# isolation-level Published articles for isolation-level. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Optimistic Locking Overview DevFeed: [Optimistic Locking Overview]() Original publisher: [Read original article]() Author: somesh sharma Published: 2025-06-19T09:02:40Z Content type: tutorial Language: en Sources: [Housing.com]() Topics: [Concurrency](), [Transactions](), [Database]() Tags: [concurrency](), [concurrency-control](), [database](), [databases](), [isolation-level](), [locks](), [optimistic-locking](), [retry](), [sql](), [stateless](), [transaction-management](), [transactions]() ### AI overview An overview of optimistic locking as a concurrency-control mechanism for database transactions. It explains how version columns detect conflicting updates, when optimistic locking is suitable, and when transaction isolation may make it unnecessary. ### Source excerpt Optimistic locking is a concurrency control mechanism where we assume that multiple transactions can safely access data without conflict, allowing them to proceed without locking the data upfront. Unlike pessimistic locking, where resources are locked to avoid conflicts, optimistic locking allows transactions to proceed without locks and checks for conflicts only when updating the data. If a conflict is detected (e.g., another transaction has already modified the data), the operation fails, and you can retry it. When Should You Use Optimistic Locking? Optimistic locking is ideal for use in scenarios where: Low contention exists: If it's unlikely that multiple users or processes will try to update the same data at the same time, optimistic locking is a good fit. This is especially true in systems where most operations involve reading data rather than writing it. High read-to-write ratio: If your application is mostly about reading data and writing happens less frequently, optimistic locking helps avoid the overhead of locking rows during reads. Non-critical updates: In cases where it's okay to retry a failed update without much impact, optimistic locking is a good choice. When conflicts arise, either the user or the system can simply retry. Stateless operations: Optimistic locking is suitable in stateless environments where holding onto locks across multiple requests or sessions isn't feasible. Long-running transactions: If your transactions take a long time to complete, holding locks during the entire process isn't feasible. Optimistic locking provides flexibility while still maintaining data integrity. How Optimistic Locking Works in Databases In databases, optimistic locking is usually implemented with a version column. Each row in the database has a version field (like a number or timestamp) that gets updated whenever the row is modified. When updating a record, the database checks if the version in the database matches the version the transaction originally read ## What write skew looks like DevFeed: [What write skew looks like]() Original publisher: [Read original article]() Author: Justin Jaffray Published: 2022-03-31T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [Database](), [Transactions](), [Concurrency]() Tags: [concurrency](), [database](), [isolation-level](), [transactions]() ### AI overview An explanatory article develops intuition for write skew and Snapshot Isolation, focusing on how transaction isolation affects correctness under concurrency. It introduces transactions, read and write sets, isolation levels, and the contrast between SERIALIZABLE and Snapshot Isolation. ### Source excerpt Syndication from What Does Write Skew Look Like by Justin Jaffray This post is about gaining intuition for Write Skew, and, by extension, Snapshot Isolation. Snapshot Isolation is billed as a transaction isolation level that offers a good mix between performance and correctness, but the precise meaning of "correctness" here is often vague. In this post I want to break down and capture exactly when the thing called "write skew" can happen. ## PostgreSQL Concurrency: an Article Series DevFeed: [PostgreSQL Concurrency: an Article Series]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2018-08-14T08:49:02Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [Concurrency](), [PostgreSQL](), [Databases](), [consistency](), [Open Source]() Tags: [acid](), [article](), [cache-invalidation](), [caching](), [concurrency](), [consistency](), [database](), [isolation-level](), [locking](), [postgresql](), [series](), [sql](), [transactions](), [triggers]() ### AI overview This article series explains how PostgreSQL handles concurrent access while maintaining data consistency. It covers data modification, isolation levels, locking, ACID transactions, concurrency-oriented data modeling, event processing, materialized views for caching, and triggers for transactionally correct cache maintenance. ### Source excerpt PostgreSQL is a relational database management system. It's even the world's most advanced open source one of them. As such, as its core, Postgres solves concurrent access to a set of data and maintains consistency while allowing concurrent operations. In the PostgreSQL Concurrency series of articles here we did see several aspects of how to handle concurrent use cases of your application design with PostgreSQL. The main thing to remember is that a Database Management System first task is to handle concurrency access to the data for you.