Tip: Database Transaction Isolation Level

Type Of Isolation Levels

Isolation levels in databases refer to the degree to which transactions are isolated from each other. These levels determine how much one transaction can see the changes made by other concurrent transactions and how these changes are reflected.

There are several isolation levels defined by the SQL standard, and different database management systems (DBMS) might implement them slightly differently. Some common isolation levels include:

1. Read Uncommitted: This is the lowest isolation level. Transactions can see uncommitted changes made by other transactions. It can lead to dirty reads, where a transaction reads data that has been modified but not committed yet.

2. Read Committed: In this level, a transaction can only see changes committed by other transactions. It avoids dirty reads but still allows non-repeatable reads since data can be changed by other transactions between reads within the same transaction.

3. *Repeatable Read: This level ensures that within the same transaction, the data remains consistent even if other transactions commit changes. It prevents non-repeatable reads but allows phantom reads, where new rows matching the search conditions might be inserted by other transactions.

4. Serializable: This is the highest isolation level. It ensures strict transaction isolation. It prevents all anomalies (dirty reads, non-repeatable reads, and phantom reads) but can lead to a higher degree of locking, potentially impacting performance.

Different database systems might have additional isolation levels or variations on these standard levels. Choosing the appropriate isolation level depends on the specific requirements of the application, considering factors such as data consistency, concurrency, and performance.

The choice of isolation level should balance between data integrity and concurrency, ensuring that transactions behave as expected while allowing the required level of simultaneous access to data without causing inconsistencies.

*The term "repeatable" refers to the ability of the transaction to "repeat" the reads of the same data multiple times within the transaction and obtain the same results each time. It highlights the guarantee that the data read by the transaction remains stable and does not change, hence making the reads "repeatable."

Use Cases for Each Isolation Level

Different isolation levels serve distinct purposes and are suitable for various scenarios. Here's a breakdown of common use cases for each isolation level:

1. Read Uncommitted:

   • Use Case: Rarely used in practice due to its potential for data inconsistency.
   • Scenario: When reading partially completed or uncommitted data doesn't significantly impact the application's correctness or when minimizing locking overhead is more critical than ensuring data consistency.
   • Example: Real-time analytics systems or reporting systems where up-to-date data is required, and the occasional discrepancy is acceptable.

2. Read Committed:

   • Use Case: Commonly used in many applications.
   • Scenario: When balancing between data consistency and concurrent access is crucial, and occasional non-repeatable reads are acceptable.
   • Example: Most online transaction processing (OLTP) systems, e-commerce applications, where data accuracy is essential, but real-time synchronization is not the highest priority.

3. Repeatable Read:

   • Use Case: When ensuring that data remains consistent throughout a transaction is crucial.
   • Scenario: In applications where long transactions are executed, and any changes to the data during the transaction can lead to inconsistencies.
   • Example: Financial systems, batch processing systems, or systems where multiple steps of a transaction are executed, and data integrity is crucial for each step.

4. Serializable:

   • Use Case: When absolute data consistency and preventing any concurrency-related anomalies are essential.
   • Scenario: In scenarios where strict data integrity is mandatory, and the system can handle potential performance trade-offs due to increased locking.
   • Example: Banking systems for fund transfers, reservation systems, or any application where avoiding all types of anomalies (dirty reads, non-repeatable reads, and phantom reads) is critical.

Choosing the appropriate isolation level depends on the specific requirements of the application, considering factors such as data integrity, consistency, concurrency, and performance. It's important to test and analyze the impact of different isolation levels on the application's behavior and performance before choosing the most suitable one for a particular use case.

Phantom Read

Phantom read is a phenomenon that can occur in a multi-user database system when a transaction performs a query multiple times within the same transaction, and between those executions, another transaction inserts or deletes rows that meet the search criteria of the initial query. As a result, the initial transaction perceives a difference in the number of rows retrieved in subsequent reads within the same transaction.

In simpler terms, a phantom read happens when a transaction performs a query, sees a certain set of rows, and then re-executes the same query within the transaction, but due to concurrent transactions inserting or deleting rows that fit the query's conditions, the second execution returns a different set of rows.

For instance:

• Transaction 1 starts and performs a SELECT query, retrieving a set of rows that meet certain criteria.
• Another transaction (Transaction 2) inserts or deletes rows that satisfy the criteria of the previous SELECT query executed by Transaction 1.
• Transaction 1 repeats the same SELECT query within the same transaction and observes additional rows that were not present in the initial result set due to the changes made by Transaction 2.

