Buckets:
| import { sql } from "drizzle-orm" | |
| export const UPSERT_CHUNK_SIZE = 500 | |
| const DAY_MS = 86_400_000 | |
| export type StatGrain = "day" | "week" | |
| export type StatBaseAggregate = { | |
| grain: StatGrain | |
| period_key: string | |
| dataset: string | |
| tier: string | |
| sessions: number | |
| requests: number | |
| input_tokens: number | |
| output_tokens: number | |
| reasoning_tokens: number | |
| cache_read_tokens: number | |
| total_tokens: number | |
| input_cost_microcents: number | |
| output_cost_microcents: number | |
| total_cost_microcents: number | |
| avg_duration_ms: number | null | |
| p50_duration_ms: number | null | |
| p95_duration_ms: number | null | |
| avg_ttfb_ms: number | null | |
| p50_ttfb_ms: number | null | |
| p95_ttfb_ms: number | null | |
| avg_output_tps: number | null | |
| success_count: number | |
| error_count: number | |
| sample_count: number | |
| } | |
| export type StatBaseRow = { | |
| grain: string | |
| period_key: string | |
| dataset?: string | |
| tier?: string | |
| client?: string | |
| source?: string | |
| sessions?: number | |
| requests?: number | |
| input_tokens?: number | |
| output_tokens?: number | |
| reasoning_tokens?: number | |
| cache_read_tokens?: number | |
| total_tokens?: number | |
| input_cost_microcents?: number | |
| output_cost_microcents?: number | |
| total_cost_microcents?: number | |
| avg_duration_ms?: number | null | |
| p50_duration_ms?: number | null | |
| p95_duration_ms?: number | null | |
| avg_ttfb_ms?: number | null | |
| p50_ttfb_ms?: number | null | |
| p95_ttfb_ms?: number | null | |
| avg_output_tps?: number | null | |
| success_count?: number | |
| error_count?: number | |
| sample_count?: number | |
| } | |
| export function toStatBaseRow(data: StatBaseAggregate) { | |
| return { | |
| grain: data.grain, | |
| period_key: data.period_key, | |
| dataset: data.dataset, | |
| tier: data.tier, | |
| client: "all", | |
| source: "all", | |
| sessions: data.sessions, | |
| requests: data.requests, | |
| input_tokens: data.input_tokens, | |
| output_tokens: data.output_tokens, | |
| reasoning_tokens: data.reasoning_tokens, | |
| cache_read_tokens: data.cache_read_tokens, | |
| total_tokens: data.total_tokens, | |
| input_cost_microcents: data.input_cost_microcents, | |
| output_cost_microcents: data.output_cost_microcents, | |
| total_cost_microcents: data.total_cost_microcents, | |
| avg_duration_ms: data.avg_duration_ms, | |
| p50_duration_ms: data.p50_duration_ms, | |
| p95_duration_ms: data.p95_duration_ms, | |
| avg_ttfb_ms: data.avg_ttfb_ms, | |
| p50_ttfb_ms: data.p50_ttfb_ms, | |
| p95_ttfb_ms: data.p95_ttfb_ms, | |
| avg_output_tps: data.avg_output_tps, | |
| success_count: data.success_count, | |
| error_count: data.error_count, | |
| sample_count: data.sample_count, | |
| } | |
| } | |
| export function synthesizeAllTierRows<T extends StatBaseRow>(rows: T[], dimensionKey: (row: T) => string) { | |
| return [ | |
| ...rows, | |
| ...Object.values( | |
| rows.reduce<Record<string, T>>((result, row) => { | |
| const key = [row.grain, row.period_key, row.dataset, row.client, row.source, dimensionKey(row)].join("\u0000") | |
| result[key] = result[key] ? combineRows(result[key], row) : { ...row, tier: "all" } | |
| return result | |
| }, {}), | |
| ), | |
| ] | |
| } | |
