Tagged articles

9 articles

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Apr 8, 2026 · Cloud Native

Go Full‑Stack Mastery: From High‑Concurrency Order Systems to Cloud‑Native Production

This comprehensive guide walks you through building a production‑grade Go order service—from understanding the high‑concurrency business scenario and Go’s runtime advantages, to designing microservice architecture, handling idempotency, outbox patterns, observability, Kubernetes deployment, incident response, and testing strategies.

Distributed ConsistencyMicroservicescloud-native

0 likes · 54 min read

Go Full‑Stack Mastery: From High‑Concurrency Order Systems to Cloud‑Native Production

Ray's Galactic Tech

Jan 18, 2026 · Backend Development

Designing a Robust Transaction System: From Domain Modeling to Distributed Consistency

This article explores how to build a reliable, scalable transaction module for e‑commerce and finance, covering business requirement analysis, domain modeling, state‑machine design, layered microservice architecture, order creation and payment flows, idempotency, anti‑oversell mechanisms, performance tuning, monitoring, and evolution strategies.

BackendDistributed ConsistencyIdempotency

0 likes · 10 min read

Designing a Robust Transaction System: From Domain Modeling to Distributed Consistency

Tech Freedom Circle

Jan 12, 2026 · Backend Development

Why Sentinel Fails for Per‑User Hourly Limits and How Redis + Lua Solves It

The article compares four user‑level rate‑limiting approaches—Sentinel hotspot parameters, Sentinel ordinary flow control with cluster mode, Redis ZSet + Lua scripts, and Guava RateLimiter—explaining why Sentinel is unsuitable for long‑window low‑frequency limits and why Redis + Lua is the optimal solution for high‑traffic e‑commerce scenarios.

Distributed ConsistencyLong WindowLua

0 likes · 26 min read

Why Sentinel Fails for Per‑User Hourly Limits and How Redis + Lua Solves It

Code Wrench

Dec 14, 2025 · Backend Development

How to Keep a Go System Stable: Middleware, Redis, MQ, and Beyond

This article breaks down the core concerns interviewers have for senior Go engineers—system stability, middleware pitfalls, Redis risk isolation, message‑queue buffering, distributed consistency, configuration management, ID design, and avalanche prevention—offering concrete insights and practical interview answers.

Distributed ConsistencyGoInterview Preparation

0 likes · 11 min read

How to Keep a Go System Stable: Middleware, Redis, MQ, and Beyond

dbaplus Community

Jul 10, 2025 · Backend Development

Ensuring Data Consistency in Microservices: From CAP to Transactional Messaging

This article examines the limitations of the CAP theorem in modern microservice architectures, introduces the BASE model for eventual consistency, and evaluates practical solutions such as write RPC retries, synchronous and transactional messaging, and local message tables to achieve reliable data consistency across distributed services.

BASE theoryCAP theoremDistributed Consistency

0 likes · 11 min read

Ensuring Data Consistency in Microservices: From CAP to Transactional Messaging

DeWu Technology

Mar 26, 2025 · Fundamentals

Consistency Challenges and Solutions in Distributed Systems: CAP, BASE, RPC, and Messaging

To address consistency problems in distributed systems, the article explains CAP and BASE trade‑offs, shows how transactional RPC and messaging—using retries, RocketMQ two‑phase commits, Spring @TransactionalEventListener, or a local message log—can ensure atomic updates, and compares their reliability, latency, and performance impacts.

BASE theoryCAP theoremDistributed Consistency

0 likes · 12 min read

Consistency Challenges and Solutions in Distributed Systems: CAP, BASE, RPC, and Messaging

ITPUB

Sep 27, 2022 · Fundamentals

Mastering Distributed Consistency: From ACID to TCC and Beyond

This article explains the fundamentals of distributed consistency, covering consistency levels, ACID and CAP principles, BASE theory, and practical protocols such as 2PC, 3PC, and TCC, while also discussing real‑world patterns like cache consistency and reliable messaging.

2PC3PCACID

0 likes · 20 min read

Mastering Distributed Consistency: From ACID to TCC and Beyond

Architect

Jun 3, 2020 · Backend Development

Elasticsearch Distributed Consistency Analysis: Data Flow, PacificA Algorithm, Sequence Numbers and Checkpoints

This article provides a detailed examination of Elasticsearch's distributed consistency mechanisms, covering the shard write path, the PacificA replication algorithm, the role of SequenceNumber and Checkpoint, and a comparison of ES's implementation with the original algorithm, based on version 6.2.

BackendCheckpointDistributed Consistency

0 likes · 23 min read

Elasticsearch Distributed Consistency Analysis: Data Flow, PacificA Algorithm, Sequence Numbers and Checkpoints

Java Backend Technology

Jan 26, 2019 · Backend Development

Hidden Pitfalls of Adding a Message Queue to Your Architecture

While message queues can decouple services and smooth traffic spikes, they also introduce availability risks, stability problems like message loss or duplication, and distributed consistency challenges that require careful high‑availability design and reliable transaction strategies.

Distributed ConsistencyMQMessage Queue

0 likes · 6 min read

Hidden Pitfalls of Adding a Message Queue to Your Architecture