How does this help with service resilience?

It includes ready-to-use implementation patterns for circuit breakers, request deduplication, and load balancing to prevent cascading failures in distributed systems.

Does this skill support both HTTP and gRPC?

Yes, it provides comprehensive patterns for building HTTP services using Axum and Hyper, as well as type-safe gRPC services using Tonic.

Can I use this for database connection management?

Yes, it provides specific guidance and code patterns for efficient connection pooling using tools like deadpool-postgres within the Tokio runtime.

Is it suitable for production environments?

Absolutely. The patterns focus on production-grade requirements like tracing, structured logging, health checks, and graceful error handling.

Tokio Networking Patterns

Name: Tokio Networking Patterns
Author: geoffjay

bygeoffjay

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Desarrollo de API

Implements production-grade network services and resilient patterns using the Tokio ecosystem, Hyper, Tonic, and Tower.

Acerca de

This skill provides a comprehensive library of networking patterns for building high-performance, resilient services in Rust. It offers standardized implementation logic for Axum-based HTTP services, Tonic-powered gRPC APIs, and Tower middleware composition. From sophisticated connection pooling and robust health checks to advanced resilience patterns like circuit breakers and request deduplication, this tool guides you through the complexities of asynchronous networking to ensure your Rust applications are scalable and production-ready.

Características Principales

Efficient connection pooling patterns for high-concurrency databases
Modular Axum HTTP service structures with state management
Type-safe gRPC service implementation using Tonic and Protobuf
Advanced Tower middleware stacks for tracing, rate limiting, and timeouts
Service resilience patterns including circuit breakers and load balancing
2 GitHub stars

Casos de Uso

Implementing standardized health, readiness, and liveness probes for Kubernetes
Adding observability and resource management layers to complex network services
Building high-throughput microservices using gRPC and REST protocols

Acerca de

Características Principales

Efficient connection pooling patterns for high-concurrency databases
Modular Axum HTTP service structures with state management
Type-safe gRPC service implementation using Tonic and Protobuf
Advanced Tower middleware stacks for tracing, rate limiting, and timeouts
Service resilience patterns including circuit breakers and load balancing
2 GitHub stars

Casos de Uso

Implementing standardized health, readiness, and liveness probes for Kubernetes
Adding observability and resource management layers to complex network services
Building high-throughput microservices using gRPC and REST protocols