A beginner’s guide to Phantom Read anomaly

Introduction Database transactions are defined by the four properties known as ACID. The Isolation Level (I in ACID) allows you to trade off data integrity for performance. The weaker the isolation level, the more anomalies can occur, and in this article, we are going to describe the Phantom Read phenomenon.

Spring request-level memoization

Introduction Memoization is a method-level caching technique for speeding-up consecutive invocations. This post will demonstrate how you can achieve request-level repeatable reads for any data source, using Spring AOP only. Spring Caching Spring offers a very useful caching abstracting, allowing you do decouple the application logic from the caching implementation details. Spring Caching uses an application-level scope, so for a request-only memoization we need to take a DIY approach.

How does Hibernate guarantee application-level repeatable reads

Introduction In my previous post I described how application-level transactions offer a suitable concurrency control mechanism for long conversations. All entities are loaded within the context of a Hibernate Session, acting as a transactional write-behind cache. A Hibernate persistence context can hold one and only one reference to a given entity. The first level cache guarantees session-level repeatable reads. If the conversation spans over multiple requests we can have application-level repeatable reads. Long conversations are inherently stateful so we can opt for detached objects or long persistence contexts. But application-level repeatable reads… Read More

A beginner’s guide to database locking and the lost update phenomena

Introduction A database is highly concurrent system. There’s always a chance of update conflicts, like when two concurring transactions try to update the same record. If there would be only one database transaction at any time then all operations would be executed sequentially. The challenge comes when multiple transactions try to update the same database rows as we still have to ensure consistent data state transitions. The SQL standard defines three consistency anomalies (phenomena): Dirty reads, prevented by Read Committed, Repeatable Read and Serializable isolation levels Non-repeatable reads, prevented by Repeatable Read… Read More

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