Claude Code
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Race Conditions Causing Data Corruption on Concurrent Updates
When multiple users or processes update the same data simultaneously, your application produces incorrect results. Inventory counts go negative, account balances are wrong, duplicate records appear, or the last write silently overwrites earlier changes without merging them.
Race conditions are among the hardest bugs to find because they're non-deterministic. They happen occasionally under load but almost never during manual testing. You might only discover them when a user complains that their changes disappeared, or when financial totals don't add up.
Claude Code generates code that works correctly for sequential operations but doesn't add concurrency controls. Every read-modify-write sequence without locking is a potential race condition waiting to be triggered under production load.
Error Messages You Might See
ERROR: could not serialize access due to concurrent update
OptimisticLockException: Row was updated by another transaction
Inventory cannot be negative: constraint violation
Duplicate entry for key 'unique_order_id'
StaleObjectStateError: Row was updated or deleted
Common Causes
- Read-modify-write without locking — Code reads a value, modifies it in application code, and writes it back. Between read and write, another request changes the value
- Missing database transactions — Multiple related operations are not wrapped in a transaction, allowing partial completion
- Optimistic concurrency not implemented — No version column or ETag to detect and reject conflicting writes
- Shared mutable state — In-memory counters, caches, or rate limiters modified by multiple async operations without synchronization
- Idempotency not enforced — Retry logic or duplicate requests cause the same operation to execute multiple times
How to Fix It
- Use atomic database operations — Replace read-modify-write with UPDATE counters SET value = value + 1 WHERE id = X
- Add optimistic locking — Include a version column and use UPDATE ... WHERE version = expected_version. Retry on conflict
- Wrap operations in transactions — Use database transactions with appropriate isolation levels (READ COMMITTED or SERIALIZABLE)
- Implement idempotency keys — Accept a client-generated idempotency key and skip duplicate operations
- Use distributed locks for critical sections — For operations spanning multiple services, use Redis-based distributed locks (Redlock)
- Test with concurrent load — Use tools like k6 or Artillery to send concurrent requests and verify data integrity
Real developers can help you.
Omar Faruk
As a Product Engineer at Klasio, I contributed to end-to-end product development, focusing on scalability, performance, and user experience.
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Get HelpFrequently Asked Questions
How do I test for race conditions?
Use a load testing tool to send 50-100 concurrent requests that modify the same record. Check if the final state is correct. For example, if 100 requests each increment a counter by 1, the final value should be exactly 100.
What's the difference between optimistic and pessimistic locking?
Optimistic locking allows concurrent reads and detects conflicts at write time (using version numbers). Pessimistic locking prevents concurrent access with database locks. Use optimistic for read-heavy workloads, pessimistic for write-heavy ones.