DevOps School

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Quick Definition

Plain-English definition A rolling update is a deployment strategy that incrementally replaces instances of an application or service with a new version, keeping the service available by updating a subset of hosts or replicas at a time.

Analogy Think of replacing tires on a touring bus one at a time while the bus continues moving; you change a small part, test stability, then continue until all tires are replaced.

Formal technical line A rolling update is an orchestrated, phased rollout that maintains capacity by sequentially draining, upgrading, verifying, and returning instances to service according to defined concurrency and health constraints.

Multiple meanings (most common first)

• Rolling update as a deployment strategy for services and application instances.
• Rolling update for database schema migration where shards or nodes are migrated in sequence.
• Rolling update for edge configurations where edge nodes are progressively updated.
• Rolling update as an OS or dependency patching process across a fleet.

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What is rolling update?

What it is / what it is NOT

• What it is: A controlled, incremental replacement process for software artifacts, configuration, or infrastructure that avoids full-system downtime by updating a subset of units at a time.
• What it is NOT: A canary or blue-green deployment by itself; rolling update is sequential and conservative rather than concurrently switching traffic between two distinct environments.

Key properties and constraints

• Phased concurrency limits the number of instances updated simultaneously.
• Health checks and readiness probes gate progress.
• Rollback capability must be considered and automated where possible.
• Stateful services require extra coordination to preserve data consistency.
• Rollouts must balance speed vs risk via batch size and pause windows.
• Observability and automated abort criteria are essential.

Where it fits in modern cloud/SRE workflows

• CI/CD pipeline produces artifacts and triggers a rolling update job.
• Orchestrators (Kubernetes, ECS, managed instance groups) manage concurrency and health gating.
• Observability systems monitor SLIs and if thresholds breach, automated rollback or pause is triggered.
• Incident response uses runbooks tailored to rolling update failures for rapid mitigation.
• Security scans can be integrated as a pre-deploy gate to prevent rolling out vulnerable images.

A text-only “diagram description” readers can visualize

• Imagine five boxes representing instances behind a load balancer. The orchestrator marks box A as draining, removes traffic, updates software, performs health checks, and returns A to the load balancer. Then it repeats sequentially for B, C, D, E. If any update fails health checks, the orchestrator stops further updates and either rolls back the failed instance or keeps it out of rotation for manual inspection.

rolling update in one sentence

A rolling update sequentially upgrades units of a service with health-gated batches to maintain availability and limit blast radius.

rolling update vs related terms (TABLE REQUIRED)

ID	Term	How it differs from rolling update	Common confusion
T1	Canary deployment	Targets a small percentage of users concurrent to main fleet	Confused as same because both reduce risk
T2	Blue-green deployment	Switches traffic between two full environments	Misunderstood as always preferable due to rollback simplicity
T3	Immutable deployment	Replaces instances with new immutable images only	People assume rolling update requires mutable in-place changes
T4	Recreate deployment	Shuts down old instances then starts new ones	Often mistaken for a safe approach when downtime is acceptable
T5	Progressive delivery	Umbrella term including canary and rollout strategies	Rolling update seen as full synonym

Row Details (only if any cell says “See details below”)

• None

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Why does rolling update matter?

Business impact (revenue, trust, risk)

• Reduces user-visible downtime, protecting revenue streams for customer-facing services.
• Preserves customer trust by enabling frequent, low-risk changes instead of rare high-risk big bangs.
• Limits blast radius so a faulty release affects a small portion of traffic or instances.

Engineering impact (incident reduction, velocity)

• Allows teams to ship smaller changes more frequently, improving mean time to deliver features.
• Reduces the chance of large, systemic failures caused by mass deployments.
• Encourages better instrumentation and automated checks which reduce toil.

SRE framing (SLIs/SLOs/error budgets/toil/on-call)

• Rolling updates directly interact with SLIs like availability and latency; teams must budget error budget consumption during rollouts.
• On-call load can be reduced by automating rollback triggers and having explicit thresholds for aborting updates.
• Toil is lowered by having standardized rollout automation and prebuilt runbooks for common failures.

3–5 realistic “what breaks in production” examples

• A new dependency increases average request latency by 30% under specific traffic patterns.
• A configuration change causes a memory leak that only manifests when a subset of instances receives background jobs.
• An updated SSL/TLS library rejects particular client cipher suites, affecting a minority of users.
• A database node upgrade causes transient replication lag and read anomalies for dependent services.
• A container image with incompatible environment variable handling fails readiness probes causing steady reduction in capacity.

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Where is rolling update used? (TABLE REQUIRED)

ID	Layer/Area	How rolling update appears	Typical telemetry	Common tools
L1	Edge network	Update edge node software in waves	Edge latency and error rates	CDN CLI and orchestration
L2	Service / compute	Replace service replicas sequentially	Pod restart counts CPU mem latency	Kubernetes rolling update
L3	Application	Update app instances across regions	Request error rate and p50 p95 latencies	CI/CD and load balancers
L4	Database	Upgrade replicas or schema by shard	Replica lag and DB errors	DB replication tools managed DB consoles
L5	Serverless	Version traffic shifting progressively	Invocation errors cold starts latency	Managed versioning and traffic split
L6	Infrastructure	Rolling OS or agent patching	Node health and boot times	Configuration management and MDM
L7	CI/CD	Auto-triggered rollout stages	Deployment success rate and time	Jenkins GitHub Actions GitLab CI

Row Details (only if needed)

• None

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When should you use rolling update?

When it’s necessary

• When zero-or-low downtime is required and capacity must be preserved.
• When services are stateful or tied to a datastore where a full switch would cause risk.
• When infrastructure or configuration change cannot use parallel environments due to cost or complexity.

