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What is non-repeatable read and fuzzy read? Clearly explain the basic concepts of database transaction isolation levels

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What are Non-Repeatable Read and Fuzzy Read in Database Transactions?

In the context of database transactions, two common issues related to isolation levels are non-repeatable read and fuzzy read. Let’s explore each of these concepts in detail.

Non-Repeatable Read:
Non-repeatable read refers to a situation in which a transaction reads the same row multiple times within the scope of the transaction, but the values retrieved each time are different. This inconsistency arises due to the concurrency of multiple transactions accessing the same data.

Consider an example where Transaction A performs a read operation on a specific row in a database. Meanwhile, Transaction B performs an update operation on the same row and commits the changes. If Transaction A reads the row again within the same transaction, it may see different values compared to the first read due to the changes made by Transaction B.

To prevent non-repeatable read, database isolation levels such as “Repeatable Read” or “Serializable” can be used. These isolation levels ensure that a transaction reads consistent data throughout its execution, regardless of other concurrent transaction activities.

Fuzzy Read:
Fuzzy read, also known as “dirty read,” occurs when a transaction reads data that has been modified by another transaction that has not yet committed those changes. This situation can lead to inconsistent and unreliable readings.

Imagine a scenario where Transaction C starts and updates a row but has not committed the changes yet. Meanwhile, Transaction D reads the same row before the changes made by Transaction C are finalized. As a result, Transaction D obtains uncommitted and potentially erroneous data.

To alleviate the issue of fuzzy reads, databases employ transaction isolation levels such as “Read Committed” or “Serializable.” These levels ensure that a transaction can only read committed data, minimizing the risk of reading uncommitted and inconsistent information.

Understanding Database Transaction Isolation Levels

To better comprehend non-repeatable read and fuzzy read, it is essential to grasp the concept of database transaction isolation levels. Isolation levels are standards that define the degree to which transactions are separated from each other and the effects of in-progress transactions on others.

Commonly used isolation levels include:

1. Read Uncommitted:
This is the lowest isolation level, where transactions can read uncommitted data and are vulnerable to non-repeatable and fuzzy reads. It provides the highest level of concurrency but sacrifices consistency.

2. Read Committed:
In this isolation level, transactions can only read committed data. It avoids non-repeatable and fuzzy reads but introduces the possibility of phantom reads. Phantom reads happen when a transaction reads different rows when executing the same query multiple times.

3. Repeatable Read:
With this isolation level, a transaction guarantees that the same set of rows are read consistently throughout the transaction. It prevents non-repeatable and fuzzy reads, as well as phantom reads. However, it does not prevent other transactions from inserting new rows into the result set.

4. Serializable:
Serializable is the highest isolation level, ensuring full data consistency. It prevents non-repeatable reads, fuzzy reads, and phantom reads by locking the data, potentially reducing concurrency. This level provides the highest degree of data integrity but may result in longer transaction times.

By selecting the appropriate isolation level based on the application’s requirements, developers can strike a balance between concurrency and consistency, addressing issues such as non-repeatable read and fuzzy read effectively.

In conclusion, non-repeatable read and fuzzy read are common challenges in database transactions that can lead to inconsistent and unreliable data. Understanding the different isolation levels and their implications is crucial for ensuring data integrity and achieving the desired level of database consistency.

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