Instrumenting the OpenClaw Rating API with OpenTelemetry

1. Introduction

The OpenClaw Rating API powers many SaaS products that need to calculate user‑generated scores, rankings, and recommendations. While the API is functionally robust, without proper observability you risk blind spots that can lead to latency spikes, silent failures, or inaccurate ratings.

OpenTelemetry, the open‑source standard for distributed tracing, metrics, and logs, provides a unified way to collect telemetry data across languages and runtimes. In this guide we walk you through a complete, production‑ready instrumentation of the OpenClaw Rating API using the OpenTelemetry SDK for Node.js, and we show how to deploy the instrumented service on Docker, Kubernetes, and the UBOS platform overview.

2. Why instrument the OpenClaw Rating API with OpenTelemetry?

• End‑to‑end tracing: Follow a request from the client, through the rating engine, to downstream services (e.g., database, cache).
• Business metrics: Export custom metrics such as rating_requests_total or rating_latency_seconds to monitor SLA compliance.
• Root‑cause analysis: Correlate traces with logs and metrics to pinpoint the exact line of code causing a slowdown.
• Vendor lock‑in avoidance: OpenTelemetry works with any backend (Jaeger, Prometheus, Datadog, etc.), giving you flexibility as your stack evolves.

3. Prerequisites

Before you start, make sure you have the following installed and configured:

1. Node.js ≥ 14 and npm ≥ 6.
2. A Git repository for the OpenClaw Rating API source code.
3. Docker ≥ 20.10 (for containerization).
4. Kubernetes cluster access (optional, for scaling).
5. An OpenTelemetry collector endpoint (e.g., OpenTelemetry.io demo collector).

4. Step‑by‑step instrumentation guide

npm install @opentelemetry/api \
  @opentelemetry/sdk-node \
  @opentelemetry/auto-instrumentations-node \
  @opentelemetry/exporter-trace-otlp-http \
  @opentelemetry/exporter-metrics-otlp-http \
  @opentelemetry/resources

// otel.js
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-http');
const { OTLPMetricExporter } = require('@opentelemetry/exporter-metrics-otlp-http');
const { Resource } = require('@opentelemetry/resources');
const { SemanticResourceAttributes } = require('@opentelemetry/semantic-conventions');

// Define service name for all telemetry
const resource = new Resource({
  [SemanticResourceAttributes.SERVICE_NAME]: 'openclaw-rating-api',
});

const traceExporter = new OTLPTraceExporter({
  url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || 'http://localhost:4318/v1/traces',
});

const metricExporter = new OTLPMetricExporter({
  url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || 'http://localhost:4318/v1/metrics',
});

const sdk = new NodeSDK({
  resource,
  traceExporter,
  metricExporter,
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start()
  .then(() => console.log('🛰️ OpenTelemetry initialized'))
  .catch((error) => console.error('Failed to start OpenTelemetry', error));

module.exports = sdk;

// ratingController.js
const { trace, context } = require('@opentelemetry/api');
const tracer = trace.getTracer('openclaw-rating');

async function calculateRating(req, res) {
  const span = tracer.startSpan('calculateRating', {
    attributes: {
      'http.method': req.method,
      'http.route': '/rating',
      'rating.inputSize': req.body.items?.length || 0,
    },
  });

  try {
    // Simulate heavy computation
    const result = await ratingEngine.compute(req.body);
    span.setAttribute('rating.result', result.score);
    res.json({ score: result.score });
  } catch (err) {
    span.recordException(err);
    span.setStatus({ code: 2, message: err.message }); // 2 = ERROR
    res.status(500).json({ error: 'Rating calculation failed' });
  } finally {
    span.end();
  }
}

module.exports = { calculateRating };

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      http:

exporters:
  logging:
    loglevel: debug
  otlp:
    endpoint: ${OTEL_EXPORTER_OTLP_ENDPOINT}
    tls:
      insecure: true

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [logging, otlp]
    metrics:
      receivers: [otlp]
      exporters: [logging, otlp]

5. Full Node.js example

The following minimal Express app demonstrates the complete flow from SDK initialization to a fully instrumented rating endpoint.