When it’s optional

• For stateless services where canary deployments are feasible and you prefer traffic-based validation.
• For low-risk cosmetic changes where rebuild overhead is minimal.

When NOT to use / overuse it

• When rapid rollback to a known-good version is required and blue-green offers a cleaner rollback.
• When changes require atomic switch-over across components that must be consistent simultaneously.
• When orchestration or health metrics are immature; rolling updates without good observability create hidden risk.

Decision checklist

• If high availability required AND instances are homogeneous -> Rolling update recommended.
• If need instant rollback to previous full environment -> Prefer blue-green.
• If behavioral differences must be validated with real user traffic -> Consider canary/progressive delivery.
• If database schema change alters data contracts across all instances -> Consider staged migration plan, not blind rolling update.

Maturity ladder

• Beginner: Use orchestrator defaults with health probes and small batch size; manual pause points.
• Intermediate: Add automated health-based aborts, staged regional rollouts, pre-deploy tests.
• Advanced: Integrate deployment with SLO-aware rollout controllers, automated rollbacks, and policy engines that factor canary telemetry and ML-based anomaly detection.

Example decision for a small team

• Small SaaS team with a single cluster: Use rolling update with a 1/3 max unavailable and manual pause between batches. Verify quick smoke tests and monitor error rate.

Example decision for a large enterprise

• Large enterprise with multi-region traffic: Use regional staged rolling updates, preflight canaries in one region, automated rollback on SLO breach, and coordination with DB migration windows.

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How does rolling update work?

Step-by-step components and workflow

1. Build: CI pipeline produces an immutable artifact.
2. Preflight: Security and integration tests run; image is signed or promoted.
3. Plan: Orchestrator calculates batch size and concurrency constraints.
4. Drain: Orchestrator marks instance to receive zero new traffic and finishes in-flight work.
5. Replace: Instance is updated or replaced with new version.
6. Verify: Readiness and health checks run; automated smoke tests execute.
7. Promote: If healthy, instance rejoins load balancer and rollout continues.
8. Abort/Rollback: If health checks fail, orchestrator stops and either reverts instance or pauses for manual action.
9. Post-deploy: Telemetry evaluated and release is closed or remediated.

Data flow and lifecycle

• User requests hit load balancer which routes to healthy instances.
• Instance marked for update is drained of new requests; in-flight requests finish.
• Orchestrator creates or patches new instance; health probes ensure service readiness.
• Monitoring ingest collects metrics, traces, and logs for the new instance.
• Automated checks validate behavior before full promotion.

Edge cases and failure modes

• Failure to drain due to long-running requests blocks rollout.
• Dependency version skew causes partial failures across updated and non-updated instances.
• Hidden data migrations required by new code produce subtle errors after some instances update.
• Network partitions or rolling update pausing in one region leaves inconsistent global state.

Short practical examples (pseudocode)

• Kubernetes: kubectl set image deployment/myapp myapp=registry/app:v2 –record then check rollout status with kubectl rollout status deployment/myapp and configure maxUnavailable in deployment spec.
• Managed instance group: Update instance template, then execute rolling update policy with configured minimal ready time and per-batch size.

Typical architecture patterns for rolling update

• Simple sequential replacement: Use for small-scale services with low throughput.
• Batch parallel replacement: Replace N instances at a time for faster rollouts.
• Region-by-region staged rollout: Use geographic stages to reduce global impact.
• Service mesh-aware rollout: Integrate with mesh for traffic shaping and gradual shift.
• SLO-driven rollout controller: Gate rollout progression based on SLO compliance.
• Side-by-side canary then rolling update: Deploy canary to validate behavior, then perform a rolling update to the rest.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Stuck draining	Nodes never leave draining state	Long running requests or hung tasks	Add timeout and kill policy; notify	High connection counts on node
F2	Readiness failures	New pods fail to become ready	Startup crash or missing config	Fail fast and rollback; fix config	Readiness probe failure rate
F3	Performance regression	Increased p95 latency after update	New code path slower or resource limits	Rollback or autoscale and patch	Spike in p95 latency metric
F4	Dependency skew	Partial failures across versions	Backward incompatible API changes	Coordinate versions or use adapter	Error traces showing mismatched schema
F5	DB inconsistency	Missing/incorrect data reads	Schema change without migration	Pre-flight migration or dual-read	Replica lag and DB error rate
F6	Image pull errors	New instances fail to start	Registry auth or tag missing	Verify registry auth and tag; retry	Pod CrashLoopBackOff or image pull errors
F7	Network policy block	Traffic not routed to new instances	New network policy rules	Revert policy and test incrementally	Connection refused errors in logs
F8	Rollout flapping	Rollout repeatedly aborts and retries	Flaky health checks or unstable infra	Stabilize checks and enforce retries	Repeated rollout events and alerts

Row Details (only if needed)

• None

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Key Concepts, Keywords & Terminology for rolling update

