observability-and-instrumentation – skill Lập trình cho agent AI

Observability and Instrumentation

Overview

Code you can't observe is code you can't operate. Observability is the ability to answer "what is the system doing and why?" from the outside, using the telemetry the code emits. Instrumentation is not a post-launch add-on — it's written alongside the feature, the same way tests are. If a feature ships without telemetry, the first user-reported bug becomes archaeology instead of a query.

When to Use

• Building any feature that will run in production
• Adding a new service, endpoint, background job, or external integration
• A production incident took too long to diagnose ("we couldn't tell what happened")
• Setting up or reviewing alerting rules
• Reviewing a PR that adds I/O, retries, queues, or cross-service calls

NOT for:

• Diagnosing a failure happening right now — use the debugging-and-error-recovery skill (observability is what makes that skill fast next time)
• Profiling and optimizing measured slowness — use the performance-optimization skill
• Launch-day monitoring checklists and rollback triggers — see the shipping-and-launch skill; this skill covers the instrumentation that feeds them

Process

1. Define "working" before instrumenting

Telemetry without a question is noise. Before adding any instrumentation, write down 2–4 questions an on-call engineer will ask about this feature:

FEATURE: checkout payment retry
QUESTIONS ON-CALL WILL ASK:
1. What fraction of payments succeed on first attempt vs after retry?
2. When a payment fails permanently, why? (provider error? timeout? validation?)
3. Is the payment provider slower than usual?
→ Every signal below must help answer one of these.

If you can't name the questions, you're not ready to instrument — you'll log everything and learn nothing.

2. Pick the right signal for each question

| Signal | Answers | Cost profile | Example |

|---|---|---|---|

| Structured log | "What happened in this specific case?" | Per-event; grows with traffic | payment_failed with provider error code |

| Metric | "How often / how fast, in aggregate?" | Fixed per series; cheap to query | p99 latency of provider calls |

| Trace | "Where did time go across services?" | Per-request; usually sampled | One slow checkout, broken down by hop |

Rule of thumb: metrics tell you that something is wrong, traces tell you where, logs tell you why.

3. Structured logging

Log events, not prose. Every log line is a JSON object with a stable event name and machine-readable fields:

// BAD: string interpolation — unqueryable, inconsistent
logger.info(`Payment ${id} failed for user ${userId} after ${n} retries`);

// GOOD: stable event name + structured fields
logger.warn({
  event: 'payment_failed',
  paymentId: id,
  provider: 'stripe',
  errorCode: err.code,
  attempt: n,
}, 'payment failed');

Log levels — use them consistently:

| Level | Meaning | On-call action |

|---|---|---|

| error | Invariant broken; someone may need to act | Investigate |

| warn | Degraded but handled (retry succeeded, fallback used) | Watch for trends |

| info | Significant business event (order placed, job finished) | None |

| debug | Diagnostic detail | Off in production by default |

Correlation IDs are mandatory. Generate (or accept) a request ID at the system boundary and attach it to every log line, span, and outbound call. Without it, you cannot reconstruct a single request from interleaved logs:

// Express: child logger per request, ID propagated downstream
app.use((req, res, next) => {
  const suppliedId = req.headers['x-request-id'];
  req.id = typeof suppliedId === 'string' && /^[A-Za-z0-9_-]{1,64}$/.test(suppliedId)
    ? suppliedId
    : crypto.randomUUID();
  req.log = logger.child({ requestId: req.id });
  res.setHeader('x-request-id', req.id);
  next();
});

Never log secrets, tokens, passwords, or full PII. This is a hard rule from the security-and-hardening skill — telemetry pipelines are a classic data-leak path. Allowlist fields; don't log whole request bodies.

4. Metrics

For request-driven services, instrument RED on every endpoint and every external dependency: Rate (requests/sec), Errors (failure rate), Duration (latency histogram, not average). For resources (queues, pools, hosts), use USE: Utilization, Saturation, Errors.

