---

Quick Definition

High Level Design (HLD) is a structured architectural description that outlines system components, their relationships, interfaces, and major data flows without delving into low-level implementation details.

Analogy: HLD is like an architect’s floor plan showing rooms, corridors, and utilities but not the electrical wiring diagrams or the paint colors.

Formal technical line: HLD defines component boundaries, interfaces, protocols, and non-functional constraints to guide detailed design and implementation.

Multiple meanings:

• Most common: Software and system architecture overview used in engineering projects.
• Other meanings:
• Network HLD — high-level network topology and segmentation.
• Data HLD — top-level data pipelines and storage strategy.
• Solution HLD — vendor/third-party integration and deployment blueprint.

---

What is High Level Design?

What it is / what it is NOT

• What it is: A concise blueprint that communicates the structure, responsibilities, and interactions of major system components.
• What it is NOT: Not a detailed implementation spec, not a sequence of low-level API calls, and not a replacement for security design docs or compliance artifacts.

Key properties and constraints

• Abstraction: Hides low-level details while exposing interfaces and contracts.
• Traceability: Links to requirements, SLIs/SLOs, and acceptance criteria.
• Modularity: Defines component boundaries that enable parallel work.
• Non-functional focus: Captures latency, throughput, fault domains, scalability targets.
• Security and compliance constraints: Identity, encryption, data residency, and access control summarized.
• Evolvability: Supports extension points and versioning expectations.

Where it fits in modern cloud/SRE workflows

• Project kickoff artifact after requirements and before detailed design or implementation.
• Alignment point for product, security, infrastructure, and SRE teams.
• Used to derive observability, SLOs, deployment strategy, and CI/CD gating.
• A living document linked to infrastructure-as-code, runbooks, and automated tests.

Diagram description (text-only)

• Imagine rectangles for major services: API Gateway, Auth, Service A, Service B, Data Lake, Batch Processor, Message Bus.
• Arrows show interactions: client -> gateway -> services -> message bus -> batch -> data lake.
• Boxes around clusters indicate K8s cluster and managed DB.
• Labels on arrows show protocols and SLOs (e.g., REST 100ms, Kafka 99.9% delivery).
• Legend indicates security boundaries and ownership.

High Level Design in one sentence

A concise architectural map that shows the major components, interactions, constraints, and non-functional requirements necessary to deliver and operate a system.

High Level Design vs related terms (TABLE REQUIRED)

ID	Term	How it differs from High Level Design	Common confusion
T1	Low Level Design	Focuses on code-level structures and implementation details	Confused as interchangeable with HLD
T2	Architecture Decision Record	Records rationale for decisions not the full component map	Seen as a substitute for design diagrams
T3	Solution Design Document	Often includes vendor contracts and deployment plan	Mistaken for a technical HLD
T4	Detailed Design Spec	Contains API definitions and data schemas	Incorrectly used before HLD is approved
T5	Runbook	Operational steps for incidents not design abstractions	Treated as a design document by non-ops teams

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

• (none)

---

Why does High Level Design matter?

Business impact (revenue, trust, risk)

• Revenue: HLD enables predictable delivery by clarifying scope and interfaces, reducing rework that delays releases.
• Trust: Clear HLD reduces surprises in compliance and security reviews, preserving customer trust.
• Risk: Identifies cross-team dependencies and single points of failure before deployment, lowering systemic risk.

Engineering impact (incident reduction, velocity)

• Incident reduction: By defining failure domains and retry semantics, HLD typically reduces cascading failures.
• Velocity: Enables parallel development by specifying interfaces and contracts early, improving sprint throughput.

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

• HLD should specify candidate SLIs and SLO ranges for each external interface and critical path.
• It identifies automation opportunities to reduce toil (deployment automation, automated failover).
• Design must reflect on-call responsibilities and escalation boundaries.

3–5 realistic “what breaks in production” examples

• Message backlog explodes because downstream consumer scaling was not specified, leading to increased latency and storage costs.
• Auth service outage due to single-region deployment causes wide service degradation.
• Schema change without contract enforcement breaks multiple services consuming the data stream.
• Misconfigured ingress leads to sudden traffic spikes bypassing rate limits and causing overload.
• Cost runaway when batch jobs scale unrestricted in cloud-managed services.

---

Where is High Level Design used? (TABLE REQUIRED)

ID	Layer/Area	How High Level Design appears	Typical telemetry	Common tools
L1	Edge and Network	Topology, CIDR, routing zones, WAF boundaries	Flow logs, latency percentiles	Load balancer, WAF, CDN
L2	Service/Application	Service map, APIs, contracts, auth	Request latency, error rate	API gateway, service mesh
L3	Data and Storage	Data flow, retention, schema ownership	Ingest rate, lag, storage growth	Data lake, message bus
L4	Platform and Orchestration	Cluster layout, node pools, scaling policy	Pod restarts, CPU, memory	Kubernetes, managed clusters
L5	CI/CD and Delivery	Pipeline stages, artifact promotion, gating	Build times, deploy frequency	CI servers, artifact repos
L6	Security and Compliance	Boundary controls, encryption, IAM models	Auth success rate, audit logs	IAM, KMS, SIEM

Row Details (only if needed)

• (none)

---

When should you use High Level Design?

