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The answer is lost update, which in hindsight is obvious once you trace through what actually ends up in the database.
Identify the problem as a lost update anomaly, then explain how it arises from concurrent read-modify-write operations without proper isolation. Briefly contrast it with similar anomalies and mention common prevention techniques to show depth.
Pro tip: Mention that lost updates are often prevented by using atomic operations (e.g., UPDATE ... SET x = x + 1) or optimistic concurrency control with version checks, which are practical in high-throughput systems.
State that this is a lost update: two transactions read the same value, modify it independently, and the second write overwrites the first, losing one update.
Describe how the lack of isolation allows both transactions to read the original value before either writes, leading to a race condition.
Distinguish lost update from dirty reads, non-repeatable reads, and write skew to show precise understanding.
Outline solutions: higher isolation levels (e.g., repeatable read, serializable), locking (pessimistic), optimistic concurrency control (versioning), or atomic operations.
Mention how databases like PostgreSQL or MySQL handle this, and trade-offs between performance and consistency.
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