4 questions developers actually get asked about race conditions — with clear, practical answers you can use to prepare.
A race condition happens when two or more processes read and write the same shared data at nearly the same time, and the final result depends on which one finishes first. The classic fingerprint is two requests passing the same availability check and both committing a change meant for only one. It shows up only under concurrent load, which is why single-user testing misses it.
Wrap the read-check-write sequence in something that only one request can hold at a time: a database row-level lock (SELECT ... FOR UPDATE), an atomic database operation (UPDATE ... WHERE stock > 0), a Redis or memcached lock, or a queue that processes requests serially. The key is that the check and the write must be indivisible. Never rely on the frontend to prevent duplicates.
A race condition is two processes corrupting shared data because they ran at the same time without coordination. A deadlock is the opposite failure mode of the fix: two processes each waiting for a lock the other holds, so neither makes progress. Poorly scoped locks cause deadlocks; missing locks cause races. Both are concurrency bugs and both require careful lock design to avoid.
A table-level lock blocks every other user who touches any row in that table, turning concurrent traffic into a single-file line and killing throughput. A row-level lock only blocks requests touching the same row at the same time, which is where the actual conflict is. Most relational databases support row-level locks natively via SELECT ... FOR UPDATE.
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