When it’s necessary

• New systems that integrate multiple teams or services.
• Significant refactors that change boundaries or data ownership.
• Regulatory or compliance projects requiring documented controls.
• Multi-cloud or hybrid deployments with cross-region considerations.

When it’s optional

• Small, single-team utilities or prototypes with limited lifetime.
• Experiments meant to validate feasibility without long-term commitments.

When NOT to use / overuse it

• Avoid heavy HLD for throwaway prototypes where speed matters over maintainability.
• Don’t overdesign: excessive HLD detail can be rigid and stifle iterations.

Decision checklist

• If external clients and multiple teams depend on the service AND you need reliability -> produce HLD.
• If the component is ephemeral AND owned by one developer -> minimal HLD or an architecture note.
• If regulatory constraints exist AND service handles sensitive data -> HLD with compliance section.
• If you need clear SLOs and on-call routing -> include SRE sections in HLD.

Maturity ladder

• Beginner: Single diagram, interfaces, owners, 1–2 SLIs, single-region plan.
• Intermediate: Failure domains, scaling patterns, CI/CD mapping, multi-region options.
• Advanced: Automated deployment blueprints, resilient patterns, cost/perf tradeoffs, observability-as-code.

Example decisions

• Small team: For a 3-person team building an internal analytics API, use a single-page HLD with service boundaries and one SLI (p95 latency) before coding.
• Large enterprise: For multi-tenant payments platform, create a full HLD including multi-region design, DR, data residency, contract testing, and SLOs per tenant.

---

How does High Level Design work?

Step-by-step

1. Inputs: Requirements, compliance constraints, expected traffic, cost targets, and existing infra inventory.
2. Define components: Services, data stores, integrations, and third-party systems.
3. Interfaces and contracts: API shapes, message schemas, auth flows, and error semantics.
4. Non-functional requirements: Latency targets, throughput, availability, cost, security.
5. Deployment model: Regions, zones, cluster topology, node classes.
6. Observability plan: SLIs, logs, traces, dashboards, and alerting strategy.
7. Validation plan: Load tests, chaos tests, and game days.
8. Handoff: Link to detailed design, IaC, and acceptance criteria.

Data flow and lifecycle

• Ingest: Client -> Gateway -> Validation Service -> Message Bus.
• Processing: Stream consumer(s) -> Enrichment -> Aggregation -> Data Store.
• Serving: Query layer reads aggregates and serves results via API.
• Retention: Hot storage for 7 days, warm for 90 days, cold archive for 7 years.
• Deletion: GDPR removal pipeline with auditing and irreversibility guarantees.

Edge cases and failure modes

• Partial failure: Downstream read replicas lag causing stale reads.
• Control plane failure: CI/CD outage preventing deployments — need manual rollback playbook.
• Network partition: Region isolation requiring failover with potential for split-brain; use leader election and quorum.

Short practical examples (pseudocode)

• Contract check pseudocode:
• receive message
• validate schema
• if invalid then send to dead-letter-topic and emit metric schema_validation_error
• Retry strategy pseudocode:
• attempt = 0
• while attempt < max and not success:
  • attempt++
  • call downstream
  • if transient error wait backoff(attempt)

Typical architecture patterns for High Level Design

• API Gateway + Microservices: Use when you need independent deployability and clear API contracts.
• Event-driven streaming: Use when decoupling, high throughput, and eventual consistency are acceptable.
• Backend-for-Frontend (BFF): Use when multiple clients need tailored aggregation layers.
• Service Mesh with Sidecars: Use when you need policy, mTLS, and observability standardized across services.
• Serverless functions: Use when request bursts are unpredictable and per-invocation cost is acceptable.
• Hybrid cloud split: Use when data residency and cloud provider lock-in must be balanced.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Downstream overload	Rising latencies and errors	Missing backpressure and retries	Add circuit breaker and rate limit	Increased p95 latency and error rate
F2	Auth service outage	401 errors across services	Single-region auth and no fallback	Multi-region auth and cache tokens	Spike in auth failures metric
F3	Schema mismatch	Consumer crashes or data loss	Unversioned schema change	Contract testing and versioning	Increase in schema_validation_error
F4	Cost spike	Unexpected cloud bill increase	Unbounded autoscaling or batch runaway	Autoscaling limits and budgets	Sudden rise in cloud spend metric
F5	Observability gap	No trace for some requests	Sampling misconfig or missing instrumentation	Instrument with consistent trace IDs	Drop in trace coverage percentage

Row Details (only if needed)

• (none)

---

Key Concepts, Keywords & Terminology for High Level Design

Glossary (40+ terms)

