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Concurrency control
Definition
Concurrency control is the set of techniques used to coordinate operations that overlap in time so they do not violate the required rules for shared data. In databases, it helps transactions run together while providing a chosen level of isolation. Techniques include locking, version-based approaches and validation of conflicts before committing changes.
What it means
Two users may read the same record before either has finished updating it. Each action can appear reasonable in isolation while their combination creates an incorrect result. Concurrency control manages these interactions. Its goal is not necessarily to run everything one after another; it is to allow useful overlap while preserving the guarantees the application requires. Those guarantees depend on the isolation level and the operations involved, so merely placing statements inside a transaction is not a complete explanation of correctness.
How it works
A locking approach reserves access to records or other resources when conflicts must be prevented. Multiversion approaches maintain versions so readers can see an appropriate snapshot while other transactions make changes. Optimistic techniques let work proceed and check for conflicts, potentially requiring a retry. Real systems combine mechanisms. Some schedules are allowed under weaker isolation but rejected under serializable isolation. Applications still need correct transaction boundaries, constraints and error handling; a concurrency mechanism cannot repair an incorrect business rule.
Three practical examples
- An inventory service coordinates competing attempts to reserve the final available item.
- A collaborative administration tool detects whether another user changed a record before accepting an update based on an older version.
- An AI dataset registry prevents two workers from independently publishing conflicting metadata for the same version, while allowing unrelated datasets to be processed concurrently.
Think of it this way
Think of several editors working on a shared document. They might reserve a section, work from versions and merge, or check for changes before saving. Each approach makes different trade-offs. The analogy is limited because database isolation has precise rules about reads, writes and committed outcomes; informal human coordination is not equivalent to a transaction protocol.
Worked example
Suppose a counter starts at 10. Worker A reads 10 and plans to store 11. Before A saves, worker B also reads 10 and plans to store 11. If both overwrite the counter with their calculated value, the final result may be 11 rather than 12. That is a lost-update pattern in this simplified read-then-write design. A correctly supported atomic increment, an appropriate lock around the operation, or a version check with retry can address this particular conflict. The exact remedy depends on the database and transaction semantics; fixing one counter does not prove that every multi-record rule is safe.
Illustrative case study
Fictional case: Meadow Stock tests simultaneous reservations rather than checking only one request at a time. The team discovers that an application-level “read availability, then update” sequence can conflict under its chosen configuration. It moves the availability condition into a suitable transactional operation and tests both successful and rejected reservations. It also handles retryable failures without duplicating a customer's request. This fictional case demonstrates why overlapping operations need deliberate tests, not a claimed deployment result.
Common misconceptions and mistakes
Do not equate concurrency with parallel execution on separate processors: operations can overlap through interleaving on one processor. Do not assume multiversion concurrency control eliminates locks or every anomaly. Serializable isolation may require transactions to abort and retry; it is not a promise that all requests succeed immediately. Locks can introduce waiting and deadlocks. Finally, external effects such as sending a message may need additional coordination because a database rollback does not automatically undo an already delivered message.
Business use
Businesses use concurrency control to protect operational rules while maintaining throughput. Identify the invariant first, such as non-negative available stock or one active assignment per resource. Then choose transaction boundaries and mechanisms that preserve it under overlap. Measure contention, wait times and retry rates as well as average response time. For AI workflows, coordinate publication, checkpoints and shared metadata explicitly, especially when many workers write results or recover after interruption.
People also ask
Does concurrency control make all transactions serial?
No. Many mechanisms allow overlap while controlling the resulting interactions.
Is a retry always safe?
No. Retry logic must account for external effects, duplicate requests and the scope of the failed transaction.
Is the strongest isolation always fastest?
No. Performance and contention depend on the workload, although weaker isolation must still meet the application's correctness requirements.
Check your understanding
Two workers each read a count of 10 and then write 11. What went wrong?
Answer: Their read-then-write operations overlapped without suitable coordination for the intended two increments, so one increment was effectively lost.
Related terms
- Locking
- transaction
- isolation level
- multiversion concurrency control
- serializability
- deadlock
- optimistic concurrency
Learn next
Write down a small invariant and draw two overlapping transaction timelines. Identify the reads and writes that could conflict, then test the database's documented behavior under the selected isolation level. Include failure and retry paths in the exercise.
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Sources
Primary references: PostgreSQL documentation, Concurrency Control, https://www.postgresql.org/docs/16/mvcc.html ; Transaction Isolation, https://www.postgresql.org/docs/16/transaction-iso.html ; Ports and Grittner, Serializable Snapshot Isolation in PostgreSQL, https://arxiv.org/abs/1208.4179 . Official and implementation-paper excerpts consulted. Product-specific behaviour and editorial review remain pending.
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