Phantom reads violate the guarantee of consistency within a transaction and can affect the correctness of operations performed within the transaction. To mitigate phantom reads, higher isolation levels like "Serializable" are used in databases. Serializable isolation prevents phantom reads by placing stronger locks or employing other mechanisms to ensure that the result set of a query remains constant within a transaction despite concurrent modifications by other transactions.

Non-Repeatable Read

A non-repeatable read is a phenomenon that occurs in a multi-user database environment when a transaction retrieves a set of data within a transaction, then re-executes the same query, and gets a different result set due to modifications committed by another transaction in between the reads.

In simpler terms, it happens when a transaction reads a row, then re-reads the same row within the same transaction, but the data has been changed or updated by another committed transaction in the interim, resulting in a different value or set of values.

For example:

1. Transaction 1 reads a row with a certain value, let's say a balance amount.
2. Meanwhile, Transaction 2 updates the same row and commits the change.
3. Transaction 1 re-reads the same row within the same transaction, but this time, it sees a different value for the balance amount, which is the updated value committed by Transaction 2.

This inconsistency between two reads within a single transaction can cause unexpected behavior and potentially impact the correctness of operations within the transaction. Non-repeatable reads are a consequence of lower isolation levels in databases, such as "Read Committed" and below, where transactions allow changes committed by other transactions to be visible during the lifetime of the transaction.

To mitigate non-repeatable reads, higher isolation levels, such as "Repeatable Read" and "Serializable," are used in databases. These isolation levels employ different mechanisms, such as locking or snapshot isolation, to ensure that once a transaction reads data, the same data remains consistent throughout the transaction, even if other transactions commit changes to the data.

Test Plan: Testing Database Isolation Levels

Objective:

The objective of this test plan is to thoroughly evaluate and compare different isolation levels in the database system to understand their behavior in concurrent transaction scenarios.

Scope:

• Database System: [Specify the database system and version]
• Isolation Levels to Test: Read Uncommitted, Read Committed, Repeatable Read, Serializable
• Test Environment: [Describe the hardware, software, and configuration details]

Resources:

• Testers: [List the team members involved]
• Database Server: [Details about the database server]
• Test Data: [Description of the sample data used for testing]

Test Cases:

Test Case 1: Test Read Uncommitted Isolation Level

1. Setup:
   • Start two concurrent transactions (Txn1 and Txn2).
   • Set Txn1 to use the Read Uncommitted isolation level.
2. Steps:
   • Txn1 reads data.
   • Txn2 updates the same data and commits.
   • Txn1 re-reads the same data.
3. Expected Result:
   • Txn1 should see the uncommitted changes made by Txn2.

Test Case 2: Test Read Committed Isolation Level

1. Setup:
   • Start two concurrent transactions (Txn1 and Txn2).
   • Set Txn1 to use the Read Committed isolation level.
2. Steps:
   • Txn1 reads data.
   • Txn2 updates the same data and commits.
   • Txn1 re-reads the same data.
3. Expected Result:
   • Txn1 should not see the uncommitted changes made by Txn2.

Test Case 3: Test Repeatable Read Isolation Level

1. Setup:
   • Start two concurrent transactions (Txn1 and Txn2).
   • Set Txn1 to use the Repeatable Read isolation level.
2. Steps:
   • Txn1 reads data.
   • Txn2 inserts new rows that match Txn1's query criteria and commits.
   • Txn1 re-reads the same data.
3. Expected Result:
   • Txn1 should see the same data as in the initial read without the newly inserted rows by Txn2.

Test Case 4: Test Serializable Isolation Level

1. Setup:
   • Start two concurrent transactions (Txn1 and Txn2).
   • Set Txn1 to use the Serializable isolation level.
2. Steps:
   • Txn1 reads data.
   • Txn2 deletes rows that match Txn1's query criteria and commits.
   • Txn1 re-reads the same data.
3. Expected Result:
   • Txn1 should see the same data as in the initial read, with no rows deleted by Txn2 visible.

Test Execution:

• Execute each test case step by step.
• Record observations, anomalies, and any unexpected behavior.
• Document any failures or inconsistencies encountered during testing.

Conclusion:

• Analyze and compare the behavior of each isolation level.
• Summarize the strengths and weaknesses of each isolation level in handling concurrency and data consistency.

Sign-off:

• Team members involved in testing review and approve the test results.