• API Gateway — Entry point that routes and enforces policies — Important for traffic control — Pitfall: overloaded single gateway.
• Availability Zone — Isolated datacenter within a region — Matters for fault isolation — Pitfall: assuming AZ independence for shared services.
• Backpressure — Mechanism to slow producers — Controls cascading failures — Pitfall: not propagated end-to-end.
• BFF (Backend for Frontend) — Backend tailored to client needs — Reduces client complexity — Pitfall: duplicated logic across BFFs.
• Canary Deployment — Gradual rollout to subset — Reduces risk of broad failure — Pitfall: incomplete rollback automation.
• Circuit Breaker — Prevent repeated calls to failing service — Prevents resource exhaustion — Pitfall: thresholds too sensitive.
• CI/CD Pipeline — Automated build, test, deploy flow — Enables fast safe changes — Pitfall: insufficient gating tests.
• Cluster Autoscaler — Adjusts nodes to demand — Controls cost and capacity — Pitfall: scale-down thrash.
• Contract Testing — Verifies producer/consumer expectations — Prevents breaking changes — Pitfall: missing negative tests.
• Data Lake — Centralized raw data store — Supports analytics and ML — Pitfall: lack of governance.
• Dead Letter Queue — Holds failed messages for inspection — Prevents data loss — Pitfall: unmonitored DLQ backlog.
• Dependency Graph — Visual of service dependencies — Helps impact analysis — Pitfall: outdated diagrams.
• Drift — Differences between declared and actual infra — Causes outages and security gaps — Pitfall: no IaC enforcement.
• Edge Cache — CDN or cache at edge — Reduces latency and origin load — Pitfall: stale cache invalidation.
• Error Budget — Allowed rate of errors over SLO — Balances innovation and reliability — Pitfall: ignored during releases.
• Event Sourcing — Persist state as sequence of events — Enables auditability — Pitfall: event incompatibility.
• Fault Domain — Group sharing a common failure cause — Used to design redundancies — Pitfall: single fault domain for entire service.
• Feature Flag — Toggle to enable features safely — Allows progressive releases — Pitfall: flag debt and poor cleanup.
• Idempotency — Safe repeated operations — Crucial for retries — Pitfall: assuming POST is idempotent.
• IAM Principle of Least Privilege — Grant minimal permissions — Reduces blast radius — Pitfall: overly broad roles.
• K8s Pod — Smallest deployable unit in Kubernetes — Hosts containers and sidecars — Pitfall: singleton pods for critical services.
• Leader Election — Mechanism for single active instance — Prevents split-brain — Pitfall: slow failover timers.
• Load Balancer — Distributes traffic across nodes — Improves availability — Pitfall: sticky sessions causing uneven load.
• Message Broker — Middleware for async messaging — Decouples producers and consumers — Pitfall: misconfigured retention.
• Multi-Region — Deploy across regions for resilience — Reduces regional risk — Pitfall: data replication lag.
• Observability — Triad of logs, metrics, traces — Enables debugging in production — Pitfall: missing correlation IDs.
• OTEL (OpenTelemetry) — Standard for telemetry collection — Simplifies instrumentation — Pitfall: incomplete instrumentation.
• Partition Tolerance — System handles broken network partitions — Trade-off in CAP theorem — Pitfall: data inconsistency.
• Rate Limiting — Control request rate per actor — Prevents overload — Pitfall: blocking legitimate traffic.
• Read Replica — Secondary DB copy for reads — Improves scalability — Pitfall: stale reads without awareness.
• Resilience Pattern — Design technique for failures — Keeps service available — Pitfall: overcomplicating simple flows.
• SLI (Service Level Indicator) — Measurable metric indicating service health — Basis for SLOs — Pitfall: selecting wrong SLI.
• SLO (Service Level Objective) — Target for an SLI over time — Guides reliability investment — Pitfall: unrealistic SLOs.
• Schema Registry — Central store for schemas used in streams — Ensures compatibility — Pitfall: registry becomes single point of failure.
• Sharding — Partition data across nodes — Scales writes and reads — Pitfall: uneven shard distribution.
• Sidecar Pattern — Companion process for cross-cutting concerns — Standardizes features — Pitfall: resource contention.
• SLA (Service Level Agreement) — Contractual uptime or penalties — Drives business-level expectations — Pitfall: misalignment with SLOs.
• Stateful vs Stateless — Whether instance keeps client state — Affects scaling and resilience — Pitfall: inappropriately making stateful services.
• Throttling — Temporarily limit throughput — Protects downstream systems — Pitfall: poor UX during throttling.
• UX Degradation Strategy — Controlled behavior when capacity is low — Preserves critical functions — Pitfall: unclear user messaging.
• Zone Awareness — Placement strategy across AZs — Improves availability — Pitfall: misconfigured affinity rules.