• Rolling update — Incremental replacement of instances — Enables low-downtime upgrades — Pitfall: assuming health checks catch all regressions
• Canary — Small subset testing with real traffic — Validates behavior at scale — Pitfall: insufficient traffic or segmentation
• Blue-green — Two identical environments with traffic switch — Simplifies rollback — Pitfall: double environment cost
• Immutable artifact — Unchanged deployable unit — Simplifies reproducibility — Pitfall: large image sizes slow rollout
• MaxUnavailable — Concurrency limit parameter — Controls capacity impact — Pitfall: overly optimistic value reduces resilience
• ProgressDeadline — Timeout for rollout progress — Prevents indefinite partial rollouts — Pitfall: too short causes false aborts
• Readiness probe — Endpoint to signal instance readiness — Gates traffic — Pitfall: misconfigured probe causes premature traffic
• Liveness probe — Endpoint to detect deadlocked processes — Enables restart — Pitfall: aggressive probe restarts healthy processes
• Drain — Stop accepting new work on an instance — Allows safe replacement — Pitfall: not draining background jobs
• Graceful shutdown — Let in-flight work finish before exit — Prevents dropped requests — Pitfall: incomplete cleanup hooks
• Health check — Automated verification step — Prevents bad instances joining pool — Pitfall: checks too shallow
• Rollback — Reverting to previous version — Mitigates failed releases — Pitfall: rollback not tested often
• Batch size — Number of units updated concurrently — Balances speed and risk — Pitfall: too large increases blast radius
• Pause window — Deliberate pause between batches — Allows observation — Pitfall: not used leads to missed regressions
• Probe timeout — Max wait for probe response — Avoids false negatives — Pitfall: long timeout delays detection
• Canary analysis — Automated evaluation of canary metrics — Improves decision-making — Pitfall: noisy metrics reduce signal
• Service mesh — Network abstraction for traffic control — Enables traffic splitting — Pitfall: added complexity in rollout
• Circuit breaker — Stops cascading failures — Protects services during rollout — Pitfall: misconfigured thresholds cause unnecessary trips
• Feature flag — Toggle behavior at runtime — Enables progressive enablement — Pitfall: flag debt if not removed
• A/B testing — Controlled variances for experiments — Useful for behavioral validation — Pitfall: confusion between experiments and stability tests
• Orchestrator — System managing deployments (e.g., k8s) — Coordinates rolling updates — Pitfall: relying on defaults without tuning
• Deployment controller — Component that executes rollout plan — Automates steps — Pitfall: controller version mismatch
• Observability — Metrics, traces, logs collection — Needed to validate rollouts — Pitfall: insufficient retention for debugging
• SLI — Service Level Indicator — Measure of service health — Pitfall: choosing meaningless SLIs
• SLO — Service Level Objective — Target for SLIs — Pitfall: unrealistic SLOs that hide failures
• Error budget — Tolerance allowed for SLO breaches — Fuel for deployments — Pitfall: ignoring burn rate during rollouts
• Burn rate — Rate of error budget consumption — Triggers throttles or aborts — Pitfall: not monitored per rollout
• Autoscaling — Adjust capacity in response to load — Helps absorb transient regressions — Pitfall: large scale leads to cost spikes
• StatefulSet rolling update — K8s pattern for stateful apps — Requires ordered updates — Pitfall: misordered updates causing data loss
• Init containers — Pre-start tasks before main container — Used for migrations — Pitfall: long init delays block readiness
• Migration strategy — Plan for data changes across versions — Ensures compatibility — Pitfall: implicit migrations during runtime
• Leader election — Single-master selection mechanism — Affects rolling behavior — Pitfall: updating leader without transfer
• Grace period — Time for processes to exit cleanly — Prevents abrupt kills — Pitfall: misaligned grace periods across services
• Health-gated rollout — Progress controlled by health signals — Reduces risk — Pitfall: tuning thresholds poorly
• Deployment pipeline — Chain of build-test-deploy stages — Integrates rolling update steps — Pitfall: lacking rollback hooks
• Policy engine — Declarative rules for deployment behavior — Enforces standards — Pitfall: complex policy leads to bottlenecks
• Artifact registry — Stores built images/artifacts — Source of truth for deploys — Pitfall: stale tags or mutable latest tags
• Drift detection — Identifies config/infrastructure divergence — Prevents unexpected behavior — Pitfall: unmonitored drift leads to failed rollouts
• Post-deploy validation — Suite of checks after rollout — Ensures correctness — Pitfall: inadequate coverage for edge cases

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How to Measure rolling update (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Deployment success rate	Fraction of rollouts that complete without manual rollback	Count successful rollouts over total	99%	See details below: M1
M2	Mean time to rollback	Time from detection to rollback completion	Timestamp differences in CI/CD logs	< 10m	See details below: M2
M3	Post-deploy error rate	Errors introduced by rollout	Compare error rate window pre and post	< 2x baseline	See details below: M3
M4	SLI availability during rollout	User-facing availability for deployment window	Requests successful divided by total	99.9%	See details below: M4
M5	P95 latency delta	Tail latency change caused by release	Compare p95 before vs after	< 20% increase	See details below: M5
M6	Resource usage spike	CPU mem change during update	Aggregated resource metrics	No more than 30% spike	See details below: M6
M7	Replica readiness time	Time for new replica to become ready	Measure ready timestamp minus start	< 60s	See details below: M7
M8	Error budget burn rate	How fast SLO is consumed during rollout	Error budget consumed per minute	Keep burn < 5x	See details below: M8
M9	Rollout aborts	Number of automated aborts per period	Count of abort events	< 1 per 30 days	See details below: M9
M10	User impact incidents	Incidents attributed to rollout	Incident tracking per rollout	Minimal and tracked	See details below: M10

Row Details (only if needed)

• M1: Count successful rollouts divided by total scheduled rollouts over a time window. Use CI/CD event logs and labels to classify success.
• M2: Use orchestration and pipeline timestamps. Include detection, decision, and completion times for full context.
• M3: Define error windows (e.g., 30m pre vs 30m post). Normalize by traffic volume and segment by region.
• M4: Use availability SLI definition relevant to user requests; ensure synthetic checks and real traffic both measured.
• M5: Baseline p95 should be computed over representative load; compare rolling 30m windows.
• M6: Include autoscaling actions; consider both percent and absolute values to assess cost impact.
• M7: Measure container/pod start to Ready state; include image pull time contributions.
• M8: Tie SLO violation calculations to the specific deployment window to decide abort or proceed.
• M9: Abort counts should include automated and manual; analyze root causes.
• M10: Ensure incidents contain deployment git tag and metadata for traceability.

