{"version":3,"file":"index.cjs","names":["getOtelLogger","currentTraceId","currentSpanId","SeverityNumber","trace","SpanKind","otelContext","SpanStatusCode","performance"],"sources":["../../src/observability/logger.ts","../../src/observability/http-instrumentation.ts","../../src/observability/worker-metrics.ts","../../src/observability/otel-worker-gauges.ts","../../src/observability/utils.ts"],"sourcesContent":["import { AnyValue, AnyValueMap, SeverityNumber } from '@opentelemetry/api-logs'\nimport {\n currentSpanId,\n currentTraceId,\n getLogger as getOtelLogger,\n} from './telemetry-system'\n\n/** @internal */\nexport type LoggerParams = {\n message: string\n trace_id?: string\n span_id?: string\n service_name?: string\n data?: unknown\n /** @deprecated Use service_name instead */\n function_name?: string\n}\n\n/**\n * Structured logger that emits logs as OpenTelemetry LogRecords.\n *\n * Every log call automatically captures the active trace and span context,\n * correlating your logs with distributed traces without any manual wiring.\n * When OTel is not initialized, Logger gracefully falls back to `console.*`.\n *\n * Pass structured data as the second argument to any log method. Using an\n * object of key-value pairs (instead of string interpolation) lets you\n * filter, aggregate, and build dashboards in your observability backend.\n *\n * @example\n * ```typescript\n * import { Logger } from 'iii-sdk'\n *\n * const logger = new Logger()\n *\n * // Basic logging, trace context is injected automatically\n * logger.info('Worker connected')\n *\n * // Structured context for dashboards and alerting\n * logger.info('Order processed', { orderId: 'ord_123', amount: 49.99, currency: 'USD' })\n * logger.warn('Retry attempt', { attempt: 3, maxRetries: 5, endpoint: '/api/charge' })\n * logger.error('Payment failed', { orderId: 'ord_123', gateway: 'stripe', errorCode: 'card_declined' })\n * ```\n */\nexport class Logger {\n private _otelLogger: ReturnType | null = null\n\n private get otelLogger() {\n // Lazy initialization: re-fetch logger if not yet available\n if (!this._otelLogger) {\n this._otelLogger = getOtelLogger()\n }\n return this._otelLogger\n }\n\n constructor(\n private readonly traceId?: string,\n private readonly serviceName?: string,\n private readonly spanId?: string,\n ) {}\n\n private emit(message: string, severity: SeverityNumber, data?: unknown): void {\n const attributes: AnyValueMap = {}\n const traceId = this.traceId ?? currentTraceId()\n const spanId = this.spanId ?? currentSpanId()\n\n if (traceId) {\n attributes.trace_id = traceId\n }\n if (spanId) {\n attributes.span_id = spanId\n }\n if (this.serviceName) {\n attributes['service.name'] = this.serviceName\n }\n if (data !== undefined) {\n attributes['log.data'] = data as AnyValue\n }\n\n if (this.otelLogger) {\n this.otelLogger.emit({\n severityNumber: severity,\n body: message,\n attributes: Object.keys(attributes).length > 0 ? attributes : undefined,\n })\n } else {\n // Fallback to console when OTEL is not available\n switch (severity) {\n case SeverityNumber.DEBUG:\n console.debug(message, data)\n break\n case SeverityNumber.INFO:\n console.info(message, data)\n break\n case SeverityNumber.WARN:\n console.warn(message, data)\n break\n case SeverityNumber.ERROR:\n console.error(message, data)\n break\n default:\n console.log(message, data)\n }\n }\n }\n\n /**\n * Log an info-level message.\n *\n * @param message - Human-readable log message.\n * @param data - Structured context attached as OTel log attributes.\n * Use key-value objects to enable filtering and aggregation in your\n * observability backend (e.g. Grafana, Datadog, New Relic).\n *\n * @example\n * ```typescript\n * logger.info('Order processed', { orderId: 'ord_123', status: 'completed' })\n * ```\n */\n info(message: string, data?: unknown): void {\n this.emit(message, SeverityNumber.INFO, data)\n }\n\n /**\n * Log a warning-level message.