---

How to Measure High Level Design (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Request p95 latency	User-perceived performance	Measure latency for successful requests	p95 < 300ms for APIs	p95 can hide long-tail p99 issues
M2	Error rate	Service reliability	Failed requests / total requests per minute	< 0.5% for critical APIs	Sparse traffic skews percentage
M3	Availability (uptime)	Service continuity	Successful requests over time window	99.9% monthly	Maintenance windows impact SLOs
M4	Queue lag	Processing delay in async flows	Max offset between head and consumer commit	Lag < 1 min for real-time	Bursty writes spike lag temporarily
M5	Deployment success rate	Delivery pipeline health	Successful deploys / total deploys	95% successful deploys	Flaky tests cause false failures
M6	Cold start time	Serverless response delay	Time from invocation to ready	Cold start < 500ms	Depends on provider and runtime
M7	Mean time to restore (MTTR)	Recovery speed after incidents	Time from incident start to recovery	MTTR < 30 min for critical	Detection latency affects MTTR
M8	Error budget burn rate	Pace of reliability loss	Error rate / budget over time	Maintain burn rate < 1x	Small windows show volatility
M9	Trace coverage	Instrumentation completeness	Traces with full path / total requests	> 80% end-to-end traces	Sampling reduces coverage
M10	Cost per transaction	Operational efficiency	Monthly cost / transactions	Varies by business; track trend	Multi-tenant costs obscure per-feature

Row Details (only if needed)

• (none)

Best tools to measure High Level Design

Provide 5–10 tools descriptions.

Tool — OpenTelemetry

• What it measures for High Level Design: Metrics, traces, logs correlation.
• Best-fit environment: Multi-cloud, microservices, hybrid.
• Setup outline:
• Instrument services with OTEL SDKs.
• Configure collectors to export to backend.
• Enforce consistent context propagation.
• Strengths:
• Vendor-neutral telemetry standard.
• Flexible pipeline and sampling.
• Limitations:
• Requires integration effort.
• Sampling and storage costs.

Tool — Prometheus

• What it measures for High Level Design: Time-series metrics for systems and services.
• Best-fit environment: Kubernetes and cloud-native infra.
• Setup outline:
• Export metrics via /metrics endpoints.
• Configure scrape jobs and alert rules.
• Use federation for scale.
• Strengths:
• Powerful query language and alerting.
• Lightweight collectors.
• Limitations:
• Not ideal for long-term retention without remote storage.
• Pull model complexity across networks.

Tool — Jaeger / Tempo

• What it measures for High Level Design: Distributed traces for latency and path analysis.
• Best-fit environment: Microservices with request flows.
• Setup outline:
• Instrument services with tracing SDKs.
• Send spans to collector and storage.
• Build trace-based alerting.
• Strengths:
• Root cause identification across services.
• Latency breakdowns per span.
• Limitations:
• Storage costs for high throughput.
• Requires consistent tracing headers.

Tool — Grafana

• What it measures for High Level Design: Dashboards combining metrics, logs, and traces.
• Best-fit environment: Cross-platform observability.
• Setup outline:
• Connect data sources.
• Build executive and ops dashboards.
• Configure alerting channels.
• Strengths:
• Visual flexibility and plugins.
• Alerting and annotations.
• Limitations:
• Dashboard sprawl risk.
• Alert fatigue if not curated.

Tool — Cloud Provider Metrics (e.g., managed monitoring)

• What it measures for High Level Design: Managed infra health, billing, and service metrics.
• Best-fit environment: Managed cloud services and PaaS.
• Setup outline:
• Enable monitoring for services.
• Export to central system or use native alerts.
• Tag resources for cost attribution.
• Strengths:
• Deep integration with managed services.
• Low instrument effort.
• Limitations:
• Vendor lock-in and inconsistent semantics across clouds.

Recommended dashboards & alerts for High Level Design

Executive dashboard

• Panels:
• Overall availability and error budget status (why: executive health).
• Top 5 SLA-producing endpoints by traffic (why: business focus).
• Cost trend and forecast (why: financial visibility).
• Major incident summary (why: current business impact).

On-call dashboard

• Panels:
• Current alerts and severity (why: triage).
• SLO burn rate and error budget per service (why: escalation).
• Recent deploys with success state (why: cause correlation).
• Dependency health map (why: impact analysis).

Debug dashboard

• Panels:
• Request waterfall for sample trace (why: latency root cause).
• Per-endpoint latency percentiles p50/p95/p99 (why: performance hotspots).
• Queue depths and consumer lag (why: async bottlenecks).
• Recent schema validation failures (why: data integrity).

Alerting guidance

• What should page vs ticket:
• Page (immediate paging): SLO breach for critical services, total outage, data loss.
• Ticket (non-urgent): Deploy failure with no immediate impact, low-priority regressions.
• Burn-rate guidance:
• Page when burn rate > 4x error budget and error budget remaining small.
• Create ticket when burn rate between 1–4x for investigation.
• Noise reduction tactics:
• Deduplicate alerts at routing layer.
• Group by root cause and service.
• Suppress during known maintenance windows.
• Use dynamic thresholds for noisy signals.

---

Implementation Guide (Step-by-step)

1) Prerequisites – Stakeholders identified: product, infra, security, SRE, data owners. – Requirements captured: scalability, latency, compliance. – Inventory of existing services and constraints.

2) Instrumentation plan – Define SLIs and tags for context propagation. – Standardize telemetry libraries and sampling policies. – Plan schema registry and contract testing.

3) Data collection – Centralize metrics, traces, and logs to chosen backends. – Use exporters/collectors and ensure authentication. – Define retention and archival policies.

