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.

Even in the 80’s, before Facebook knew everything there was to know about us, we as an industry had vast reams of data we needed to be able to answer questions about. To deal with this, data analysts were starting to flex their JOIN muscles in increasingly creative ways. But back in that day and age, we had neither machine learning nor rooms full of underpaid Excel-proficient interns to save us from problems we didn’t understand; we were on our own.

The development of the relational model heralded a big step forward for the world of databases. A few years later, SQL introduced a rich vocabulary for data manipulation: filters, projections, and—most importantly—the mighty join. Joins meant that analysts could construct new reports without having to interact with those eggheads in engineering, but more importantly, the existence of complex join queries meant that theoreticians had an interesting new NP-hard problem to fawn over for the next five decades.

We are excited to announce support for JSON in our 2.0 release (coming in April) and available now via our most recent 2.0 Beta release. Now you can use both structured and semi-structured data within the same database. No longer will you need to sacrifice ACID guarantees, accuracy, or the ability to scale in order to use multiple data models within the same database. This post will explain how we implemented JSON and give you a few examples of how JSON can be used to model your data.