What is System Design?

Quick Definition

System Design in plain English: System Design is the process of defining the structure, components, interfaces, and behavior of a system to meet functional and nonfunctional requirements.

Analogy: Designing a distributed software system is like planning a city: decide roads, utilities, zoning, traffic rules, and emergency response so people and goods flow reliably.

Formal technical line: System Design is the engineering discipline that maps requirements to architectures, defines component interactions, and prescribes operational practices to achieve target qualities such as availability, scalability, security, and performance.

If System Design has multiple meanings:

Most common meaning: Architectural design for distributed software systems and services.
Other meanings:
Design of hardware or embedded systems — focuses on circuits, boards, and firmware.
Enterprise systems design — aligns business processes, data models, and multiple large applications.
UX-oriented system design — emphasizes user flows across multiple systems.

What it is / what it is NOT

It is a disciplined activity that converts requirements into an architecture and operational plan covering components, communication, data flow, and failure modes.
It is NOT only high-level box-and-arrow diagrams; it also includes API contracts, data schemas, capacity planning, observability design, deployment models, and operational runbooks.
It is NOT a one-time activity; it is iterative across product, infra, and ops lifecycles.

Key properties and constraints

Functional requirements: features, APIs, latency targets.
Nonfunctional requirements: availability, scalability, durability, consistency, security, cost.
Constraints: budget, team skills, regulatory compliance, vendor lock-in, deployment model.
Trade-offs: consistency vs availability, latency vs cost, complexity vs velocity.

Where it fits in modern cloud/SRE workflows

Upstream: product and requirements discovery.
Core: architecture and component design, interfaces, data schemas.
Downstream: CI/CD pipelines, observability and SLOs, runbooks, incident response.
Continuous loop: design informs operations and incidents drive design changes.

A text-only “diagram description” readers can visualize

Users and external systems send requests to the edge (CDN, API gateway).
Edge routes to load balancers which forward to stateless service instances in multiple zones.
Services use sharded databases for state and message queues for async work.
Observability pipelines collect traces, metrics, logs, and expose SLO dashboards.
CI/CD pipelines build, test, and deploy artifacts with canary gates and automated rollback.
Security controls (IAM, WAF, secrets manager) protect traffic and data.

System Design in one sentence

System Design is the practice of selecting and composing components, interfaces, and operational processes to meet functional and nonfunctional requirements under real-world constraints.

System Design vs related terms (TABLE REQUIRED)

ID	Term	How it differs from System Design	Common confusion
T1	Architecture	Focuses on high-level structure and constraints	Confused as full design including ops
T2	Software Design	Emphasizes code-level structure and patterns	Mistaken for system scope and ops
T3	Solution Design	Often business-focused implementation plan	Assumed identical to technical system design
T4	Infrastructure Design	Focuses on servers, networks, storage	Mistaken as including application logic
T5	DevOps	Cultural practices and automation	Confused as purely tooling not design
T6	SRE	Operational reliability practices and SLOs	Mistaken for upfront architecture only
T7	Data Engineering	Focuses on pipelines and schemas	Confused about system-level nonfunctional needs

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

None required.

Why does System Design matter?

Business impact (revenue, trust, risk)

System Design directly influences uptime and user experience, which affect revenue and customer trust.
Poor design can cause outages, data loss, or compliance failures, increasing cost and legal risk.
Thoughtful design enables predictable scaling for growth and controlled cost.

Engineering impact (incident reduction, velocity)

Clear designs reduce ambiguity and rework, increasing developer velocity.
Well-instrumented systems reduce mean time to detect and mean time to repair, lowering incident impact.
Good interfaces and modularization enable parallel development and safer changes.

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

System Design should incorporate SLIs and SLOs as first-class artifacts to guide capacity, testing, and runbooks.
Error budgets drive release velocity decisions and canary sizes.
Design choices that reduce operational toil (automation, self-healing) improve on-call experience.

3–5 realistic “what breaks in production” examples

Database connection storm: many instances re-establishing connections after failover causes CPU exhaustion on the DB host.
Unbounded queue growth: downstream service slowness causes message backlog and memory pressure in brokers.
Configuration drift: environment configuration differs between staging and prod, leading to runtime failures.
Network partition: cross-AZ latency spikes cause leader election thrashing if not designed for partition tolerance.
Unexpected schema change: a rollout adds a non-null column without migration, causing consumer crashes.

Where is System Design used? (TABLE REQUIRED)

ID	Layer/Area	How System Design appears	Typical telemetry	Common tools
L1	Edge and networking	Rate limiting, CDN, gateway design	Request rate and errors	Load balancer metrics
L2	Service and application	APIs, stateless services, scaling rules	Latency, error rate, throughput	APM and service meshes
L3	Data and storage	Sharding, replication, retention policies	Storage latency, backlogs	Databases and data pipelines
L4	Platform and orchestration	Kubernetes topology, autoscaling	Pod restarts, node pressure	K8s control plane metrics
L5	CI CD and deployment	Pipelines, canary policies, rollbacks	Build times, deploy success	CI/CD server metrics
L6	Observability and ops	Telemetry pipelines, alert rules	SLI/SLO, traces, logs	Monitoring and tracing tools
L7	Security and compliance	Access controls, encryption, audits	Auth success, policy violations	IAM and secrets managers

Row Details (only if needed)

None required.