4) SLO design – Map user journeys to SLIs. – Set conservative starting SLOs and iterate after telemetry baseline. – Define error budget policies and escalation steps.

5) Dashboards – Build one executive, one on-call, and per-service debug dashboards. – Standardize panels and naming conventions. – Add annotations for deploys and incidents.

6) Alerts & routing – Define alert severity and paging rules. – Implement grouping, dedupe, and downstream suppression. – Integrate with incident management workflow and notification channels.

7) Runbooks & automation – Author playbooks for common failures tied to HLD failure domains. – Automate runbook steps where possible (rollbacks, scaling). – Keep runbooks versioned alongside HLD.

8) Validation (load/chaos/game days) – Run load tests against staging with production-like traffic patterns. – Conduct chaos experiments on fault domains defined in HLD. – Run game days to validate runbook effectiveness.

9) Continuous improvement – Monthly review of SLOs, error budget consumption, and postmortems. – Update HLD for significant architectural changes. – Automate drift detection between HLD and deployed infra.

Checklists

Pre-production checklist

• Stakeholders have reviewed and signed off HLD.
• SLIs defined and instrumentation in place.
• CI/CD pipeline for canary and rollback configured.
• Compliance signoffs for sensitive data pathways.
• Load test plan and acceptance criteria documented.

Production readiness checklist

• Observability coverage > 80% for critical paths.
• Automated failover and rollback tested.
• Cost alerting and budgets in place.
• Runbooks accessible with contact routing.
• Security scans and IAM least privilege applied.

Incident checklist specific to High Level Design

• Verify HLD component owning the failing path.
• Check SLO burn rate and whether paging is required.
• Run diagnostic commands and collect traces and logs.
• Execute runbook steps; if unresolved escalate per policy.
• Post-incident update HLD and runbook with root cause.

Example: Kubernetes

• Ensure pod anti-affinity across AZs.
• Verify horizontal pod autoscaler metrics and limits.
• Confirm liveness/readiness probes are tuned.
• Good: p95 latency stable under scaled load.

Example: Managed cloud service (serverless)

• Ensure concurrency limits and reserved capacity set.
• Verify cold-start metrics and provisioned concurrency if needed.
• Good: cold start < target and cost within budget.

---

Use Cases of High Level Design

Provide 8–12 concrete use cases.

1) Multi-tenant SaaS API – Context: SaaS serving multiple customers in one service. – Problem: Isolation, performance, and billing. – Why HLD helps: Defines tenant isolation boundaries and routing, tenancy model, and SLOs per tenant class. – What to measure: Per-tenant error rate, latency, cost per tenant. – Typical tools: API gateway, service mesh, tenant tagging.

2) Real-time analytics pipeline – Context: Event stream ingestion and near-real-time metrics. – Problem: Backpressure, schema evolution, retention. – Why HLD helps: Specifies streaming platform, retention tiers, and consumer responsibilities. – What to measure: Ingest rate, lag, event loss. – Typical tools: Kafka, Flink, schema registry.

3) Payment processing service – Context: Financial transactions with regulatory constraints. – Problem: High availability and data residency. – Why HLD helps: Lays out multi-region failover and audit trails. – What to measure: Transaction success rate, latency, audit completeness. – Typical tools: Managed DB, KMS, HSM.

4) Edge caching for global app – Context: Global user base with latency-sensitive content. – Problem: Latency and inconsistent content. – Why HLD helps: CDN placement, cache invalidation strategy. – What to measure: Cache hit ratio, TTL effectiveness. – Typical tools: CDN, origin failover.

5) Legacy migration to microservices – Context: Monolith moving to services. – Problem: Data ownership and incremental cutover. – Why HLD helps: Defines strangler pattern, API facades, and data sync. – What to measure: Error rate during migration window, data drift. – Typical tools: Message bus, API gateway.

6) Serverless ingestion endpoint – Context: High bursts of short-lived requests. – Problem: Cold starts and concurrency limits. – Why HLD helps: Set concurrency provisioning and fallback. – What to measure: Cold start latency, throttles. – Typical tools: Serverless functions, managed queues.

7) Observability platform rollout – Context: Centralizing telemetry across services. – Problem: Inconsistent telemetry and blind spots. – Why HLD helps: Standardizes instrumentation and exporters. – What to measure: Trace coverage, metric completeness. – Typical tools: OTEL, Prometheus, Grafana.

8) Multi-cloud DR plan – Context: Need resilient operations across clouds. – Problem: Replication and failover complexity. – Why HLD helps: Defines failover path, replication lag, and cost tradeoffs. – What to measure: RTO, RPO, failover test success. – Typical tools: Cross-region replication, CDN, DNS failover.

9) Batch ETL for analytics – Context: Nightly jobs producing aggregates. – Problem: Performance variability and cost spikes. – Why HLD helps: Schedules, resource sizing, and data partitioning. – What to measure: Job duration, retry counts, compute cost. – Typical tools: Managed batch services, data warehouse.

10) Mobile backend with offline sync – Context: Mobile clients sync intermittently. – Problem: Conflict resolution and data staleness. – Why HLD helps: Define sync protocol, conflict strategies. – What to measure: Sync success rate, conflict frequency. – Typical tools: Sync service, conflict resolver.