Best tools to measure rolling update

Tool — Prometheus

• What it measures for rolling update: Metrics for pod readiness, latency, error rates, resource usage.
• Best-fit environment: Kubernetes and cloud environments with metric exporters.
• Setup outline:
• Instrument application with metrics endpoints.
• Deploy node and app exporters.
• Configure scrape jobs per namespace.
• Define recording rules for rollout windows.
• Integrate with alert manager.
• Strengths:
• Flexible query language and alerting integration.
• Wide ecosystem of exporters.
• Limitations:
• Requires storage planning and retention management.
• Long-term analysis needs secondary store.

Tool — Grafana

• What it measures for rolling update: Visualization of SLIs, deployment timelines, and comparative metrics.
• Best-fit environment: Teams needing dashboards for exec and on-call.
• Setup outline:
• Connect data sources (Prometheus, Loki, Tempo).
• Build dashboards with deployment panels.
• Add templating for environment and service.
• Strengths:
• Rich visualization and templating.
• Alerting rules and annotations.
• Limitations:
• Dashboard sprawl if not governed.
• Alerting across multiple sources can be complex.

Tool — Datadog

• What it measures for rolling update: Correlated metrics, tracing, and deployment events with anomaly detection.
• Best-fit environment: Managed SaaS observability with integrated APM.
• Setup outline:
• Install agents on hosts or use k8s integration.
• Tag events with deployment metadata.
• Configure monitors for rollout windows.
• Strengths:
• Unified telemetry and out-of-the-box dashboards.
• ML anomaly detection.
• Limitations:
• Cost scales with telemetry volume.
• Vendor lock-in considerations.

Tool — New Relic

• What it measures for rolling update: Application performance, traces, deployment markers, error rates.
• Best-fit environment: Enterprise SaaS-first observability.
• Setup outline:
• Instrument applications with agents.
• Add deployment metadata to events.
• Configure alerts tied to SLOs.
• Strengths:
• Rich APM features and distributed tracing.
• Limitations:
• Pricing and data ingestion limits.
• Integration complexity for custom telemetry.

Tool — OpenTelemetry + Tempo + Loki

• What it measures for rolling update: Traces for understanding request path regressions and logs for debugging.
• Best-fit environment: Teams building open observability stacks.
• Setup outline:
• Instrument code with OpenTelemetry SDKs.
• Export traces to Tempo and logs to Loki.
• Correlate traces with metrics for pre/post comparisons.
• Strengths:
• Vendor-neutral and flexible.
• Limitations:
• Operational overhead for storage and scaling.
• Requires integration effort.

Recommended dashboards & alerts for rolling update

Executive dashboard

• Panels:
• Overall deployment success rate (M1): shows % of successful rollouts.
• Current active rollouts: list of services and phase.
• Error budget consumption aggregated: highlights risky rollouts.
• Top impacted regions/components: focuses executive attention.
• Why: Provides quick health and business impact snapshot.

On-call dashboard

• Panels:
• Active rollout timeline and current batch progress.
• Post-deploy error rate and p95 latency comparisons.
• Pod readiness and restart counts.
• Recent rollback events and reasons.
• Why: Enables rapid triage and decision-making during rollout.

Debug dashboard

• Panels:
• Per-instance logs and recent stack traces.
• Traces for recent failing requests with versions annotated.
• Resource usage heatmap by node.
• Probe success/failure timelines per pod.
• Why: Surface root cause quickly and allow rollback or patch.

Alerting guidance

• Page vs ticket:
• Page: Production availability SLO breach or an automated rollout abort that leaves capacity insufficient.
• Ticket: Non-critical increase in retryable errors or deployment failure that does not affect SLOs.
• Burn-rate guidance:
• If burn rate exceeds 5x planned for > 10 minutes during rollout, pause and evaluate.
• Apply per-service thresholds aligned to error budget.
• Noise reduction tactics:
• Use dedupe by deployment tag and group alerts by service and region.
• Suppress alerts for healthy automated retries; require persistent failures before paging.
• Use alert templates to include deployment metadata for quick context.

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Implementation Guide (Step-by-step)

1) Prerequisites – Immutable artifact in registry with immutable tags. – Health probes and readiness checks defined. – Observability in place for key SLIs and logs. – CI/CD pipeline capable of orchestrating deployment operations and rollbacks. – Runbook for rollout failures and rollback procedures.

2) Instrumentation plan – Instrument request success/failure counters, latency histograms, and business-specific SLIs. – Add deployment metadata (git sha, image tag, cohort) to metrics, logs, and traces. – Ensure synthetic and real-user monitoring cover same user paths.

3) Data collection – Centralize metrics, traces, and logs with timestamped deployment annotations. – Capture deployment events, rollout stages, and aborts in a single stream for correlation.

4) SLO design – Choose SLIs relevant to user experience (availability, latency). – Set SLO targets with realistic error budgets considering deployment traffic. – Define thresholds for abort triggers tied to burn rate and absolute errors.

5) Dashboards – Create exec, on-call, and debug dashboards as described above. – Add deployment timeline panel that shows instances updated and timestamps.

6) Alerts & routing – Configure monitors for SLO breaches, readiness failures, and unusual resource spikes. – Route SLO breaches to paging; route deployment failures to distribution lists and tickets if noncritical.

7) Runbooks & automation – Maintain runbooks for common failures including step-by-step rollback and mitigation commands. – Automate rollback for common deterministic failures (readiness failure, image pull errors). – Add preflight checks to prevent bad artifacts from being rolled out.

