promotel

promotel: Prometheus to OpenTelemetry conversion library for JavaScript/TypeScript

A pure TypeScript implementation for converting Prometheus metrics to OpenTelemetry Protocol (OTLP) format. Promotel provides fast, reliable conversion between Prometheus and OTLP formats with full type safety and comprehensive protobuf support.

Features

• 🚀 High Performance: Zero-copy protobuf parsing and generation
• 🛡️ Type Safe: Full TypeScript support with generated protobuf types
• 🔄 Format Flexibility: Prometheus content negotiation between protobuf and text formats
• 🌐 HTTP Integration: Built-in HTTP client for fetching and pushing metrics
• 🔧 Prom-Client Integration: Direct integration with prom-client registries
• 🏗️ Pipeline Architecture: Flexible metrics pipeline with pluggable sources
• ⚡ CLI Tool: Standalone server for automated metric conversion
• 📊 Complete Metric Support: Counters, gauges, histograms, and summaries

Installation

npm install @platformatic/promotel

Requirements: Node.js 18+

Quick Start

Standalone Server

The easiest way to get started is with the CLI server for automated conversion:

npx @platformatic/promotel \
  --prometheus-url http://my-app:9090/metrics \
  --otlp-url http://otel-collector:4318/v1/metrics \
  --interval 30000 \
  --service-name my-service \
  --service-version 1.0.0

This will automatically fetch metrics from your Prometheus endpoint every 30 seconds and push them to your OpenTelemetry collector.

In-Process Bridge

For Node.js applications using prom-client, use the automated bridge:

import { PromClientBridge } from '@platformatic/promotel';
import { register } from 'prom-client';

const bridge = new PromClientBridge({
  registry: register, // Your prom-client registry
  otlpEndpoint: 'http://otel-collector:4318/v1/metrics',
  interval: 15000, // 15 seconds
  conversionOptions: {
    serviceName: 'my-app',
    serviceVersion: '1.2.3'
  }
});

bridge.start(); // Begins automatic metric collection and pushing

HTTP-Based Pipeline

For fetching from external Prometheus endpoints:

import { gather, dispatch, convert } from '@platformatic/promotel';

// Gather from Prometheus endpoint
const writeRequest = await gather({
  url: 'http://app:9090/metrics'
});

// Convert to OTLP
const otlpData = convert(writeRequest);

// Dispatch to OTLP endpoint
await dispatch(otlpData, {
  url: 'http://otel-collector:4318/v1/metrics'
});

API Reference

Core Functions

parse(text: string): prometheus.IWriteRequest

Parses Prometheus text exposition format into protobuf WriteRequest.

const writeRequest = parse(`
# HELP cpu_usage CPU usage percentage
# TYPE cpu_usage gauge
cpu_usage{cpu="0"} 45.2
cpu_usage{cpu="1"} 38.7
`);

convert(writeRequest, options?): opentelemetry.proto.collector.metrics.v1.IExportMetricsServiceRequest

Converts Prometheus WriteRequest to OTLP format.

Parameters:

• writeRequest: Prometheus protobuf WriteRequest
• options: Conversion options (optional)
  • serviceName?: string - Service name (default: 'promotel-js')
  • serviceVersion?: string - Service version (default: '0.1.0')
  • defaultTimestamp?: number - Default timestamp in milliseconds

const otlpData = convert(writeRequest, {
  serviceName: 'payment-service',
  serviceVersion: '2.1.0',
  defaultTimestamp: Date.now()
});

encode(data): Uint8Array

Encodes OTLP ExportMetricsServiceRequest to protobuf bytes.

const bytes = encode(otlpData);
// Send bytes over HTTP, gRPC, etc.

HTTP Client Functions

gather(options): Promise<prometheus.IWriteRequest>

Fetches metrics from a Prometheus HTTP endpoint with content negotiation.

Options:

• url: string - Prometheus metrics endpoint URL
• headers?: Record<string, string> - Additional HTTP headers
• signal?: AbortSignal - Abort signal for request cancellation

const writeRequest = await gather({
  url: 'https://my-app.com/metrics',
  headers: { 'Authorization': 'Bearer token123' },
  signal: AbortSignal.timeout(5000)
});

dispatch(data, options): Promise<void>

Pushes OTLP data to an OpenTelemetry collector endpoint.

Options:

• url: string - OTLP metrics endpoint URL
• headers?: Record<string, string> - Additional HTTP headers
• signal?: AbortSignal - Abort signal for request cancellation

await dispatch(otlpData, {
  url: 'http://jaeger:14268/api/traces',
  headers: { 'Content-Encoding': 'gzip' }
});

Prom-Client Integration

getFromRegistry(options?): Promise<prometheus.IWriteRequest>

Extracts metrics from a prom-client registry as protobuf WriteRequest.

Options:

• registry?: Registry - Prom-client registry (defaults to default registry)

import { register, Counter } from 'prom-client';

const counter = new Counter({
  name: 'my_counter',
  help: 'Example counter',
  registers: [register]
});

const writeRequest = await getFromRegistry({ registry: register });

Classes

PromClientBridge

Automated bridge that periodically converts metrics from a prom-client registry and pushes to an OTLP endpoint.

