This article explains why database transactions are a core backend skill, breaks down the four ACID properties, clarifies dirty read, non‑repeatable read and phantom read, compares the four isolation levels, and offers practical guidelines for choosing the right level in production.
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.
This article explains what database transactions are, why they matter, details MySQL’s transaction commands, the ACID properties, isolation levels, lock types, MVCC, how to use InnoDB, handle errors, employ savepoints, and provides concrete SQL examples for creating, committing, rolling back, and managing transactions.
This article explains what OLTP (Online Transaction Processing) is, outlines its key characteristics, and details how MySQL—through ACID‑compliant transactions, the InnoDB storage engine, various indexing strategies, fast locking mechanisms, query optimization, and high‑availability features—effectively supports high‑concurrency, low‑latency transactional workloads.
This article explains the concept and importance of MySQL database transactions, describes the ACID properties, shows how to use transaction control statements and savepoints, and demonstrates each isolation level (read uncommitted, read committed, repeatable read, serializable) with practical SQL examples.
This article explains how MySQL's SELECT ... FOR UPDATE statement acquires either row-level or table-level locks depending on the presence of indexes or primary keys, and demonstrates the behavior with multiple transaction examples and a sample table definition.
This article explains the concept of database transactions, the ACID properties, how MySQL implements transactions with InnoDB, how to manage autocommit, the essential transaction control statements, a practical example using savepoints, and the four isolation levels supported by MySQL.
This article explains how a plain SELECT does not lock data, while SELECT … FOR UPDATE adds a pessimistic lock whose scope—row or table—depends on whether the query uses an indexed or primary‑key column, and demonstrates the behavior with SQL examples and transaction scenarios.
The article explains how Airbnb’s payment platform uses a generic idempotency library called Orpheus, combined with Java lambda‑driven transaction composition, to guarantee data consistency, avoid double charges, and handle retries in a low‑latency micro‑service architecture.
This article presents a chronological overview of Optimistic Concurrency Control (OCC), covering its early theoretical foundations, key research papers, prototype implementations such as MVCC+OCC+2PC and Hekaton, and its adoption in modern distributed NewSQL databases like Megastore, F1, and MaaT, highlighting both advantages and challenges.
This article explains the concept of database transactions, their ACID properties, and provides a step‑by‑step Node.js example using the Bookshelf.js ORM to safely perform multi‑table operations within a transaction.
This article explains how atomic operations, Java concurrency primitives, database transaction mechanisms, and scalability techniques combine to ensure data consistency and high performance in large‑scale e‑commerce systems under extreme traffic loads.