8) Validation (load/chaos/game days) – Run load tests against new version in staging that mimic production. – Execute chaos testing to verify graceful handling of instance termination and network faults. – Hold game days that simulate rollback scenarios and practice manual interventions.

9) Continuous improvement – After each rollout, capture lessons in postmortems. – Adjust probe thresholds, batch sizes, and rollout cadence based on observed metrics. – Automate repetitive fixes and grow test coverage for regression areas.

Checklists

Pre-production checklist

• Artifact signed and stored with immutable tag.
• Unit/integration/security tests passed.
• Health probes implemented and verified.
• Preflight smoke tests in staging passed.
• Monitoring instrumentation present and annotated.

Production readiness checklist

• Rollout concurrency and maxUnavailable set appropriate to capacity.
• Rollback plan defined and automated rollback enabled.
• SLOs and alerts active for deployment window.
• Backup or migration strategy validated for stateful services.
• Runbook available with contact points and escalation paths.

Incident checklist specific to rolling update

• Identify deployment ID and affected instances.
• Pause rollout and isolate affected cohort.
• Compare metrics pre/post with timestamps and annotate events.
• Decide on rollback vs patch-forward based on impact and risk.
• Execute rollback and verify recovery, then run postmortem.

Kubernetes example

• Ensure Deployment.spec.strategy.rollingUpdate has maxUnavailable and maxSurge values set.
• Verify readinessProbe and livenessProbe are defined.
• Use kubectl rollout status deployment/ and kubectl rollout undo if needed.
• “Good” looks like: new pods pass readiness checks, p95 latency and error rates remain within SLO.

Managed cloud service example (e.g., managed instance group)

• Update instance template with new image and start rolling update with defined minimal action batch size and health check.
• Confirm health checks and load balancer behavior pre-deploy.
• “Good” looks like: instances transition to healthy and serve traffic without capacity loss.

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Use Cases of rolling update

1) Zero-downtime API version upgrade – Context: Public API needs minor request handling fix. – Problem: Cannot drop traffic to update all nodes. – Why rolling update helps: Replaces nodes one-by-one preserving capacity. – What to measure: Error rate, p95 latency, request success across versions. – Typical tools: Kubernetes, Prometheus, Grafana.

2) Container runtime patching – Context: CVE in container runtime requires patching hosts. – Problem: Patching all hosts would cause service disruption. – Why rolling update helps: Patch subset of nodes and migrate workloads. – What to measure: Pod eviction success, crash loops, region availability. – Typical tools: Configuration management, cluster autoscaler.

3) Database replica upgrade – Context: DB engine patch requires rolling across replicas. – Problem: Simultaneous upgrade risks data inconsistency. – Why rolling update helps: Upgrade secondary replicas incrementally and re-sync. – What to measure: Replica lag and query errors. – Typical tools: Managed DB consoles, replication tools.

4) Edge CDN config rollout – Context: New caching rules to reduce origin load. – Problem: Faulty rules could break content delivery. – Why rolling update helps: Apply changes to edge nodes gradually. – What to measure: Cache hit ratio and 5xx error rates. – Typical tools: CDN control plane with staged rollout.

5) Service mesh sidecar update – Context: Sidecar proxy needs minor behavior fix. – Problem: Updating all sidecars could induce traffic disruption. – Why rolling update helps: Update sidecars sequentially while verifying routing. – What to measure: Request success and proxy restart counts. – Typical tools: Service mesh control plane and CI/CD.

6) Feature flag remove-and-clean – Context: Removing a long-lived feature flag after migration. – Problem: Removing all code at once may expose bugs. – Why rolling update helps: Remove flag and deploy incrementally while monitoring. – What to measure: Business metric changes and error rates. – Typical tools: Feature flagging system and progressive rollout.

7) Rolling OS security patch – Context: Critical OS patch required across VM fleet. – Problem: Mass reboots would reduce capacity. – Why rolling update helps: Reboot hosts in batches coordinate with autoscaler. – What to measure: Node readiness and job queue backpressure. – Typical tools: MDM, orchestration, and job schedulers.

8) Serverless runtime upgrade – Context: Managed platform upgrades runtime version. – Problem: Cold starts or function failures may occur. – Why rolling update helps: Traffic shifting and staged versioning reduce exposure. – What to measure: Invocation error rate and cold-start frequency. – Typical tools: Managed serverless versioning and traffic split.

9) CI/CD pipeline agent update – Context: Updating build agents image. – Problem: Broken agents stall pipelines. – Why rolling update helps: Update agents progressively to preserve pipeline capacity. – What to measure: Job success rate and queue length. – Typical tools: Runner orchestration and artifact registry.

10) Security policy rollout in the network – Context: New security group rules to harden services. – Problem: Incorrect rule blocks legitimate traffic. – Why rolling update helps: Apply rules to small subset and monitor. – What to measure: Connection refused counts and legitimate traffic drop. – Typical tools: IaC and firewall management.

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Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes microservice rollout

Context: A payment microservice running as a Kubernetes Deployment needs a bugfix release. Goal: Deploy v2 with zero downtime and maintain availability SLO. Why rolling update matters here: Stateful aspects are minimal but capacity must remain for peak traffic. Architecture / workflow: CI builds image, pushes to registry, annotates image with git sha, CD triggers kubectl set image on Deployment with maxUnavailable: 1. Step-by-step implementation:

• Run unit and integration tests in CI.
• Deploy to staging and run smoke tests and synthetic transactions.
• Tag image and trigger rolling update in production with maxUnavailable: 1 and readinessProbe configured.
• Monitor p99 p95 latency and error rate for 30 minutes per batch.
• Abort and rollback if error budget burn exceeds thresholds. What to measure: Pod readiness time, p95 latency, post-deploy error rate. Tools to use and why: Kubernetes for rollout orchestration, Prometheus/Grafana for monitoring, CI/CD for automation. Common pitfalls: Readiness probe misconfigured causing traffic to hit unhealthy pods. Validation: Synthetic transactions succeed and SLOs hold for 2 hours post-rollout. Outcome: v2 rolled out across cluster with no SLO breach.