| export function collapseRows<T extends StatBaseRow>(rows: T[], dimensionKey: (row: T) => string) { | |
| return Object.values( | |
| rows.reduce<Record<string, T>>((result, row) => { | |
| const key = [row.grain, row.period_key, row.dataset, row.tier, row.client, row.source, dimensionKey(row)].join( | |
| "\u0000", | |
| ) | |
| result[key] = result[key] ? combineRows(result[key], row) : row | |
| return result | |
| }, {}), | |
| ) | |
| } | |
| export function combineRows<T extends StatBaseRow>(left: T, right: T): T { | |
| return { | |
| ...left, | |
| sessions: (left.sessions ?? 0) + (right.sessions ?? 0), | |
| requests: (left.requests ?? 0) + (right.requests ?? 0), | |
| input_tokens: (left.input_tokens ?? 0) + (right.input_tokens ?? 0), | |
| output_tokens: (left.output_tokens ?? 0) + (right.output_tokens ?? 0), | |
| reasoning_tokens: (left.reasoning_tokens ?? 0) + (right.reasoning_tokens ?? 0), | |
| cache_read_tokens: (left.cache_read_tokens ?? 0) + (right.cache_read_tokens ?? 0), | |
| total_tokens: (left.total_tokens ?? 0) + (right.total_tokens ?? 0), | |
| input_cost_microcents: (left.input_cost_microcents ?? 0) + (right.input_cost_microcents ?? 0), | |
| output_cost_microcents: (left.output_cost_microcents ?? 0) + (right.output_cost_microcents ?? 0), | |
| total_cost_microcents: (left.total_cost_microcents ?? 0) + (right.total_cost_microcents ?? 0), | |
| avg_duration_ms: weightedAverage(left.avg_duration_ms, left.requests, right.avg_duration_ms, right.requests), | |
| p50_duration_ms: null, | |
| p95_duration_ms: null, | |
| avg_ttfb_ms: weightedAverage(left.avg_ttfb_ms, left.requests, right.avg_ttfb_ms, right.requests), | |
| p50_ttfb_ms: null, | |
| p95_ttfb_ms: null, | |
| avg_output_tps: weightedAverage(left.avg_output_tps, left.requests, right.avg_output_tps, right.requests), | |
| success_count: (left.success_count ?? 0) + (right.success_count ?? 0), | |
| error_count: (left.error_count ?? 0) + (right.error_count ?? 0), | |
| sample_count: (left.sample_count ?? 0) + (right.sample_count ?? 0), | |
| } | |
| } | |
| export function statPeriodKey(row: StatBaseRow) { | |
| return [row.grain, row.period_key, row.dataset, row.tier, row.client, row.source].join("\u0000") | |
| } | |
| export function statRowScope(rows: StatBaseRow[]) { | |
| if (rows.length === 0) return | |
| return { | |
| grains: unique(rows.map((row) => row.grain)), | |
| periodKeys: unique(rows.map((row) => row.period_key)), | |
| datasets: unique(rows.map((row) => row.dataset ?? "all")), | |
| clients: unique(rows.map((row) => row.client ?? "all")), | |
| sources: unique(rows.map((row) => row.source ?? "all")), | |
| } | |
| } | |
| export function periodKeyFor(grain: StatGrain, periodStart: Date) { | |
| if (grain === "week") return isoWeekId(periodStart) | |
| return utcDateId(periodStart) | |
| } | |
| export function startOfUtcDay(value: Date) { | |
| return new Date(Date.UTC(value.getUTCFullYear(), value.getUTCMonth(), value.getUTCDate())) | |
| } | |
| export function startOfIsoWeek(value: Date) { | |
| return new Date( | |
| Date.UTC(value.getUTCFullYear(), value.getUTCMonth(), value.getUTCDate() - (value.getUTCDay() || 7) + 1), | |
| ) | |
| } | |
| export function isoWeekId(value: Date) { | |
| const thursday = new Date( | |
| Date.UTC(value.getUTCFullYear(), value.getUTCMonth(), value.getUTCDate() + 4 - (value.getUTCDay() || 7)), | |
