Hello there! Let’s keep exploring transactions by discussing isolation levels.
An isolation level refers to the degree to which concurrent transactions are isolated from each other.
Consider an inventory management system where two employees update the inventory for a given product simultaneously:
Alice starts a transaction to add 100 units.
At the same time, Bob starts a transaction to deduct 50 units of the same product.
An isolation level covers a range of guarantees:
In a system with a strong isolation level, Bob's transaction does not see Alice's update until Alice’s transaction is committed. This ensures that Bob only works with a consistent view of the data. This is referred to as serializability, the isolation level that guarantees fully ACID-compliant transactions.
Conversely, with a weaker isolation level, Bob might see an uncommitted state due to Alice’s ongoing transaction. This could lead to inconsistencies, for example, if Alice later decides not to commit her transaction.
Here’s an overview of the different isolation levels:
Isolation levels are a trade-off between data integrity and performance. The stronger the isolation level, the more reliable and consistent the data—but at the cost of potential delays and reduced throughput. Conversely, weaker isolation levels can lead to performance improvements but also increase the risk of anomalies1.
As a software engineer, understanding and choosing the appropriate isolation level is crucial for multiple reasons, such as performance tuning and ensuring data integrity. For example, in the past, I faced a significant business issue with customer data because we didn’t properly tune our PostgreSQL config.
There’s no one-size-fits-all solution; the right choice of isolation level depends on the application’s needs.
Tomorrow, we will explore the concept of consistency models.
An anomaly refers to undesirable behavior that can occur when multiple transactions execute concurrently. Each of these boxes solves one or multiple anomalies.