Notes:

• Consider using variations in test data and different types of queries to cover diverse scenarios.
• Ensure proper backups of the database before running tests to avoid data loss or corruption.
• Document all configurations, test results, and observations thoroughly for future reference.

This test plan provides a structured approach to testing different isolation levels in a database system, covering various scenarios to evaluate their behavior under concurrency. Adjustments to the test cases and plan might be necessary based on specific database features and functionalities.

Coding Examples

For NestJS applications using TypeORM, here's an example of how you can set the isolation level for a transaction:

import { Injectable } from '@nestjs/common'; import { InjectRepository } from '@nestjs/typeorm'; import { Repository, Connection } from 'typeorm'; @Injectable() export class YourService { constructor( @InjectRepository(YourEntity) private readonly repository: Repository<YourEntity>, private readonly connection: Connection, ) {} async performTransaction() { await this.connection.transaction(async (manager) => { try { // Set the isolation level for the transaction using SQL statements await manager.query('SET TRANSACTION ISOLATION LEVEL READ COMMITTED'); // Replace with your desired isolation level // Perform your TypeORM operations within the transaction // For example: const result = await manager.getRepository(YourEntity).find(); // Commit the transaction await manager.commitTransaction(); } catch (error) { // Handle exceptions or rollback the transaction if needed await manager.rollbackTransaction(); } }); } }

Here's an example of how you can set the isolation level for a LINQ to SQL transaction using ADO.NET:

using (var dbContext = new YourLinqToSqlDataContext()) { using (var transaction = new System.Transactions.TransactionScope( System.Transactions.TransactionScopeOption.Required, new System.Transactions.TransactionOptions { IsolationLevel = System.Transactions.IsolationLevel.ReadCommitted // Set your desired isolation level here })) { try { // Perform your LINQ to SQL operations within the transaction // For example: var result = dbContext.YourTable.Where(x => x.SomeCondition).ToList(); // Commit the transaction transaction.Complete(); } catch (Exception ex) { // Handle exceptions or rollback the transaction if needed // transaction.Dispose(); // This automatically rolls back if not completed } } }

Serializable Isolation Level Consideration

Serializable isolation level ensures strong data consistency by preventing anomalies like dirty reads, non-repeatable reads, and phantom reads. However, achieving this level of isolation often involves increased locking and serialization of transactions, which can impact performance, especially in highly concurrent systems. Here are some strategies to potentially improve performance while using the Serializable isolation level:

1. Optimize Transactions:

• Reduce Transaction Scope: Keep transactions as short and focused as possible to minimize the duration of locks.
• Avoid Unnecessary Locks: Acquire locks only when necessary to prevent unnecessary contention.

2. Indexing and Query Optimization:

• Use Proper Indexing: Ensure that the database schema is properly indexed to speed up queries and minimize lock contention.
• Optimize Queries: Write efficient and optimized SQL queries to reduce the time data remains locked.

3. Application-Level Optimization:

• Batch Operations: Perform bulk operations wherever possible to minimize the number of individual database transactions.
• Cache Data: Implement caching mechanisms to reduce the need for frequent database accesses.

4. Pessimistic vs. Optimistic Locking:

• Consider Optimistic Locking: In some scenarios, optimistic locking (where conflicts are detected at the time of committing transactions) might be more suitable than pessimistic locking (acquiring locks before making changes).

5. Database-Specific Optimization Techniques:

• Database-specific Features: Some databases offer specific features or hints to fine-tune performance in high-concurrency scenarios. Explore such features provided by your database system.

6. Scale and Distributed Systems:

• Distributed Transactions: In distributed systems, consider breaking down transactions or using distributed transaction models to reduce contention and improve scalability.

7. Thorough Testing and Profiling:

• Performance Testing: Continuously test and profile your application under load to identify performance bottlenecks.
• Monitoring and Optimization: Monitor database performance metrics and optimize based on real-time usage patterns.

8. Consider Relaxing Isolation if Appropriate:

• Relax Isolation Level if Feasible: If strict serialization is not critical for all parts of the application, consider relaxing the isolation level where possible to improve concurrency.

9. Application Design Considerations:

• Design for Concurrency: Consider architectural changes that might reduce the need for stringent isolation levels while maintaining data consistency.

Keep in mind that while improving performance with Serializable isolation, it's crucial to maintain the required level of data consistency. Each optimization should be carefully evaluated and tested to ensure it doesn’t compromise data integrity or consistency in the system. Balancing performance with data consistency is often a delicate trade-off, and the approach might vary based on the specific requirements and constraints of your application.