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The article explains how distributed applications handle failures through business compensation mechanisms—defining rollback and retry strategies, consistency models, and practical considerations to maintain eventual consistency across multiple services.
This article explains the concepts of eventual consistency, hinted handoff queues and anti‑entropy in distributed databases, illustrates how they work with XDB Enterprise examples, and shows how AE restores data integrity after node failures or network partitions.
This article explains consistency in distributed systems, distinguishing strong and eventual consistency, outlines the CAP and FLP impossibility theorems, and details common consensus mechanisms such as two‑phase commit, three‑phase commit, and the Paxos algorithm.
This article explains how distributed applications handle failures through business compensation mechanisms, detailing rollback and retry strategies, consistency models like ACID and BASE, and practical design considerations for microservice architectures.
This article compiles a technical interview recap covering MySQL MVCC, atomicity and persistence mechanisms, operating‑system page cache and deadlock concepts, TCP reliability and flow‑control, Redis persistence and clustering, as well as distributed transaction and consensus fundamentals.
This article explains what logs are, when to record them, their value in large‑scale architectures, and how to build effective logging, metrics, and tracing platforms using tools such as ELK, Prometheus, and SkyWalking, while also presenting good and bad logging practices and sample batch‑log retrieval code.
This article explains the basic concepts, dimensions, and typical algorithms of rate limiting, discusses various implementation strategies such as token bucket, leaky bucket, and sliding window, and reviews practical solutions using Nginx, Guava, Redis, Sentinel, and Tomcat for both single‑node and distributed environments.
This article explains the concept of consistency in distributed systems, distinguishes strong and weak (eventual) consistency, outlines typical use cases and challenges, and reviews key protocols such as 2‑Phase Commit, 3‑Phase Commit, Paxos, and Raft, while referencing the FLP and CAP theorems.
This article explains what logs are, why they are essential in large‑scale distributed architectures, the capabilities required of log‑operation tools, and how logs integrate with metrics and tracing within APM and observability frameworks, illustrated with practical examples and Go code for batch log queries.
The article examines logging’s critical role in distributed systems, detailing its purpose, severity levels, and value for debugging, performance, security, and auditing, while highlighting challenges of inconsistent formats and traceability, and reviewing observability pillars, ELK and tracing tools, and practical implementation best practices.