4d:I[707087,["/_next/static/immutable/chunks/3arlv5bvukoo2.js","/_next/static/immutable/chunks/39v64hmfyo-sr.js","/_next/static/immutable/chunks/2nvof3-29tvc3.js","/_next/static/immutable/chunks/3tjobt77z_9dj.js","/_next/static/immutable/chunks/3upr08c9vtj8w.js","/_next/static/immutable/chunks/2rwy6tq861e-y.js","/_next/static/immutable/chunks/2xxcqy6ppds3l.js","/_next/static/immutable/chunks/2hlz_ivp-qrnh.js","/_next/static/immutable/chunks/0p6vao205o1jc.js","/_next/static/immutable/chunks/11mfl5ymqld9-.js","/_next/static/immutable/chunks/2zoahorp76dll.js","/_next/static/immutable/chunks/1ww0328si-u8x.js","/_next/static/immutable/chunks/1_kyvgkyokw92.js","/_next/static/immutable/chunks/1d0aij627lbx2.js","/_next/static/immutable/chunks/1ud4fi_ghnsx-.js"],"RegistryDependencies"] 4e:I[314491,["/_next/static/immutable/chunks/3arlv5bvukoo2.js","/_next/static/immutable/chunks/39v64hmfyo-sr.js","/_next/static/immutable/chunks/2nvof3-29tvc3.js","/_next/static/immutable/chunks/3tjobt77z_9dj.js","/_next/static/immutable/chunks/3upr08c9vtj8w.js","/_next/static/immutable/chunks/2rwy6tq861e-y.js","/_next/static/immutable/chunks/2xxcqy6ppds3l.js","/_next/static/immutable/chunks/2hlz_ivp-qrnh.js","/_next/static/immutable/chunks/0p6vao205o1jc.js","/_next/static/immutable/chunks/11mfl5ymqld9-.js","/_next/static/immutable/chunks/2zoahorp76dll.js","/_next/static/immutable/chunks/1ww0328si-u8x.js","/_next/static/immutable/chunks/1_kyvgkyokw92.js","/_next/static/immutable/chunks/1d0aij627lbx2.js","/_next/static/immutable/chunks/1ud4fi_ghnsx-.js"],"FilesView"] 4f:T78d,import type { Column, SQL } from "drizzle-orm"; import type { PgDatabase, PgTable } from "drizzle-orm/pg-core"; /** * Maps tableSchema field keys to Drizzle table columns. * This is the bridge between your table UI schema and your database. * * @example * const columnMapping = { * level: logs.level, * date: logs.date, * latency: logs.latency, * "timing.dns": logs.timingDns, * } satisfies ColumnMapping; */ export type ColumnMapping = Record; export type DrizzleDB = PgDatabase; /** Sort descriptor matching the URL state shape. */ export type SortDescriptor = { id: string; desc: boolean } | null; /** Cursor-based pagination params. */ export type CursorPaginationParams = { cursor: Date | number | null; direction: "prev" | "next"; size: number; cursorColumn: Column; /** * Unique column ordered last, so rows sharing a cursor value have one * defined order instead of whatever the plan happens to produce. */ tiebreakColumn?: Column; }; /** * Everything a caller needs to write its own aggregate SQL against the same * filtered set the handler queried. * * The three things such a caller actually needs — the resolved time range, the * bucket size, and the composed WHERE — are all computed inside the handler * already. Handing back only `SQL[]` meant every caller re-derived them. */ export type DrizzleQueryScope = { readonly db: DrizzleDB; readonly table: PgTable; readonly columns: ColumnMapping; /** The fully filtered set. */ readonly where: SQL | undefined; /** The set slider bounds are computed over (date + non-slider filters). */ readonly whereWithoutSliders: SQL | undefined; /** Resolved from an explicit date filter, else from MIN/MAX of the set. */ readonly range: { from: Date; to: Date } | null; /** Bucket size for `range`, with the interval ladder already applied. */ readonly bucketMs: number; }; 50:T4053,import { evaluateIntervalMs } from "@/lib/data-table/interval"; import type { FacetMetadataSchema } from "@/lib/data-table/types"; import type { Filters } from "@/lib/filters"; import { and, count, eq, sql, type Column, type SQL } from "drizzle-orm"; import { getTableConfig, type SelectedFields as PgSelectedFields, type PgTable, } from "drizzle-orm/pg-core"; import { computeFacets } from "./facets"; import { buildWhereConditions } from "./filters"; import { buildCursorPagination } from "./pagination"; import { buildOrderBy } from "./sorting"; import type { ColumnMapping, DrizzleDB, DrizzleQueryScope, SortDescriptor, } from "./types"; /** * Derive slider, facet, and date keys from