\n *\n * @param message - Human-readable log message.\n * @param data - Structured context attached as OTel log attributes.\n * Use key-value objects to enable filtering and aggregation in your\n * observability backend (e.g. Grafana, Datadog, New Relic).\n *\n * @example\n * ```typescript\n * logger.warn('Retry attempt', { attempt: 3, maxRetries: 5, endpoint: '/api/charge' })\n * ```\n */\n warn(message: string, data?: unknown): void {\n this.emit(message, SeverityNumber.WARN, data)\n }\n\n /**\n * Log an error-level message.\n *\n * @param message - Human-readable log message.\n * @param data - Structured context attached as OTel log attributes.\n * Use key-value objects to enable filtering and aggregation in your\n * observability backend (e.g. Grafana, Datadog, New Relic).\n *\n * @example\n * ```typescript\n * logger.error('Payment failed', { orderId: 'ord_123', gateway: 'stripe', errorCode: 'card_declined' })\n * ```\n */\n error(message: string, data?: unknown): void {\n this.emit(message, SeverityNumber.ERROR, data)\n }\n\n /**\n * Log a debug-level message.\n *\n * @param message - Human-readable log message.\n * @param data - Structured context attached as OTel log attributes.\n * Use key-value objects to enable filtering and aggregation in your\n * observability backend (e.g. Grafana, Datadog, New Relic).\n *\n * @example\n * ```typescript\n * logger.debug('Cache lookup', { key: 'user:42', hit: false })\n * ```\n */\n debug(message: string, data?: unknown): void {\n this.emit(message, SeverityNumber.DEBUG, data)\n }\n}\n","import { context as otelContext, propagation, SpanKind, SpanStatusCode, trace, type Tracer } from '@opentelemetry/api'\n\nconst SAFE_REQUEST_HEADERS = ['content-type', 'accept'] as const\nconst SAFE_RESPONSE_HEADERS = ['content-type'] as const\n\nexport interface TracedFetchInit extends RequestInit {\n /** Tracer used to create the client span for the outgoing request. */\n tracer?: Tracer\n}\n\n/**\n * Execute a fetch request inside an OTel CLIENT span.\n *\n * Mirrors the Rust execute_traced_request shape: injects W3C traceparent into\n * outgoing headers, records HTTP semantic-convention attributes, and sets\n * ERROR span status for HTTP responses with status >= 400 or network errors.\n *\n * @param input - The resource to fetch: a URL string, `URL`, or `Request`.\n * @param init - Fetch options, plus an optional `tracer` to create the span.\n */\nexport async function executeTracedRequest(\n input: RequestInfo | URL,\n init?: TracedFetchInit,\n): Promise {\n const tracer = init?.tracer ?? trace.getTracer('iii-node-sdk')\n const rawUrl = typeof input === 'string' ? input : input instanceof URL ? input.toString() : input.url\n let url: URL | null\n try {\n url = new URL(rawUrl)\n } catch {\n url = null\n }\n const method = (init?.method ?? (typeof input === 'object' && 'method' in input ? input.method : 'GET') ?? 'GET').toUpperCase()\n const name = url?.pathname ? `${method} ${url.pathname}` : method\n\n return tracer.startActiveSpan(\n name,\n {\n kind: SpanKind.CLIENT,\n attributes: {\n 'http.request.method': method,\n 'url.full': url?.toString() ?? rawUrl,\n 'network.protocol.name': 'http',\n ...(url\n ? {\n 'server.address': url.hostname,\n 'url.scheme': url.protocol.replace(':', ''),\n 'url.path': url.pathname,\n }\n : {}),\n ...(url?.port ? { 'server.port': Number(url.port) } : {}),\n ...(url?.search ? { 'url.query': url.search.slice(1) } : {}),\n },\n },\n async (span) => {\n try {\n // Seed with the Request's own headers so they survive when caller\n // passes a Request object; init.headers (if any) then overrides.