When should you use System Design?

When it’s necessary

New services that must scale, be highly available, or handle sensitive data.
Systems with multiple teams contributing or when integration boundaries matter.
Projects with regulatory, compliance, or security requirements.
When cost or performance targets are constrained.

When it’s optional

Small, short-lived prototypes or internal tools with low user and reliability expectations.
One-off scripts or experiments where speed is prioritized over durability.

When NOT to use / overuse it

Over-designing for hypothetical scale that may never materialize.
Spending months on perfect diagrams before validating with a minimal prototype.
Applying enterprise patterns to simple apps causing unnecessary complexity.

Decision checklist

If expected concurrent users > 1000 and SLA > 99% -> perform full System Design.
If latency budget < 100 ms and multi-region required -> include distributed data design.
If small team and MVP horizon < 3 months -> prefer simple design and iterate.

Maturity ladder

Beginner: Single service, simple datastore, single-region deploys, basic monitoring.
Intermediate: Service boundaries, retries and timeouts, basic SLOs, autoscaling.
Advanced: Multi-region active-active, strict SLOs, automated recovery, cross-team observability.

Example decision for small teams

Small startup with a single microservice and low traffic: choose managed DB, single region, simple health checks and basic SLOs.

Example decision for large enterprises

Large enterprise requiring 99.99% across regions, PCI compliance, and multi-team ownership: design multi-region active-active with replication, strict access controls, and disaster recovery playbooks.

How does System Design work?

Explain step-by-step

Components and workflow

Requirements and constraints gathering: functional features, traffic profiles, compliance.
Define SLIs/SLOs and acceptance criteria.
Sketch high-level architecture: edge, services, storage, async paths.
Design interfaces and contracts: API schemas, message formats.
Capacity and cost estimation: sizing, autoscaling rules.
Plan observability: metrics, traces, logs, alerts, dashboards.
Define deployment and CI/CD strategy with canaries and rollbacks.
Create runbooks, incident response plan, and game days.
Iterate based on load tests, chaos testing, and production telemetry.

Data flow and lifecycle

Ingest -> validate -> process sync/async -> persist -> index/replicate -> serve -> archive/retain.
Consider idempotency, deduplication, causal ordering, and schema evolution.

Edge cases and failure modes

Partial failures between services: use retries with backoff, circuit breakers, and bulkheads.
State divergence after network partition: choose consistency model and reconciliation strategy.
Burst traffic: design request throttling and graceful degradation.
Data corruption: immutable event logs and versioned schemas for recovery.

Short practical examples (pseudocode)

Retry with circuit breaker pseudocode:
attempt request
if failures exceed threshold open circuit for cooldown
when half-open try single probe
API contract example:
POST /orders { id, userId, items[] } responds 202 for async or 201 for sync

Typical architecture patterns for System Design

Layered microservices: Use when you need clear bounded contexts and independent deployability.
Event-driven architecture: Use when decoupling, async reliability, and eventual consistency are required.
CQRS and Event Sourcing: Use when read/write workloads have different patterns and auditability is essential.
Serverless functions + managed services: Use for spiky workloads and when operational overhead must be minimized.
Shared-nothing sharded databases: Use for large-scale OLTP with horizontal scaling needs.
Data mesh: Use for decentralized data ownership with cross-team data product boundaries.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	DB connection storm	Increased DB CPU and errors	Bulk reconnects after failover	Connection pooling and backoff	DB connection error rate
F2	Queue backlog	Growing queue length and lag	Downstream slowness or consumer crash	Autoscale consumers and backpressure	Queue depth and consumer lag
F3	API latency spike	HTTP p95/p99 increase	Hot partition or GC pauses	Shard, tune GC, add capacity	Traces showing slow spans
F4	Resource exhaustion	OOM or CPU saturation	Memory leak or traffic spike	Limit resources and restart policy	Node resource pressure metrics
F5	Config drift	Service errors in prod only	Unversioned config or manual edits	GitOps and immutable configs	Config checksum mismatch
F6	Deployment regression	Spike in errors after deploy	Missing tests or bad rollback	Canary gating and quick rollback	Error rate by deploy version

Row Details (only if needed)

None required.

