View Categories

How do you implement concurrency in SQL?

Parthiban Kannan
Updated on December 2, 2025

2 min read

Especially when designing systems that handle multiple simultaneous users or transactions. Concurrency control in SQL ensures that when multiple users try to access or modify data at the same time, the data integrity remains consistent and no conflicts occur — like lost updates or dirty reads.

When I talk about implementing concurrency, I usually start by saying that it’s about balancing data accuracy with performance. SQL databases handle concurrency using transactions and isolation levels, and I’ve applied a few key strategies depending on the workload and requirements.

Let me explain it step by step like I would in an interview —

In general, concurrency is managed using locking mechanisms and isolation levels. When multiple users perform operations, the database decides how much visibility one transaction has into another’s uncommitted changes.

For example, SQL Server and most RDBMSs provide these isolation levels:

  • READ UNCOMMITTED: Allows dirty reads — maximum concurrency but low consistency.
  • READ COMMITTED: Prevents dirty reads — the default level in many systems.
  • REPEATABLE READ: Ensures a transaction sees the same rows throughout its lifetime.
  • SERIALIZABLE: Strictest — fully isolates transactions, but at the cost of performance.
  • SNAPSHOT: Uses row versioning to avoid blocking readers and writers.

Now, how I’ve implemented concurrency in practice:

In a high-traffic order processing system, we had multiple users updating stock quantities at the same time. To avoid conflicts like overselling, we implemented optimistic concurrency control using a version column.
Each record had a RowVersion column, and when a user attempted to update a record, we checked if the row’s version matched the one the user initially read:

🗃️
filename.sql 
UPDATE Products
SET Quantity = Quantity - 1, RowVersion = RowVersion + 1
WHERE ProductID = @ProductID AND RowVersion = @OldRowVersion;

If the update affected zero rows, it meant another user had already modified it — the application caught this and re-tried or alerted the user.
This approach ensures data integrity without using heavy locks, which is ideal for high-concurrency environments.

In other cases, like financial transactions, we can’t afford concurrent writes that might cause inconsistencies. So, I’ve used pessimistic locking — explicitly locking records while a transaction is being processed. For example:

🗃️
filename.sql 
SELECT * FROM Accounts WITH (UPDLOCK, ROWLOCK)
WHERE AccountID = @AccountID;

This ensures no other transaction can modify the row until the current one completes.
It’s more consistent but can lead to blocking and deadlocks if not designed carefully.

Challenges I’ve faced:

  • One major challenge is deadlocks, where two transactions hold locks that each other needs. I’ve handled that by keeping transactions short, accessing tables in a consistent order, and using TRY...CATCH with retry logic.
  • Another issue is long-running transactions holding locks for too long, impacting concurrency. I fixed that by batching updates and using snapshot isolation where appropriate.

Limitations:

  • Pessimistic locking reduces concurrency — fine for critical updates, but not scalable for heavy read workloads.
  • Optimistic concurrency requires additional logic in the application layer to handle conflicts.
  • Snapshot isolation increases tempdb usage (in SQL Server) because it stores row versions — so it needs monitoring.

Alternatives or supporting strategies:

  • Implement row versioning or timestamp columns for optimistic concurrency.
  • Use retry mechanisms for transient concurrency conflicts.
  • Apply partitioning or sharding to distribute workloads and reduce contention on hot tables.
  • Introduce message queues or service buses for async updates in highly concurrent systems.

In summary — I’d say concurrency in SQL is about choosing the right isolation level and locking strategy for your workload.
For read-heavy systems, I prefer snapshot isolation or optimistic concurrency.
For write-heavy or transactional systems, I go with pessimistic locking and tighter transaction scopes.

In one of my past projects, switching from pessimistic to optimistic concurrency using RowVersion reduced blocking by nearly 60%, improving response times significantly — that experience really reinforced how important it is to choose the right concurrency model for the business scenario.

Scroll to Top