declared filter semantics. * * These are pass groupings for the three-pass strategy, not semantics — the * semantics live in `Filters` and are the same everywhere. */ function deriveKeys(filters: Filters) { const sliderKeys: string[] = []; const facetKeys: string[] = []; const dateKeys: string[] = []; for (const { key, type } of filters.specs) { if (type === "slider") { sliderKeys.push(key); facetKeys.push(key); } if (type === "checkbox") facetKeys.push(key); if (type === "input") facetKeys.push(key); if (type === "timerange") dateKeys.push(key); } return { sliderKeys, facetKeys, dateKeys }; } /** * The date range the user asked for, if any. * * Read through `filters.plan` rather than off the raw search values, so the * single-date and reversed-bounds cases are normalized the same way they are * for the WHERE clause. */ function resolveExplicitRange( filters: Filters, search: Record, dateKeys: string[], ): { from: Date; to: Date } | null { for (const op of filters.plan(search, { only: dateKeys })) { if (op.op === "dateRange") return { from: op.from, to: op.to }; } return null; } /** MIN/MAX over the filtered set, for when no explicit range was given. */ async function discoverRange( db: DrizzleDB, table: PgTable, column: Column, where: SQL | undefined, ): Promise<{ from: Date; to: Date } | null> { const result = await db .select({ from: sql`MIN(${column})`, to: sql`MAX(${column})`, }) .from(table) .where(where); const row = result[0]; if (!row?.from || !row?.to) return null; const from = new Date(row.from); const to = new Date(row.to); if (Number.isNaN(from.getTime()) || Number.isNaN(to.getTime())) return null; return { from, to }; } /** * One config shape. * * The old two-shape union existed because `tableSchema` lives in a * `"use client"` file and could not be imported on the server, so the server * had to be handed three untyped `string[]`s instead. `defineFilters` accepts * `SchemaJSON`, so the declaration now crosses that boundary as data and one * field carries the semantics. */ export type DrizzleHandlerConfig = { db: DrizzleDB; table: PgTable; /** Built with `defineFilters(tableSchema.definition | schemaJson | specs)`. */ filters: Filters; columnMapping: ColumnMapping; /** * The schema key rows are paged by. A `Date` or numeric column, since the * cursor is serialized as a number. * * `scope.range` and `scope.bucketMs` are read off THIS column when no * explicit date filter is set, so a non-time cursor (an auto-increment id, * say) still paginates correctly but hands aggregate callers a range built * from `new Date(id)`. Page by your time column unless you have no use for * `scope.range`. */ cursorColumn: string; /** * The schema key rows are ordered by last, to break ties on the cursor * column. Must be unique — the primary key, normally, which is what this * defaults to when the table declares a single-column one. * * Without it `ORDER BY date DESC` leaves rows sharing a timestamp in * whatever order the plan produced, and that order is not stable: updating a * row moves it in the heap, and a different plan reads it back differently. * The page a row lands on stays correct either way — the boundary logic * below refuses to split a tied group — but the rows visibly shuffle within * their group between refetches. * * Set explicitly for a table with a composite primary key, or none. */ tiebreakColumn?: string; defaultSize?: number; /** * Extra projected fields merged into each row — columns that are never * filtered or sorted, joins, computed SQL. * * `columnMapping` already declares the schema-key ↔ Drizzle-column * relationship, so it doubles as the projection. Anything the wire contract * needs but the filters do not goes here. */ select?: Record; }; export type DrizzleHandlerResult> = { /** Keyed by SCHEMA keys — `"timing.dns"`, never `timingDns`. */ data: TRow[]; facets: Record; totalRowCount: number; filterRowCount: number; nextCursor: number | null; prevCursor: number | null; /** * One typed handle for callers writing their own aggregate SQL. * * Replaces `allConditions: SQL[]`, which leaked just enough to