Key Concepts, Keywords & Terminology for System Design

Glossary (40+ terms). Each entry: Term — definition — why it matters — common pitfall

Availability — Percentage of time system is usable — Drives SLA targets and redundancy — Pitfall: ignoring partial outages
Scalability — Ability to handle growth in load — Determines partitioning and autoscaling — Pitfall: vertical scale assumptions
Latency — Time to respond to a request — Affects UX and SLA — Pitfall: optimizing p50 only
Throughput — Work completed per time unit — Drives capacity planning — Pitfall: confusing throughput with concurrency
Durability — Probability data is preserved — Guides backup and replication — Pitfall: assuming single replica is enough
Consistency — Guarantees about data visibility — Influences design of stateful components — Pitfall: expecting strong consistency everywhere
Availability zone — Isolated failure domain in cloud — Used for fault tolerance — Pitfall: cross AZ latency costs
Region — Geographically separate cloud group — Used for disaster recovery — Pitfall: data residency constraints
Sharding — Partitioning data for scale — Improves performance and parallelism — Pitfall: hot shards
Replication — Copying data across nodes — Provides redundancy — Pitfall: replication lag
Leader election — Choosing a primary node — Enables coordination — Pitfall: split brain without quorum
Circuit breaker — Prevents cascading failures — Improves system stability — Pitfall: misconfiguring thresholds
Bulkhead — Isolating resources per component — Limits blast radius — Pitfall: over-isolation increases cost
Backpressure — Slowdown propagation to avoid overload — Stabilizes downstream systems — Pitfall: not propagating signals
Idempotency — Safe repeated operations — Prevents duplicates — Pitfall: not designing idempotent APIs
Eventual consistency — Convergence over time — Enables higher availability — Pitfall: user confusion with stale reads
Strong consistency — Immediate visibility after write — Simplifies correctness — Pitfall: reduced availability under partition
Saga pattern — Distributed transaction pattern — Provides eventual consistency across services — Pitfall: complex compensation logic
CQRS — Separation of read and write models — Optimizes different workloads — Pitfall: sync complexity
Event sourcing — Persist events as primary store — Enables auditability — Pitfall: event schema evolution issues
Message queue — Async communication primitive — Decouples producers and consumers — Pitfall: unbounded queue growth
Pub/sub — Publish-subscribe messaging — Useful for broadcast scenarios — Pitfall: managing ordering and duplicates
IdP — Identity provider for authentication — Centralizes auth — Pitfall: single point of failure
IAM — Role and permission management — Critical for least privilege — Pitfall: overly broad roles
Zero trust — Security model assuming no implicit trust — Reduces lateral movement risk — Pitfall: complexity without tooling
Observability — Ability to understand internal state from outputs — Essential for troubleshooting — Pitfall: insufficient signal fidelity
Telemetry — Metrics, logs, and traces — Core for SLOs and alerts — Pitfall: siloed signals
SLI — Service level indicator — Measured attribute of reliability — Pitfall: choosing irrelevant SLIs
SLO — Service level objective — Target for SLIs guiding operations — Pitfall: unrealistic targets
Error budget — Allowable unreliability window — Balances release velocity and reliability — Pitfall: ignored in release processes
Toil — Manual, repetitive operational work — Reducing toil improves productivity — Pitfall: manual runbook steps not automated
Canary deployment — Small-scale rollout to detect regressions — Reduces blast radius — Pitfall: insufficient traffic split
Blue-green deployment — Switch traffic between environments — Minimizes downtime — Pitfall: double costs during transition
Autoscaling — Dynamic instance scaling — Matches resources to load — Pitfall: oscillation with bad thresholds
Chaos engineering — Intentional failure testing — Validates resilience — Pitfall: not running in production-like environment
Rate limiting — Controlling request rate — Protects downstream systems — Pitfall: poor client signaling
Throttling — Slowing request processing — Protects resources — Pitfall: poor fallback UX
Immutable infrastructure — Treat infra as code and immutable artifacts — Improves reproducibility — Pitfall: too many unique images
GitOps — Git as single source for ops changes — Improves auditability — Pitfall: merging untested changes
Service mesh — Infrastructure layer for service-to-service traffic — Enables observability and routing — Pitfall: performance overhead

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

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Request success rate	Reliability of service	Successful responses divided by total	99.9% for user-facing APIs	Ignore retries may inflate rate
M2	Request latency p95	Perceived performance	p95 of request duration	p95 < 300 ms typical	p50 hides tail problems
M3	Error budget burn rate	Pace of reliability loss	Error budget used per time window	Alert at burn rate >3x	Short windows noisy
M4	Queue depth	Backlog magnitude	Messages pending in queue	Keep steady or decreasing	Transient spikes common
M5	Downstream latency	Impact of dependencies	Time spent calling dependencies	Keep contribution < 30% of total	Shared infra skews numbers
M6	Deployment success rate	CI/CD health	Successful deployments per attempts	99%+ deploy success	Rollbacks mask defects
M7	Mean time to detect	Observability effectiveness	Time from incident start to detection	Lower is better; target depends	Alerts can be noisy
M8	Mean time to repair	Operational responsiveness	Time from detection to resolution	SLO dependent; reduce via runbooks	Lack of playbooks increases MTTR
M9	Resource utilization	Cost and saturation risk	CPU, memory, IO usage	Keep headroom for spikes	Overprovisioning wastes cost
M10	Data loss incidents	Durability issues	Count of data loss events	Zero data loss expected	Hidden data corruption cases

Row Details (only if needed)

None required.