---

Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes: Multi-region API with failover

Context: Public API deployed on K8s across two regions.
Goal: 99.95% availability with automated failover.
Why High Level Design matters here: Defines region topology, data replication, and failover path without dictating implementation details.
Architecture / workflow: Client -> Global LB -> Region primary -> K8s services -> Database with cross-region replicas -> Replication lag monitor.
Step-by-step implementation:

• Define HLD with regions, databases, and failover triggers.
• Implement multi-cluster deployment using GitOps.
• Add health checks and failover automation in LB.
• Create runbooks and SLOs. What to measure: Region availability, replication lag, p99 latency, failover time.
  Tools to use and why: K8s, service mesh, global load balancer, managed DB for replication for reduced ops.
  Common pitfalls: Assuming synchronous replication; not testing failover.
  Validation: Inject regional failover in staging, verify failover time and data consistency.
  Outcome: Predictable failover behavior and recorded SLO adherence.

Scenario #2 — Serverless ingestion with provisioned concurrency

Context: High-volume ingestion spikes from IoT devices, using managed serverless functions.
Goal: Minimize cold starts and control cost.
Why High Level Design matters here: Captures concurrency model, throttling, downstream buffers, and cost targets.
Architecture / workflow: Device -> API Gateway -> Lambda with provisioned concurrency -> Kinesis -> Consumer.
Step-by-step implementation:

• HLD defines concurrency and buffer sizing.
• Configure provisioned concurrency and burst queue.
• Add DLQ and monitoring.
• Define SLOs and error budgets. What to measure: Cold start rate, throttled invocations, DLQ size.
  Tools to use and why: Serverless platform, managed streaming, observability from cloud provider.
  Common pitfalls: Unbounded provisioned concurrency costs; insufficient DLQ handling.
  Validation: Load tests with burst patterns and monitor cost.
  Outcome: Stable latency and controlled spend during spikes.

Scenario #3 — Incident-response / postmortem

Context: Partial outage caused by a schema change that propagated to producers.
Goal: Restore service and prevent recurrence.
Why High Level Design matters here: HLD should have identified schema ownership, contract test gates, and rollback paths.
Architecture / workflow: Producers -> Schema Registry -> Consumers.
Step-by-step implementation:

• Immediate: Revert change or route to previous schema.
• Runbook: Isolate faulty producer, reprocess DLQ.
• Postmortem: Update HLD to require schema contract tests in pipeline. What to measure: Time to detect, MTTR, number of affected messages.
  Tools to use and why: Schema registry, DLQ, CI pipeline.
  Common pitfalls: No automatic contract test gating causing production schema changes.
  Validation: Replay tests and CI gating demonstration.
  Outcome: Reduced likelihood of schema-induced outages.

Scenario #4 — Cost vs performance optimization

Context: Data-heavy aggregation service with rising compute costs.
Goal: Reduce cost per query while maintaining p95 latency.
Why High Level Design matters here: Identifies hot paths and potential for caching, pre-aggregation, and tiered storage.
Architecture / workflow: Ingest -> Batch aggregation -> Hot cache -> API.
Step-by-step implementation:

• Instrument queries and cost attribution.
• Add pre-aggregation for common queries.
• Introduce cache with TTL based on freshness requirements. What to measure: Cost per transaction, p95 latency, cache hit ratio.
  Tools to use and why: Data warehouse, caching layer, cost monitoring.
  Common pitfalls: Overcaching leading to stale results; not tagging costs.
  Validation: A/B testing pre-aggregation vs on-the-fly queries.
  Outcome: Measurable cost savings with maintained latency.

---

Common Mistakes, Anti-patterns, and Troubleshooting

List of mistakes with symptom -> root cause -> fix (15–25 items)

1) Symptom: Sudden spike in error budget. -> Root cause: Uncontrolled deploy with breaking change. -> Fix: Rollback, add pre-deploy contract tests, gate deploys on SLO health.

2) Symptom: DLQ backlog grows. -> Root cause: Consumer scaling not specified. -> Fix: Define scaling policy and autoscaling for consumers, add alert for DLQ increase.

3) Symptom: High p99 latency only at peak times. -> Root cause: Missing capacity planning and burst handling. -> Fix: Implement rate limits, queue buffering, and reserve capacity.

4) Symptom: Flaky alerts every deploy. -> Root cause: Alerts tied to transient events during deployment. -> Fix: Suppress deployment-related alerts and use deploy annotations.

5) Symptom: Inconsistent tracing. -> Root cause: Not propagating trace headers. -> Fix: Enforce middleware to inject/propagate trace IDs.

6) Symptom: Unauthorized access incidents. -> Root cause: Over-permissive IAM roles. -> Fix: Apply least privilege, audit roles regularly.

7) Symptom: Cost spike after traffic growth. -> Root cause: Autoscaler scaling without bounds. -> Fix: Set max limits and implement cost alerts.

8) Symptom: Data drift between systems. -> Root cause: No schema/version contract. -> Fix: Use schema registry and consumer-driven contract tests.