Scenario #2 — Serverless function version upgrade

Context: A managed serverless function needs dependency patch. Goal: Shift traffic gradually to new version to detect regression. Why rolling update matters here: Full switch could break clients due to subtle dependency differences. Architecture / workflow: Use versioned functions with traffic splitting to send 10% traffic to new version and monitor. Step-by-step implementation:

• Package function and upload versioned artifact.
• Create traffic split: 10% to v2, 90% to v1.
• Monitor logs, error rate, and cold-start metrics over 1 hour.
• Increase to 50% then 100% if stable; rollback immediately if errors spike. What to measure: Invocation success rate, cold starts, p95 latency. Tools to use and why: Managed serverless versioning and observability console. Common pitfalls: Dependency size increases cold-start times, causing apparent regressions. Validation: Steady error rate and acceptable cold-start profile at 100%. Outcome: Safe progressive promotion to v2.

Scenario #3 — Incident-response postmortem of failed rollout

Context: A rolling update caused a cascading failure during peak traffic. Goal: Restore service and produce postmortem with action items. Why rolling update matters here: Rollout exacerbated the issue by progressively reducing capacity. Architecture / workflow: Orchestrator paused mid-rollout with multiple failed batches and high error budget burn. Step-by-step implementation:

• Pause rollout and revert to previous image for failed cohorts.
• Restore capacity by scaling unaffected nodes or routing traffic to healthy regions.
• Collect deployment IDs, metrics, and traces for analysis.
• Postmortem: identify root cause, update runbooks, adjust probe thresholds, and automate rollback. What to measure: Time to detect, time to rollback, error budget consumed. Tools to use and why: Observability for tracing, incident management for coordination. Common pitfalls: Lack of annotated deployment metadata makes correlation slow. Validation: Rollback successful and service meets SLO within 15 minutes. Outcome: Action items include adding stricter preflight checks and automated abort at lower thresholds.

Scenario #4 — Cost vs performance trade-off during rolling update

Context: New version increases memory usage leading to higher costs. Goal: Roll out while controlling cost impact and ensuring SLOs. Why rolling update matters here: Gradual rollout allows observing cost/perf trade-off before full promotion. Architecture / workflow: Launch new version with initial 20% rollout, monitor memory usage and autoscaler behavior. Step-by-step implementation:

• Deploy with resource requests/limits tuned conservatively.
• Monitor memory usage, autoscaler events, and cost metrics.
• If memory-induced autoscaling increases cost beyond threshold, pause and optimize image or resource settings. What to measure: Memory per pod, number of scaled pods, cost per traffic unit. Tools to use and why: Cloud billing APIs and monitoring. Common pitfalls: Resource limits too low causing OOM kills that disrupt rollout. Validation: Achieve acceptable cost/perf ratio and SLOs; proceed to full rollout. Outcome: Either rollout completed with optimized resources or version rolled back.

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Common Mistakes, Anti-patterns, and Troubleshooting

List of mistakes with symptom -> root cause -> fix

1) Symptom: New pods fail readiness probes -> Root cause: Probe endpoint missing or misconfigured -> Fix: Update readiness probe path and test in staging. 2) Symptom: Rollout stuck on draining nodes -> Root cause: Long-running requests or missing graceful shutdown -> Fix: Implement /shutdown hook and set terminationGracePeriodSeconds. 3) Symptom: Increased p95 latency after update -> Root cause: Unoptimized code or resource limits too low -> Fix: Profile code, increase resources or tune autoscaler. 4) Symptom: Repeated rollback loops -> Root cause: Automated rollback misconfigured to retry without addressing root cause -> Fix: Add manual gate after x retries and investigate logs. 5) Symptom: Deployment events lack context -> Root cause: No deployment metadata in logs/metrics -> Fix: Tag telemetry with git sha, image tag, and environment. 6) Symptom: High error budget burn during rollout -> Root cause: SLO thresholds not integrated into rollout logic -> Fix: Tie rollout controller to SLOs and abort on burn thresholds. 7) Symptom: Flaky readiness checks cause false aborts -> Root cause: Unreliable dependent services in check -> Fix: Improve probe design to check essential components only. 8) Symptom: Canary shows no user traffic -> Root cause: Incorrect routing or sampling -> Fix: Ensure canary region or cohort receives representative traffic. 9) Symptom: Image pull rate limits -> Root cause: Pulling latest for many pods concurrently -> Fix: Use private registry with caching and stagger pulls. 10) Symptom: Observability gaps for new version -> Root cause: Missing instrumentation in new code -> Fix: Add metrics and traces and fail deployment if not present. 11) Symptom: Manual rollback takes too long -> Root cause: No automated rollback scripts -> Fix: Implement automated rollback with validated steps. 12) Symptom: Secret/config mismatch -> Root cause: New version expects different config keys -> Fix: Validate config parity in staging and use feature toggles if needed. 13) Symptom: DB schema errors after some nodes updated -> Root cause: Uncoordinated schema change -> Fix: Use backward compatible migrations and dual-read patterns. 14) Symptom: Alerts flood during rollout -> Root cause: Lack of suppression and grouping -> Fix: Group alerts by deployment ID and suppress noncritical alerts during automated retries. 15) Symptom: Cross-region inconsistency -> Root cause: Rolling update applied unevenly and cache invalidation differs -> Fix: Coordinate cache invalidation and regional rollout windows. 16) Symptom: Security regression after update -> Root cause: New image with outdated packages or config exposing endpoints -> Fix: Add vulnerability scans to preflight and harden runtime policies. 17) Symptom: Service unavailable after many batches -> Root cause: MaxUnavailable misconfigured too high -> Fix: Reduce concurrency and increase verification time. 18) Symptom: Hidden dependency failure -> Root cause: Dependency version skew across instances -> Fix: Version lock dependencies and orchestrate dependent component updates. 19) Symptom: Log volume spike causing ingestion delays -> Root cause: Debug logs enabled in new release -> Fix: Adjust logging level and use sample rate for high-volume paths. 20) Symptom: Missing rollback artifacts -> Root cause: Old images pruned prematurely -> Fix: Keep rollback images for minimum retention window. 21) Symptom: Health checks succeed but behavior wrong -> Root cause: Probes too superficial -> Fix: Expand post-deploy validation tests for critical flows. 22) Symptom: On-call confusion during rollout -> Root cause: No clear runbook or ownership -> Fix: Assign deploy owner and include runbook link in deployment event. 23) Symptom: Metrics delayed and misleading -> Root cause: Metric aggregation latency or batch intervals -> Fix: Use high-resolution metrics during deployment windows. 24) Symptom: Release blocked due to policy engine -> Root cause: Policy rules too strict or misconfigured -> Fix: Review policies and add exemptions for emergency fixes. 25) Symptom: Cost spikes during rollout -> Root cause: Uncontrolled autoscaling due to resource needs -> Fix: Temporarily cap autoscaler or tune resource requests.

