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default async function (req: Request): Promise {\n const url = new URL(req.url);\n\n // Extract parameters\n const workspaceId = url.searchParams.get(\"workspaceId\");\n const spaceId = url.searchParams.get(\"spaceId\");\n const folderDataSourceId = url.searchParams.get(\"folderDataSourceId\");\n const mode = url.searchParams.get(\"mode\") || \"month\";\n const table = url.searchParams.get(\"table\") || \"users\";\n let start = url.searchParams.get(\"start\");\n let end = url.searchParams.get(\"end\");\n\n // Get API key\n const apiKey = Deno.env.get(\"DUST_API_KEY\");\n\n // Baby-proof: Clean up parameters\n if (mode === \"month\") {\n end = null;\n if (start && start.length > 7) {\n start = start.substring(0, 7);\n }\n } else if (mode === \"range\") {\n if (start && end && start === end) {\n console.log(\"Auto-converting to month mode (start === end)\");\n if (start.length > 7) {\n start = start.substring(0, 7);\n }\n end = null;\n }\n }\n\n // Log parameters\n console.log(\"Parameters:\", {\n workspaceId,\n spaceId,\n folderDataSourceId,\n mode,\n table,\n start,\n end,\n });\n\n // Validate required parameters\n if (!workspaceId || !folderDataSourceId || !spaceId || !start || !apiKey) {\n return Response.json({\n error: \"Missing required parameters\",\n required: [\n \"workspaceId\",\n \"spaceId\",\n \"folderDataSourceId\",\n \"start\",\n \"DUST_API_KEY\",\n ],\n received: {\n workspaceId,\n spaceId,\n folderDataSourceId,\n start,\n hasApiKey: !!apiKey,\n },\n }, { status: 400 });\n }\n\n if (mode === \"range\" && !end) {\n return Response.json({\n error: \"Parameter 'end' is required when mode is 'range'\",\n }, { status: 400 });\n }\n\n // Determine format based on table type\n const format = table === \"all\" ? \"csv\" : \"json\";\n\n // Build query parameters for Dust API\n const params = new URLSearchParams({\n mode: mode,\n table: table,\n format: format,\n start: start,\n });\n\n if (end) params.append(\"end\", end);\n\n // Fetch usage data from Dust API\n const dustApiUrl =\n `https://dust.tt/api/v1/w/${workspaceId}/workspace-usage?${params.toString()}`;\n\n try {\n console.log(`Fetching from: ${dustApiUrl}`);\n const response = await fetch(dustApiUrl, {\n method: \"GET\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Accept\": format === \"csv\" ? \"text/csv\" : \"application/json\",\n },\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(`Dust API error: ${response.status} - ${errorText}`);\n return Response.json({\n error: `Dust API returned ${response.status}`,\n message: errorText,\n }, { status: response.status });\n }\n\n let usageData;\n if (format === \"csv\") {\n const csvText = await response.text();\n usageData = parseCSV(csvText);\n console.log(`Parsed CSV: ${usageData.length} rows`);\n } else {\n usageData = await response.json();\n console.log(\n `Received JSON: ${\n Array.isArray(usageData) ? usageData.length : 0\n } rows`,\n );\n }\n\n // Smart upsert: reuse existing table or create new\n const tableResult = await smartUpsertData(\n workspaceId,\n spaceId,\n folderDataSourceId,\n table,\n usageData,\n apiKey,\n start,\n end,\n );\n\n return Response.json({\n success: true,\n message: \"Data successfully pushed to Dust folder\",\n table: table,\n format: format,\n rowCount: Array.isArray(usageData) ? usageData.length : 0,\n tableId: tableResult.tableId,\n batches: tableResult.batches,\n tableCreated: tableResult.tableCreated,\n tableReused: tableResult.tableReused,\n