Understanding Phantom Reads Problem with hands on examples

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Phantom read is a common problem that occurs in database transactions, particularly when the transaction isolation level is set to “read committed.” In this article, we will provide a brief overview of the read committed isolation level, explain what the phantom read problem is, and illustrate it through a practical example to highlight its consequences. We will also discuss the recommended solution, which involves changing the isolation level or using a different locking mechanism to mitigate this problem. It is crucial to address the phantom read problem by modifying the isolation level or implementing appropriate measures to ensure data consistency. So, let’s delve into this issue with a detailed example.

Schema Setup and Problem Introduction

To better understand the phantom read problem, let’s consider a scenario involving a social media website. The schema includes a “users” table with ID and name columns, a “post” table storing user posts with a post ID and user ID, and a “user stats” table tracking the total number of posts made by each user. The user stats table enables efficient rendering of user profiles without the need for repetitive post count computations.

Transaction Process and Inconsistency

When a user publishes a post, three actions need to be performed within a single transaction: inserting the post into the “post” table, updating the user stats in the “user stats” table, and retrieving all posts made by the user for the response. However, if the transaction isolation level is set to read committed, the phantom read problem can arise. This problem occurs when another transaction inserts new rows between the queries executed within the ongoing transaction.

Practical Example

In our example, we simulate two API calls that attempt to publish posts for the same user almost simultaneously. Transaction 1 inserts a post and updates the user stats, while transaction 2 inserts another post. Until either transaction commits, inconsistencies arise. When transaction 2 commits, a phantom row appears in the “post” table. Consequently, when reading all posts for that user, five rows are returned instead of the expected four. This inconsistency in the returned data creates a poor user experience and illustrates the phantom read problem.

Resolving the Phantom Read Problem

To address the phantom read problem, there are two common approaches. The first option is to use locking mechanisms to prevent concurrent modifications and ensure consistency. The second option is to change the isolation level from read committed to repeatable read, which eliminates phantom reads entirely but may affect system throughput. It is important to note that different database systems may have varying recommendations and guarantees regarding the phantom read problem, so consulting the documentation specific to the database being used is advised.

Conclusion

Understanding and mitigating the phantom read problem is crucial for maintaining data consistency in database transactions. By comprehending the implications of different isolation levels and implementing appropriate solutions, developers and engineers can ensure accurate and reliable data operations. Whether through locking mechanisms or isolation level adjustments, addressing the phantom read problem is essential for maintaining a high level of consistency and delivering a seamless user experience. Stay tuned for more articles on database engineering and related topics.

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