Observability pitfalls (at least five covered above)

• Missing deployment metadata prevents traceability.
• Low-resolution metrics hide short-lived regressions.
• Overly broad readiness probes mask internal failures.
• Alert fatigue from ungrouped alerts leads to ignored issues.
• No synthetic checks for critical user journeys.

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Best Practices & Operating Model

Ownership and on-call

• Assign deployment owner per release who coordinates the rollout and authorizes pause/rollback.
• Primary on-call should be able to pause and rollback automatically; secondary on-call handles deeper investigation.

Runbooks vs playbooks

• Runbooks: Step-by-step deterministic procedures for known failure modes (rollback, scale).
• Playbooks: Higher-level decision frameworks for ambiguous incidents (decide to rollback vs patch-forward).

Safe deployments (canary/rollback)

• Combine rolling updates with canaries for higher confidence.
• Automate rollback for deterministic failures and provide one-click rollback for manual decision cases.
• Keep last known-good image/pattern accessible for immediate rollback.

Toil reduction and automation

• Automate preflight checks and automated aborts based on SLOs.
• Automatically tag and annotate all telemetry with deployment metadata.
• Automate common fixes such as configuration syncs or secret propagation.

Security basics

• Integrate vulnerability scanning and policy checks as pre-deploy gates.
• Limit who can trigger production rollouts and require approvals for high-risk changes.
• Ensure secrets and config are injected securely and validated pre-deploy.

Weekly/monthly routines

• Weekly: Review recent rollouts and any near-miss aborts.
• Monthly: Audit deployment policies, retention for rollback artifacts, and runbook currency.
• Quarterly: Run game days that simulate rollback and cross-team coordination.

What to review in postmortems related to rolling update

• Time to detect and time to rollback.
• Decision rationale and whether automatic abort thresholds were appropriate.
• Any missing telemetry or runbook steps.
• Follow-up actions: probe tuning, automation, and policy changes.

What to automate first

• Automated tagging of deployment metadata in telemetry.
• Automated health-gated abort and rollback for clear deterministic failures.
• Automated preflight smoke tests that fail fast.

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Tooling & Integration Map for rolling update (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Orchestrator	Coordinates rolling replacement of instances	CI/CD load balancer metrics	K8s, managed instance groups
I2	CI/CD	Triggers rollout and rollback operations	Registry orchestrator ticketing	Automates deployment lifecycle
I3	Observability	Collects metrics traces logs for validation	Orchestrator CI/CD alerts	Prometheus Grafana Datadog
I4	Feature flagging	Controls runtime behavior during rollout	App SDK CI/CD	Useful for gradual enablement
I5	Service mesh	Enables traffic shifting and routing	Orchestrator observability	Adds traffic steering capabilities
I6	Policy engine	Enforces deployment rules and approvals	CI/CD orchestrator	Prevents risky rollouts
I7	Artifact registry	Stores immutable images/artifacts	CI/CD orchestrator	Ensure immutable tags
I8	Secret manager	Provides secure config at deploy time	Orchestrator app runtime	Validate secrets preflight
I9	Chaos engineering	Validates resilience during rollout	CI/CD observability	Use for game days
I10	Incident management	Pages and tracks rollout incidents	Observability CI/CD	Annotate incidents with deployment IDs

Row Details (only if needed)

• None

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Frequently Asked Questions (FAQs)

How do I choose between rolling update and blue-green?

Choose blue-green when instant rollback and complete environment parity are critical and cost is acceptable; choose rolling update when preserving capacity and minimizing duplicate environment cost matters.

How do I decide batch size for rolling update?

Base batch size on capacity needs, typical traffic distribution, and acceptable blast radius; start small and increase as confidence grows.

What SLOs should I watch during a rolling update?

Availability SLI and latency p95/p99 for user-critical paths are primary; also monitor error rates and resource exhaustion.

How do I automate rollback?

