Learn how to build high‑performance, secure gRPC services in Rust using the Tonic framework, covering its core components, advantages, installation steps, project setup, protobuf definition, code generation, server implementation, and client creation with detailed code examples.
This article explains why extending gRPC is necessary, outlines the go‑zero directory structure, describes how go‑zero adapts gRPC through a wrapper generated by goctl, and walks through service initialization and startup, highlighting metrics, etcd registration, interceptors, and health checks.
This article examines common short‑term failure causes in shared‑resource environments, explains gRPC’s retry configuration options and implementation details, and provides practical guidance on setting retry policies, backoff strategies, and status code handling to improve service resilience.
This article explores the fundamentals of load balancing in microservice architectures, detailing both stateless and stateful strategies such as round‑robin, random, hash, and weighted algorithms, and demonstrates how go‑zero and gRPC implement these mechanisms through custom balancers, service discovery, and request scheduling.
This article explains how a gRPC bidirectional streaming read method was added to Ozone to reduce chunk‑by‑chunk request gaps, describes the client‑side implementation, presents single‑ and multi‑threaded performance tests showing roughly 30% faster reads, and outlines future enhancements such as pre‑fetching.
This article explains the motivations behind creating a new distributed scheduling framework, compares existing solutions, and details the design choices—including gRPC communication, protobuf serialization, a custom NameServer for load balancing, and a built‑in message queue with persistence and retry mechanisms—to handle frequent task creation and dynamic parameter changes in a high‑concurrency environment.
The article shares Google SRE veteran Minghua Ye’s insights on App Engine’s evolution, emphasizing the critical role of automatic scalability, distributed locks, service discovery, load balancing, and open‑source tools like gRPC, Protobuf, gflags, glog, and Googletest in building reliable, high‑traffic cloud services.
A mobile app gateway for e‑commerce platforms consolidates API exposure, connection pooling, HTTP/2 and QUIC multiplexing, unified encryption, custom DNS, caching and degradation strategies, while supporting heterogeneous back‑ends via gRPC and proxy services, thereby improving stability, speed, capacity planning and revenue.
The 2018 developer‑tool roundup highlights Elasticsearch for log processing, gRPC for high‑performance RPC, the CNCF ecosystem (Kubernetes, Prometheus, etc.), Python’s AI dominance, cross‑platform Mini‑Programs, VSCode’s plugin‑rich IDE, Vue.js front‑end simplicity, GraphQL’s flexible APIs, and notes a shift toward mobile, cloud‑native infrastructure and commercial open‑source licensing.
The article details how a sudden surge in goroutine and heap usage was traced to repeatedly creating gRPC clients instead of reusing a singleton, leading to blocked goroutines and TCP connections, and explains using pprof, stack traces, and tools like goleak to detect and prevent such leaks.
This comprehensive guide walks you through creating a gRPC user service in Go and PHP, from defining protobuf messages and generating code, implementing server and client stubs, adding client, server, and bidirectional streaming, securing communication with TLS certificates, and managing request deadlines with timeout controls.
The article explains gRPC fundamentals, illustrating service definition in .proto files, synchronous and asynchronous client and server implementations with CompletionQueue and CallData state machines, streaming RPC patterns, HTTP/2 protocol details, metadata handling, and the structure of generated stub and service code.
The article surveys the open‑source microservice ecosystem, comparing RPC‑only frameworks like gRPC, language‑specific solutions such as Spring Cloud, emerging service‑mesh platforms, and Tencent’s multi‑language TARS which integrates full governance features, and explains their architectures, capabilities, and future direction.
The Shopee Games team created a custom Protobuf‑driven API gateway that unifies external HTTP and internal RPC traffic, automatically generates routing, authentication, logging and Swagger from proto annotations, shares capacity across services, and boosts performance and resource utilization for their micro‑service‑based casual gaming platform.
This article recounts the migration of the Core Course (CC) service from a custom Ruby RPC framework to a Go‑based gRPC solution, detailing technical choices, performance bottlenecks, testing strategies, monitoring setup, and the substantial improvements achieved after launch.
This article shares the engineering team’s experience of building a high‑growth, reliable backend for English Fluently, covering inter‑service communication with gRPC, service discovery, Docker‑based deployment, health‑checking, monitoring, autoscaling, Kubernetes orchestration, and multi‑cell availability strategies.
This article shares Laiye Technology's practical experience with Protobuf and gRPC, covering performance benefits, common pitfalls such as naming and compatibility issues, and the engineering solutions they implemented—including naming conventions, review processes, unified CI compilation, automatic client generation, version tagging, and Swagger documentation—to streamline large‑scale microservice development.
This article shares Tubi’s experience using Elixir‑gRPC in production, covering performance optimizations, stability measures, HTTP/2 edge‑case handling, and practical code examples for efficient Protobuf processing; it also discusses Envoy sidecar integration, interceptor usage, and lessons learned from real‑world deployments.
This guide explains the core concepts of TensorFlow Serving, shows how to prepare Docker images, save TensorFlow 2.x models in various formats, configure version policies, warm‑up models, start the service, and invoke it via gRPC or HTTP with complete code examples.
This article explains the core concepts of TensorFlow Serving, shows how to prepare the environment with Docker, convert TensorFlow 2.x models to the SavedModel format, configure version policies, warm‑up the service, and invoke predictions via gRPC or HTTP interfaces.