Best tools to measure System Design

Tool — Prometheus

What it measures for System Design: Time-series metrics from services and infra.
Best-fit environment: Kubernetes native and cloud VMs.
Setup outline:
Export metrics via client libraries.
Run Prometheus server or managed equivalent.
Configure scrape targets and retention.
Define recording rules for SLIs.
Integrate with alertmanager.
Strengths:
Powerful query language and ecosystem.
Works well with Kubernetes.
Limitations:
Single-server scaling complexity and long-term storage needs.

Tool — OpenTelemetry

What it measures for System Design: Traces, metrics, logs in a unified format.
Best-fit environment: Polyglot services needing distributed traces.
Setup outline:
Instrument apps with SDKs.
Configure exporters to backend.
Sample and tag spans with service metadata.
Strengths:
Vendor neutral and rich context.
Limitations:
Initial instrumentation effort and sampling design.

Tool — Grafana

What it measures for System Design: Visualization of metrics, traces, and logs.
Best-fit environment: Cross-team dashboards across infra and apps.
Setup outline:
Connect datasources.
Define panels and alert rules.
Share dashboards and folders.
Strengths:
Flexible visualizations and alerts.
Limitations:
Dashboards need maintenance and can become stale.

Tool — Jaeger / Tempo

What it measures for System Design: Distributed tracing and latency analysis.
Best-fit environment: Microservices with complex call graphs.
Setup outline:
Collect spans via OpenTelemetry.
Configure sampling and retention.
Use trace IDs in logs for correlation.
Strengths:
Root-cause latency analysis.
Limitations:
Storage cost for high-volume tracing.

Tool — Datadog / New Relic (managed APM)

What it measures for System Design: Full-stack observability including APM, logs, and infra.
Best-fit environment: Teams preferring integrated managed observability.
Setup outline:
Install agents or use SDKs.
Configure dashboards, SLOs, and alerts.
Strengths:
Rapid time-to-value and integrations.
Limitations:
Cost and vendor lock-in.

Recommended dashboards & alerts for System Design

Executive dashboard

Panels: Global SLO compliance, revenue-impacting services, critical incidents open, weekly trend of error budget usage.
Why: Provides leadership view of reliability vs business impact.

On-call dashboard

Panels: Active alerts by severity, recent deploys, top failing endpoints, SLO burn rates, current incident runbook link.
Why: Focuses on immediate operational triage and actionability.

Debug dashboard

Panels: Recent traces for top slow endpoints, histogram of latencies, dependency latencies, pod logs snippets, node resource metrics.
Why: Assists engineers in root cause analysis during an incident.

Alerting guidance

What should page vs ticket:
Page (pager duty) for SLO breaches, production incidents causing user impact, safety/security incidents.
Ticket for degradations without immediate user impact, non-urgent anomalies, and backlog items.
Burn-rate guidance:
Alert when error budget burn rate > 2x for short window (e.g., 1 hour) and >1.5x for 24-hour window.
Noise reduction tactics:
Deduplicate alerts by routing key.
Group similar alerts by service and endpoint.
Suppress alerts during known maintenance windows.

Implementation Guide (Step-by-step)

1) Prerequisites – Requirements documented including SLIs/SLOs. – Version-controlled infra and app repos. – Observability basics: metrics, traces, logs collectors. – Team roles and on-call rotation defined.

2) Instrumentation plan – Identify top user journeys and critical paths. – Add metrics for request counts, latencies, errors, and business metrics. – Instrument traces for cross-service calls and add useful tags. – Ensure logs are structured and include trace IDs.

3) Data collection – Deploy collectors and configure retention and sampling. – Ensure telemetry is high-fidelity for top 5% of traffic and sampled elsewhere. – Centralize storage for analysis and long-term SLIs.

4) SLO design – Pick 1–3 SLIs per service (latency, availability, error rate). – Translate business impact to SLO targets. – Define error budget and burn policies.

5) Dashboards – Build three dashboards: exec, on-call, debug. – Add SLO status panel and deploy-version breakdown.

6) Alerts & routing – Define alerts for SLO breaches and immediate symptoms. – Map alerts to on-call roles and escalation policy. – Implement throttling to prevent alert storms.

7) Runbooks & automation – Create runbooks per common incident with steps and playbook links. – Automate common recovery steps (scale up, restart, reroute).

8) Validation (load/chaos/game days) – Run load tests reflecting realistic traffic and edge cases. – Run chaos experiments on failover, network partition, and pod node failures. – Execute game days with stakeholders practicing runbooks.

9) Continuous improvement – Postmortem incidents and add remediation tasks. – Update SLOs and instrumentation after validation. – Regularly revisit architecture with traffic growth.

Checklists

Pre-production checklist

SLIs defined and instrumented.
Health checks and graceful shutdown implemented.
CI/CD pipelines automated and tested.
Canary deployment configured.
Secrets stored in a manager.

