Conquering Concurrency in Spring Boot: Strategies and Solutions

How to Tackle Concurrency in Spring Boot: A Comprehensive Guide to Prevent Data Conflicts and Handle High Traffic with 6 Proven Solutions.

Concurrency issues can lead to unpredictable behavior in your Spring Boot application. Luckily, there are several alternatives to avoid such problems. In this article, we’ll explore six popular alternatives and their pros and cons.

1. Use Versioning

One of the simplest alternatives to avoid concurrency issues in Spring Boot is to use versioning. By versioning your data, you can ensure that each request operates on a specific version of the data. This ensures that changes made by one request do not affect other requests.

Pros

• Easy to implement.
• Minimal overhead on application performance.

Cons

• Can lead to data redundancy.

This alternative to solve the concurrency problem in Spring Boot is to use the JPA @Version annotation. This annotation is used to control the version of an entity object in the database and detect concurrency conflicts.

To use this technique, we need to add a version field to our entity and annotate it with @Version. When an entity object is updated, JPA will automatically increment the value of the version field in the database. If two or more threads attempt to update the same entity object at the same time, JPA will detect the concurrency conflict and throw an OptimisticLockException.

For example, suppose we have a Resource entity with a version field:

@Entity
public class Resource {
    
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    
    private String name;
    
    private String description;
    
    @Version
    private Integer version;

    // Getters y setters
}

In this case, the version field is annotated with @Version, indicating that it is used to control the version of the entity in the database.

We can use this entity in our controller to update a resource as follows:

@RestController
public class MyController {

    @Autowired
    private MyRepository repository;

    @PutMapping("/resources/{id}")
    public Resource updateResource(@PathVariable Long id, @RequestBody Resource resource) {
        Resource existingResource = repository.findById(id).orElse(null);
        if (existingResource == null) {
            throw new ResourceNotFoundException();
        }
        existingResource.setName(resource.getName());
        existingResource.setDescription(resource.getDescription());
        return repository.save(existingResource);
    }
}

In this case, we do not need to add any explicit locks in the code since concurrency conflict detection is handled automatically by JPA. If two or more threads attempt to update the same entity object at the same time, JPA will throw an OptimisticLockException and one of the threads will have to handle the exception and retry the update operation.

2. Use Transactional

The concurrency problem in Spring Boot is to use the @Transactional annotation. This annotation ensures that the data is accessed and modified in a transaction, which means that multiple threads cannot modify the same data at the same time. If two or more threads try to access the same data, one thread must wait for the transaction to complete before modifying the data.

To use this technique, we must annotate the method that modifies the data with @Transactional. Spring will create a transaction around the method and commit the changes to the database when the method completes successfully.

Here’s an example of using the @Transactional annotation in a service class:

@Service
public class MyService {
    
    @Autowired
    private MyRepository repository;

    @Transactional
    public void updateData(Long id, String newData) {
        MyEntity entity = repository.findById(id).orElse(null);
        if (entity == null) {
            throw new EntityNotFoundException();
        }
        entity.setData(newData);
        repository.save(entity);
    }
}

In this example, the updateData method is annotated with @Transactional, which ensures that the data is accessed and modified within a transaction. If two threads try to access the same data at the same time, one of the threads must wait for the transaction to complete before modifying the data.

One of the advantages of using the @Transactional annotation is that it is easy to use and does not require any additional code to handle concurrency. However, it can affect performance because it creates a transaction for each method call, which can increase the overhead.

In conclusion, the @Transactional annotation is a simple and effective way to handle concurrency in Spring Boot applications. While it may have some performance impact, it is a good option for applications that do not require high concurrency or where the performance impact is acceptable.

Pros

• Simple to use.
• Can be applied at the method level.

Cons

• May impact application performance.
• Can lead to deadlocks.

3. Use Pessimistic Locking

how using pessimistic locking in Spring Boot can help prevent concurrency issues, along with pros and cons, example code, and a brief conclusion.

Pessimistic locking is a technique used to prevent concurrent access to shared resources by explicitly locking them. In Spring Boot, you can use the @Lock annotation provided by JPA to implement pessimistic locking.

To use pessimistic locking, you need to specify the lock mode for the query in which you want to perform locking. You can do this using the @Lock annotation, as shown in the following example:

@Transactional
public void processOrder(Long orderId) {
    Order order = orderRepository.findById(orderId);
    entityManager.lock(order, LockModeType.PESSIMISTIC_WRITE);

    // perform some operations on the order

    orderRepository.save(order);
}

### or other alternative

@Repository
public interface MyRepository extends JpaRepository<MyEntity, Long> {
 
    @Lock(LockModeType.PESSIMISTIC_WRITE)
    MyEntity findById(Long id);
 
}

In this example, we are using LockModeType.PESSIMISTIC_WRITE to lock the entity when we retrieve it from the database.

