While attending a session on OutSystems Tech Talk: Performance Best Practices in OutSystems 11, one of the key tips discussed was data model design best practices. One that stood out to me, and which I’ve seen my team grapple with in real-world scenarios, was the recommendation to "avoid data access concurrency" issues.

This is a topic that keeps growing in importance as my experience deepens, and I thought I’d develop it into an article. Let’s explore what data concurrency is, why it matters, and how to handle it effectively.

What is Data Access Concurrency?

In essence, data access concurrency refers to situations where multiple processes or threads attempt to access or modify the same data simultaneously. If not managed properly, concurrency can lead to serious problems like data corruption or degraded performance, especially in systems designed for parallel operations (e.g., databases, multi-threaded applications, or cloud-based architectures like OutSystems).

Managing concurrency is crucial to ensuring data integrity, consistency, and optimal performance in these systems.

Concurrency Pitfalls: Race Conditions and Deadlocks

Before we dive into management strategies, let’s look at the conditions you want to avoid:

Race Conditions

A race condition occurs when two or more processes simultaneously access the same data, and the final result depends on the order of execution. The timing discrepancy can cause unpredictable behavior, leading to data corruption or inconsistencies.

Imagine two threads trying to update a shared record at the same time. If both execute simultaneously, they might overwrite each other's changes without realizing it, creating flawed data.

Deadlocks

A deadlock is another concurrency nightmare. It happens when two or more processes are stuck in a stalemate, each waiting for the other to release a resource (lock). This results in a system standstill, where none of the processes can proceed. In database environments, deadlocks can severely affect performance, forcing the system to abort transactions.

Using Locks to Manage Concurrency

To prevent race conditions and deadlocks, we use locks to control data access.

Locks are mechanisms that restrict access to shared resources, ensuring that only one process or thread can modify data at a time. When used correctly, they protect data integrity but must be managed carefully to avoid performance degradation or deadlocks.

Types of Locks:

Read Locks (Shared Locks): Multiple threads can read data simultaneously, but no writing is allowed. This is useful when ensuring that read operations don’t interfere with each other.
Write Locks (Exclusive Locks): Only one thread can write to the data, and no other operations (read or write) can access it until the lock is released.

Isolation Levels: Balancing Performance and Consistency

In database systems, isolation levels determine the visibility of changes made by one transaction to others. Different levels offer trade-offs between data consistency and performance, and selecting the right level can prevent issues like dirty reads, non-repeatable reads, or phantom reads.

Here are the common isolation levels:

Read Uncommitted:
- Transactions can see uncommitted changes from other transactions. This is the least strict level and offers the highest performance but at the cost of potential data inconsistencies.
Read Committed:
- Transactions can only see committed changes. This level is a good balance between performance and consistency, as it avoids reading uncommitted data.
Repeatable Read:
- Ensures that if a transaction reads data, it will see the same data throughout its execution. It protects against non-repeatable reads but can lead to performance bottlenecks in high-transaction environments.
Serializable:
- The strictest isolation level, ensuring full isolation by making transactions appear as though they were executed sequentially. It provides the highest level of consistency but can significantly impact performance in large systems.

Optimistic vs. Pessimistic Concurrency Control

Concurrency control can generally be classified into two approaches: Optimistic and Pessimistic. Each has its own use cases, depending on the likelihood of conflicts.

Optimistic Concurrency Control

In optimistic concurrency, conflicts are considered rare. Therefore, multiple transactions proceed without locking resources, and checks for conflicts are made only when changes are being committed. This approach works well in environments where transactions mostly don’t interfere with each other, offering better performance.

Pessimistic Concurrency Control

In pessimistic concurrency, we assume that conflicts are likely. As a result, locks are placed early in the process to prevent other transactions from accessing the same data. This approach is ideal in systems with high contention for resources, but it may introduce performance overhead due to frequent locking.

Why Concurrency Management Matters

Proper concurrency management ensures:

Data Integrity: Preventing corruption by coordinating access to shared resources.
Consistency: Ensuring transactions yield reliable outcomes, regardless of how they're interleaved.
System Performance: Avoiding bottlenecks or crashes that can result from deadlocks or unnecessary locking.

In complex applications like those built on OutSystems, the performance impact of concurrency issues can scale rapidly. This makes it critical to design the data model and access layers with concurrency in mind, using the right balance of locking mechanisms, isolation levels, and concurrency control strategies.

Final Thoughts

The topic of data access concurrency is vast and continues to evolve as my understanding deepens through real-world experience. Managing concurrency isn't just about preventing conflicts—it's about finding the right trade-off between data integrity, performance, and scalability. Whether you’re working with a high-transactional system or a distributed architecture like OutSystems, mastering concurrency control can dramatically improve your application’s stability and responsiveness.

Stay tuned as I continue to expand on this topic in future posts, diving deeper into specific case studies and optimization techniques!

Managing Data Concurrency