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MaintainX Observability

maintainx-observability

Implement comprehensive observability for MaintainX integrations.

SKILL.md

Full skill instructions

MaintainX Observability

Overview

Implement metrics, structured logging, and alerting for MaintainX integrations to ensure reliability and rapid issue detection.

Prerequisites

  • MaintainX integration deployed
  • Node.js 18+
  • Monitoring platform (Prometheus/​Grafana, Datadog, or CloudWatch)

Instructions

Step 1: Prometheus Metrics

// src/​observability/​metrics.ts
import { Counter, Histogram, Gauge, Registry } from 'prom-client';

const register = new Registry();

export const metrics = {
  apiRequests: new Counter({
    name: 'maintainx_api_requests_total',
    help: 'Total MaintainX API requests',
    labelNames: ['method', 'endpoint', 'status'],
    registers: [register],
  }),

  apiLatency: new Histogram({
    name: 'maintainx_api_latency_seconds',
    help: 'MaintainX API request latency',
    labelNames: ['method', 'endpoint'],
    buckets: [0.1, 0.25, 0.5, 1, 2.5, 5, 10],
    registers: [register],
  }),

  rateLimitHits: new Counter({
    name: 'maintainx_rate_limit_hits_total',
    help: 'Times rate limited by MaintainX API',
    registers: [register],
  }),

  workOrdersProcessed: new Counter({
    name: 'maintainx_work_orders_processed_total',
    help: 'Work orders processed',
    labelNames: ['action', 'status'],
    registers: [register],
  }),

  syncLag: new Gauge({
    name: 'maintainx_sync_lag_seconds',
    help: 'Seconds since last successful sync',
    registers: [register],
  }),
};

export { register };

Step 2: Instrumented API Client

// src/​observability/​instrumented-client.ts
import axios, { AxiosInstance } from 'axios';
import { metrics } from './​metrics';

export function createInstrumentedClient(apiKey: string): AxiosInstance {
  const client = axios.create({
    baseURL: 'https://api.getmaintainx.com/​v1',
    headers: { Authorization: `Bearer ${apiKey}`, 'Content-Type': 'application/​json' },
    timeout: 30_000,
  });

  client.interceptors.request.use((config) => {
    (config as any).__startTime = process.hrtime.bigint();
    return config;
  });

  client.interceptors.response.use(
    (response) => {
      const elapsed = Number(process.hrtime.bigint() - (response.config as any).__startTime) / 1e9;
      const endpoint = response.config.url?.split('?')[0] || 'unknown';

      metrics.apiRequests.inc({
        method: response.config.method?.toUpperCase() || 'GET',
        endpoint,
        status: String(response.status),
      });
      metrics.apiLatency.observe(
        { method: response.config.method?.toUpperCase() || 'GET', endpoint },
        elapsed,
      );
      return response;
    },
    (error) => {
      const status = error.response?.status || 0;
      const endpoint = error.config?.url?.split('?')[0] || 'unknown';

      metrics.apiRequests.inc({
        method: error.config?.method?.toUpperCase() || 'GET',
        endpoint,
        status: String(status),
      });

      if (status === 429) {
        metrics.rateLimitHits.inc();
      }
      throw error;
    },
  );

  return client;
}

Step 3: Structured Logging

// src/​observability/​logger.ts

type LogLevel = 'debug' | 'info' | 'warn' | 'error';

interface LogEntry {
  level: LogLevel;
  message: string;
  service: string;
  timestamp: string;
  [key: string]: any;
}

class StructuredLogger {
  private service: string;

  constructor(service: string) {
    this.service = service;
  }

  private log(level: LogLevel, message: string, data?: Record<string, any>) {
    const entry: LogEntry = {
      level,
      message,
      service: this.service,
      timestamp: new Date().toISOString(),
      ...data,
    };
    // JSON output for log aggregation (ELK, CloudWatch, Datadog)
    console.log(JSON.stringify(entry));
  }

  info(message: string, data?: Record<string, any>) { this.log('info', message, data); }
  warn(message: string, data?: Record<string, any>) { this.log('warn', message, data); }
  error(message: string, data?: Record<string, any>) { this.log('error', message, data); }
  debug(message: string, data?: Record<string, any>) { this.log('debug', message, data); }
}

export const logger = new StructuredLogger('maintainx-integration');

// Usage
logger.info('Work order created', { workOrderId: 12345, priority: 'HIGH' });
logger.error('API call failed', { endpoint: '/​workorders', status: 500, retryCount: 2 });

Step 4: Health and Metrics Endpoints

// src/​observability/​server.ts
import express from 'express';
import { register, metrics } from './​metrics';

const app = express();

// Prometheus scrape endpoint
app.get('/​metrics', async (req, res) => {
  res.set('Content-Type', register.contentType);
  res.end(await register.metrics());
});

// Health check with metrics
app.get('/​health', async (req, res) => {
  const health = {
    status: 'healthy',
    uptime: process.uptime(),
    metrics: {
      totalRequests: await metrics.apiRequests.get(),
      rateLimitHits: await metrics.rateLimitHits.get(),
      syncLagSeconds: (await metrics.syncLag.get()).values[0]?.value || 0,
    },
  };
  res.json(health);
});

app.listen(9090, () => logger.info('Metrics server on :9090'));

Step 5: Alerting Rules (Prometheus)

# prometheus/​alerts.yml
groups:
  - name: maintainx
    rules:
      - alert: MaintainXHighErrorRate
        expr: rate(maintainx_api_requests_total{status=~"5.."}[5m]) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "MaintainX API error rate > 10%"

      - alert: MaintainXHighLatency
        expr: histogram_quantile(0.95, rate(maintainx_api_latency_seconds_bucket[5m])) > 5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "MaintainX API p95 latency > 5s"

      - alert: MaintainXRateLimited
        expr: rate(maintainx_rate_limit_hits_total[5m]) > 0
        for: 1m
        labels:
          severity: warning
        annotations:
          summary: "MaintainX API rate limiting detected"

      - alert: MaintainXSyncStale
        expr: maintainx_sync_lag_seconds > 900
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "MaintainX sync lag > 15 minutes"

Output

  • Prometheus metrics (request count, latency histogram, rate limit counter, sync lag gauge)
  • Instrumented axios client automatically recording metrics on every API call
  • Structured JSON logging for all operations
  • /​metrics endpoint for Prometheus scraping
  • Alerting rules for error rate, latency, rate limits, and sync staleness

Error Handling

IssueCauseSolution
Metrics endpoint 500prom-client not initializedEnsure Registry is created before metrics
Missing labelsMetric name mismatchCheck labelNames match inc()/observe() calls
Log volume too highDebug logging in productionSet LOG_LEVEL=info in production
Stale sync alertSync job stoppedCheck cron schedule, restart sync process

Resources

Next Steps

For incident response, see maintainx-incident-runbook.

Examples

Datadog integration using DogStatsD:

import StatsD from 'hot-shots';

const dogstatsd = new StatsD({ prefix: 'maintainx.' });

// Record API call
dogstatsd.increment('api.requests', 1, { endpoint: '/​workorders', status: '200' });
dogstatsd.histogram('api.latency', 0.45, { endpoint: '/​workorders' });

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