A fleeting mayfly in the world, a single grain in the boundless sea.
The article explains how databases use Multi-Version Concurrency Control, Undo Logs, ReadViews, snapshot reads, and both pessimistic and optimistic locking to allow readers to see a stable historical version while writers modify data without causing inconsistency or performance loss.
The article uses a simple money‑transfer scenario to introduce database transactions, explains the four ACID properties, illustrates common concurrency anomalies such as dirty reads, non‑repeatable reads and phantom reads, and outlines the four isolation levels with their trade‑offs and default settings in MySQL and Oracle.
The article explains how database indexes act like a book's table of contents, reducing full‑table scans and disk I/O by using B+Tree structures, and compares clustered, non‑clustered, and covering indexes while highlighting their benefits and trade‑offs.
This article walks through a complete e‑commerce request path, illustrating how an API gateway, load balancer, service registry, Redis cache, Kafka queue and the database cooperate, and explains where CAP/BASE, distributed transaction patterns and service‑governance mechanisms fit in the overall diagram.
The article explains how using a message queue such as Kafka lets an order service return quickly by handling non‑critical tasks like SMS, points, and logging asynchronously, thereby improving latency, decoupling services, and smoothing traffic spikes while also covering Kafka's core concepts and trade‑offs.
The article explains how caching, especially with Redis, reduces database load in high‑traffic scenarios, but also details three cache failure modes—avalanche, penetration, and breakdown—and provides concrete mitigation techniques to keep systems stable.
The article explains how microservice decomposition introduces challenges such as locating services, routing external requests, balancing load, isolating failures, limiting traffic, and degrading non‑essential features, and it details the six core mechanisms—service discovery, API gateway, load balancing, circuit breaking, rate limiting, and degradation—that together form a complete service‑governance solution.
The article explains why a single order operation can involve multiple services and databases, introduces the challenges of distributed transactions, and compares three common solutions—Two‑Phase Commit, TCC, and Saga—detailing their mechanisms, strengths, weaknesses, and suitable scenarios.
The article explains the CAP theorem and BASE model by walking through a concrete scenario of two data centers losing network connectivity, showing how architects must choose between consistency and availability and illustrating typical CP and AP use cases.
The author describes creating a lightweight local dashboard that monitors Binance Alpha, crypto and US stocks, records purchase quantities and amounts, auto‑calculates averages and profits, and evolves through concrete AI prompts and iterative UI tweaks, all stored in a local JSON file.
