Data & Telemetry Expert (OpenTelemetry 1.x / ClickHouse Edition)

English | Bahasa Indonesia

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English

Orchestration & Integration

Connects and orchestrates with relevant domain skills like brainstorming, zero-to-prod-orchestrator, and project-context-mapper to ensure cohesive execution.

Description

Expert guide for production observability, product analytics, and data pipelines. Covers OpenTelemetry 1.x (stable, vendor-neutral traces/metrics/logs), PostHog (open-source product analytics), ClickHouse (OLAP analytics database), Grafana stack, and AI agent observability patterns.

Trigger Conditions

• Adding distributed tracing to a microservice or Next.js application.
• Setting up structured logging and metrics collection.
• Implementing product analytics (funnel analysis, feature flags, session replay).
• Building a high-performance analytics pipeline with ClickHouse.
• Monitoring AI agent runs, LLM token costs, and response quality.

OpenTelemetry 1.x — Vendor-Neutral Observability

OpenTelemetry (OTel) is the CNCF standard for generating traces, metrics, and logs from any application.

Three Pillars of OTel

Signal	What It Captures	Example
Traces	Request flow across services	GET /api/users → DB query → cache
Metrics	Numeric measurements over time	http_requests_total, db_query_duration
Logs	Structured event records	{"level":"error","msg":"DB timeout"}

Next.js 15 + OTel Instrumentation

// instrumentation.ts (Next.js built-in OTel support)
export async function register() {
  if (process.env.NEXT_RUNTIME === 'nodejs') {
    const { NodeSDK } = await import('@opentelemetry/sdk-node');
    const { OTLPTraceExporter } = await import('@opentelemetry/exporter-trace-otlp-http');
    const { OTLPMetricExporter } = await import('@opentelemetry/exporter-metrics-otlp-http');
    const { PeriodicExportingMetricReader } = await import('@opentelemetry/sdk-metrics');
    const { Resource } = await import('@opentelemetry/resources');
    const { SEMRESATTRS_SERVICE_NAME } = await import('@opentelemetry/semantic-conventions');

    const sdk = new NodeSDK({
      resource: new Resource({
        [SEMRESATTRS_SERVICE_NAME]: 'my-saas-app',
      }),
      traceExporter: new OTLPTraceExporter({
        url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT,
      }),
      metricReader: new PeriodicExportingMetricReader({
        exporter: new OTLPMetricExporter(),
        exportIntervalMillis: 30_000,
      }),
    });

    sdk.start();
  }
}

Custom Spans for Business Logic

import { trace, SpanStatusCode } from '@opentelemetry/api';

const tracer = trace.getTracer('my-service', '1.0.0');

async function processOrder(orderId: string) {
  return tracer.startActiveSpan('processOrder', async (span) => {
    span.setAttribute('order.id', orderId);
    span.setAttribute('order.source', 'api');

    try {
      const order = await db.order.findUnique({ where: { id: orderId } });
      span.setAttribute('order.amount', order.amount);

      const result = await chargeCustomer(order);
      span.setStatus({ code: SpanStatusCode.OK });
      return result;
    } catch (error) {
      span.recordException(error as Error);
      span.setStatus({ code: SpanStatusCode.ERROR, message: String(error) });
      throw error;
    } finally {
      span.end();
    }
  });
}

ClickHouse — High-Performance Analytics Database

ClickHouse is the 2026 standard for analytical workloads — ingests billions of events and queries them in milliseconds:

-- Create an events table optimized for time-series analytics
CREATE TABLE events (
    event_id     UUID DEFAULT generateUUIDv4(),
    workspace_id String,
    user_id      String,
    event_name   LowCardinality(String),
    properties   JSON,
    timestamp    DateTime64(3, 'UTC'),
    date         Date DEFAULT toDate(timestamp)
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (workspace_id, event_name, timestamp)
TTL date + INTERVAL 1 YEAR;

-- Query: Funnel analysis — users who signed up then upgraded
SELECT
    countIf(event_name = 'signup') AS signups,
    countIf(event_name = 'plan_upgraded') AS upgrades,
    round(countIf(event_name = 'plan_upgraded') / countIf(event_name = 'signup') * 100, 2) AS conversion_rate
FROM events
WHERE workspace_id = 'ws_abc'
  AND timestamp >= now() - INTERVAL 30 DAY;

