How would you design a scalable microservices architecture?

Long Answer

A production-grade microservices architecture is a sociotechnical design. The structure of teams, the boundaries of services, and the contracts between them determine speed and reliability more than any framework choice. The goal is to let services evolve independently while the whole system stays coherent and observable.

1) Service boundaries by domain, not layers
Start from domain modeling. Draw bounded contexts such as Identity, Catalog, Orders, Payments, Fulfillment, and Reporting. Each service owns a capability end to end: its business rules, its storage, and its external integrations. Avoid layering services by technical concern (for example, “all controllers in one service”), because that recreates a distributed monolith. Boundaries should minimize chatty cross-calls and reflect language that a non-technical domain expert would recognize.

2) Data ownership and a single system of record
Every entity has one owner service. Other services read through contracts or replicated projections, never through shared tables. Use an outbox in the owning service to publish domain events when state changes. Downstream services consume events to maintain read models or trigger workflows. This pattern removes direct coupling while keeping data fresh enough for business needs. For sensitive data, publish hashes or references instead of raw fields and apply field level encryption where appropriate.

3) API contracts and versioning discipline
Contracts are products. Document them with OpenAPI for REST and Protocol Buffers for gRPC. Prefer small, purpose-built endpoints over giant kitchen sink interfaces. Version additively: new fields are optional, old fields are deprecated with timelines, and breaking changes use a new major version side by side. Add consumer-driven contract tests so producers cannot ship a change that breaks a real consumer. Pin client code generation to explicit versions to avoid surprise upgrades.

4) Communication patterns fit for purpose
Choose the simplest pattern that meets performance and coupling needs. Use REST for public and cross-team integration where readability, caching, and evolvability matter. Use gRPC for high-throughput internal calls that require low latency and strong typing, such as pricing or recommendation services. Use events for workflows that cross boundaries or for read model fan-out. Events should be factual statements (“OrderPlaced”, “PaymentCaptured”), immutable, and versioned. Consumers must be idempotent and tolerant of reordering. Keep the event catalog discoverable with schemas and example payloads.

5) Consistency models and sagas
Accept that cross-service transactions are rare. Use sagas to coordinate multi-step flows with compensations. Begin with choreography (services reacting to events). Move to orchestration when visibility, audit, or complex compensations are required. Timeouts, retries with jitter, and dead-letter queues are non-negotiable. Expose state machines and correlation identifiers to aid support and debugging.

6) Failure isolation, backpressure, and time budgets
Protect the system from correlated failure. Enforce timeouts on all calls. Apply bulkheads and per-route concurrency limits so a slow dependency does not starve unrelated traffic. Add circuit breakers and degrade gracefully with cached or partial responses. For asynchronous work, use bounded queues and backpressure. Document time budgets per request so teams know what they can spend.

7) Observability and governance
Instrument every service with structured logs, metrics, and traces. Propagate correlation identifiers across REST, gRPC, and events. Track rate, errors, duration, queue depth, and consumer lag. Create golden dashboards per domain and set service level objectives that reflect user journeys. Lightweight governance aligns teams: naming conventions, error envelopes, retry policies, and security baselines. A platform team maintains templates, libraries, and a service catalog so new services start healthy.

8) Security and multi-tenancy
Authenticate at the edge, authorize in each service. Include tenant identifiers in tokens and event payloads. Scope caches and indexes by tenant. Guard public REST with rate limits and abuse detection. For internal traffic, use mutual transport layer security and signed tokens with short lifetimes. Encrypt sensitive fields at rest and in transit.

9) Delivery and evolution
Automate everything: schema checks, contract tests, linting, dependency scanning, and performance smoke tests. Use progressive delivery with canaries. Keep one service per repository or a well-structured monorepo with clear ownership. Publish library updates and contract changes through a change log and upgrade playbooks. Make rollbacks safe and regular.

By centering domains, enforcing data ownership, publishing high quality API contracts, and selecting REST, gRPC, and event patterns deliberately, you gain independent deployability without chaos. The result is a microservices architecture that scales in traffic and in teams.

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Table

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Common Mistakes

• Splitting by technical layers, creating a distributed monolith with chatty REST calls.
  
• Sharing a database or tables across services, breaking data ownership and coupling deployments.
  
• Publishing unversioned API contracts or making silent breaking changes.
  
• Using synchronous calls for every interaction instead of events and read models.
  
• Omitting idempotency, retries, and dead-letter queues, so consumers duplicate effects.
  
• Ignoring timeouts and circuit breakers, allowing slow dependencies to cascade.
  
• No event catalog or schema registry, leaving payloads tribal and inconsistent.
  
• Weak observability: missing traces, no correlation identifiers, and no consumer lag metrics.
  