As with tracing, the vendor-neutral path is the OpenTelemetry metrics API (same SDK and context as step 5). The example below uses Prometheus' prom-client — one common backend choice, not the only one; the RED/USE and cardinality rules are identical either way.

import { Histogram } from 'prom-client';

const httpDuration = new Histogram({
  name: 'http_request_duration_seconds',
  help: 'HTTP request duration',
  labelNames: ['method', 'route', 'status_class'],  // '2xx', not '200'
  buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
});

Cardinality is the failure mode. Every unique label combination is a separate time series. Labels must come from small, fixed sets (route template, status class, provider name). Never use user IDs, raw URLs, error messages, or other unbounded values as labels — that belongs in logs and traces.

OK as label:    route="/api/tasks/:id"   status_class="5xx"   provider="stripe"
NEVER a label:  user_id, email, request_id, full URL, error message text

Do not rely on averages alone; inspect percentiles and sample size: an average hides the 1% of users having a terrible time. Use histograms and read p50/p95/p99.

5. Distributed tracing

Use OpenTelemetry — it's the vendor-neutral standard, and auto-instrumentation covers HTTP, gRPC, and common DB clients with near-zero code:

// tracing.ts — must be imported before anything else
import { NodeSDK } from '@opentelemetry/sdk-node';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';

const sdk = new NodeSDK({
  serviceName: 'checkout-service',
  instrumentations: [getNodeAutoInstrumentations()],
});
sdk.start();

Add manual spans only around meaningful internal units of work (e.g., applyDiscounts, chargeProvider) and attach the attributes on-call will filter by. Propagate context across every async boundary — HTTP headers, queue message metadata — or the trace dies at the gap. Choose a documented sampling policy. Tail sampling cannot recover spans dropped by an upstream head sampler; retaining every error requires the collector to receive those traces. Measure cost and data exposure.

6. Alerting

Alert on symptoms users feel, not on causes:

SYMPTOM (page-worthy):           CAUSE (dashboard, not a page):
error rate > 1% for 5 min        CPU at 85%
p99 latency > 2s                 one pod restarted
queue age > 10 min               disk at 70%

Prefer actionable symptom/SLO alerts; capacity and cause-based alerts can also be appropriate when they identify an imminent actionable failure. Neither category guarantees complete detection.

Rules for every alert you create:

1. It must be actionable. If the response is "ignore it, it self-heals", delete the alert.

2. It links to a runbook — even three lines: what it means, first query to run, escalation path.

3. It has a threshold and duration justified by the SLO or by historical data, not by a guess.

4. Use two severities only: page (user-facing, act now) and ticket (degradation, act this week). A third tier becomes noise that trains people to ignore everything.

7. Verify the telemetry itself

Instrumentation is code; it can be wrong. Before calling the work done, trigger the paths and look at the actual output:

• Force an error in staging → find it in the logs by requestId, confirm fields are structured (not [object Object])
• Send test traffic → confirm metric series appear with the expected labels and sane values
• Follow one request across services in the tracing UI → no broken spans
• Test each new alert in authorized staging with an approved test destination; restore any changed thresholds and avoid paging unrelated responders

Common Rationalizations

| Rationalization | Reality |

|---|---|

| "I'll add logging after it works" | "After" becomes "after the first incident", which is the most expensive moment to discover you're blind. Instrument as you build. |

| "More logs = more observability" | Unstructured noise makes incidents slower, not faster. Three queryable events beat three hundred prose lines. |

| "console.log is fine for now" | Unstructured output can't be filtered, correlated, or alerted on. The structured logger costs five extra minutes once. |

| "We can just look at the dashboards when something breaks" | Dashboards built without defined questions show you everything except the answer. Start from on-call questions. |

| "Alert on everything important, we'll tune later" | A noisy pager trains people to ignore

… (nội dung đầy đủ có trong gói tải về)