Production readiness checklist

Dashboards and alerts configured.
Runbooks and on-call contacts available.
Autoscaling validated under load.
Backups and recovery procedures tested.
Compliance controls in place.

Incident checklist specific to System Design

Identify impacted SLOs and error budget status.
Narrow blast radius by disabling offending feature or traffic.
Collect traces and logs tied by trace ID.
Execute runbook steps and document timeline.
Postmortem with corrective actions and owners.

Examples

Kubernetes example: Ensure Liveness and Readiness probes, HorizontalPodAutoscaler configured, resource requests/limits set, Prometheus scraping configured, and canary deployment via deployment strategy with rolling updates and pod disruption budgets.
Managed cloud service example: For a managed queue service, configure dead-letter queues, visibility timeout, retention policies, and alarms on visible messages count and consumer lag.

What to verify and what “good” looks like

Health checks succeed under expected load.
P95 latency below target for 95% of traffic in load test.
SLO breach alerts fire and map to correct runbook actions.
Canaries detect regressions within expected time window.

Use Cases of System Design

Provide 8–12 use cases

1) Global API gateway for multi-region services – Context: Customer-facing APIs need low latency and failover. – Problem: Single-region outages cause customer impact. – Why System Design helps: Multi-region routing, active-passive or active-active strategies. – What to measure: Per-region latency, failover time, DNS TTL issues. – Typical tools: Global load balancer, service mesh, multi-region DB replicas.

2) Event-driven order processing – Context: High volume e-commerce orders with asynchronous fulfillment. – Problem: Peak bursts and downstream slow services. – Why System Design helps: Queueing and backpressure to decouple workloads. – What to measure: Queue depth, consumer lag, order processing time. – Typical tools: Managed message queues, consumer autoscaling.

3) Real-time analytics pipeline – Context: Streaming user events ingested and transformed into metrics. – Problem: Late-arriving events and backfill needs. – Why System Design helps: Sharding and windowing, watermarking, retention policies. – What to measure: Event lag, processing throughput, data completeness. – Typical tools: Stream processors and object storage.

4) Multi-tenant SaaS database isolation – Context: Tenant resource interference affecting others. – Problem: Noisy neighbor causing latency spikes. – Why System Design helps: Tenant sharding, resource quotas, isolation. – What to measure: Tenant-specific latency and resource utilization. – Typical tools: Logical sharding, dedicated clusters for high-tier tenants.

5) Cost-optimized batch processing – Context: Nightly ETL with large compute needs. – Problem: High cost for constant provisioned clusters. – Why System Design helps: Spot instances, autoscaling, serverless batch. – What to measure: Job completion time, cost per run. – Typical tools: Serverless functions, managed batch services.

6) Compliance and audit logging – Context: Financial services with strict retention and audit. – Problem: Incomplete or untrusted logs. – Why System Design helps: Immutable event stores and retention policies. – What to measure: Log completeness and audit retrieval latency. – Typical tools: Append-only storage and SIEM integration.

7) Feature flag rollout system – Context: Gradual releases and A/B testing. – Problem: Risk of full-scale rollout causing regressions. – Why System Design helps: Canary flags, percentage rollouts, targeted cohorts. – What to measure: Feature impact on errors and latency. – Typical tools: Feature flagging platforms and telemetry hooks.

8) Distributed cache consistency – Context: Cache invalidation across regions. – Problem: Stale reads causing user confusion. – Why System Design helps: TTL strategies, versioned keys, cache warming. – What to measure: Cache hit ratio and stale read rate. – Typical tools: Global cache systems and pub/sub invalidation.

9) Backup and DR for critical DB – Context: Critical transactional database with RPO/RTO targets. – Problem: Long restore times and data loss risk. – Why System Design helps: Continuous backup, point-in-time recovery, tested DR failover. – What to measure: Backup completion, restore time, restore correctness. – Typical tools: Managed DB snapshots and replication.

10) Throttling external API calls – Context: Third-party rate limits and billing costs. – Problem: Exceeding quotas or unexpected charges. – Why System Design helps: Client-side rate limiters, batching, adaptive throttling. – What to measure: Throttled request count and retry success rate. – Typical tools: Token bucket implementations and retry queues.

Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes: Multi-tenant web service autoscaling

Context: A SaaS platform hosts tenant websites on Kubernetes with variable traffic patterns. Goal: Maintain p95 latency under 200 ms while minimizing cost. Why System Design matters here: Autoscaling, resource isolation, and observability prevent noisy-neighbor incidents. Architecture / workflow: Ingress -> API gateway -> namespace-per-tenant -> HorizontalPodAutoscaler per deployment -> PostgreSQL with connection pooler. Step-by-step implementation:

Define SLIs: p95 latency, error rate.
Add metrics for per-tenant latency and CPU.
Configure HPA based on custom metric (request-per-second per pod).
Implement resource quotas per namespace and connection pooler.
Canary deploy HPA rules and test scaling. What to measure: Per-tenant p95 latency, pod startup time, DB connection saturation. Tools to use and why: Kubernetes, Prometheus, Grafana, metrics-server, KEDA. Common pitfalls: HPA reacts too slowly; DB connections exhausted during scale-up. Validation: Load tests with tenant churn and chaos test node failure. Outcome: Stable latency under load, controlled cost, and fewer tenant-impact incidents.