Implement health-gated aborts in your orchestrator that automatically trigger rollback when predefined SLI thresholds or probe failures occur.

How does canary differ from rolling update?

Canary exposes a new version to a subset of traffic for validation; rolling update replaces instances in batches across the whole fleet.

What’s the difference between maxSurge and maxUnavailable?

maxSurge controls how many extra instances can be created during rollout; maxUnavailable limits how many instances can be down simultaneously.

How do I handle database schema changes with rolling updates?

Use backward-compatible schema changes, dual-read patterns, or a staged migration with feature flags; avoid breaking reads/writes mid-rollout.

How do I measure impact from a rolling update?

Compare SLIs in defined pre/post windows, track error budget burn and use deployment-tagged traces and logs.

How do I test rollbacks safely?

Exercise rollback in staging and during game days; ensure the old artifact is retained and verified to be compatible.

How do I handle long-running connections during rolling update?

Implement connection draining and ensure terminationGracePeriodSeconds covers typical request duration.

How do I limit noise from alerts during rollouts?

Group alerts by deployment and suppress known noncritical alerts during controlled retries; focus alerts on SLO breaches.

How do I incorporate ML-based anomaly detection in rollouts?

Use anomaly signals as an additional gate but validate against known false positives and tie trigger to human confirmation for non-deterministic patterns.

How do I roll out changes across regions?

Use region-by-region staged rollouts with region-specific monitoring and pause windows to observe regional behavior.

How do I prevent resource-related cost spikes during rollouts?

Set autoscaler caps, monitor resource usage, and tune resource requests; use staged rollout to observe cost impact before full promotion.

How do I ensure security during a rolling update?

Integrate vulnerability scans and policy checks in preflight; restrict deployment permissions and validate secrets injection.

How do I track which deployment caused an incident?

Annotate metrics, logs, and traces with deployment metadata (git sha, image tag) and include it in incident payloads.

What’s the difference between rolling update and progressive delivery?

Progressive delivery is a broader term that includes canary, feature flags, and traffic shaping; rolling update specifically describes instance-level phased replacement.

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Conclusion

Summary Rolling update is a pragmatic, low-downtime deployment strategy that replaces instances in phases while preserving capacity and minimizing blast radius. It requires solid observability, automated gating, and clear runbooks to be effective. When combined with canaries, feature flags, and SLO-aware automation, rolling updates enable frequent safe releases.

Next 7 days plan (5 bullets)

• Day 1: Audit current deployment configs for health probes maxUnavailable maxSurge and add missing probes.
• Day 2: Ensure deployment artifacts are immutable and add deployment metadata tagging to metrics/logs.
• Day 3: Create or update on-call runbook for rollout abort and rollback; practice rollback in staging.
• Day 4: Build a minimal rollout dashboard with deployment success rate and SLI comparisons.
• Day 5: Implement an automated preflight smoke test in CI and tie it as a gate for production rollouts.
• Day 6: Run a tabletop exercise with on-call team simulating a failed rolling update.
• Day 7: Review game day results, update SLO thresholds and automated abort policies.

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Appendix — rolling update Keyword Cluster (SEO)

• Primary keywords
• rolling update
• rolling deployment
• rolling update strategy
• rolling update Kubernetes
• rolling update vs canary
• rolling update best practices
• rolling update tutorial
• rolling update guide
• rolling update SLO

• rolling update observability

• Related terminology

• canary deployment
• blue-green deployment
• immutable deployment
• maxUnavailable
• maxSurge
• readiness probe
• liveness probe
• deployment rollback
• deployment abort
• deployment batch size
• deployment pause window
• progressive delivery
• SLI SLO error budget
• post-deploy validation
• deployment metadata tagging
• deployment dashboard
• deployment automation
• deployment runbook
• deployment policy engine
• deployment orchestration
• artifact registry deployment
• CI CD rolling update
• Kubernetes rolling update example
• managed instance group rolling update
• serverless traffic splitting
• traffic shifting strategies
• service mesh rollout
• canary analysis
• rollout health gating
• rollback automation
• rollout failure modes
• rollout diagnostics
• rollout observability
• rollout metrics deployment success rate
• rollout error budget burn
• rollout p95 latency delta
• rollout resource spike
• rollout readiness time
• rollout abort reason
• rollout incident postmortem
• rollout game day
• safe deployment patterns
• staged regional rollout
• database migration rollout
• stateful rolling update
• feature flag progressive rollout
• rollback artifacts retention
• deployment ownership best practices
• deployment security preflight
• vulnerability scan pre-deploy
• deployment synthetic testing
• deployment anomaly detection
• rollout cost performance tradeoff
• rollout autoscaling impact
• deployment staging validation
• deployment traceability
• deployment annotation
• deployment CI pipeline gate
• deployment monitoring alerts
• deployment alert grouping
• deployment noise reduction
• deployment throttling by SLO
• rolling update checklist
• rolling update checklist Kubernetes
• rolling update checklist serverless
• rolling update troubleshooting
• rolling update failure mitigation
• rolling update mitigation strategies
• rolling update modernization
• rolling update and chaos testing
• rolling updates at scale
• multi-region rolling updates
• blue-green vs rolling update
• canary vs rolling update
• rollout automation priorities
• rollout automation first steps
• onboarding rolling update practices
• deployment pipeline best practices
• deployment policy enforcement
• deployment audit trail
• deployment cost monitoring
• deployment KPIs and metrics
• deployment SLIs and alerts
• deployment latency monitoring
• deployment error rate tracking
• deployment readiness verification
• deployment resiliency patterns
• deployment orchestration tools
• deployment integration map
• deployment observability tools
• deployment runtime security