period: { start, end, mode },\n }, {\n headers: { \"Access-Control-Allow-Origin\": \"*\" },\n });\n } catch (error) {\n console.error(\"Error:\", error);\n return Response.json({\n error: \"Failed to fetch or push workspace usage\",\n details: error.message,\n stack: error.stack,\n }, { status: 500 });\n }\n}\n\n// Improved CSV parser\nfunction parseCSV(csvText: string): any[] {\n const lines = csvText.trim().split(\"\\n\");\n if (lines.length === 0) return [];\n\n // Parse header - handle quoted headers\n const headerLine = lines[0];\n const headers: string[] = [];\n let current = \"\";\n let inQuotes = false;\n\n for (let i = 0; i < headerLine.length; i++) {\n const char = headerLine[i];\n if (char === '\"') {\n inQuotes = !inQuotes;\n } else if (char === \",\" && !inQuotes) {\n headers.push(current.trim().replace(/^\"|\"$/g, \"\"));\n current = \"\";\n } else {\n current += char;\n }\n }\n headers.push(current.trim().replace(/^\"|\"$/g, \"\"));\n\n console.log(`CSV Headers: ${headers.join(\", \")}`);\n\n const rows = [];\n for (let i = 1; i < lines.length; i++) {\n const line = lines[i];\n if (!line.trim()) continue;\n\n const values: string[] = [];\n current = \"\";\n inQuotes = false;\n\n for (let j = 0; j < line.length; j++) {\n const char = line[j];\n if (char === '\"') {\n inQuotes = !inQuotes;\n } else if (char === \",\" && !inQuotes) {\n values.push(current.trim().replace(/^\"|\"$/g, \"\"));\n current = \"\";\n } else {\n current += char;\n }\n }\n values.push(current.trim().replace(/^\"|\"$/g, \"\"));\n\n // Skip rows that don't have enough values\n if (values.length < headers.length) {\n console.log(\n `Skipping malformed row ${i}: only ${values.length} values for ${headers.length} headers`,\n );\n continue;\n }\n\n const row: any = {};\n headers.forEach((header, index) => {\n const value = values[index] || \"\";\n // Try to parse numbers\n if (value && !isNaN(Number(value)) && value !== \"\") {\n row[header] = Number(value);\n } else {\n row[header] = value;\n }\n });\n\n rows.push(row);\n }\n\n console.log(`Parsed ${rows.length} valid rows from CSV`);\n return rows;\n}\n\n// Normalize column names to STRICT lowercase alphanumeric + underscore\nfunction normalizeColumnName(name: string): string {\n return name\n .toLowerCase() // Force lowercase\n .replace(/[^a-z0-9]+/g, \"_\") // Only a-z, 0-9, underscore\n .replace(/^_+|_+$/g, \"\") // Remove leading/trailing underscores\n .replace(/_+/g, \"_\"); // Collapse multiple underscores\n}\n\n// Normalize all keys in a row object\nfunction normalizeRowData(row: any): any {\n const normalized: any = {};\n for (const [key, value] of Object.entries(row)) {\n const normalizedKey = normalizeColumnName(key);\n normalized[normalizedKey] = value;\n }\n return normalized;\n}\n\n// Validate that all keys are lowercase alphanumeric\nfunction validateRowKeys(row: any): { valid: boolean; invalidKeys: string[] } {\n const invalidKeys = Object.keys(row).filter((key) =>\n !/^[a-z0-9_]+$/.test(key)\n );\n return {\n valid: invalidKeys.length === 0,\n invalidKeys,\n };\n}\n\n// List all tables in a data source\nasync function listTablesInDataSource(\n workspaceId: string,\n spaceId: string,\n dataSourceId: string,\n apiKey: string,\n): Promise> {\n const listUrl =\n `https://dust.tt/api/v1/w/${workspaceId}/spaces/${spaceId}/data_sources/${dataSourceId}/tables`;\n\n console.log(`Listing tables from: ${listUrl}`);\n\n try {\n const response = await fetch(listUrl, {\n method: \"GET\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n },\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(`Failed to list tables: ${response.status} - ${errorText}`);\n return [];\n }\n\n const data = await response.json();\n\n // Handle different response formats\n const tables = data.tables || data.data || [];\n\n console.log(`Found ${tables.length} existing tables`);\n if (tables.length > 0) {\n console.log(\n \"Existing tables:\",\n tables.map((t: any) => `${t.name || t.table_id} (id: ${t.table_id})`)\n .join(\", \"),\n );\n }\n\n return tables;\n } catch (error) {\n console.error(`Error listing tables: ${error.message}`);\n return [];\n }\n}\n\n// Find existing table by matching name pattern\nfunction findMatchingTable(\n existingTables: Array<{ table_id: string; name: string }>,\n tableName: string,\n dateStr: string,\n): { table_id: string; name: string } | null {\n // Normalize the search pattern\n const searchPattern = `${tableName}_${dateStr}`.toLowerCase().replace(\n /[^a-z0-9_]/g,\n \"_\",\n );\n\n // Look for exact match\n const exactMatch = existingTables.find((t) =>\n t.name === searchPattern ||\n t.table_id === searchPattern\n );\n\n if (exactMatch) {\n console.log(\n `Found exact match: ${exactMatch.name} (${exactMatch.table_id})`,\n );\n return exactMatch;\n }\n\n // Look for partial match (starts with pattern)\n const partialMatch = existingTables.find((t) =>\n t.name.startsWith(searchPattern) ||\n t.table_id.startsWith(searchPattern)\n );\n\n if (partialMatch) {\n console.log(\n `Found partial match: ${partialMatch.name} (${partialMatch.table_id})`,\n );\n return partialMatch;\n }\n\n return null;\n}\n\n// Generate a stable row ID based on the data content (STRICT LOWERCASE)\nfunction generateRowId(tableName: string, row: any, dateStr: string): string {\n const identifiers: string[] = [];\n\n // Common identifying fields (already normalized to lowercase)\n const idFields = [\n \"user_id\",\n \"userid\",\n \"email\",\n \"assistant_id\",\n \"assistantid\",\n \"date\",\n \"day\",\n \"workspace_id\",\n \"message_id\",\n ];\n\n for (const field of idFields) {\n if (row[field]) {\n identifiers.push(String(row[field]));\n }\n }\n\n // If no identifiers found, create hash from all values\n if (identifiers.length === 0) {\n const jsonStr = JSON.stringify(row);\n const simpleHash = jsonStr.split(\"\").reduce((acc, char) => {\n return ((acc << 5) - acc) + char.charCodeAt(0);\n }, 0);\n const cleanTableName = tableName.toLowerCase().replace(/[^a-z0-9]/g, \"_\");\n const cleanDateStr = dateStr.toLowerCase().replace(/[^a-z0-9]/g, \"_\");\n return `${cleanTableName}_${cleanDateStr}_${Math.abs(simpleHash)}`;\n }\n\n // Create stable ID from identifiers - FORCE LOWERCASE AND CLEAN\n const idString = identifiers\n .join(\"_\")\n .toLowerCase()\n .replace(/[^a-z0-9_]/g, \"_\")\n .replace(/_+/g, \"_\")\n .replace(/^_+|_+$/g, \"\");\n\n const cleanTableName = tableName.toLowerCase().replace(/[^a-z0-9]/g, \"_\");\n const cleanDateStr = dateStr.toLowerCase().replace(/[^a-z0-9]/g, \"_\");\n\n return `${cleanTableName}_${cleanDateStr}_${idString}`;\n}\n\n// Smart upsert: Check for existing table, reuse or create\nasync function smartUpsertData(\n workspaceId: string,\n spaceId: string,\n folderDataSourceId: string,\n tableName: string,\n data: any,\n apiKey: string,\n start: string,\n end?: string,\n) {\n let dataArray = Array.isArray(data) ? data : [data];\n\n console.log(\"\\n=== RAW DATA BEFORE NORMALIZATION ===\");\n console.log(\"Sample raw row:\", JSON.stringify(dataArray[0], null, 2));\n