How to Use Transaction Isolation Levels to Control Concurrency

1. Introduction

When you work with databases, you often need to perform transactions that involve multiple operations on the data. For example, you might want to transfer money from one account to another, or update the inventory of a product after a sale. Transactions are a way of ensuring that the data remains consistent and accurate throughout the process.

However, transactions can also cause problems when multiple users or applications try to access or modify the same data at the same time. This is called concurrency, and it can lead to issues such as lost updates, dirty reads, non-repeatable reads, and phantom reads. These issues can compromise the integrity and reliability of the data, and cause unexpected results or errors.

To prevent or control these issues, databases use different levels of isolation for transactions. Transaction isolation levels are a way of defining how much a transaction can see or affect the changes made by other concurrent transactions. The higher the isolation level, the more the transaction is protected from the effects of concurrency, but the lower the performance and scalability of the database.

In this tutorial, you will learn about the different transaction isolation levels and how they affect concurrency and consistency. You will also learn how to choose the right transaction isolation level for your database applications. By the end of this tutorial, you will be able to:

• Explain what transaction isolation levels are and why they are important.
• Describe the four standard transaction isolation levels: READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, and SERIALIZABLE.
• Identify the advantages and disadvantages of each transaction isolation level.
• Select the appropriate transaction isolation level for your database applications.

Let’s get started!

2. What are Transaction Isolation Levels?

Transaction isolation levels are a way of defining how much a transaction can see or affect the changes made by other concurrent transactions. They are based on the concept of ACID properties, which are the four essential characteristics of a reliable database system: Atomicity, Consistency, Isolation, and Durability.

Atomicity means that a transaction is either completed in full or not at all. Consistency means that a transaction preserves the integrity and validity of the data. Isolation means that a transaction operates independently of other transactions. Durability means that the effects of a transaction are permanent and persistent.

However, achieving full isolation can be costly and impractical, as it can reduce the performance and scalability of the database. Therefore, most database systems allow different levels of isolation, which trade off some consistency for better concurrency. The SQL standard defines four transaction isolation levels, which are supported by most relational database management systems (RDBMS):

• READ UNCOMMITTED: The lowest level of isolation, which allows a transaction to read uncommitted changes made by other transactions. This can lead to dirty reads, where a transaction reads data that is not yet final and may be rolled back.
• READ COMMITTED: The default level of isolation for most RDBMS, which prevents dirty reads by allowing a transaction to read only committed changes made by other transactions. However, this can still lead to non-repeatable reads, where a transaction reads the same data twice and gets different results, or phantom reads, where a transaction reads a set of data and finds new rows added or deleted by other transactions.
• REPEATABLE READ: A higher level of isolation, which prevents non-repeatable reads by locking the data read by a transaction until it commits or rolls back. However, this can still lead to phantom reads, as new rows can be inserted or deleted by other transactions.
• SERIALIZABLE: The highest level of isolation, which prevents phantom reads by locking the entire range of data accessed by a transaction until it commits or rolls back. This ensures that a transaction can be executed in isolation, as if no other transactions were running concurrently. However, this can also cause the most concurrency issues, such as deadlocks, where two transactions wait for each other to release their locks.

In the following sections, you will learn more about each transaction isolation level and see some examples of how they work.

2.1. READ UNCOMMITTED

READ UNCOMMITTED is the lowest level of transaction isolation, which allows a transaction to read uncommitted changes made by other transactions. This means that a transaction can see the intermediate state of the data, before it is finalized by another transaction. This can lead to a phenomenon called dirty reads, where a transaction reads data that is not yet final and may be rolled back.

Dirty reads can cause serious problems for the consistency and accuracy of the data. For example, suppose you have a table called accounts that stores the balance of different users. You have two concurrent transactions: T1 and T2. T1 transfers $100 from user A to user B, and T2 reads the balance of user A. The following table shows the possible scenarios:

In this scenario, T2 reads the balance of user A after T1 has updated it, but before T1 has committed the transaction. This means that T2 sees a dirty value of $900, which may not be the final value if T1 rolls back. This can cause T2 to make incorrect decisions based on the incorrect data.

To avoid dirty reads, you should use a higher level of transaction isolation, such as READ COMMITTED, which ensures that a transaction can only read committed changes made by other transactions. However, READ COMMITTED also has some drawbacks, which you will learn in the next section.

2.2. READ COMMITTED

READ COMMITTED is the default level of transaction isolation for most RDBMS, which prevents dirty reads by allowing a transaction to read only committed changes made by other transactions. This means that a transaction cannot see the intermediate state of the data, but only the final state after another transaction has committed or rolled back. This can improve the consistency and accuracy of the data, but it can also introduce some new problems.

One of the problems that READ COMMITTED can cause is non-repeatable reads, where a transaction reads the same data twice and gets different results. This can happen when another transaction modifies the data between the two reads. For example, suppose you have the same table accounts as before, and two concurrent transactions: T1 and T2. T1 transfers $100 from user A to user B, and T2 reads the balance of user A twice. The following table shows the possible scenarios:

In this scenario, T2 reads the balance of user A before and after T1 has committed the transaction. This means that T2 sees a different value of $900 in the second read, which is not the same as the first read. This can cause T2 to make incorrect decisions based on the inconsistent data.

To avoid non-repeatable reads, you should use a higher level of transaction isolation, such as REPEATABLE READ, which ensures that a transaction can read the same data consistently throughout its execution. However, REPEATABLE READ also has some drawbacks, which you will learn in the next section.

2.3. REPEATABLE READ

REPEATABLE READ is a higher level of transaction isolation, which prevents non-repeatable reads by locking the data read by a transaction until it commits or rolls back. This means that a transaction can read the same data consistently throughout its execution, regardless of the changes made by other transactions. This can improve the consistency and accuracy of the data, but it can also introduce some new problems.