make callers * re-derive the range, the bucket size, and the WHERE composition by hand — * with `db: any` at the boundary and no test at any level. */ scope: DrizzleQueryScope; }; /** * The table's primary key, when it is a single column — the default tiebreak. * * A composite key would need every one of its columns to be a valid tiebreak, * and one of them alone is not unique, so those tables get `undefined` and * have to name a `tiebreakColumn` themselves. */ function singleColumnPrimaryKey(table: PgTable): Column | undefined { const config = getTableConfig(table); const inline = config.columns.filter((column) => column.primary); if (inline.length === 1) return inline[0]; if (config.primaryKeys.length === 1) { const columns = config.primaryKeys[0]?.columns; if (columns?.length === 1) return columns[0]; } return undefined; } /** * Create a high-level query handler that encapsulates the three-pass filtering * strategy, faceted search, counts, and cursor pagination. * * Chart data and percentiles are intentionally excluded — handle them in user-land. * * @example * ```ts * // `tableSchema` is importable (not a "use client" file) * const handler = createDrizzleHandler({ * db, * table: logs, * filters: defineFilters(tableSchema.definition), * columnMapping, * cursorColumn: "date", * }); * * // `tableSchema` is "use client" — the declaration crosses as data * const handler = createDrizzleHandler({ * db, * table: logs, * filters: defineFilters(schemaJson), * columnMapping, * cursorColumn: "date", * }); * * const result = await handler.execute(search); * ``` */ export function createDrizzleHandler(config: DrizzleHandlerConfig) { const { db, table, filters, columnMapping, cursorColumn, tiebreakColumn, defaultSize = 40, select: extraSelect, } = config; const { sliderKeys, facetKeys, dateKeys } = deriveKeys(filters); const cursorCol = columnMapping[cursorColumn]; if (!cursorCol) { throw new Error( `cursorColumn "${cursorColumn}" not found in columnMapping`, ); } let tiebreakCol: Column | undefined; if (tiebreakColumn !== undefined) { tiebreakCol = columnMapping[tiebreakColumn]; if (!tiebreakCol) { throw new Error( `tiebreakColumn "${tiebreakColumn}" not found in columnMapping`, ); } } else { tiebreakCol = singleColumnPrimaryKey(table); } // Fail loudly at construction, not silently at query time. // // `buildWhereConditions` and `buildOrderBy` both skip a key that is missing // from the mapping. `ColumnMapping` is `Record`, so a typo // was not a type error either — the filter just stopped filtering, with no // error, no warning, and no failing test. const unmapped = filters.specs .map((spec) => spec.key) .filter((key) => !columnMapping[key]); if (unmapped.length > 0) { throw new Error( `[createDrizzleHandler] These filterable columns are missing from columnMapping:\n` + unmapped.map((key) => ` - ${key}`).join("\n") + `\n\n Fix: add them, e.g.\n` + ` columnMapping: {\n` + unmapped .map( (key) => ` ${JSON.stringify(key)}: table.${key.replace(/[.\-_](\w)/g, (_, c) => c.toUpperCase())},`, ) .join("\n") + `\n }`, ); } // Rows come back keyed by SCHEMA keys because the projection IS the mapping. // Callers used to hand-write the inverse of this — twice, and the two copies // had already drifted. // `ColumnMapping` is driver-agnostic (`Column`) by design, while `db.select` // wants pg's narrower `SelectedFields`. Every value here is a real pg column // or aliased SQL; narrowing `ColumnMapping` itself would make the public type // pg-specific for every consumer. const projection = { ...columnMapping, ...