Introduction In this article, we are going to see why the default strong consistency guarantees offered by YugabyteDB allow you to design applications that are more resilient than when using traditional relational database systems. If you’re new to YugabyteDB, check out this article first, in which I explain what YugabyteDB is and why you should consider using it.
Introduction It’s hard to imagine that a race condition bug could lead to the bankruptcy of a given online service, isn’t it? In this article, I’m going to show you how a race condition led to the bankruptcy of Flexcoin in 2014.
Introduction In this article, I’m going to explain what is the difference between optimistic and pessimistic locking, as well as when you should employ one or the other concurrency control strategies.
Introduction In my previous post I demonstrated how you can scale optimistic locking through write-concerns splitting. Version-less optimistic locking is one lesser-known Hibernate feature. In this post, I’ll explain both the good and the bad parts of this approach. Version-less optimistic locking Optimistic locking is commonly associated with a logical or physical clocking sequence, for both performance and consistency reasons. The clocking sequence points to an absolute entity state version for all entity state transitions. To support legacy database schema optimistic locking, Hibernate added a version-less concurrency control mechanism. To enable this… Read More
Introduction All database statements are executed within the context of a physical transaction, even when we don’t explicitly declare transaction boundaries (BEGIN/COMMIT/ROLLBACK). Data integrity is enforced by the ACID properties of database transactions. Logical vs Physical transactions A logical transaction is an application-level unit of work that may span over multiple physical (database) transactions. Holding the database connection open throughout several user requests, including user think time, is definitely an anti-pattern. A database server can accommodate a limited number of physical connections, and often those are reused by using connection pooling. Holding… Read More
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 read, prevented by Repeatable Read… Read More