Observability Slo

Observability and Reliability

The question this discipline answers is not "is the server up". It is:

When a user has a bad experience right now, can we tell — and can we find out why without deploying new code?

A system you cannot debug from its own output is a system you debug by guessing.

The three signals, and what each is for

You need all three, and they must be linked: a metric spike leads to an exemplar trace, which leads to the log lines for that request. Three disconnected tools mean three separate investigations and a much longer time-to-diagnosis.

Use OpenTelemetry as the instrumentation layer — it keeps the vendor decision reversible, which matters because observability vendors are expensive and switching is otherwise a rewrite.

Structured logging

{"ts":"2026-08-24T16:18:12.451Z","level":"error","msg":"payment.capture_failed",
 "trace_id":"4bf92f...","span_id":"00f067...","request_id":"req_01HQ8",
 "user_id":"usr_01HQ8","org_id":"org_01HQ8","payment_id":"pay_01HQ8",
 "provider":"stripe","error_code":"card_declined","duration_ms":842}

Rules

1. JSON, not prose. Grep does not scale; structured queries do.
2. A correlation id on every line, generated at the edge (or accepted from the client), propagated through every service, job and outbound call. This single field is the difference between a two-minute investigation and a two-hour one.
3. Log events, not sentences. payment.capture_failed with fields beats "Failed to capture payment for user 123" — the first is queryable and aggregatable, the second is a string.
4. Levels with meaning. error = a human should look. warn = degraded but handled. info = business events worth keeping. debug = off in production. If everything is error, nothing is.
5. Never log secrets or personal data. Tokens, passwords, card numbers, full request bodies, authorization headers. Implement redaction as a layer in the logger, not as a habit at each call site. Logs are frequently the least protected copy of your data, and they are shipped to third parties.
6. Log at boundaries: request in/out, external call in/out, job start/end, state transitions. Not every line of business logic.
7. Retention bounded deliberately — a cost control and a privacy obligation.

Metrics

The four that matter for a request-serving system (RED / golden signals):

Rate        requests per second, by route and status class
Errors      error rate, by route and error class
Duration    p50, p95, p99 latency, by route
Saturation  how full the constrained resource is — pool, queue, memory, CPU

Plus, for anything with a queue: queue depth and oldest-message age. Oldest-message age is the metric that catches a stalled worker, and it is the one teams forget.

Plus business metrics: signups, payments, core actions completed. A technical dashboard showing green while zero orders are being placed is a monitoring failure. Business metrics catch the outages that infrastructure metrics cannot see.

Cardinality is the cost driver. Never put a user id, request id, full URL path with parameters, or an unbounded value in a metric label. Those belong in traces and logs. One careless label can multiply your bill by a thousand.

Percentiles, not averages. An average latency of 200ms is consistent with 90% of users at 50ms and 10% at 1.5 seconds. Average hides exactly what you need to see. And never average percentiles across instances — that is not a percentile.

SLIs, SLOs and error budgets

SLI — a measurement of user experience. SLO — the target. Error budget — how much you are permitted to miss.

Start with two per critical journey:

Availability SLI  = successful requests / total requests           (excluding 4xx client errors)
Latency SLI       = requests faster than threshold / total requests

SLO: 99.9% availability over 30 days   → error budget: 43 minutes
     99% of requests under 300ms

Choosing the number. 99.9% is a reasonable default for a paid product. Each additional nine multiplies cost roughly tenfold. 99.99% is not achievable if you depend on services that do not offer it — your ceiling is the product of your dependencies' availability. Do not publish an SLA you cannot structurally meet.

The error budget is the point. It converts reliability from an argument into arithmetic:

• Budget remaining → ship features, take risks, deploy on Friday.
• Budget exhausted → reliability work takes priority until it recovers.

Agree this policy before you need it, with whoever owns the roadmap. Its value is that it ends the recurring argument between shipping and stability by making the trade-off explicit and measurable.

Measure the SLI from the user's side where you can — real user monitoring, or a synthetic probe from outside your network. A server-side metric cannot see the outage where your load balancer was unreachable.

Alerting

Alert on symptoms, not causes. Page when users are having a bad time. CPU at 90% with healthy latency and no errors is not an incident; it is a well-utilised machine.

Every paging alert must satisfy all four:

1. Urgent — it needs action now, not tomorrow.
2. Actionable — there is something a human can do.
3. Linked to a runbook — the alert names the document.
4. Real — it is not a known-noisy alert people have learned to dismiss.

An alert failing any of these should be downgraded or deleted. Alert fatigue is the primary cause of missed incidents — every noisy page trains the on-call engineer to dismiss the next one.

Burn-rate alerting beats simple thresholds: alert when you are consuming the error budget fast enough to exhaust it early. Use a fast burn (page) and a slow burn (ticket) so a slow degradation is caught without a 3am page for it.

Tracing

• Instrument every service boundary, database call and external HTTP call.
• Propagate W3C traceparent across every hop, including queues (put it in the message).
• Sample intelligently: keep all errors and slow requests, sample the fast successful ones (1–10%). Tail-based sampling if your backend supports it.
• Add span attributes for the identifiers you will search by: user, org, resource id. Never for secrets.
• Link traces to logs by trace id, and to metrics by exemplar.

Traces earn their cost the first time a latency problem is in a dependency you did not suspect.

The minimum viable setup

For a small project with no observability, in priority order:

1. Error tracking (Sentry or equivalent) with source maps and release tagging. Highest value per hour of setup, by a wide margin.
2. Structured logs with a correlation id, shipped somewhere queryable.
3. An uptime check from outside your network, on the critical path — not on /health, which is famously green while the app is broken.
4. One dashboard: request rate, error rate, p95 latency, plus your single most important business metric.
5. One alert that pages a human on user-visible errors.
6. Then traces, SLOs and the rest.

Steps 1–5 are a day of work and cover most of the value.

Incidents

DETECT    → alert, or a customer report (count how often it is the second —
            that ratio is a monitoring KPI)
DECLARE   → name it. A named incident has an owner
ROLES     → Incident Lead (coordinates, does not debug), Comms, Operators
MITIGATE  → stop the bleeding. Roll back, disable the flag, scale up.
            RESTORING SERVICE COMES BEFORE FINDING THE CAUSE
COMMUNICATE → status page and internal updates on a fixed cadence, even when
            the update is "still investigating"
RESOLVE   → confirm recovery with metrics, not with hope
LEARN     → blameless postmortem within a week

The postmortem must be blameless. Not out of politeness — because a culture that blames individuals produces engineers who hide information, and hidden information is how the same incident happens twice. Ask what about the system allowed a reasonable person to make that decision.

Every postmortem produces owned, dated action items, tracked like any other work. A postmortem whose actions are never done is a document, not a learning process.

Review checklist

• Structured JSON logs with a correlation id propagated end to end
• Secrets and personal data redacted by a logging layer
• Error tracking wired, with releases and source maps
• RED metrics per route; queue depth and oldest-message age where relevant
• At least one business metric monitored
• Distributed tracing across services, with errors always sampled
• SLIs measured from the user's perspective; SLOs defined and published
• An error budget policy agreed with the roadmap owner
• Alerts page on symptoms; every page is urgent, actionable and runbooked
• External uptime check on the real user path
• Runbooks for the top failure modes, linked from alerts
• A named on-call rotation and escalation path
• Postmortems are blameless and produce owned action items
• Metric cardinality bounded; log retention deliberate

References

• references/telemetry-setup.md — instrumentation, per stack
• references/slo-and-incidents.md — SLO maths, error budgets, incident process, postmortem template