서비스 메시를 활용한 gRPC 트래픽 관리

As gRPC microservices multiply, cross-cutting concerns — routing, retries, mTLS, observability — clutter every service. A service mesh moves these into the platform layer.

A mesh injects a sidecar proxy (e.g. Envoy) next to each pod. All gRPC traffic flows through it, so the proxy can apply policy without changing app code.

Two halves:

• Data plane: the proxies that move traffic
• Control plane: configures the proxies (Istiod in Istio)

You write high-level rules; the control plane programs each proxy.

Plain L4 load balancers pin all streams of a connection to one backend, unbalancing gRPC. The mesh proxy is L7 and balances per-request, spreading RPCs evenly.

An Istio VirtualService routes traffic by rules — host, headers, or weights. Here all traffic goes to the v1 subset.

Split traffic by percentage to roll out a new version gradually and watch its error rate before shifting more.

The mesh can retry failed RPCs without app code. Configure attempts and per-try timeout, and limit retries to safe (idempotent) methods.

Set request timeouts and outlier detection so unhealthy backends are ejected automatically, protecting the system from cascading failures.

The mesh issues and rotates certificates, encrypting all service-to-service gRPC with mutual TLS automatically — no app changes required.

Because all traffic passes through proxies, the mesh emits metrics, distributed traces, and access logs for every gRPC call, feeding dashboards like Kiali and Grafana.

Meshes add value but also cost:

• Extra latency per hop via the sidecar
• Higher resource usage
• Operational complexity

Adopt when service count and policy needs justify it.

You learned mesh traffic management:

• Sidecar proxies apply policy without app changes
• L7 proxies load-balance gRPC per request
• VirtualService rules enable canary releases by weight
• Built-in retries, timeouts, circuit breaking, and mTLS
• Free observability — at the cost of latency and complexity