Scenario #2 — Serverless/managed-PaaS: Autoscaling event processor

Context: Image processing pipeline with spiky traffic from uploads. Goal: Process images within 60 seconds with minimal operational overhead. Why System Design matters here: Serverless reduces ops but requires careful throttling and retries. Architecture / workflow: Upload -> object storage event -> managed function -> async job state stored in managed DB. Step-by-step implementation:

Use managed queue for bursts and DLQ.
Configure function concurrency and retry policies.
Instrument start-to-finish tracing to measure processing time. What to measure: Function invocation duration, queue depth, DLQ rate. Tools to use and why: Managed functions, managed queues, cloud object storage. Common pitfalls: Throttling by provider and cold start latency. Validation: Upload burst tests and verify DLQ handling. Outcome: Meet processing SLA with low ops and predictable cost.

Scenario #3 — Incident-response/postmortem: Large-scale outage due to leader election

Context: Distributed coordination service experienced split-brain during AZ network flaps. Goal: Restore availability and prevent recurrence. Why System Design matters here: Election algorithm and quorum rules were misaligned with topology. Architecture / workflow: Services rely on coordination cluster for leader info; failover triggers leader election. Step-by-step implementation:

Detect SLO breach and runbook to isolate affected cluster.
Promote failover to healthy region with controlled traffic reroute.
Postmortem to identify quorum misconfiguration. What to measure: Leader change frequency, service error rate, SLO impact. Tools to use and why: Tracing, cluster metrics, runbooks. Common pitfalls: Not testing network partitions and incorrect quorum settings. Validation: Simulate network partition in staging and verify graceful leader election. Outcome: Revised quorum policy, automated detection for leader flapping.

Scenario #4 — Cost/performance trade-off: Read replica placement

Context: Global read-heavy application with users across regions. Goal: Reduce read latency for international users while controlling replication cost. Why System Design matters here: Replica placement affects latency and replication lag. Architecture / workflow: Primary DB in home region with read replicas in target regions. Step-by-step implementation:

Identify top read paths and regions with latency problems.
Create read replicas in two secondary regions and route reads via geo-aware routing.
Monitor replication lag and adjust replication topology. What to measure: Read latency by region, replication lag, cost per replica. Tools to use and why: Managed DB replicas, CDN for static assets, APM. Common pitfalls: Strongly consistent reads directed to replicas causing stale results. Validation: Synthetic reads across regions and verify stale read window acceptable. Outcome: Improved latency with controlled additional cost and documented consistency trade-offs.

Common Mistakes, Anti-patterns, and Troubleshooting

List of 20 mistakes with symptom -> root cause -> fix

1) Symptom: High error rate after deploy -> Root cause: No canary and untested change -> Fix: Implement canary deployments and rollout health checks. 2) Symptom: Sudden DB CPU spike -> Root cause: Unindexed query or N+1 query -> Fix: Add index, optimize query, add query caching. 3) Symptom: Alert storm on flapping dependency -> Root cause: Alert rules fire on individual host metrics -> Fix: Aggregate alerts by service and add suppression windows. 4) Symptom: Long tail latencies -> Root cause: GC pauses on old JVMs -> Fix: Tune GC or upgrade runtime and instrument heap metrics. 5) Symptom: Queue backlog growth -> Root cause: Consumer crash loop or bottleneck -> Fix: Autoscale consumers, add DLQ, fix consumer bug. 6) Symptom: Cost spike -> Root cause: Resource overprovisioning and runaway scale rules -> Fix: Add budget limits and schedule scale policies. 7) Symptom: Inconsistent data between regions -> Root cause: Replication lag and eventual consistency assumptions -> Fix: Document consistency model and route critical reads to primary. 8) Symptom: Secrets leaked in logs -> Root cause: Lack of log sanitization -> Fix: Implement structured logging and scrub secrets before ingestion. 9) Symptom: Deployment fails due to config mismatch -> Root cause: Manual config changes in production -> Fix: Adopt GitOps and immutable configs. 10) Symptom: On-call overload -> Root cause: High toil and manual remediation -> Fix: Automate routine fixes and improve runbooks. 11) Symptom: Observability blind spots -> Root cause: Missing instrumentation on critical paths -> Fix: Instrument traces and add business metrics. 12) Symptom: Pager noise from flapping threshold -> Root cause: Poor alert thresholds and sensitivity -> Fix: Adjust thresholds, add multi-condition alerts. 13) Symptom: Hot hotspot in DB shard -> Root cause: Poor shard key choice -> Fix: Re-shard or introduce request hashing and cache. 14) Symptom: Client-side retries amplify load -> Root cause: Aggressive retry without jitter -> Fix: Exponential backoff with jitter and circuit breaker. 15) Symptom: Stale cache reads -> Root cause: No invalidation strategy -> Fix: Versioned keys and pub/sub invalidation. 16) Symptom: Long restores for backups -> Root cause: Cold backup strategy and no PITR -> Fix: Enable incremental backups and test restores. 17) Symptom: Race conditions in distributed tasks -> Root cause: No idempotency or locking -> Fix: Implement idempotent operations and distributed locks. 18) Symptom: Lack of ownership for services -> Root cause: Unknown maintainers and handoffs -> Fix: Define service owners and on-call rotation. 19) Symptom: Security misconfiguration discovered -> Root cause: Loose IAM roles and missing least privilege -> Fix: Rotate credentials and enforce least privilege policies. 20) Symptom: Performance regressions unnoticed -> Root cause: No performance gates in CI -> Fix: Add performance tests to CI and block bad changes.