9) Symptom: Slow failover during region outage. -> Root cause: Long health check intervals. -> Fix: Tighter health checks and automated failover thresholds.

10) Symptom: Production differs from HLD. -> Root cause: Drift due to manual changes. -> Fix: Enforce IaC and drift detection.

11) Symptom: No visibility in incidents. -> Root cause: Missing logs/traces for new service. -> Fix: Add telemetry instrumentation before rollout.

12) Symptom: High tail latencies from cold starts. -> Root cause: Serverless functions not provisioned. -> Fix: Use provisioned concurrency and warmers.

13) Symptom: Confusing ownership during incidents. -> Root cause: Undefined component owners in HLD. -> Fix: Assign and document owners with escalation paths.

14) Symptom: Overly complex HLD blocking progress. -> Root cause: Overdesign and unnecessary detail. -> Fix: Simplify HLD to the necessary abstraction and add extension notes.

15) Symptom: Alert storms during network partitions. -> Root cause: Alert rules not grouping by root cause. -> Fix: Group alerts, add deduplication, and implement suppression.

16) Symptom: Observability gaps for third-party integration. -> Root cause: No telemetry emitted at integration boundary. -> Fix: Add telemetry wrappers and external call metrics.

17) Symptom: Stale cache causing user complaints. -> Root cause: Hard TTLs not tied to data volatility. -> Fix: Use dynamic TTLs and cache invalidation hooks.

18) Symptom: Long-running queries blocking DB. -> Root cause: No read replicas or inefficient queries. -> Fix: Add read replicas, query optimization, and circuit breakers.

19) Symptom: Unable to reproduce production failure. -> Root cause: Missing fidelity in staging env. -> Fix: Improve staging parity and synthetic traffic.

20) Symptom: Manual postmortems not acted on. -> Root cause: No ownership for action items. -> Fix: Assign owners and track remediation in backlog.

Observability-specific pitfalls (at least 5 included above)

• Missing correlation IDs -> Add consistent header propagation.
• Under-sampling traces -> Adjust sampling strategy for key paths.
• Metric cardinality explosion -> Limit labels and use aggregation.
• No alert dedupe -> Add grouping rules and suppression windows.
• Logs not structured -> Move to structured logs for parsing.

---

Best Practices & Operating Model

Ownership and on-call

• Assign component owners in HLD and list on-call rotations.
• On-call should have access to runbooks and tooling for fast mitigation.

Runbooks vs playbooks

• Runbooks: Step-by-step operational procedures for incidents.
• Playbooks: Decision trees for high-level incident strategy.
• Keep both versioned and linked from HLD.

Safe deployments (canary/rollback)

• Use automated canaries, with SLO-based gating for promotion.
• Ensure automated rollback triggers on error budget burn or deploy-time SLI degradation.

Toil reduction and automation

• Automate repetitive tasks first: deploys, rollbacks, and repeated diagnostics.
• Instrument common triage commands into runbooks.

Security basics

• Apply secure defaults: mTLS, encryption at rest, least privilege IAM.
• Include threat model summary in HLD and required controls.

Weekly/monthly routines

• Weekly: Review open alerts and incident actions.
• Monthly: SLO review, capacity forecast, dependency review.

What to review in postmortems related to HLD

• Whether HLD accurately captured failure domain.
• Missing SLOs or instrumentation that hindered diagnosis.
• Ownership or runbook gaps.

What to automate first

• Deploy rollback pipeline.
• SLO alert routing and burn-rate detection.
• DLQ monitoring and auto-retry orchestration.
• Telemetry coverage checks in CI.

---

Tooling & Integration Map for High Level Design (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Telemetry	Collects metrics logs traces	OTEL exporters, Prometheus	Central source for observability
I2	CI/CD	Automates build and deploy	Git, artifact repository	Gate with contract tests
I3	IaC	Declarative infra provisioning	Cloud APIs, secret stores	Prevents drift
I4	Message Bus	Async decoupling and buffering	Consumers, schema registry	Critical for scaling
I5	API Management	Gateway, auth, rate limits	IAM, service mesh	Entry point for clients
I6	Data Store	Persistent storage for events and state	Backups, replication	Choose per access pattern
I7	Cost Monitoring	Tracks spend and trends	Billing APIs, tags	Alert on anomalies
I8	Secret Management	Stores keys and secrets	KMS, CI/CD	Rotate and audit regularly
I9	Security Posture	Scans and policy enforcement	IaC scanning, SIEM	Shift-left security
I10	Incident Mgmt	Pager, ticketing, postmortem	Alerting, chatops	Link alerts to runbooks

Row Details (only if needed)

• (none)

---

Frequently Asked Questions (FAQs)

How do I decide what belongs in HLD versus detailed design?

HLD should include components, interfaces, and non-functional constraints; leave implementation specifics, schemas, and code-level decisions to the detailed design and ADRs.

How do I measure if an HLD is “good enough”?

A good HLD answers who owns components, how data flows, expected SLOs, major failure modes, and deployment boundaries; it should enable teams to implement without repeated clarifications.

