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Microservices Architecture

⬢ 層級 1技術
高
薪資影響
10 個月
學習時長
困難
難度
12
職業
一覽

Microservices is the architectural pattern for building large-scale systems as independent services deployed separately. Career path: Practitioner (work on one service, REST APIs, Docker, $120-170k) → Designer (decompose monoliths, service mesh, saga patterns, distributed transactions, $145-200k) → Architect (company-wide platforms, migration strategy, cost optimization, $180-300k+). Trade-offs: operational complexity vs independent scaling and team autonomy. Prerequisites: distributed-systems, backend-development, system-design-architecture, devops-ci-cd. Modern stack: Kubernetes + service mesh (Istio/Linkerd) + event streaming (Kafka) + distributed tracing (Jaeger).

什麼是 Microservices Architecture

Microservices is the architectural pattern for building large-scale systems as independent services that communicate via APIs, each with its own database, deployment pipeline, and team ownership. Unlike monolithic applications (one codebase, one database, deployed together), microservices decompose the system along business domains: a payment service, inventory service, order service, each running separately, scaling independently, and deployable without coordination. Netflix pioneered the pattern in 2009 when a single shared database became a bottleneck; today, Uber, Amazon, and Meta run 500+ interdependent microservices. The trade-off is clear: operational complexity increases dramatically (distributed debugging, network failures, data consistency), but you gain independent scaling, team autonomy, and technology flexibility. Microservices is not about the size of the service (common myth: "should be <100 lines"). It's about bounded contexts (Domain-Driven Design) and organizational alignment: one service per team, one team per service, different scaling and deployment schedules.

🔧 工具與生態
KubernetesDockerIstioEnvoyKongLinkerdgRPCKafkaOpenTelemetryJaegerConsulHelm

💰 各地區薪資

地區初級中級高級
USA$130,000$185,000$260,000
UK£80,000£125,000£170,000
EU€85,000€130,000€185,000
CANADACA$135,000CA$190,000CA$270,000

❓ 常見問題

When should I NOT use microservices?
Microservices add operational complexity (deployment, monitoring, debugging across services). Avoid if: (1) small team (<10 engineers, monolith is faster), (2) low concurrency (scaling only one part isn't valuable), (3) tight latency budgets (network calls add milliseconds), (4) simple domain (CRUD app ≠ needs services). Rule of thumb: start with monolith, split when team/traffic justifies the complexity.
Should I always start with a monolith?
Yes, 'monolith first' is the industry consensus (Sam Newman, Martin Fowler). Microservices shine when you outgrow a monolith: multiple teams, different scaling needs, separate deployment cycles. At startup stage (< 50 engineers), a well-structured monolith is faster than coordinating 10+ services. Netflix started monolith, migrated to microservices at ~300 engineers. Airbnb, Stripe, etc. kept monoliths much longer than expected.
How do I handle distributed transactions across services?
ACID transactions don't exist across services (you can't lock rows in another service's DB). Use Saga pattern instead: (1) orchestration-based (centralized coordinator calls services sequentially) or (2) choreography-based (services emit events, other services react). Example: Order Service reserves inventory (Inventory Service), charges payment (Payment Service), if payment fails publish CompensationRequested, Inventory Service releases reservation. Eventual consistency: accept that orders may be temporarily inconsistent (payment pending) but converge within seconds/minutes.
How do I define service boundaries?
Use domain-driven design (DDD): each service = one business capability (User Management, Order Processing, Payment). Boundaries = what would require different teams or scaling independently. Test: can two services be deployed at different schedules without coordination? If not, they're too coupled. Anti-pattern: 'Service per database table' (leads to chatty services). Pro tip: service boundaries emerge over time, start with obvious splits (auth service, API service), refine as you learn.
How do I ensure observability in a distributed system?
Three pillars: (1) Logs (ELK, Splunk, CloudWatch), what happened where. (2) Metrics (Prometheus, Datadog), throughput, latency, error rate per service. (3) Traces (Jaeger, Zipkin, Datadog), follow one request across all services with unique trace ID. Correlation IDs essential: attach X-Trace-ID to every request header so you can join logs/metrics/traces. Without tracing, debugging '500 error in production' = nightmare in 10+ services.
How do I coordinate deployments across microservices?
Don't coordinate, each service deploys independently (that's the point). Use blue-green deployments (new version runs alongside old, flip traffic instantly), canary deployments (1% traffic to new version, monitor, 100% if good), or feature flags (deploy code but disable feature until ready). Database migrations trickier: deploy backward-compatible migrations first (add column but don't use it), then deploy code using new column, then remove old column in next release. Tools: Kubernetes handles this via rolling updates.
What organizational changes come with microservices (Conway's Law)?
System architecture mirrors organization structure (Conway's Law). Microservices = one service per team. Requires: (1) clear ownership (who's on-call for the payment service?), (2) async communication (don't wait for other team's code review), (3) clear APIs and SLAs (service X promises 99.9% uptime, <100ms latency). At scale, you need: platform teams (provide Kubernetes, monitoring, CI/CD), infrastructure teams (run the platform). Small team adopting microservices without org change = suffering (meetings to deploy together defeats the purpose).

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