console.log(\"Raw row keys:\", Object.keys(dataArray[0]));\n\n // CRITICAL: Normalize BEFORE any other processing\n dataArray = dataArray.map((row) => normalizeRowData(row));\n\n // Check if we have any data\n if (dataArray.length === 0) {\n console.log(\"⚠️ No data returned from API for this period\");\n return {\n tableId: null,\n rowCount: 0,\n batches: 0,\n tableCreated: false,\n tableReused: false,\n message: \"No data available for the specified period\",\n };\n }\n\n // Check if first row exists and is valid\n if (!dataArray[0] || typeof dataArray[0] !== \"object\") {\n console.error(\"❌ Invalid data structure:\", dataArray);\n throw new Error(\"Invalid data returned from API\");\n }\n\n console.log(\"\\n=== DATA AFTER NORMALIZATION ===\");\n console.log(\"Sample normalized row:\", JSON.stringify(dataArray[0], null, 2));\n console.log(\"Normalized row keys:\", Object.keys(dataArray[0]));\n\n // Generate consistent identifiers\n const dateStr = end\n ? `${start.replace(/-/g, \"\")}_to_${end.replace(/-/g, \"\")}`\n : start.replace(/-/g, \"\");\n\n console.log(`\\n=== Smart Upsert for ${tableName} (${dateStr}) ===`);\n\n // Step 1: List existing tables\n const existingTables = await listTablesInDataSource(\n workspaceId,\n spaceId,\n folderDataSourceId,\n apiKey,\n );\n\n // Step 2: Check if matching table exists\n const matchingTable = findMatchingTable(existingTables, tableName, dateStr);\n\n let tableId: string;\n let tableCreated = false;\n let tableReused = false;\n\n if (matchingTable) {\n // Reuse existing table\n tableId = matchingTable.table_id;\n tableReused = true;\n console.log(`✓ Reusing existing table: ${matchingTable.name} (${tableId})`);\n } else {\n // Create new table with unique name (lowercase, alphanumeric + underscore only)\n tableId = `${tableName}_${dateStr}`.toLowerCase().replace(\n /[^a-z0-9_]/g,\n \"_\",\n );\n console.log(`Creating new table: ${tableId}`);\n\n const schema = inferSchemaFromData(dataArray[0]);\n\n console.log(\"\\n=== SCHEMA BEING CREATED ===\");\n console.log(JSON.stringify(schema, null, 2));\n\n const tableUrl =\n `https://dust.tt/api/v1/w/${workspaceId}/spaces/${spaceId}/data_sources/${folderDataSourceId}/tables`;\n\n const tableResponse = await fetch(tableUrl, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n table_id: tableId,\n name: tableId, // Use tableId for name too (ensures uniqueness AND lowercase)\n title: `${tableName} (${dateStr})`,\n description: `Workspace usage data for ${tableName}. Period: ${start}${\n end ? ` to ${end}` : \"\"\n }. Last updated: ${new Date().toISOString()}`,\n schema: schema,\n }),\n });\n\n if (!tableResponse.ok) {\n const error = await tableResponse.text();\n console.error(`Failed to create table: ${error}`);\n throw new Error(`Failed to create table: ${error}`);\n }\n\n console.log(`✓ Table created successfully`);\n tableCreated = true;\n }\n\n // Step 3: Prepare and VALIDATE rows\n console.log(\"\\n=== PREPARING ROWS ===\");\n\n const allRows = dataArray.map((row, index) => {\n // Validate each row's keys\n const validation = validateRowKeys(row);\n if (!validation.valid) {\n console.error(\n `\\n❌ Row ${index} has INVALID KEYS:`,\n validation.invalidKeys,\n );\n console.error(\"Problematic row:\", JSON.stringify(row, null, 2));\n throw new Error(\n `Row ${index} contains invalid keys: ${\n validation.invalidKeys.join(\", \")\n }. ` +\n `Keys must be lowercase alphanumeric with underscores only.