| ) | |
| return `${thursday.getUTCFullYear()}-W${String(Math.ceil(((thursday.getTime() - Date.UTC(thursday.getUTCFullYear(), 0, 1)) / DAY_MS + 1) / 7)).padStart(2, "0")}` | |
| } | |
| function utcDateId(value: Date) { | |
| return `${value.getUTCFullYear()}-${String(value.getUTCMonth() + 1).padStart(2, "0")}-${String(value.getUTCDate()).padStart(2, "0")}` | |
| } | |
| export function rankBy<T extends StatBaseRow>(rows: T[], value: (row: T) => number) { | |
| return new Map(rows.toSorted((a, b) => value(b) - value(a)).map((row, index) => [row, index + 1])) | |
| } | |
| export function rankRowsWithMarketShare<T extends StatBaseRow>( | |
| rows: T[], | |
| groupKey: (row: T) => string = statPeriodKey, | |
| ) { | |
| return Object.values( | |
| rows.reduce<Record<string, T[]>>((result, row) => { | |
| const key = groupKey(row) | |
| result[key] = [...(result[key] ?? []), row] | |
| return result | |
| }, {}), | |
| ).flatMap((group) => { | |
| const tokens = group.reduce((sum, row) => sum + (row.total_tokens ?? 0), 0) | |
| const requests = group.reduce((sum, row) => sum + (row.requests ?? 0), 0) | |
| const sessions = group.reduce((sum, row) => sum + (row.sessions ?? 0), 0) | |
| const tokenRanks = rankBy(group, (row) => row.total_tokens ?? 0) | |
| const requestRanks = rankBy(group, (row) => row.requests ?? 0) | |
| const sessionRanks = rankBy(group, (row) => row.sessions ?? 0) | |
| const costRanks = rankBy(group, (row) => row.total_cost_microcents ?? 0) | |
| return group.map((row) => ({ | |
| ...row, | |
| market_share_tokens: share(row.total_tokens, tokens), | |
| market_share_requests: share(row.requests, requests), | |
| market_share_sessions: share(row.sessions, sessions), | |
| rank_by_tokens: tokenRanks.get(row) ?? null, | |
| rank_by_requests: requestRanks.get(row) ?? null, | |
| rank_by_sessions: sessionRanks.get(row) ?? null, | |
| rank_by_cost: costRanks.get(row) ?? null, | |
| })) | |
| }) | |
| } | |
| export function share(value: number | null | undefined, total: number) { | |
| if (total <= 0) return null | |
| return Number(((value ?? 0) / total).toFixed(6)) | |
| } | |
| export function chunks<T>(items: T[], size: number) { | |
| return Array.from({ length: Math.ceil(items.length / size) }, (_, index) => | |
| items.slice(index * size, (index + 1) * size), | |
| ) | |
| } | |
| function unique(values: string[]) { | |
| return [...new Set(values)] | |
| } | |
| export function inserted(column: string) { | |
| return sql.raw(`values(\`${column}\`)`) | |
| } | |
| export function weightedAverage( | |
| left: number | null | undefined, | |
| leftWeight = 0, | |
| right: number | null | undefined, | |
| rightWeight = 0, | |
| ) { | |
| const totalWeight = | |
| (left === null || left === undefined ? 0 : leftWeight) + (right === null || right === undefined ? 0 : rightWeight) | |
| if (totalWeight === 0) return null | |
| return Number((((left ?? 0) * leftWeight + (right ?? 0) * rightWeight) / totalWeight).toFixed(2)) | |
| } | |
| export function normalizeTier(value: string) { | |
| if (value === "Paid") return "Zen" | |
| return value | |
| } | |
| export function normalizeCountry(value: string | undefined) { | |
| if (!value || value.length !== 2) return "ZZ" | |
| return value.toUpperCase() | |
| } | |
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