\n const baseHeaders =\n typeof input === 'object' && 'headers' in input ? input.headers : undefined\n const headers = new Headers(baseHeaders)\n if (init?.headers) {\n for (const [k, v] of new Headers(init.headers).entries()) headers.set(k, v)\n }\n const carrier: Record = {}\n propagation.inject(otelContext.active(), carrier)\n for (const [k, v] of Object.entries(carrier)) headers.set(k, v)\n\n for (const h of SAFE_REQUEST_HEADERS) {\n const v = headers.get(h)\n if (v) span.setAttribute(`http.request.header.${h}`, v)\n }\n\n const response = await fetch(input, { ...init, headers })\n span.setAttribute('http.response.status_code', response.status)\n const cl = response.headers.get('content-length')\n if (cl) span.setAttribute('http.response.body.size', Number(cl))\n for (const h of SAFE_RESPONSE_HEADERS) {\n const v = response.headers.get(h)\n if (v) span.setAttribute(`http.response.header.${h}`, v)\n }\n\n if (response.status >= 400) {\n span.setStatus({ code: SpanStatusCode.ERROR, message: String(response.status) })\n span.setAttribute('error.type', String(response.status))\n } else {\n span.setStatus({ code: SpanStatusCode.OK })\n }\n return response\n } catch (err) {\n const error = err as Error\n span.recordException(error)\n span.setStatus({ code: SpanStatusCode.ERROR, message: error.message })\n span.setAttribute('error.type', error.name)\n throw err\n } finally {\n span.end()\n }\n },\n )\n}\n","/**\n * Worker metrics collection for the III Node SDK.\n *\n * Collects CPU, memory, and event loop metrics for worker health monitoring.\n * Uses the Node.js built-in `monitorEventLoopDelay` API for accurate\n * event loop lag measurements.\n */\n\nimport { type IntervalHistogram, monitorEventLoopDelay, performance } from 'node:perf_hooks'\n\n/**\n * Worker metrics data structure used internally for OTEL metric collection.\n * \n */\nexport type WorkerMetrics = {\n memory_heap_used?: number\n memory_heap_total?: number\n memory_rss?: number\n memory_external?: number\n cpu_user_micros?: number\n cpu_system_micros?: number\n cpu_percent?: number\n event_loop_lag_ms?: number\n uptime_seconds?: number\n timestamp_ms: number\n runtime: string\n}\n\n/**\n * Configuration options for the WorkerMetricsCollector.\n */\nexport interface WorkerMetricsCollectorOptions {\n /**\n * Event loop delay histogram resolution in milliseconds.\n * Lower values provide more accurate measurements but use more resources.\n * @default 20\n */\n eventLoopResolutionMs?: number\n}\n\n/**\n * Collects worker resource metrics including CPU, memory, and event loop lag.\n *\n * Uses the Node.js `monitorEventLoopDelay` API for high-precision event loop\n * delay measurements instead of manual `setImmediate` timing.\n *\n * @example\n * ```typescript\n * const collector = new WorkerMetricsCollector()\n *\n * // Collect metrics periodically\n * setInterval(() => {\n * const metrics = collector.collect()\n * console.log('CPU:', metrics.cpu_percent, '%')\n * console.log('Event Loop Lag:', metrics.event_loop_lag_ms, 'ms')\n * }, 5000)\n *\n * // Clean up when done\n * collector.stopMonitoring()\n * ```\n */\nexport class WorkerMetricsCollector {\n private readonly startTime: number\n private lastCpuUsage: NodeJS.CpuUsage\n private lastCpuTime: number\n private eventLoopHistogram: IntervalHistogram | null = null\n\n /**\n * Creates a new WorkerMetricsCollector instance.\n *\n * @param options - Configuration options\n */\n constructor(options: WorkerMetricsCollectorOptions = {}) {\n this.startTime = Date.now()\n this.lastCpuUsage = process.cpuUsage()\n this.lastCpuTime = performance.now()\n this.startEventLoopMonitoring(options.eventLoopResolutionMs ?? 20)\n }\n\n /**\n * Starts the event loop delay histogram monitoring.\n *\n * @param resolutionMs - Histogram resolution in milliseconds\n */\n private startEventLoopMonitoring(resolutionMs: number): void {\n // Sanitize resolution: must be a positive finite number, minimum 1ms\n const safeResolutionMs =\n Number.isFinite(resolutionMs) && resolutionMs > 0 ? Math.max(1, Math.floor(resolutionMs)) : 20 // Default fallback\n\n this.eventLoopHistogram = monitorEventLoopDelay({ resolution: safeResolutionMs })\n this.eventLoopHistogram.enable()\n }\n\n /**\n * Stops the event loop monitoring and releases resources.