Observability pitfalls (at least 5 included above):

Missing trace IDs in logs causing correlation issues. Fix: ensure trace ID propagation.
Metrics without cardinality control leading to storage blowup. Fix: sanitize labels and use histograms.
Alerts on raw metrics rather than SLOs leading to noise. Fix: alert on SLO burn rate.
High sampling dropping critical traces. Fix: adaptive sampling for high-error traces.
Unstructured logs that are hard to query. Fix: structured JSON logs and index key fields.

Best Practices & Operating Model

Ownership and on-call

Assign clear service ownership including code, infra, and runbook ownership.
Rotate on-call with documented handover and escalation policies.
Include reliability objectives as part of team responsibilities.

Runbooks vs playbooks

Runbooks: Step-by-step remediation for known incidents.
Playbooks: Higher-level decision guides for ambiguous incidents.
Maintain both in version control and link from alerts.

Safe deployments

Use canary or staged rollout with automated gates based on SLOs.
Implement fast rollback based on deploy metadata and health signals.

Toil reduction and automation

Automate routine tasks: certificate rotation, scaling, failover.
Automate diagnostics collection for common incidents.
“What to automate first”: health checks and automated restart workflows, then scale automation, then automated remediation for well-understood failures.

Security basics

Enforce least privilege via IAM roles and service identities.
Use secrets manager and avoid static credentials in code.
Encrypt data at rest and in transit and log access attempts for audit.

Weekly/monthly routines

Weekly: Review open incidents, alert tuning, and error budget consumption.
Monthly: Runbook drills, dependency review, and capacity planning.
Quarterly: Game day and disaster recovery test.

What to review in postmortems related to System Design

Root cause and contributing design decisions.
Gaps in observability and missing SLIs.
Required architecture changes and owners.
Deployment or process changes to avoid recurrence.

Tooling & Integration Map for System Design (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Metrics store	Stores time series metrics	Instrumentation and dashboards	Use long retention for SLOs
I2	Tracing backend	Stores distributed traces	OpenTelemetry and logs	High value for latency root cause
I3	Logging pipeline	Collects and indexes logs	App logs and traces	Ensure structured logs and PIIs handled
I4	CI CD	Automates build and deployment	Repos and testing frameworks	Gate deploys with tests and SLO checks
I5	Feature flags	Controls feature rollouts	SDKs in services	Useful for canarying features
I6	Message broker	Async decoupling of services	Producers and consumers	Monitor queue depth closely
I7	DB as a service	Persistent storage	ORM and backup tools	Configure replicas and PITR
I8	IAM and secrets	Identity and secrets management	Apps and infra	Centralize rotation and audit logs
I9	Load balancer	Traffic routing and TLS	DNS and ingress controllers	Global routing for multi-region
I10	Chaos tooling	Failure injection for testing	Orchestration and infra	Run in controlled environments

Row Details (only if needed)

None required.

Frequently Asked Questions (FAQs)

How do I pick SLIs for my service?

Choose metrics that reflect user experience: success rate, latency on critical paths, and business transactions. Start small and iterate.

How do I balance cost and availability?

Quantify business impact of downtime, set SLOs, and optimize architecture to meet targets at minimal cost, using tiered replication and autoscaling.

How do I instrument services for traces?

Use OpenTelemetry SDKs, propagate trace IDs in requests and logs, and export to a tracing backend with sampling rules.

What’s the difference between SLO and SLA?

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

What’s the difference between observability and monitoring?

Monitoring checks known conditions with alerts; observability enables exploration to understand unknown unknowns.

What’s the difference between architecture and system design?

Architecture focuses on structure and constraints; system design covers architecture plus operational practices and implementation details.

How do I avoid alert fatigue?

Alert on SLO burn rates, group alerts, and add suppression during maintenance. Set priority levels for paging vs ticketing.

How do I design for multi-region?

Design stateless services, choose replication strategy for data, use geo-aware routing, and plan for cross-region failover.

How do I migrate a monolith to microservices?

Start by identifying bounded contexts, extract small services incrementally, and use API gateways and event-driven patterns for integration.