Constructor Options:

• registry: Registry - Prom-client registry
• otlpEndpoint: string | OTLPEndpointOptions - OTLP endpoint configuration
• interval?: number - Collection interval in milliseconds (default: 60000)
• conversionOptions?: ConversionOptions - OTLP conversion options
• onError?: (error: Error) => void - Error handler

Methods:

• start(): void - Start periodic collection
• stop(): void - Stop periodic collection
• collectAndPush(): Promise<void> - Manual collection trigger

const bridge = new PromClientBridge({
  registry: myRegistry,
  otlpEndpoint: {
    url: 'http://collector:4318/v1/metrics',
    headers: { 'x-api-key': 'secret' }
  },
  interval: 30000,
  onError: (error) => console.error('Bridge error:', error)
});

bridge.start();

// Later...
bridge.stop();

MetricsPipeline

Flexible pipeline for metrics collection, conversion, and delivery with pluggable data sources.

Constructor Options:

• source: MetricsSource - Metrics data source implementation
• otlpEndpoint: string | OTLPEndpointOptions - OTLP endpoint configuration
• interval?: number - Collection interval in milliseconds (default: 60000)
• conversionOptions?: ConversionOptions - OTLP conversion options
• onError?: (error: Error) => void - Error handler

Custom Source Example:

class DatabaseMetricsSource implements MetricsSource {
  async fetch(): Promise<prometheus.IWriteRequest> {
    const metrics = await this.queryDatabase();
    return this.convertToPrometheus(metrics);
  }
}

const pipeline = new MetricsPipeline({
  source: new DatabaseMetricsSource(),
  otlpEndpoint: 'http://collector:4318/v1/metrics'
});

CLI Tool

The CLI provides a standalone server for automated metric conversion:

npx @platformatic/promotel \
  --prometheus-url http://app:9090/metrics \
  --otlp-url http://collector:4318/v1/metrics \
  --interval 30000 \
  --service-name my-service \
  --service-version 1.0.0

Options:

• --prometheus-url - Source Prometheus metrics endpoint
• --otlp-url - Destination OTLP endpoint
• --interval - Collection interval in milliseconds (default: 60000)
• --service-name - Service name for OTLP data (default: 'promotel-js')
• --service-version - Service version for OTLP data (default: '0.1.0')
• --prometheus-headers - JSON string of headers for Prometheus requests
• --otlp-headers - JSON string of headers for OTLP requests

Protobuf Types

Promotel exports generated protobuf types for direct usage:

import { prometheus, opentelemetry } from '@platformatic/promotel';

// Prometheus types
const writeRequest: prometheus.IWriteRequest = {
  timeseries: [],
  metadata: []
};

// OpenTelemetry types
const exportRequest: opentelemetry.proto.collector.metrics.v1.IExportMetricsServiceRequest = {
  resource_metrics: []
};

Error Classes

import { PrometheusParseError, OTLPConversionError } from '@platformatic/promotel';

try {
  parse('invalid metrics');
} catch (error) {
  if (error instanceof PrometheusParseError) {
    console.log(`Parse error at line ${error.lineNumber}: ${error.message}`);
  }
}

try {
  await dispatch(data, { url: 'invalid-url' });
} catch (error) {
  if (error instanceof OTLPConversionError) {
    console.log(`OTLP error: ${error.message}`);
  }
}

Advanced Usage

Low-Level Conversion

For direct conversion of Prometheus data without the automated pipelines:

import { parse, convert, encode } from '@platformatic/promotel';

// Parse Prometheus text format
const prometheusText = `
# HELP http_requests_total Total HTTP requests
# TYPE http_requests_total counter
http_requests_total{method="GET",status="200"} 1027
`;

const writeRequest = parse(prometheusText);
const otlpData = convert(writeRequest, {
  serviceName: 'my-service',
  serviceVersion: '1.0.0'
});

// Encode as protobuf for transmission
const bytes = encode(otlpData);

Working with Protobuf Types

import { prometheus, encodeWriteRequest, decodeWriteRequest } from '@platformatic/promotel';

// Create WriteRequest manually
const writeRequest: prometheus.IWriteRequest = {
  timeseries: [{
    labels: [{ name: 'job', value: 'api' }],
    samples: [{ value: 42, timestamp: Date.now() }]
  }],
  metadata: [{
    type: prometheus.MetricMetadata.MetricType.GAUGE,
    metric_family_name: 'memory_usage',
    help: 'Memory usage in bytes'
  }]
};

// Serialize to bytes
const bytes = encodeWriteRequest(writeRequest);

// Parse from bytes
const decoded = decodeWriteRequest(bytes);

Custom Metrics Pipeline

class CloudWatchSource implements MetricsSource {
  async fetch(): Promise<prometheus.IWriteRequest> {
    const metrics = await this.getCloudWatchMetrics();
    return this.transformToPrometheus(metrics);
  }

  private async getCloudWatchMetrics() {
    // Fetch from CloudWatch API
  }

  private transformToPrometheus(metrics: any[]): prometheus.IWriteRequest {
    // Convert CloudWatch metrics to Prometheus format
  }
}

const pipeline = new MetricsPipeline({
  source: new CloudWatchSource(),
  otlpEndpoint: process.env.OTLP_ENDPOINT!,
  conversionOptions: {
    serviceName: 'cloudwatch-bridge'
  },
  onError: (error) => {
    console.error('Pipeline failed:', error);
    // Send to monitoring system
  }
});

Development

Building

npm install
npm run build

Testing

# Run all tests
npm test

# Run tests with test environment
npm run test:with-env

# Type checking
npm run typecheck

# Linting
npm run lint

Protobuf Generation

# Fetch latest protobuf definitions
npm run fetch:protos

# Generate TypeScript types
npm run generate:types

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Run npm run lint and npm run typecheck
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

License

MIT © Platformatic Inc.