• Over-centralized governance that blocks teams, or zero governance that invites drift.
  
  

Sample Answers (Junior / Mid / Senior)

Junior:
“I would group features into services that own their data and expose simple REST endpoints. Each service would publish events when state changes so others can update their views. I would version API contracts and avoid shared tables.”

Mid:
“My microservices architecture defines bounded contexts with single data ownership. Public traffic uses REST with OpenAPI, internal low-latency paths use gRPC with Protobuf, and cross-service workflows use events with an outbox. Consumers are idempotent with retries and dead-letter queues. Contracts evolve additively and are protected by consumer-driven tests.”

Senior:
“I align team boundaries to domains and treat contracts as products. Each service is the system of record and emits versioned events. I select REST, gRPC, or events per interaction, enforce time budgets and bulkheads, and drive sagas for multi-step flows. Observability spans traces and lag. Governance is light but strict on versioning, security, and reliability.”

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Evaluation Criteria

A strong answer frames microservices architecture around domain-driven boundaries, strict data ownership, and explicit API contracts. It should assign REST to public and coarse interactions, gRPC to internal low-latency paths, and events to decouple workflows and read models. It should cover additive versioning, consumer-driven contract tests, sagas, idempotency, retries, and dead-letter queues. It should address timeouts, circuit breakers, and bulkheads, plus observability with tracing and correlation identifiers. Red flags include shared databases, unversioned contracts, synchronous everything, and missing resilience or telemetry.

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Preparation Tips

• Map domains and choose service boundaries that minimize cross-calls.
  
• Assign data ownership and design outbox events for each entity.
  
• Define API contracts: OpenAPI for REST, Protobuf for gRPC; publish examples and change logs.
  
• Create a small saga using events with idempotent consumers, retries, and a dead-letter queue.
  
• Add timeouts, circuit breakers, and bulkheads; write a failure injection test.
  
• Enable tracing with correlation identifiers that propagate across REST, gRPC, and events.
  
• Set service level objectives and dashboards for rate, errors, duration, and consumer lag.
  
• Practice additive versioning: introduce a field, support both shapes, deprecate later.
  
• Prepare a runbook for rollbacks, replaying events, and pausing consumers safely.
  
  

Real-world Context

A retailer moved from a shared database to data ownership per service with an outbox. Orders emitted “OrderPlaced” and “OrderPaid”; Inventory consumed them to reserve stock. Public traffic stayed REST, while pricing queries switched to gRPC for lower latency. Reporting abandoned live joins and built projections from events. When Payments suffered a third-party slowdown, circuit breakers and bulkheads prevented a cascade; Orders served cached status and queued retries. Additive contract changes landed safely because consumer-driven tests ran in continuous integration. With tracing, the team cut time to diagnose from hours to minutes. The microservices architecture delivered faster features and steadier operations.

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Key Takeaways

• Define service boundaries by domain and enforce single data ownership.
  
• Treat API contracts as products with additive versioning and consumer tests.
  
• Use REST, gRPC, and events deliberately to balance coupling and latency.
  
• Coordinate cross-service flows with sagas, idempotency, retries, and dead-letter queues.
  
• Guard reliability with timeouts, circuit breakers, bulkheads, and end-to-end observability.
  
  

Practice Exercise

Scenario:
You are designing a checkout platform with Catalog, Cart, Orders, Payments, and Inventory. Traffic is high, latency targets are tight, and multiple teams will ship features independently.

Tasks:

1. Draw service boundaries and assign data ownership for products, carts, orders, payments, and stock. For each entity, specify which events the owning service will publish.
   
2. Define API contracts: public REST endpoints for browsing and checkout, internal gRPC methods for pricing and availability, and an event schema catalog for “CartUpdated”, “OrderPlaced”, “PaymentCaptured”, and “StockReserved”. Describe versioning and deprecation rules.
   
3. Design workflows: a saga for order placement that reserves stock, charges payment, confirms order, or compensates by releasing stock and refunding. State how timeouts, retries with jitter, and dead-letter queues are handled.
   
4. Specify resilience: per-route time budgets, circuit breakers around Payments, bulkheads so Cart and Search remain responsive during incidents.
   
5. Plan observability: correlation identifiers across REST, gRPC, and events; dashboards for rate, errors, duration, queue lag, and reservation failures.
   
6. Provide rollout and governance: consumer-driven contract tests in continuous integration, canary deploys, and a change log. Include a playbook to roll back a bad version and to replay events when a consumer is fixed.
   
   

Deliverable:
A concise blueprint and runbook demonstrating a pragmatic microservices architecture with clear boundaries, strong API contracts, explicit data ownership, and balanced REST, gRPC, and event communication.