Once an entity is locked, other transactions trying to access or update the same entity will have to wait until the lock is released. This ensures that only one transaction can access the entity at a time, preventing concurrency issues.

Pros:

• Pessimistic locking provides a strong and reliable way to prevent concurrency issues.
• It ensures that only one transaction can access a shared resource at a time, preventing conflicts and inconsistencies.

Cons:

• Pessimistic locking can have negative impact on performance, as it can cause transactions to wait for a lock to be released.
• If locks are held for too long, it can cause other transactions to wait for extended periods of time, potentially leading to deadlocks.

In conclusion, pessimistic locking is a powerful technique that can help prevent concurrency issues in Spring Boot applications. However, it should be used with caution, as it can have a negative impact on performance and can lead to deadlocks if not used properly. It is important to carefully evaluate the trade-offs and determine whether pessimistic locking is the best solution for your specific use case.

4. Optimistic Locking in Spring Boot

Optimistic locking is a concurrency control technique that allows multiple transactions to access the same data simultaneously by allowing them to read the data without acquiring locks, and only acquiring locks when a transaction attempts to update the data. This approach assumes that conflicts are rare and that most transactions will succeed without blocking each other.

Optimistic locking works by adding a version field to each record in the database. The version field is updated each time the record is updated. When a transaction attempts to update a record, it checks the current version number against the version number it read earlier. If the version number has not changed, the transaction updates the record and increments the version number. If the version number has changed, it indicates that another transaction has updated the record in the meantime, and the transaction must be retried.

Implementing Optimistic Locking in Spring Boot

In Spring Boot, optimistic locking can be implemented using the @Version annotation. This annotation can be added to a field in an entity class to indicate that it should be used for optimistic locking. Here is an example:

@Entity
public class Product {
   @Id
   @GeneratedValue(strategy = GenerationType.IDENTITY)
   private Long id;
   private String name;
   private double price;
   @Version
   private int version;
   // getters and setters
}

In this example, the version field is annotated with @Version, indicating that it should be used for optimistic locking.

To use optimistic locking in a Spring Boot application, you simply need to annotate the repository method with @Transactional and catch the OptimisticLockingFailureException:

@Transactional
public Product updateProduct(Product product) {
   Product existingProduct = productRepository.findById(product.getId())
            .orElseThrow(() -> new ResourceNotFoundException("Product not found"));
   if (existingProduct.getVersion() != product.getVersion()) {
      throw new OptimisticLockingFailureException("Product has been modified by another transaction");
   }
   existingProduct.setName(product.getName());
   existingProduct.setPrice(product.getPrice());
   existingProduct.setVersion(existingProduct.getVersion() + 1);
   return productRepository.save(existingProduct);
}

In this example, the updateProduct method checks the version number of the existing product against the version number of the incoming product. If they do not match, it throws an OptimisticLockingFailureException, indicating that the product has been modified by another transaction. If they do match, it updates the product and increments the version number.

Pros:

• Optimistic locking is a non-blocking approach that allows multiple transactions to access the same data simultaneously, improving system concurrency and scalability.
• Optimistic locking is generally faster and less resource-intensive than pessimistic locking because it does not require acquiring and releasing locks.

Cons:

• Optimistic locking assumes that conflicts are rare and that most transactions will succeed without blocking each other, which may not be the case in some systems.
• Optimistic locking can result in more transaction retries, which can increase system overhead and reduce performance.

Optimistic locking is a powerful concurrency control technique that can help improve system concurrency and scalability. It works by allowing multiple transactions to read the same data without acquiring locks, and only acquiring locks when a transaction attempts to update the data. While optimistic locking may not be suitable for all systems, it is a useful tool to have in your toolbox when designing high-concurrency systems.

5: Lock class

The java.util.concurrent.locks.Lock interface provides more fine-grained locking mechanisms. It provides a way to create locks that can be used to control access to a shared resource in a multi-threaded environment. Locks are similar to synchronized blocks, but they provide more control over the locking and unlocking of the shared resource.