`,\n );\n }\n\n return {\n row_id: generateRowId(tableName, row, dateStr),\n value: row,\n };\n });\n\n console.log(`✓ All ${allRows.length} rows validated successfully`);\n\n console.log(\"\\n=== SAMPLE VALIDATED ROW ===\");\n console.log(\"row_id:\", allRows[0].row_id);\n console.log(\"value keys:\", Object.keys(allRows[0].value));\n console.log(\n \"Full sample:\",\n JSON.stringify(allRows[0], null, 2).substring(0, 800),\n );\n\n // Step 4: Create batches\n const MAX_BATCH_SIZE_BYTES = 800 * 1024;\n const batches = createBatches(allRows, MAX_BATCH_SIZE_BYTES);\n\n console.log(\n `\\nCreated ${batches.length} batches from ${allRows.length} rows`,\n );\n\n // Step 5: Push batches\n const rowsUrl =\n `https://dust.tt/api/v1/w/${workspaceId}/spaces/${spaceId}/data_sources/${folderDataSourceId}/tables/${tableId}/rows`;\n\n let totalPushed = 0;\n\n for (let i = 0; i < batches.length; i++) {\n const batch = batches[i];\n\n console.log(\n `\\nPushing batch ${i + 1}/${batches.length}: ${batch.length} rows`,\n );\n\n // Only truncate if this is a fresh table AND first batch\n const batchPayload = {\n rows: batch,\n truncate: tableCreated && i === 0,\n };\n\n const payloadSize =\n new TextEncoder().encode(JSON.stringify(batchPayload)).length;\n\n console.log(\n `Batch ${i + 1} payload size: ${(payloadSize / 1024).toFixed(2)}KB`,\n );\n\n const rowsResponse = await fetch(rowsUrl, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(batchPayload),\n });\n\n if (!rowsResponse.ok) {\n const error = await rowsResponse.text();\n console.error(`\\n❌ Failed to push batch ${i + 1}: ${error}`);\n\n // DEBUG: Log the problematic batch\n console.error(\"\\n=== PROBLEMATIC BATCH SAMPLE ===\");\n console.error(JSON.stringify(batch[0], null, 2).substring(0, 1000));\n\n throw new Error(\n `Failed to push batch ${i + 1}/${batches.length}: ${error}`,\n );\n }\n\n totalPushed += batch.length;\n console.log(\n `✓ Batch ${i + 1} pushed. Total: ${totalPushed}/${allRows.length}`,\n );\n\n if (i < batches.length - 1) {\n await new Promise((resolve) => setTimeout(resolve, 200));\n }\n }\n\n console.log(\n `\\n✓✓✓ Upsert complete: ${totalPushed} rows in table ${tableId} ✓✓✓`,\n );\n\n return {\n tableId,\n rowCount: totalPushed,\n batches: batches.length,\n tableCreated,\n tableReused,\n };\n}\n\n// Create batches under size limit\nfunction createBatches(rows: any[], maxSizeBytes: number): any[][] {\n const batches: any[][] = [];\n let currentBatch: any[] = [];\n let currentSize = 0;\n\n const OVERHEAD = 100;\n\n for (const row of rows) {\n const rowSize = new TextEncoder().encode(JSON.stringify(row)).length;\n\n if (\n currentSize + rowSize + OVERHEAD > maxSizeBytes && currentBatch.length > 0\n ) {\n batches.push(currentBatch);\n currentBatch = [row];\n currentSize = rowSize;\n } else {\n currentBatch.push(row);\n currentSize += rowSize;\n }\n }\n\n if (currentBatch.length > 0) {\n batches.push(currentBatch);\n }\n\n return batches;\n}\n\n// Infer schema from data\nfunction inferSchemaFromData(sample: any) {\n if (!sample || typeof sample !== \"object\") {\n return [];\n }\n\n return Object.keys(sample).map((key) => {\n const value = sample[key];\n let value_type = \"text\";\n\n if (typeof value === \"number\") {\n value_type = Number.isInteger(value) ? \"int\" : \"float\";\n } else if (typeof value === \"boolean\") {\n value_type = \"bool\";\n }\n\n return {\n name: normalizeColumnName(key),\n value_type,\n };\n });\n}","deleted","label",[99],"permalink","https://www.val.town/x/wesype/Test-Dust-Usage-Analytics/code/main.ts","extension","actionData","errors"]