Everything in Monitoring & SRE, page 6
Essential tags for a request latency metric
Add method, route template, status code, and maybe service or region; they enable slicing while staying low-cardinality.
Structured vs unstructured logging
Unstructured logs are free-text lines; structured logs are machine-readable key-value or JSON, enabling reliable parsing, filtering, and aggregation.
The three pillars of observability
Metrics show what and when via cheap aggregates, logs give detailed per-event context, traces show where across services.
Fixing watermelon SLOs that hide unhappy users
Green-outside, red-inside SLOs come from server-side measurement, coarse aggregation, wrong events, and lax thresholds; fix by measuring at the client and using meaningful SLIs.
Launching a risky feature with no error budget left
Bring burn history, what consumed the budget, blast radius, and rollback safety; offer mitigations like flags and canaries.
Setting SLOs across a dependency chain
Serial dependencies multiply, so each backend needs a tighter SLO than the target; mitigate with redundancy, caching, and graceful degradation.
Handling planned maintenance in SLOs and error budgets
Either charge maintenance to the error budget or formally exclude it via a maintenance window; each trades honesty against fairness.
Defining SLOs for a new critical service
Start from user journeys, pick SLIs, involve product, engineering, and business stakeholders, set realistic targets iteratively.
Diagnosing a healthy p50 but breaching p99
One percent of requests are slow, hurting power users and fan-out calls; investigate GC, locks, contention, cold caches, retries.
Proposing availability and latency SLIs for an auth API
Availability as the ratio of successful valid requests; latency as the fraction served under a threshold; measure at the edge from the user's view.
What is an error budget?
Error budget is one minus the SLO, the allowed unreliability; it gates feature velocity versus reliability work.
Relationship between SLI, SLO, and SLA
SLI is the measured metric, SLO is the internal target for that metric, SLA is the external contract with consequences.
Symptom-based vs cause-based alerting
Symptom alerts fire on user-visible impact, cause alerts on internal conditions; page on symptoms to cut noise and catch unforeseen failures.
Head-based vs tail-based trace sampling
Head-based decides at trace start cheaply but blindly; tail-based waits for the full trace to keep errors and slow requests.
What is high-cardinality data in Prometheus?
Cardinality is the count of unique label combinations; each is a separate series; explosion blows up memory and query cost.
Black-box vs white-box monitoring for legacy apps
Black-box probes from outside, white-box reads internal state; start black-box for fast user-facing signal.
How does distributed tracing work?
Trace ID ties one request together, span IDs are individual operations, context propagates via headers.
Structured vs unstructured logging: why it matters
Unstructured logs are free-text lines hard to parse; structured logs are machine-readable key-value or JSON records; structure enables reliable querying…
Why use latency percentiles over the average?
Averages hide the tail and are skewed by outliers, so most users can suffer while the mean looks fine; use percentiles; prioritize p99 when tail users are high-value or fan-out…
What are the Four Golden Signals?
Name latency, traffic, errors, and saturation; explain each briefly; describe measuring latency as a distribution separating success from failure.
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