// server.js
require('dotenv').config();
require('./otel'); // Initialize OpenTelemetry first

const express = require('express');
const bodyParser = require('body-parser');
const { calculateRating } = require('./ratingController');

const app = express();
app.use(bodyParser.json());

// Health check (auto‑instrumented)
app.get('/health', (req, res) => res.send('OK'));

// Rating endpoint (custom spans)
app.post('/rating', calculateRating);

// Global error handler (captures unhandled errors)
app.use((err, req, res, next) => {
  console.error(err);
  res.status(500).json({ error: 'Unexpected error' });
});

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
  console.log(`🚀 OpenClaw Rating API listening on port ${PORT}`);
});

Run the service locally with node server.js. You should see the OpenTelemetry initialization message, and traces will be streamed to the collector.

6. Deployment tips (Docker, Kubernetes, UBOS)

# Dockerfile
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build   # if you have a build step

FROM node:18-alpine
WORKDIR /app
COPY --from=builder /app .
ENV NODE_ENV=production
EXPOSE 3000
CMD ["node", "server.js"]

# docker-compose.yml
version: '3.8'
services:
  rating-api:
    build: .
    ports:
      - "3000:3000"
    environment:
      - OTEL_EXPORTER_OTLP_ENDPOINT=http://collector:4318
    depends_on:
      - collector

  collector:
    image: otel/opentelemetry-collector:latest
    command: ["--config=/etc/collector/otel-collector-config.yaml"]
    volumes:
      - ./otel-collector-config.yaml:/etc/collector/otel-collector-config.yaml
    ports:
      - "4318:4318"

apiVersion: apps/v1
kind: Deployment
metadata:
  name: openclaw-rating-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: rating-api
  template:
    metadata:
      labels:
        app: rating-api
    spec:
      containers:
        - name: api
          image: your-registry/openclaw-rating-api:latest
          ports:
            - containerPort: 3000
          env:
            - name: OTEL_EXPORTER_OTLP_ENDPOINT
              value: "http://otel-collector:4318"
        - name: otel-collector
          image: otel/opentelemetry-collector:latest
          args: ["--config=/etc/collector/otel-collector-config.yaml"]
          volumeMounts:
            - name: collector-config
              mountPath: /etc/collector
      volumes:
        - name: collector-config
          configMap:
            name: otel-collector-config

7. Best‑practice observability patterns

Beyond basic instrumentation, adopt these patterns to get the most out of OpenTelemetry.

a. Correlate traces, metrics, and logs

• Inject the same trace_id into log statements (e.g., using winston or pino).
• Export metrics with the same service name and version tags used for traces.
• Use a backend that supports unified queries (e.g., Grafana Loki + Tempo).

b. Use semantic conventions

Follow the OpenTelemetry semantic conventions for attribute naming. This ensures that dashboards and alerts work out‑of‑the‑box across tools.

c. Sample high‑traffic endpoints

For endpoints that receive thousands of requests per second, enable probabilistic sampling to keep data volume manageable while still capturing enough traces for analysis.

d. Export custom business metrics

Define metrics that reflect the health of the rating algorithm, such as:

e. Automate alerting

Set SLO‑based alerts on the metrics above. For example, trigger a PagerDuty incident if rating_error_rate exceeds 1 % over a 5‑minute window.

8. Related guide

For a deeper dive into building AI‑enhanced monitoring dashboards on UBOS, explore the AI SEO Analyzer template, which demonstrates how to visualize OpenTelemetry data alongside SEO metrics.

9. Conclusion and call to action

Instrumenting the OpenClaw Rating API with OpenTelemetry transforms a black‑box service into a transparent, observable component of your architecture. By following the steps above, you gain:

• Real‑time traceability of every rating request.
• Custom business metrics that align with product goals.
• Scalable deployment patterns for Docker, Kubernetes, and UBOS.
• Best‑practice observability patterns that reduce MTTR.

Ready to level up your API observability? Check out UBOS pricing plans and spin up a fully managed OpenTelemetry pipeline in minutes.

Happy tracing! 🚀