0, 1, 0, 0, 0, 0))
const end = new Date(now)
end.setUTCHours(23, 59, 59, 999)
return { start, end }
}
return null
}
/**
* Normalize date values to handle strings, numbers, and Date objects
* Returns ISO string for PostgreSQL/MySQL, Unix timestamp for SQLite, or null
* Ensures dates are in the correct format for each database engine
*/
private normalizeDate(value: any): string | number | null {
Iif (!value) return null
// If it's already a Date object, validate and convert to appropriate format
Iif (value instanceof Date) {
if (isNaN(value.getTime())) return null
// Return timestamp for integer-based databases, ISO string for others
// SQLite stores timestamps as Unix seconds (not milliseconds)
if (this.databaseAdapter.isTimestampInteger()) {
return this.databaseAdapter.getEngineType() === 'sqlite'
? Math.floor(value.getTime() / 1000)
: value.getTime()
}
// PostgreSQL and MySQL need ISO strings, not Date objects
return value.toISOString()
}
// If it's a number, assume it's a timestamp
Iif (typeof value === 'number') {
// If it's a reasonable Unix timestamp in seconds (10 digits), convert to milliseconds
// Otherwise assume it's already in milliseconds
const timestamp = value < 10000000000 ? value * 1000 : value
const date = new Date(timestamp)
if (isNaN(date.getTime())) return null
// Return timestamp for integer-based databases, ISO string for others
// SQLite stores timestamps as Unix seconds (not milliseconds)
if (this.databaseAdapter.isTimestampInteger()) {
return this.databaseAdapter.getEngineType() === 'sqlite'
? Math.floor(timestamp / 1000)
: timestamp
}
// PostgreSQL and MySQL need ISO strings, not Date objects
return date.toISOString()
}
// If it's a string, try to parse it as a Date
Eif (typeof value === 'string') {
// Check if it's a date-only string (YYYY-MM-DD format)
// Parse as UTC midnight to avoid timezone/DST issues
if (/^\d{4}-\d{2}-\d{2}$/.test(value.trim())) {
const parsed = new Date(value + 'T00:00:00Z')
if (isNaN(parsed.getTime())) return null
// Return timestamp for integer-based databases, ISO string for others
// SQLite stores timestamps as Unix seconds (not milliseconds)
Eif (this.databaseAdapter.isTimestampInteger()) {
return this.databaseAdapter.getEngineType() === 'sqlite'
? Math.floor(parsed.getTime() / 1000)
: parsed.getTime()
}
// PostgreSQL and MySQL need ISO strings, not Date objects
return parsed.toISOString()
}
// For other formats (with time components), use default parsing
const parsed = new Date(value)
if (isNaN(parsed.getTime())) return null
// Return timestamp for integer-based databases, ISO string for others
// SQLite stores timestamps as Unix seconds (not milliseconds)
Eif (this.databaseAdapter.isTimestampInteger()) {
return this.databaseAdapter.getEngineType() === 'sqlite'
? Math.floor(parsed.getTime() / 1000)
: parsed.getTime()
}
// PostgreSQL and MySQL need ISO strings, not Date objects
return parsed.toISOString()
}
// Try to parse any other type as date
const parsed = new Date(value)
if (isNaN(parsed.getTime())) return null
// Return timestamp for integer-based databases, ISO string for others
// SQLite stores timestamps as Unix seconds (not milliseconds)
if (this.databaseAdapter.isTimestampInteger()) {
return this.databaseAdapter.getEngineType() === 'sqlite'
? Math.floor(parsed.getTime() / 1000)
: parsed.getTime()
}
// PostgreSQL and MySQL need ISO strings, not Date objects
return parsed.toISOString()
}
/**
* Build GROUP BY fields from dimensions and time dimensions
* Works for both single and multi-cube queries
*/
buildGroupByFields(
cubes: Map<string, Cube> | Cube,
query: SemanticQuery,
context: QueryContext,
queryPlan?: any // Optional QueryPlan for CTE handling
): (SQL | AnyColumn)[] {
const groupFields: (SQL | AnyColumn)[] = []
// Only add GROUP BY if we have measures (aggregations)
const hasMeasures = query.measures && query.measures.length > 0
if (!hasMeasures) {
return []
}
// Convert single cube to map for consistent handling
const cubeMap = cubes instanceof Map ? cubes : new Map([[cubes.name, cubes]])
// Add dimensions to GROUP BY
if (query.dimensions) {
for (const dimensionName of query.dimensions) {
const [cubeName, fieldName] = dimensionName.split('.')
const cube = cubeMap.get(cubeName)
Eif (cube && cube.dimensions && cube.dimensions[fieldName]) {
// Check if this dimension is from a CTE cube
const isFromCTE = queryPlan?.preAggregationCTEs?.some((cte: any) => cte.cube.name === cubeName)
if (isFromCTE) {
// For dimensions from CTE cubes, check if this is a join key that maps to the main table
const cteInfo = queryPlan.preAggregationCTEs.find((cte: any) => cte.cube.name === cubeName)
const matchingJoinKey = cteInfo.joinKeys.find((jk: JoinKeyInfo) => jk.targetColumn === fieldName)
Iif (matchingJoinKey && matchingJoinKey.sourceColumnObj) {
// Use the source column from the main table for GROUP BY instead of the CTE dimension
groupFields.push(matchingJoinKey.sourceColumnObj)
} else {
// This dimension from CTE cube is not a join key - we need to reference it from the CTE
// But only if it was included in the CTE selections
const cteDimensionExpr = sql`${sql.identifier(cteInfo.cteAlias)}.${sql.identifier(fieldName)}`
groupFields.push(cteDimensionExpr)
}
} else {
// Regular dimension from non-CTE cube
const dimension = cube.dimensions[fieldName]
const dimensionExpr = resolveSqlExpression(dimension.sql, context)
groupFields.push(dimensionExpr)
}
}
}
}
// Add time dimensions to GROUP BY
if (query.timeDimensions) {
for (const timeDim of query.timeDimensions) {
const [cubeName, fieldName] = timeDim.dimension.split('.')