One of the problems that REPEATABLE READ can cause is phantom reads, where a transaction reads a set of data and finds new rows added or deleted by other transactions. This can happen when another transaction inserts or deletes rows that match the criteria of the query used by the first transaction. For example, suppose you have the same table accounts as before, and two concurrent transactions: T1 and T2. T1 transfers $100 from user A to user B, and T2 reads the number of users with a balance greater than $500. The following table shows the possible scenarios:

In this scenario, T2 reads the number of users with a balance greater than $500 before and after T1 has committed the transaction. This means that T2 sees a different value of 2 in the second read, which is not the same as the first read. This can cause T2 to make incorrect decisions based on the inconsistent data.

To avoid phantom reads, you should use the highest level of transaction isolation, such as SERIALIZABLE, which ensures that a transaction can read the same set of data throughout its execution, regardless of the changes made by other transactions. However, SERIALIZABLE also has some drawbacks, which you will learn in the next section.

2.4. SERIALIZABLE

SERIALIZABLE is the highest level of transaction isolation, which prevents phantom reads by locking the entire range of data accessed by a transaction until it commits or rolls back. This means that a transaction can read the same set of data throughout its execution, regardless of the changes made by other transactions. This ensures that a transaction can be executed in isolation, as if no other transactions were running concurrently. This can improve the consistency and accuracy of the data, but it can also reduce the concurrency and performance of the database.

One of the problems that SERIALIZABLE can cause is deadlocks, where two transactions wait for each other to release their locks. This can happen when two transactions access the same data in a different order. For example, suppose you have the same table accounts as before, and two concurrent transactions: T1 and T2. T1 transfers $100 from user A to user B, and T2 transfers $200 from user B to user A. The following table shows the possible scenarios:

In this scenario, T1 and T2 both read and write the balance of user A and user B, but in a different order. This means that T1 locks the row of user A, and T2 locks the row of user B. Then, T1 tries to read the row of user B, but it is locked by T2. Similarly, T2 tries to read the row of user A, but it is locked by T1. This creates a deadlock, where both transactions are waiting for each other to release their locks, and neither can proceed.

To avoid deadlocks, you should use a lower level of transaction isolation, such as READ COMMITTED, which does not lock the data for the entire duration of the transaction. However, READ COMMITTED also has some drawbacks, which you have learned in the previous section.

As you can see, each transaction isolation level has its own advantages and disadvantages, and there is no one-size-fits-all solution. In the next section, you will learn how to choose the right transaction isolation level for your database applications, based on your requirements and preferences.

3. How to Choose the Right Transaction Isolation Level?

Choosing the right transaction isolation level for your database applications depends on several factors, such as your data requirements, your performance goals, and your concurrency issues. There is no single best choice for all situations, as each level has its own trade-offs and implications. Therefore, you need to consider the pros and cons of each level and weigh them against your specific needs and preferences.

Here are some general guidelines to help you choose the right transaction isolation level:

• If you need the highest level of consistency and accuracy for your data, and you can tolerate the lowest level of concurrency and performance, you should use SERIALIZABLE. This level guarantees that your transactions will execute as if they were running in a serial order, without any interference from other transactions. However, this level also has the highest risk of deadlocks, which can block your transactions and cause errors or timeouts.
• If you need a high level of consistency and accuracy for your data, but you also want to improve the concurrency and performance of your database, you should use REPEATABLE READ. This level guarantees that your transactions will read the same data consistently throughout their execution, regardless of the changes made by other transactions. However, this level can still cause phantom reads, which can affect the integrity and validity of your data.
• If you need a moderate level of consistency and accuracy for your data, and you want to avoid the problems of dirty reads and non-repeatable reads, you should use READ COMMITTED. This level guarantees that your transactions will read only committed changes made by other transactions, which can improve the reliability and correctness of your data. However, this level can still cause performance issues, as it can lock the data for a short period of time during the read operation.
• If you need the highest level of concurrency and performance for your database, and you can tolerate the lowest level of consistency and accuracy for your data, you should use READ UNCOMMITTED. This level allows your transactions to read uncommitted changes made by other transactions, which can improve the speed and scalability of your database. However, this level can also cause serious data quality issues, such as dirty reads, which can compromise the integrity and validity of your data.

As you can see, choosing the right transaction isolation level is not a simple task, as it involves balancing different aspects of your database applications. You should always test and evaluate the impact of each level on your data and performance, and adjust your choice accordingly. You should also be aware of the default level of your RDBMS, and how to change it if needed.

In the next and final section, you will learn how to change the transaction isolation level of your RDBMS, and how to check the current level of your transactions.

4. Conclusion

In this tutorial, you have learned about the different transaction isolation levels and how they affect concurrency and consistency in your database applications. You have also learned how to choose the right transaction isolation level for your specific needs and preferences, and how to change and check the transaction isolation level of your RDBMS.

Here are some key points to remember:

• Transaction isolation levels are a way of defining how much a transaction can see or affect the changes made by other concurrent transactions.
• The SQL standard defines four transaction isolation levels: READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, and SERIALIZABLE.
• Each transaction isolation level has its own advantages and disadvantages, and there is no one-size-fits-all solution for all situations.
• To choose the right transaction isolation level, you need to consider your data requirements, your performance goals, and your concurrency issues.
• To change the transaction isolation level of your RDBMS, you need to use the appropriate SQL command or configuration option.
• To check the current transaction isolation level of your RDBMS, you need to use the appropriate SQL query or function.

We hope you have enjoyed this tutorial and found it useful for your database projects. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!