Isolation Levels
Learn how SQL isolation levels prevent concurrency issues while balancing consistency and performance.
We'll cover the following...
Imagine an online ticketing system for a blockbuster movie premiere.
There’s only one seat left, and two customers, Alex and Ben, click the “Buy Now” button simultaneously. In a poorly designed system, both transactions might check the seat’s availability, see that it’s open, and proceed to sell it.
The result?
Two customers have tickets for the same seat, leading to confusion and frustration. This is precisely the kind of chaos that transaction isolation is designed to prevent. It ensures that even when thousands of users are interacting with the database simultaneously, their operations don't corrupt the data or lead to illogical outcomes.
By the end of this lesson, we will have a solid understanding of what isolation levels are and how they help us manage this controlled chaos.
Specifically, in this lesson, we will learn about:
The common concurrency problems: dirty reads, non-repeatable reads, and phantom reads.
The four standard SQL isolation levels are designed to prevent these problems.
The trade-offs between data consistency and system performance for each level.
How to choose and set the appropriate isolation level for our transactions.
Let’s get started and explore the mechanisms that ensure our data remains consistent and reliable in a busy, multi-user environment.
Isolation levels
We’ve learned that the Isolation property in ACID ensures that concurrent transactions don’t interfere with one another. It makes transactions appear as if they’re executed one after another, even though they actually run concurrently.
However, enforcing perfect isolation can be resource-intensive and may slow down the database.
In practice, perfect isolation isn’t always necessary. For instance, an application that only reads blog posts has very different consistency requirements than a banking application that transfers funds. To manage this, database systems offer several isolation levels, each providing a different balance between consistency and performance.
Lower isolation levels allow more concurrency and improve performance, but increase the risk of data anomalies.
Higher isolation levels provide stronger data consistency but may ...