const cube = cubeMap.get(cubeName)
Eif (cube && cube.dimensions && cube.dimensions[fieldName]) {
// Check if this time dimension is from a CTE cube
const isFromCTE = queryPlan?.preAggregationCTEs?.some((cte: any) => cte.cube.name === cubeName)
if (isFromCTE) {
// For time dimensions from CTE cubes, check if this is a join key that maps to the main table
const cteInfo = queryPlan.preAggregationCTEs.find((cte: any) => cte.cube.name === cubeName)
const matchingJoinKey = cteInfo.joinKeys.find((jk: JoinKeyInfo) => jk.targetColumn === fieldName)
Iif (matchingJoinKey && matchingJoinKey.sourceColumnObj) {
// Use the source column from the main table for GROUP BY with time granularity
// const dimension = cube.dimensions[fieldName] // Unused
const timeExpr = this.buildTimeDimensionExpression(
matchingJoinKey.sourceColumnObj,
timeDim.granularity,
context
)
groupFields.push(timeExpr)
} else {
// This time dimension from CTE cube is not a join key - reference it from the CTE
// The CTE already has the time dimension expression applied, so just reference the column
const cteDimensionExpr = sql`${sql.identifier(cteInfo.cteAlias)}.${sql.identifier(fieldName)}`
groupFields.push(cteDimensionExpr)
}
} else {
// Regular time dimension from non-CTE cube
const dimension = cube.dimensions[fieldName]
const timeExpr = this.buildTimeDimensionExpression(
dimension.sql,
timeDim.granularity,
context
)
groupFields.push(timeExpr)
}
}
}
}
// Note: We used to add join keys from CTEs to GROUP BY, but this is unnecessary
// Join keys are only needed for the JOIN condition, not for grouping
// The GROUP BY should only contain columns that are actually selected or used for aggregation
return groupFields
}
/**
* Build ORDER BY clause with automatic time dimension sorting
*/
buildOrderBy(query: SemanticQuery, selectedFields?: string[]): SQL[] {
const orderClauses: SQL[] = []
// Get all selected fields (measures + dimensions + timeDimensions)
const allSelectedFields = selectedFields || [
...(query.measures || []),
...(query.dimensions || []),
...(query.timeDimensions?.map(td => td.dimension) || [])
]
// First, add explicit ordering from query.order
if (query.order && Object.keys(query.order).length > 0) {
for (const [field, direction] of Object.entries(query.order)) {
// Validate that the field exists in the selected fields
if (!allSelectedFields.includes(field)) {
throw new Error(`Cannot order by '${field}': field is not selected in the query`)
}
// Use Drizzle's built-in asc/desc functions for proper ordering
const orderClause = direction === 'desc' ? desc(sql.identifier(field)) : asc(sql.identifier(field))
orderClauses.push(orderClause)
}
}
// Then, automatically add time dimension sorting for any time dimensions not explicitly ordered
if (query.timeDimensions && query.timeDimensions.length > 0) {
const explicitlyOrderedFields = new Set(Object.keys(query.order || {}))
// Sort time dimensions by their dimension name to ensure consistent ordering
const sortedTimeDimensions = [...query.timeDimensions].sort((a, b) =>
a.dimension.localeCompare(b.dimension)
)
for (const timeDim of sortedTimeDimensions) {
if (!explicitlyOrderedFields.has(timeDim.dimension)) {
// Automatically sort time dimensions in ascending order (earliest to latest)
orderClauses.push(asc(sql.identifier(timeDim.dimension)))
}
}
}
return orderClauses
}
/**
* Collect numeric field names (measures + numeric dimensions) for type conversion
* Works for both single and multi-cube queries
*/
collectNumericFields(cubes: Map<string, Cube> | Cube, query: SemanticQuery): string[] {
const numericFields: string[] = []
// Convert single cube to map for consistent handling
const cubeMap = cubes instanceof Map ? cubes : new Map([[cubes.name, cubes]])
// Add all measure fields (they are always numeric)
if (query.measures) {
numericFields.push(...query.measures)
}
// Add numeric dimension fields
if (query.dimensions) {
for (const dimensionName of query.dimensions) {
const [cubeName, fieldName] = dimensionName.split('.')
const cube = cubeMap.get(cubeName)
Eif (cube) {
const dimension = cube.dimensions[fieldName]
if (dimension && dimension.type === 'number') {
// Use the full name (with prefix) as it appears in the result
numericFields.push(dimensionName)
}
}
}
}
return numericFields
}
/**
* Apply LIMIT and OFFSET to a query with validation
* If offset is provided without limit, add a reasonable default limit
*/
applyLimitAndOffset<T>(query: T, semanticQuery: SemanticQuery): T {
// If offset is provided without limit, add a reasonable default limit
let effectiveLimit = semanticQuery.limit
if (semanticQuery.offset !== undefined && semanticQuery.offset > 0 && effectiveLimit === undefined) {
effectiveLimit = 50 // Default limit when offset is used
}
let result = query
if (effectiveLimit !== undefined) {
if (effectiveLimit < 0) {
throw new Error('Limit must be non-negative')
}
result = (result as any).limit(effectiveLimit)
}
if (semanticQuery.offset !== undefined) {
if (semanticQuery.offset < 0) {
throw new Error('Offset must be non-negative')
}
result = (result as any).offset(semanticQuery.offset)
}
return result
}
} |