Non-Repeatable Read is a database anomaly that occurs in the context of transactions, which are sequences of database operations that should behave as single, indivisible units of work. Non-Repeatable Read happens when a transaction reads the same row twice, and gets different data each time. This is usually due to another transaction modifying the data after the first read.
Here is an example:
- Transaction T1 reads row R1 from the database.
- Transaction T2 updates row R1 and commits.
- Transaction T1 reads row R1 again and sees a different value than it did the first time.
This inconsistency is problematic because it breaks the illusion of transactions running in isolation, leading to potential inaccuracies in data processing.
Preventing Non-Repeatable Read Anomaly
Prevention of non-repeatable reads typically involves setting a higher level of transaction isolation. Most databases support multiple levels of isolation, including “Read Uncommitted”, “Read Committed”, “Repeatable Read”, and “Serializable”.
To prevent non-repeatable reads, you can use either “Repeatable Read” or “Serializable” isolation level.
- In “Repeatable Read” isolation, once a transaction reads a row, no other transaction can modify that row until the first transaction finishes. This can potentially lead to issues with concurrency and long-running transactions, as it can cause lock contention.
- In “Serializable” isolation, transactions are fully isolated from each other. This is the highest level of isolation and can resolve issues like non-repeatable reads and phantom reads. However, it can significantly impact performance due to its strict locking mechanism.
Here is an example in SQL how you can set the isolation level:
This example sets the transaction isolation level to “Repeatable Read”, starts a new transaction, performs some database operations, and then commits the transaction. During this transaction, any row that’s read won’t be able to be updated by other transactions, preventing non-repeatable reads.
Keep in mind that these settings are often a balance between data consistency and performance. While higher isolation levels like “Serializable” can prevent anomalies like non-repeatable reads, they can also reduce concurrency and throughput in your database operations.