How do I keep HLD in sync with changes?

Use IaC, link HLD to PRs for architecture changes, and schedule regular reviews; guard manual changes with drift detection.

How do I pick SLIs for a new service?

Choose metrics that reflect user experience: latency, errors, and availability for the critical paths; start conservative and iterate.

What’s the difference between HLD and a detailed architecture diagram?

HLD is high-level abstraction showing components and contracts; detailed diagrams include API schemas, sequence diagrams, and code-level packages.

What’s the difference between HLD and an ADR?

HLD captures architectural structure; ADR explains why particular architectural decisions were made.

What’s the difference between SLO and SLA?

SLO is an internal reliability target; SLA is a contractual guarantee often with penalties.

How do I prioritize which failure modes to mitigate first?

Prioritize by user impact, recovery complexity, and likelihood; focus on high-impact, high-likelihood issues first.

How do I document ownership in HLD?

Include a simple owner table mapping components to teams and primary on-call contacts, and update via PRs.

How do I estimate costs for alternatives in HLD?

Use cost models and representative workloads; run small-scale benchmarks and include cost per unit metrics.

How do I test HLD assumptions?

Implement smoke tests, load tests, and chaos experiments targeting the assumptions and boundaries.

How do I integrate compliance needs into HLD?

Add a compliance section with data classifications, required controls, and where evidence lives.

How do I decide between serverless and containerized approach?

Compare cost at scale, cold-start tolerance, control needs, and vendor dependencies; HLD should document tradeoffs.

How do I ensure observability is sufficient?

Define required SLIs and target coverage, instrument critical paths, and validate with simulated incidents.

How do I handle third-party dependencies in HLD?

Document integration points, expected SLAs, fallbacks, and testing strategies for third-party failures.

How do I model multi-region data consistency?

State RTO/RPO targets and choose replication strategy (sync vs async) based on those targets.

How do I communicate HLD to non-technical stakeholders?

Provide an executive summary focusing on risks, costs, and timelines accompanied by simplified diagrams.

---

Conclusion

High Level Design is the essential bridge between requirements and implementation: it clarifies component boundaries, non-functional constraints, and operational expectations to reduce risk and accelerate delivery.

Next 7 days plan

• Day 1: Gather stakeholders and capture top-level requirements and constraints.
• Day 2: Draft component diagram and ownership map.
• Day 3: Define candidate SLIs, initial SLOs, and observability requirements.
• Day 4: Identify failure domains and write basic runbooks for top 3 risks.
• Day 5–7: Validate HLD via tabletop exercises and update based on feedback.

---

Appendix — High Level Design Keyword Cluster (SEO)

• Primary keywords
• High Level Design
• HLD architecture
• system high level design
• high level system design
• architecture high level diagram
• HLD document
• high level design example
• cloud high level design
• high level design principles

• HLD template

• Related terminology

• architecture decision record
• service level objective
• service level indicator
• error budget
• observability best practices
• open telemetry instrumentation
• API gateway design
• service mesh design
• microservices HLD
• event driven architecture
• data pipeline design
• schema registry importance
• dead letter queue handling
• circuit breaker pattern
• canary deployment strategy
• rollback automation
• chaos engineering playbook
• runbook authoring
• incident management workflow
• multi region deployment
• availability zone awareness
• capacity planning guidelines
• cost optimization strategies
• serverless cold start mitigation
• autoscaling policies
• backpressure strategies
• contract testing pipeline
• CI CD architecture
• infrastructure as code design
• telemetry correlation
• trace coverage metric
• p95 p99 latency goals
• queue lag monitoring
• DLQ processing
• least privilege IAM
• encryption at rest in HLD
• data retention policy design
• GDPR data deletion flow
• leader election design
• sharding strategies
• caching patterns for performance
• backend for frontend pattern
• hot warm cold storage tiers
• deployment pipeline gating
• observability dashboards
• synthetic monitoring approach
• A B testing infra decisions
• hybrid cloud architecture
• vendor lock in considerations
• telemetry sampling strategy
• metric cardinality control
• alert deduplication techniques
• burn rate alert strategy
• subsystem ownership model
• dependency graph mapping
• drift detection tools
• postmortem action tracking
• safety and security checks
• compliance mapping in HLD
• cost per transaction measurement
• read replica design
• conflict resolution for sync
• replication lag monitoring
• payload validation at ingress
• schema versioning best practice
• telemetry export pipeline
• dashboard for executives
• on call dashboard layout
• debug dashboard panels
• SLIs for async systems
• SLO starting points
• observability as code
• versioned runbooks
• game day validation
• chaos experiments for HLD
• performance profiling in cloud
• tagging strategy for cost allocation
• telemetry retention policy
• sample HLD checklist
• production readiness criteria
• pre production checklist items
• incident checklist for HLD
• managed service tradeoffs
• serverless vs containerized decision
• hybrid storage design
• aggregation and pre computing
• cache invalidation patterns
• API contract enforcement
• message broker selection
• paid operations and SRE model
• toil reduction automation
• monitoring third party services
• escalation policy mapping
• health check configurations
• readiness and liveness probe best practice