Here is an example of using Lock to control access to a shared resource:

@RestController
public class MyController {

    @Autowired
    private MyRepository repository;
    
    private Lock lock = new ReentrantLock();

    @PutMapping("/resources/{id}")
    public Resource updateResource(@PathVariable Long id, @RequestBody Resource resource) {
        lock.lock();
        try {
            Resource existingResource = repository.findById(id).orElse(null);
            if (existingResource == null) {
                throw new ResourceNotFoundException();
            }
            existingResource.setName(resource.getName());
            existingResource.setDescription(resource.getDescription());
            return repository.save(existingResource);
        } finally {
            lock.unlock();
        }
    }
}

In this example, we have a Spring Boot controller that handles PUT requests to update a resource. The MyRepository is injected via Spring's @Autowired annotation.

We’re using the ReentrantLock class to create a lock that ensures that only one thread can update the resource at a time. We create an instance of the ReentrantLock class and then acquire the lock using lock.lock().

Inside the try block, we fetch the existing resource from the repository using its id. If the resource doesn’t exist, we throw a ResourceNotFoundException. Otherwise, we update the resource with the new data, save it using the repository, and return the updated resource.

Finally, we release the lock using lock.unlock() inside the finally block.

Pros:

• This approach ensures that only one thread can access the critical section of code at a time, thereby preventing concurrent updates.
• Locking is a well-established technique and is widely used in concurrent programming.

Cons:

• Using locks can introduce performance overhead, especially if the critical section is held for a long time.
• If a thread holds the lock for too long, it can cause other threads to wait for an extended period, leading to potential deadlock issues.

In conclusion, while locking can be an effective way to prevent concurrency issues, it should be used with caution and only in situations where other approaches are not viable. It’s essential to consider the trade-offs and potential performance impact of locking when implementing this solution.

6. Atomic Variables

The Atomic Variables solution uses the java.util.concurrent.atomic package to provide thread-safe access to a shared variable. The package provides classes such as AtomicInteger, AtomicLong, and AtomicReference that allow for atomic operations on the variables.

Example

Let’s take the same example of updating a resource as we did with the Lock solution. Here's how we can use AtomicReference to ensure thread-safety:

@RestController
public class MyController {

    @Autowired
    private MyRepository repository;

    private AtomicReference<Resource> resource = new AtomicReference<>();

    @PutMapping("/resources/{id}")
    public Resource updateResource(@PathVariable Long id, @RequestBody Resource updatedResource) {
        Resource existingResource = repository.findById(id).orElse(null);
        if (existingResource == null) {
            throw new ResourceNotFoundException();
        }

        resource.set(existingResource);

        resource.updateAndGet(r -> {
            r.setName(updatedResource.getName());
            r.setDescription(updatedResource.getDescription());
            return r;
        });

        return repository.save(resource.get());
    }
}

In the example above, we’re using the AtomicReference class to store the Resource object. The updateAndGet method is used to update the Resource object atomically.

Pros

• The Atomic Variables solution provides a simple and efficient way to ensure thread-safety without the use of locks or synchronized blocks.
• It is highly performant, and is suitable for use in high-concurrency scenarios.

Cons

• The Atomic Variables solution may not be suitable for use with more complex data structures or in scenarios where multiple variables need to be updated atomically.
• It can also be more difficult to reason about than other solutions such as Locking or Optimistic Locking.

In conclusion, the Atomic Variables solution is a powerful tool for ensuring thread-safety in high-concurrency scenarios. It provides a simple and efficient way to update shared variables without the need for locks or synchronized blocks. However, it may not be suitable for more complex scenarios, and can be more difficult to reason about than other solutions.

Conclusion

In the IT market, the most commonly used alternatives to avoid concurrency issues in Spring Boot are:

1. Use Versioning
2. @Transactional
3. Pessimistic Locking
4. Optimistic Locking in Spring Boot
5. Lock Classes
6. Atomic Variables

Each of these alternatives has its own advantages and disadvantages, and the choice of one depends on the specific use case and project requirements. In general, locking techniques (optimistic and pessimistic) and transactionality are very common in most projects that require concurrency management.

On the other hand, atomic operations and synchronized methods are suitable for simple and low complexity use cases. Distributed concurrency management and asynchronous processing are more advanced techniques used in high scalability and performance environments.

It is important to note that these alternatives are not mutually exclusive, and it is possible to combine several of them in a solution to achieve the best results in terms of performance and data consistency.

Sources:

• Spring Framework Documentation: https://docs.spring.io/spring-framework/docs/current/reference/html/data-access.html#transaction-declarative-annotations
• Baeldung: https://www.baeldung.com/jpa-pessimistic-locking
• Vlad Mihalcea’s Blog: https://vladmihalcea.com/jpa-entity-locking-soft-locking-vs-hard-locking/