(extraSelect ?? {}), } as PgSelectedFields; return { /** Derived keys (exposed for advanced use cases) */ sliderKeys, facetKeys, dateKeys, async execute( search: Record, ): Promise { const size = typeof search.size === "number" ? search.size : defaultSize; const sort = (search.sort as SortDescriptor) ?? null; const cursor = (search.cursor as Date | number | null) ?? null; const direction = (search.direction as "prev" | "next") ?? "next"; // --- Three-pass filtering strategy --- // Pass 1: Date range conditions only const dateConditions = buildWhereConditions( filters, search, columnMapping, { only: dateKeys, }, ); // Pass 2: Date + non-slider filters (for slider facet bounds) const nonSliderConditions = buildWhereConditions( filters, search, columnMapping, { exclude: [...sliderKeys, ...dateKeys] }, ); const pass2Conditions = [...dateConditions, ...nonSliderConditions]; // Pass 3: All conditions including sliders const sliderConditions = buildWhereConditions( filters, search, columnMapping, { only: sliderKeys }, ); const allConditions = [...pass2Conditions, ...sliderConditions]; const allWhereForScope = allConditions.length > 0 ? and(...allConditions) : undefined; const pass2Where = pass2Conditions.length > 0 ? and(...pass2Conditions) : undefined; // Resolve the time range once, here, rather than leaving every caller // writing custom aggregate SQL to rediscover it. An explicit date filter // wins; otherwise it comes from MIN/MAX over the filtered set. const explicitRange = resolveExplicitRange(filters, search, dateKeys); // --- Facets + range (parallel) --- const [sliderFacets, otherFacets, discoveredRange] = await Promise.all([ computeFacets(db, table, columnMapping, pass2Conditions, sliderKeys, { sliderKeys, }), computeFacets( db, table, columnMapping, allConditions, facetKeys.filter((k) => !sliderKeys.includes(k)), ), // Skipped entirely when the range is already known, so the common // "user picked a date range" path costs nothing extra. explicitRange ? Promise.resolve(null) : discoverRange(db, table, cursorCol, allWhereForScope), ]); const range = explicitRange ?? discoveredRange; const facets = { ...sliderFacets, ...otherFacets }; // --- Counts (parallel) --- const allWhere = allWhereForScope; const [totalResult, filterResult] = await Promise.all([ db.select({ total: count() }).from(table), db.select({ total: count() }).from(table).where(allWhere), ]); const totalRowCount = totalResult[0]?.total ?? 0; const filterRowCount = filterResult[0]?.total ?? 0; // --- Sort + Cursor Pagination --- const orderBy = buildOrderBy(columnMapping, sort); const { cursorCondition, orderBy: cursorOrderBy, tiebreakOrderBy, needsReverse, } = buildCursorPagination({ cursor, direction, size, cursorColumn: cursorCol, tiebreakColumn: tiebreakCol, }); const dataConditions = cursorCondition ? [...allConditions, cursorCondition] : allConditions; const dataWhere = dataConditions.length > 0 ? and(...dataConditions) : undefined; // Cursor first, then the caller's sort, then the tiebreak — the last // one is what makes the order total, so nothing may follow it. const orderClauses = sql.join( [cursorOrderBy, orderBy, tiebreakOrderBy].filter( (clause): clause is SQL => clause !== undefined, ), sql`, `, ); // One extra row reveals whether the page boundary splits a group of // rows sharing the same cursor value. const rows = await db .select(projection) .from(table) .where(dataWhere) .orderBy(orderClauses) .limit(size + 1); // Read by SCHEMA key. This used to read `row[cursorCol.name]` — the SQL // column name. They coincide for `date`; for any cursor column whose DB // name differs from its key (`timing_dns` vs `"timing.dns"`) the lookup // was `undefined`, so `boundaryValue` was null, the whole tie-snapping // block was skipped, and `nextCursor` came back null — silently ending // pagination after one page. const getCursorValue = (row: Record): number | null => { if (!row) return null; const val = row[cursorColumn]; if (val instanceof Date) return val.getTime(); if (typeof val === "number") return val; return null; }; // --- Page boundary must fall between cursor values --- // // The next page is fetched with a strict `<` (or `>`) predicate on the // cursor column, so any row sharing the boundary value that did not fit // on this page would never be returned by any page. Rather than split a // group of tied rows, end the page before it and let the group start the // next one. let page = rows; const boundaryValue = rows.length > size ? getCursorValue(rows[size]) : null; // A cursor column that yields neither a Date nor a number cannot be // serialized into a cursor at all, so leave those tables untouched. if (boundaryValue !