How do I ensure data consistency across services?

Document consistency needs, use idempotency and versioned events, and implement reconciliation jobs for eventual consistency.

How do I test reliability before production?

Use load testing, chaos experiments, and canary deployments in staging or pre-production environments that resemble prod.

How do I measure SLO burn rate?

Compute error budget consumed per time window; burn rate = observed error rate divided by allowed error rate over a rolling window.

How do I set alert thresholds?

Base alerts on SLOs and historical baselines; avoid raw metric thresholds without context.

How do I reduce toil for on-call engineers?

Automate common remediation, build reliable runbooks, and reduce noisy alerts.

How do I protect secrets in CI/CD?

Use secrets managers and inject secrets at runtime; avoid storing in source control or logs.

How do I choose between serverless and containers?

Pick serverless for event-driven, spiky workloads with minimal ops; containers for more control and steady workloads.

How do I manage schema changes safely?

Use backward-compatible changes, rolling deploys, dual-read/write patterns, and schema migration tools.

How do I prioritize design improvements?

Rank by impact on SLOs, revenue risk, and operational toil; fix high-impact, low-effort items first.

Conclusion

Summary System Design is a pragmatic engineering discipline blending architecture, operations, and continuous validation to meet business and technical requirements. It requires clear SLIs/SLOs, automation, observability, and a cycle of testing and learning.

Next 7 days plan (5 bullets)

Day 1: Document top 3 user journeys and define 2 candidate SLIs.
Day 2: Instrument metrics and basic traces for critical paths.
Day 3: Create on-call dashboard and SLO status panel.
Day 5: Run a targeted load test and collect telemetry.
Day 7: Run a short game day and update one runbook based on findings.

Appendix — System Design Keyword Cluster (SEO)

Primary keywords

System Design
Distributed system design
Cloud system design
Scalable architecture
High availability design
Reliability engineering
SLO design
Service design
Microservices architecture
Event-driven design

Related terminology

Observability metrics
Distributed tracing
Error budget
SLIs
SLOs
Incident response
Runbooks
Canary deployment
Blue green deployment
Autoscaling
Horizontal scaling
Vertical scaling
Sharding strategy
Database replication
Leader election
Circuit breaker pattern
Bulkhead isolation
Backpressure pattern
Idempotency design
Eventual consistency
Strong consistency
CQRS pattern
Event sourcing pattern
Message queue design
Pub sub architecture
Service mesh design
Kubernetes architecture
Serverless design
Managed PaaS design
CI CD pipeline design
GitOps workflow
Secrets management
Identity and access management
Zero trust architecture
Immutable infrastructure
Chaos engineering
Load testing strategies
Capacity planning
Cost optimization
Observability pipeline
Telemetry collection
Metric aggregation
Log structuring
Trace propagation
Latency optimization
Throughput tuning
Durable storage
Backup and restore
Disaster recovery planning
Data retention policy
Retention and archiving
Query performance tuning
Indexing strategies
Caching strategies
Cache invalidation
Hot partition mitigation
Retry with jitter
Exponential backoff
Rate limiting strategies
Throttling techniques
Distributed locks
Distributed transactions
Saga orchestration
Compensation logic design
Security compliance controls
PCI compliance architecture
GDPR data handling
Audit logging practices
Postmortem process
Root cause analysis
Remediation planning
Automation of remediation
On call rotation best practices
Toil reduction strategies
Metrics based alerting
Alert deduplication
Alert grouping
Burn rate alerts
Paging policies
Ticketing integration
Dashboard design patterns
Executive reliability dashboard
On-call triage dashboard
Debugging dashboards
Telemetry sampling
Cardinality control
Label management for metrics
Recording rules
Long term metrics storage
Tracing sampling strategies
Trace retention management
Correlation IDs in logs
Structured logging best practices
Searchable logs
Observability cost control
Tracing overhead mitigation
Monitoring agent deployment
Agentless telemetry
SDK instrumentation
Polyglot instrumentation
Vendor neutral telemetry
OpenTelemetry adoption
Managed observability platforms
Vendor lock in considerations
Integration testing for architecture
Performance regression testing
Feature flag rollouts
A B testing infrastructure
Multi region failover
Geo aware routing
DNS failover strategies
Health check design
Graceful shutdown handling
Circuit breaker tuning
Bulkhead sizing
Pod disruption budgets
Stateful application in Kubernetes
StatefulSet design
Connection pooling strategies
DB connection pooling
Read replica placement
Replication lag monitoring
Point in time recovery
Incremental backups
Snapshot based backups
Cost per query optimization
Query caching techniques
CDN for static assets
Edge caching considerations
API gateway design
Throttle and quota enforcement
API contract versioning
Schema evolution practices
Versioned APIs
Deprecation strategy
Consumer driven contracts
Integration testing on deploy
Contract testing frameworks
Data mesh principles
Data product ownership
Decentralized data governance
Observability as code
Infrastructure as code
Policy as code practices
Continuous verification practices