// Node.js ClickHouse client
import { createClient } from '@clickhouse/client';

const client = createClient({ url: process.env.CLICKHOUSE_URL });

await client.insert({
  table: 'events',
  values: [{
    workspace_id: 'ws_abc',
    user_id: 'user_123',
    event_name: 'page_view',
    properties: { path: '/dashboard', referrer: 'google.com' },
    timestamp: new Date().toISOString(),
  }],
  format: 'JSONEachRow',
});

PostHog — Open-Source Product Analytics

// Next.js + PostHog (client-side)
import posthog from 'posthog-js';

posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
  api_host: process.env.NEXT_PUBLIC_POSTHOG_HOST ?? 'https://app.posthog.com',
  capture_pageview: false, // Manual with App Router
});

// Track custom events
posthog.capture('feature_used', {
  feature: 'ai_assistant',
  plan: user.plan,
  workspace_id: workspace.id,
});

// Feature flags
if (posthog.isFeatureEnabled('new-dashboard')) {
  return <NewDashboard />;
}

AI Agent Observability

Track LLM costs, latency, and quality for production AI applications:

// Custom OTel attributes for LLM calls
span.setAttribute('llm.model', 'claude-4-sonnet');
span.setAttribute('llm.input_tokens', response.usage.input_tokens);
span.setAttribute('llm.output_tokens', response.usage.output_tokens);
span.setAttribute('llm.cost_usd', calculateCost(response.usage));
span.setAttribute('llm.latency_ms', Date.now() - startTime);
span.setAttribute('llm.cached', response.usage.cache_read_input_tokens > 0);

Backend tracing tools for LLM: LangSmith (LangChain/LangGraph), OpenAI Tracing (Agents SDK), Langfuse (open-source, any LLM).

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Bahasa Indonesia

Integrasi Orkestrasi

Terhubung dan mengorkestrasi skill domain yang relevan seperti brainstorming, zero-to-prod-orchestrator, dan project-context-mapper untuk memastikan eksekusi yang kohesif.

Deskripsi

Panduan ahli untuk observabilitas produksi, analitik produk, dan pipeline data. Mencakup OpenTelemetry 1.x (stabil, vendor-neutral traces/metrics/logs), PostHog (analitik produk open-source), ClickHouse (database analitik OLAP), dan pola observabilitas agen AI.

Kondisi Pemicu

• Menambahkan distributed tracing ke microservice atau aplikasi Next.js.
• Menyiapkan structured logging dan pengumpulan metrik.
• Mengimplementasikan analitik produk (analisis funnel, feature flags, session replay).
• Membangun pipeline analitik berkinerja tinggi dengan ClickHouse.
• Memantau run agen AI, biaya token LLM, dan kualitas respons.

OpenTelemetry 1.x — Observabilitas Vendor-Neutral

Tiga pilar OTel:

• Traces: Aliran permintaan antar layanan.
• Metrics: Pengukuran numerik dari waktu ke waktu.
• Logs: Catatan peristiwa terstruktur.

Integrasikan dengan Next.js 15 melalui file instrumentation.ts bawaan — OTel SDK otomatis mendistribusikan trace ke backend pilihan (Grafana Tempo, Jaeger, Honeycomb, Datadog, dll.).

ClickHouse — Database Analitik Berkinerja Tinggi

ClickHouse adalah standar 2026 untuk workload analitik — menyerap miliaran event dan melakukan query dalam milidetik. Gunakan engine MergeTree dengan partisi per bulan dan pengurutan berdasarkan kolom yang sering di-filter.

PostHog — Analitik Produk Open-Source

PostHog menyediakan analisis funnel, feature flags, session replay, dan A/B testing dalam satu platform yang dapat di-self-host. Integrasikan dengan Next.js App Router menggunakan posthog-js.

Observabilitas Agen AI

Lacak biaya LLM, latensi, dan kualitas untuk aplikasi AI produksi menggunakan custom OTel attributes. Gunakan LangSmith, OpenAI Tracing, atau Langfuse (open-source) sebagai backend tracing LLM.