gRPC for Service-to-Service Communication

gRPC for Service-to-Service Communication#

gRPC is a high-performance RPC framework that uses HTTP/2 for transport and Protocol Buffers (protobuf) for serialization. For service-to-service communication within a microservices architecture, gRPC offers significant advantages over REST: strongly typed contracts, efficient binary serialization, streaming support, and code generation in every major language.

Why gRPC for Internal Services#

REST with JSON is the standard for public APIs. For internal service-to-service calls, gRPC is often the better choice.

Message Queue Selection and Patterns

Message Queue Selection and Patterns#

Every microservice architecture eventually needs asynchronous communication. Synchronous HTTP calls between services create tight coupling, cascading failures, and latency chains. Message queues decouple producers from consumers, absorb traffic spikes, and enable event-driven workflows. The hard part is picking the right one.

Core Concepts That Apply Everywhere#

Before comparing specific systems, understand the delivery guarantees they can offer:

  • At-most-once: The message might be lost, but it is never delivered twice. Fast, no overhead, acceptable for metrics or logs where occasional loss is tolerable.
  • At-least-once: The message is guaranteed to arrive, but might arrive more than once. The consumer must handle duplicates (idempotency). This is the most common choice.
  • Exactly-once: The message arrives exactly once. This is extremely hard to achieve in distributed systems. Kafka offers it within its ecosystem via transactional producers and consumers, but end-to-end exactly-once across system boundaries requires idempotent consumers anyway.

Ordering matters too. Some systems guarantee order within a partition or queue. Others provide no ordering at all. If your consumers process messages out of order, you need to handle that in application logic or choose a system that preserves order.

Monolith to Microservices: When and How to Decompose

Monolith to Microservices#

The decision to break a monolith into microservices is one of the most consequential architectural choices a team makes. Get it right and you unlock independent deployment, team autonomy, and targeted scaling. Get it wrong and you trade a manageable monolith for a distributed monolith – all the complexity of microservices with none of the benefits.

When to Stay with a Monolith#

Microservices are not an upgrade from monoliths. They are a different set of tradeoffs. A well-structured monolith is the right choice in many situations.

Rate Limiting Implementation Patterns

Rate Limiting Implementation Patterns#

Rate limiting controls how many requests a client can make within a time period. It protects services from overload, ensures fair usage across clients, prevents abuse, and provides a mechanism for graceful degradation under load. Every production API needs rate limiting at some layer.

Algorithm Comparison#

Fixed Window#

The simplest algorithm. Divide time into fixed windows (e.g., 1-minute intervals) and count requests per window. When the count exceeds the limit, reject requests until the next window starts.

Service-to-Service Authentication and Authorization

Service-to-Service Authentication and Authorization#

In a microservice architecture, services communicate over the network. Without authentication, any process that can reach a service can call it. Without authorization, any authenticated caller can do anything. Zero-trust networking assumes the internal network is hostile and requires every service-to-service call to be authenticated, authorized, and encrypted.

Mutual TLS (mTLS)#

Standard TLS has the client verify the server’s identity. Mutual TLS adds the reverse – the server also verifies the client’s identity. Both sides present certificates. This provides three things: encryption in transit, server authentication, and client authentication.