== null) { const overflowRow = rows[size]; page = rows.slice(0, size); while ( page.length > 0 && getCursorValue(page[page.length - 1]) === boundaryValue ) { page.pop(); } // Degenerate case: a single cursor value spans the whole page, so // there is no boundary to retreat to. Return the entire tied group — // overflowing `size` is the only way to make progress without // dropping rows. if (page.length === 0) { page = await db .select(projection) .from(table) .where( and(...dataConditions, eq(cursorCol, overflowRow[cursorColumn])), ) .orderBy(orderClauses); } } if (needsReverse) { page.reverse(); } // --- Cursors --- const lastRow = page[page.length - 1]; const firstRow = page[0]; const nextCursor = lastRow ? getCursorValue(lastRow) : null; const prevCursor = firstRow ? getCursorValue(firstRow) : new Date().getTime(); const scope: DrizzleQueryScope = { db, table, columns: columnMapping, where: allWhereForScope, whereWithoutSliders: pass2Where, range, bucketMs: evaluateIntervalMs( range ? range.to.getTime() - range.from.getTime() : 0, ), }; return { data: page, facets, totalRowCount, filterRowCount, nextCursor, prevCursor, scope, }; }, }; } 51:Tf06,import type { FacetMetadataSchema } from "@/lib/data-table/types"; import { and, count, max, min, sql, type SQL } from "drizzle-orm"; import type { PgTable } from "drizzle-orm/pg-core"; import type { ColumnMapping, DrizzleDB } from "./types"; /** * Compute faceted counts for filter fields via SQL. * * For each key: * - Slider fields: SELECT MIN(col), MAX(col), COUNT(*) * - Array columns: unnest + GROUP BY for unique values * - Standard columns: GROUP BY for unique values with counts * * All facet queries run in parallel via Promise.all. */ export async function computeFacets( db: DrizzleDB, table: PgTable, mapping: ColumnMapping, baseConditions: SQL[], facetKeys: string[], options?: { /** Keys that should return min/max instead of grouped values */ sliderKeys?: string[]; }, ): Promise> { const whereCondition = baseConditions.length > 0 ? and(...baseConditions) : undefined; const sliderKeys = options?.sliderKeys ?? []; const queries = facetKeys.map(async (key) => { const column = mapping[key]; if (!column) return [key, null] as const; if (sliderKeys.includes(key)) { const result = await db .select({ min: min(column), max: max(column), total: count(), }) .from(table) .where(whereCondition); const row = result[0]; return [ key, { rows: [], total: Number(row?.total ?? 0), min: row?.min != null ? Number(row.min) : undefined, max: row?.max != null ? Number(row.max) : undefined, } satisfies FacetMetadataSchema, ] as const; } // Array columns: unnest + GROUP BY if (column.dataType === "array") { const raw = await db.execute( sql`SELECT val as value, COUNT(*)::int as total FROM ( SELECT unnest(${column}) as val FROM ${table} ${whereCondition ? sql`WHERE ${whereCondition}` : sql``} ) sub GROUP BY val ORDER BY total DESC`, ); const result: { value: string; total: number }[] = Array.isArray(raw) ? raw : (raw as { rows: { value: string; total: number }[] }).rows; const total = result.reduce((sum, r) => sum + Number(r.total), 0); return [ key, { rows: result.map((r) => ({ value: r.value, total: Number(r.total), })), total, } satisfies FacetMetadataSchema, ] as const; } // Standard column: GROUP BY const raw = await db.execute( sql`SELECT ${column} as value, COUNT(*)::int as total FROM ${table} ${whereCondition ? sql`WHERE ${whereCondition}` : sql``} GROUP BY ${column} ORDER BY total DESC`, ); const result: { value: string | number | boolean; total: number }[] = Array.isArray(raw) ? raw : ( raw as { rows: { value: string | number | boolean; total: number }[]; } ).rows; const total = result.reduce((sum, r) => sum + Number(r.total), 0); let minVal: number | undefined; let maxVal: number | undefined; if (result.length > 0 && typeof result[0].value === "number") { minVal = Math.min(...result.map((r) => Number(r.value))); maxVal = Math.max(...result.map((r) => Number(r.value))); } return [ key, { rows: result.map((r) => ({ value: r.value, total: Number(r.total), })), total, min: minVal, max: maxVal, } satisfies FacetMetadataSchema, ] as const; }); const results = await Promise.all(queries); return Object.fromEntries(results.filter(([, v]) => v !