PostgreSQL triggers and isolation levels
Introduction In this article, we are going to see how the PostgreSQL isolation levels guarantee read and write consistency when executing database triggers. While relational database systems provide strong data integrity guarantees, it’s very important to understand how the underlying transactional engine works in order to choose the right design for your data access layer.
How does MVCC (Multi-Version Concurrency Control) work
Introduction In this article, I’m going to explain how the MVCC (Multi-Version Concurrency Control) mechanism works using PostgreSQL as a reference implementation. In Concurrency Control theory, there are two ways you can deal with conflicts: You can avoid them, by employing a pessimistic locking mechanism (e.g. Read/Write locks, Two-Phase Locking) You can allow conflicts to occur, but you need to detect them using an optimistic locking mechanism (e.g. logical clock, MVCC) Because MVCC (Multi-Version Concurrency Control) is such a prevalent Concurrency Control technique (not only in relational database systems, in this article,… Read More
A beginner’s guide to the Write Skew anomaly, and how it differs between 2PL and MVCC
Introduction Unlike SQL Server which, by default, relies on the 2PL (Two-Phase Locking) to implement the SQL standard isolation levels, Oracle, PostgreSQL, and MySQL InnoDB engine use MVCC (Multi-Version Concurrency Control), so handling the Write Skew anomaly can differ from one database to the other. However, providing a truly Serializable isolation level on top of MVCC is really difficult, and, in this post, I’ll demonstrate that it’s very difficult to prevent the Write Skew anomaly without resorting to pessimistic locking.
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 read, prevented by Repeatable Read… Read More
