Read Committed Isolation: Ensuring Data Integrity in Concurrent Transactions

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Introduction

This article is part of a series on Transaction in Distributed System. If you’re new to the concept of database transactions, you might want to start with our introductory article on Understanding Database Transactions. In this installment, we will explore Read Committed Isolation, one of the most commonly used isolation levels in relational databases.

What is Read Committed Isolation?

Read Committed Isolation ensures that any data read by a transaction is committed at the moment it is read or write. It is a widely used isolation level in relational databases that provides two key guarantees: no dirty reads and no dirty writes.

No Dirty Reads: Ensuring Data Consistency

A dirty read occurs when a transaction reads data that has been modified by another transaction but not yet committed. If the modifying transaction is rolled back, the read data becomes invalid, leading to potential inconsistencies and erroneous results. Preventing dirty reads is crucial for maintaining the integrity and reliability of the data being processed.

Why is it Important to Prevent Dirty Reads?

Preventing dirty reads is essential because it ensures that transactions only work with stable and reliable data. Allowing dirty reads can lead to several issues:

• Inconsistent Data: Transactions might make decisions based on uncommitted and potentially invalid data, resulting in incorrect outcomes.
• Data Corruption: If a transaction reads and processes dirty data, subsequent operations might propagate errors, leading to widespread data corruption.
• Business Logic Errors: Applications relying on accurate data for decision-making processes might perform incorrect actions, affecting business operations and user trust.

Example:

Consider a banking application where Transaction A is updating a customer’s account balance to include a recent deposit. Meanwhile, Transaction B reads the account balance to display it to the user. If Transaction B reads the balance while Transaction A’s update is still in progress and uncommitted, Transaction B might show an incorrect balance to the user. If Transaction A subsequently rolls back its changes, the balance read by Transaction B becomes invalid, leading to confusion and potential user dissatisfaction.

How Read Committed Isolation Prevents Dirty Reads

Read Committed Isolation prevents dirty reads by ensuring that a transaction can only read data that has already been committed by other transactions. This means that any uncommitted changes made by other transactions remain invisible until they are fully committed. By doing so, Read Committed Isolation maintains data consistency and prevents the issues associated with dirty reads.

No Dirty Writes: Preserving Data Integrity

What is a Dirty Write?

A dirty write occurs when a transaction overwrites data that has been modified but not yet committed by another transaction. If the first transaction rolls back its changes after the second transaction has already written new data, it can lead to inconsistencies and lost updates, compromising the integrity of the database.

Why is it Important to Prevent Dirty Writes?

Preventing dirty writes is crucial for several reasons:

• Consistency of Data: Ensuring that transactions do not overwrite uncommitted data preserves the consistency and accuracy of the database.
• Prevention of Lost Updates: When dirty writes occur, there is a risk of losing updates made by transactions that were not yet committed, leading to data loss and potential errors.
• Reliability of Applications: Applications that rely on the database for critical operations must work with reliable and stable data to function correctly and deliver accurate results to users.

Example:

Imagine an inventory management system where two transactions are updating the stock level of a product. Transaction A starts by decreasing the stock level by 10 units but hasn’t committed yet. Concurrently, Transaction B reads the current stock level and then attempts to increase it by 5 units. If Transaction B writes its update while Transaction A’s changes are still uncommitted, and Transaction A then rolls back, the final stock level becomes inconsistent, leading to an inaccurate inventory count.

How Read Committed Isolation Prevents Dirty Writes

Read Committed Isolation prevents dirty writes by ensuring that a transaction can only write to data that has been committed by other transactions. This means that any uncommitted changes made by other transactions are not visible, and thus, cannot be overwritten. By doing so, Read Committed Isolation maintains the integrity of data and prevents the occurrence of lost updates and inconsistencies.

Implementing No Dirty Reads and No Dirty Writes

How Databases Prevent Dirty Writes

To prevent dirty writes, databases use row-level locking to ensure that transactions do not overwrite uncommitted changes made by other transactions. This mechanism is crucial for maintaining data integrity and consistency. Here’s how it works:

1. Row-Level Locking: When a transaction wants to update a row, the database automatically places an exclusive lock on that row. This lock prevents other transactions from reading or writing to the same row until the lock is released (i.e., when the transaction commits or rolls back).
2. Automatic Locking in Read Committed Mode: In Read Committed isolation mode, this row-level locking happens automatically. When a transaction modifies a row, it locks the row to ensure that other transactions cannot write to it until the transaction is complete.
3. Waiting for Locks: If another transaction attempts to update the same row while it is locked, it must wait until the first transaction releases the lock. This ensures that only committed data is written, preventing dirty writes and maintaining data consistency.

How Databases Prevent Dirty Reads

Preventing dirty reads is essential to ensuring that transactions work with stable and consistent data. Dirty reads occur when a transaction reads data that has been modified by another transaction but not yet committed. There are two main strategies to prevent dirty reads:

1. Row-Level Locking: Similar to preventing dirty writes, databases can use row-level locking to prevent dirty reads. However, this approach can lead to performance issues, especially with long-running transactions.
2. Maintaining Multiple Versions of Data: A more efficient way to prevent dirty reads involves the database maintaining both the old committed value and the new value set by a transaction that holds a write lock. While the transaction is ongoing, other transactions read the old committed value.

Conclusion

Read Committed Isolation plays a crucial role in balancing data consistency and performance in relational databases. By preventing dirty reads and dirty writes, it ensures that transactions operate on stable and reliable data. The mechanisms of row-level locking and maintaining multiple versions of data are central to achieving these guarantees, each with its own advantages and trade-offs.

Understanding Read Committed Isolation helps lay the foundation for exploring more advanced isolation levels and their impact on database consistency and concurrency.

In the next article of this series, we will delve into Snapshot Isolation. We will examine how it provides a consistent view of the database at the start of a transaction, addressing some of the limitations of Read Committed Isolation and offering stronger guarantees for data integrity. Stay tuned!

Ref: This article was inspired by the book “Designing Data-Intensive Applications” by Martin Kleppmann, which provides in-depth insights into the complexities of database design and transaction management.