== null)); } 52:Tccf,import type { FilterOp, Filters, FilterSelection, } from "@/lib/filters"; import { and, between, eq, gte, ilike, inArray, lte, sql, type Column, type SQL, } from "drizzle-orm"; import type { ColumnMapping } from "./types"; /** * Compile one canonical op to SQL. * * Exhaustive over the six `FilterOp` members with no default branch. The op * union is closed precisely so that adding a seventh fails to compile here * instead of silently matching nothing. * * Nothing in this function inspects the shape of a filter *value* — it only * ever sees an op that the declared semantics already chose. That is why a * numeric checkbox can no longer compile to `BETWEEN`. */ function toSQL(op: FilterOp, column: Column): SQL { switch (op.op) { case "substring": // Case-insensitive, matching the in-memory engine. // NOTE: LIKE metacharacters in the value are deliberately not escaped — // see `sql-injection.test.ts`, which pins this as safe-but-broadening. return ilike(column, `%${op.value}%`); case "equals": return eq(column, op.value); case "oneOf": return inArray(column, op.values); case "overlaps": { // Postgres array overlap. The column is a set on both sides. // // The literal is cast to the column's OWN array type, read off the // schema. `&&` resolves no implicit casts whatsoever: `integer[] && // text[]` is a type error, and so is `integer[] && numeric[]`. This used // to emit `::text[]` unconditionally, so `col.array(col.number())` — a // combination the builder permits and its docstring endorses — failed at // query time with `operator does not exist`. // // Deriving the cast from the declared `itemKind` cannot work either: a // `number` item says nothing about whether the column is `integer[]`, // `bigint[]` or `double precision[]`, and Postgres rejects all three // mismatches. The column knows, so the column is asked. // // `sql.raw` is safe here for the same reason `${column}` is: the type // comes from the Drizzle schema, never from a request. const arrayType = column.getSQLType(); return sql`${column} && ARRAY[${sql.join( op.values.map((value) => sql`${value}`), sql`, `, )}]::${sql.raw(arrayType)}`; } case "numberRange": return between(column, op.min, op.max); case "dateRange": return and(gte(column, op.from), lte(column, op.to))!; } } /** * Build WHERE conditions from declared filter semantics and a column mapping. * * @param filters - The declared semantics, from `defineFilters(...)` * @param values - Current filter values from parsed search params * @param mapping - Maps filter keys to Drizzle columns * @param selection - `{ only, exclude }` to limit keys (for the three-pass strategy) */ export function buildWhereConditions( filters: Filters, values: Record, mapping: ColumnMapping, selection?: FilterSelection, ): SQL[] { const conditions: SQL[] = []; for (const op of filters.plan(values, selection)) { const column = mapping[op.key]; if (!column) continue; conditions.push(toSQL(op, column)); } return conditions; } 53:T587,import { asc, desc, gt, lt, type SQL } from "drizzle-orm"; import type { CursorPaginationParams } from "./types"; /** * Build cursor-based pagination conditions and ordering. * * - direction "next": fetch rows BEFORE cursor (older) → ORDER BY cursorCol DESC * - direction "prev": fetch rows AFTER cursor (newer) → ORDER BY cursorCol ASC * * `tiebreakOrderBy` follows the cursor's direction rather than being fixed, so * that a "prev" page — fetched ascending and reversed in the handler — ends up * in the same total order as the "next" pages around it. */ export function buildCursorPagination(params: CursorPaginationParams): { cursorCondition: SQL | undefined; orderBy: SQL; tiebreakOrderBy: SQL | undefined; needsReverse: boolean; } { const { cursor, direction, cursorColumn, tiebreakColumn } = params; const cursorValue = cursor instanceof Date ? 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