\n * Should be called when the collector is no longer needed.\n */\n public stopMonitoring(): void {\n if (this.eventLoopHistogram) {\n this.eventLoopHistogram.disable()\n this.eventLoopHistogram = null\n }\n }\n\n /**\n * Collects current worker metrics.\n *\n * This method calculates CPU usage since the last collection,\n * reads memory usage, and gets event loop delay statistics.\n * The event loop histogram is reset after each collection for\n * accurate per-interval measurements.\n *\n * @returns Current worker metrics snapshot\n */\n collect(): WorkerMetrics {\n const memoryUsage = process.memoryUsage()\n const cpuUsage = process.cpuUsage()\n const now = performance.now()\n\n // Calculate CPU percentage since last collection\n const cpuDelta = {\n user: cpuUsage.user - this.lastCpuUsage.user,\n system: cpuUsage.system - this.lastCpuUsage.system,\n }\n const timeDelta = (now - this.lastCpuTime) * 1000 // Convert ms to microseconds\n const cpuPercent = timeDelta > 0 ? ((cpuDelta.user + cpuDelta.system) / timeDelta) * 100 : 0\n\n // Update state for next collection\n this.lastCpuUsage = cpuUsage\n this.lastCpuTime = now\n\n // Get event loop lag from histogram (in nanoseconds, convert to ms)\n let eventLoopLagMs = 0\n if (this.eventLoopHistogram) {\n // Mean is in nanoseconds, convert to milliseconds\n eventLoopLagMs = this.eventLoopHistogram.mean / 1_000_000\n // Reset histogram for next collection interval\n this.eventLoopHistogram.reset()\n }\n\n return {\n memory_heap_used: memoryUsage.heapUsed,\n memory_heap_total: memoryUsage.heapTotal,\n memory_rss: memoryUsage.rss,\n memory_external: memoryUsage.external,\n cpu_user_micros: cpuUsage.user,\n cpu_system_micros: cpuUsage.system,\n cpu_percent: Math.min(cpuPercent, 100), // Cap at 100%\n event_loop_lag_ms: eventLoopLagMs,\n uptime_seconds: Math.floor((Date.now() - this.startTime) / 1000),\n timestamp_ms: Date.now(),\n runtime: 'node',\n }\n }\n}\n","import type { Meter, BatchObservableResult, Observable } from '@opentelemetry/api'\nimport { WorkerMetricsCollector } from './worker-metrics'\n\nexport interface WorkerGaugesOptions {\n /** Stable identifier of the worker the gauges are reported for. */\n workerId: string\n /** Human-readable name of the worker. */\n workerName?: string\n}\n\nlet registeredGauges = false\nlet metricsCollector: WorkerMetricsCollector | null = null\nlet registeredMeter: Meter | null = null\nlet registeredBatchCallback: ((observableResult: BatchObservableResult) => void) | null = null\nlet registeredObservables: Observable[] = []\n\n/**\n * Register OpenTelemetry observable gauges that report worker resource metrics\n * (CPU, memory, event-loop) on each collection cycle.\n */\nexport function registerWorkerGauges(meter: Meter, options: WorkerGaugesOptions): void {\n if (registeredGauges) {\n return\n }\n\n const { workerId, workerName } = options\n const baseAttributes = {\n 'worker.id': workerId,\n ...(workerName && { 'worker.name': workerName }),\n }\n\n metricsCollector = new WorkerMetricsCollector()\n\n const memoryHeapUsed = meter.createObservableGauge('iii.worker.memory.heap_used', {\n description: 'Worker heap memory used in bytes',\n unit: 'bytes',\n })\n\n const memoryHeapTotal = meter.createObservableGauge('iii.worker.memory.heap_total', {\n description: 'Worker total heap memory in bytes',\n unit: 'bytes',\n })\n\n const memoryRss = meter.createObservableGauge('iii.worker.memory.rss', {\n description: 'Worker resident set size in bytes',\n unit: 'bytes',\n })\n\n const memoryExternal = meter.createObservableGauge('iii.worker.memory.external', {\n description: 'Worker external memory in bytes',\n unit: 'bytes',\n })\n\n const cpuPercent = meter.createObservableGauge('iii.worker.cpu.percent', {\n description: 'Worker CPU usage percentage',\n unit: '%',\n })\n\n const cpuUserMicros = meter.createObservableGauge('iii.worker.cpu.user_micros', {\n description: 'Worker CPU user time in microseconds',\n unit: 'us',\n })\n\n const cpuSystemMicros = meter.createObservableGauge('iii.worker.cpu.system_micros', {\n description: 'Worker CPU system time in microseconds',\n unit: 'us',\n })\n\n const eventLoopLag = meter.createObservableGauge('iii.worker.event_loop.lag_ms', {\n description: 'Worker event loop lag in milliseconds',\n unit: 'ms',\n })\n\n const uptimeSeconds = meter.createObservableGauge('iii.worker.uptime_seconds', {\n description: 'Worker uptime in seconds',\n unit: 's',\n })\n\n const batchCallback = (observableResult: BatchObservableResult) => {\n if (!metricsCollector) return\n\n const metrics = metricsCollector.collect()\n\n if (metrics.memory_heap_used !== undefined) {\n observableResult.observe(memoryHeapUsed, metrics.memory_heap_used, baseAttributes)\n }\n if (metrics.memory_heap_total !== undefined) {\n observableResult.observe(memoryHeapTotal, metrics.memory_heap_total, baseAttributes)\n }\n if (metrics.memory_rss !== undefined) {\n observableResult.observe(memoryRss, metrics.memory_rss, baseAttributes)\n }\n if (metrics.memory_external !== undefined) {\n observableResult.observe(memoryExternal, metrics.memory_external, baseAttributes)\n }\n if (metrics.cpu_percent !== undefined) {\n observableResult.observe(cpuPercent, metrics.cpu_percent, baseAttributes)\n }\n if (metrics.cpu_user_micros !== undefined) {\n observableResult.observe(cpuUserMicros, metrics.cpu_user_micros, baseAttributes)\n }\n if (metrics.cpu_system_micros !== undefined) {\n observableResult.observe(cpuSystemMicros, metrics.cpu_system_micros, baseAttributes)\n }\n if (metrics.event_loop_lag_ms !== undefined) {\n observableResult.observe(eventLoopLag, metrics.event_loop_lag_ms, baseAttributes)\n }\n if (metrics.uptime_seconds !== undefined) {\n observableResult.observe(uptimeSeconds, metrics.uptime_seconds, baseAttributes)\n }\n }\n\n meter.addBatchObservableCallback(batchCallback, [\n memoryHeapUsed,\n memoryHeapTotal,\n memoryRss,\n memoryExternal,\n cpuPercent,\n cpuUserMicros,\n cpuSystemMicros,\n eventLoopLag,\n uptimeSeconds,\n ])\n\n registeredMeter = meter\n registeredBatchCallback = batchCallback\n registeredObservables = [\n memoryHeapUsed,\n memoryHeapTotal,\n memoryRss,\n memoryExternal,\n cpuPercent,\n cpuUserMicros,\n cpuSystemMicros,\n eventLoopLag,\n uptimeSeconds,\n ]\n\n registeredGauges = true\n}\n\n/**\n * Stop and unregister the worker resource gauges.\n */\nexport function stopWorkerGauges(): void {\n // Remove the batch observable callback before stopping\n if (registeredMeter && registeredBatchCallback) {\n registeredMeter.removeBatchObservableCallback(registeredBatchCallback, registeredObservables)\n }\n\n if (metricsCollector) {\n metricsCollector.stopMonitoring()\n metricsCollector = null\n }\n\n registeredMeter = null\n registeredBatchCallback = null\n registeredObservables = []\n registeredGauges = false\n}\n","/**\n * Safely stringify a value, handling circular references, BigInt, and other edge cases.\n * Returns \"[unserializable]\" if serialization fails for any reason.\n *\n * @param value - The value to serialize to JSON.\n */\nexport function safeStringify(value: unknown): string {\n const seen = new WeakSet()\n\n try {\n const result = JSON.stringify(value, (_key, val) => {\n // Handle BigInt\n if (typeof val === 'bigint') {\n return val.toString()\n }\n\n // Handle circular references\n if (val !== null && typeof val === 'object') {\n if (seen.has(val)) {\n return '[Circular]'\n }\n seen.add(val)\n }\n\n return val\n })\n return result ?? '[unserializable]'\n } catch {\n return '[unserializable]'\n 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