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How overlapping requests lose an update
Imagine a row stores a value of 10. Request A reads it, then request B reads it before A commits. Each request calculates a replacement value in Python. If both save, the later write can replace the earlier one using a value calculated from stale data. The final result reflects one write rather than both.
This often escapes manual testing because requests are handled one at a time during a simple test. Under concurrent traffic, their read-and-write steps can overlap. PostgreSQL’s Read Committed isolation documentation explains that an ordinary SELECT sees data committed before that query began; a later query may see newer committed data, but that does not make an already loaded Python object current.
Illustration: reserving the last seat
Suppose one seat remains. Two requests both read a count of 1 and each decides it can accept a reservation, then each writes 0. The stored count is 0, but two reservations may have been accepted. This is an illustrative scenario, not a report of a verified production incident.
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Choose a fix based on the invariant
First identify what must remain true—for example, a count must not drop below zero, or a reservation may be accepted only when capacity remains. Then decide whether the change can be expressed as one database update or requires a coordinated read, check, and write.
| Approach | Best fit | Important trade-off |
|---|---|---|
| Database-side update | Simple arithmetic or conditional change expressible in one SQL update | Check the affected-row count and make sure the predicate captures the business rule. |
| Row lock with a transaction | A workflow that must read current state, check it, and then change it | Competing work can wait; keep the transaction short and verify backend support. |
| Serializable isolation | Broader invariants that need stricter isolation | Transactions can fail with serialization errors and need deliberate retry handling. |
Use a database-side update for simple changes
When the invariant fits a single conditional update, let the database calculate from the stored value rather than saving a replacement calculated from an earlier model instance. Django’s QuerySet update() documentation describes how a database-level update avoids the race window between loading an object and saving it.
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For example, a conditional decrement can update a count only when it is still positive. The application should inspect how many rows the update affected: a successful update means the condition matched; no affected row can mean capacity was already exhausted. The precise expression and business response depend on the model and workflow. A single update is not a universal replacement for a process that needs several reads, checks, or related changes.
Use select_for_update() for a multi-step workflow
If the application must inspect a row, decide whether an action is allowed, and then modify it, lock the row while that decision is made. Django’s select_for_update() documentation describes locking matched rows until the transaction ends on supported database backends.
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transaction.atomic(). - Evaluate the queryset with
select_for_update()inside that transaction to retrieve the row that must be protected. - Check the current state and perform the related update before leaving the transaction block.
- Keep the block focused on the necessary database work so competing operations do not wait longer than needed.
Django’s transaction documentation states: “If the block is successfully completed, the changes are committed to the database.” That all-or-nothing transaction boundary does not, by itself, lock every row read inside the block. The row lock comes from evaluating select_for_update() on a backend that supports it.
Consider stronger isolation only with retry handling
PostgreSQL Serializable isolation can help enforce broader invariants, but it is not a set-and-forget fix. PostgreSQL’s Serializable isolation documentation says applications must be prepared to retry transactions after serialization failures. Django’s database documentation is relevant when evaluating how Django interacts with database behavior. Choose this approach deliberately, with a retry strategy and an understanding of the workload; the available evidence does not establish it as universally faster.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check the database backend before relying on locks
select_for_update() behavior varies by engine and version. Django documents that SQLite does not add SELECT ... FOR UPDATE, so this API provides no row-lock protection there. Support for options such as nowait, skip_locked, and of also varies across MySQL and MariaDB versions. Confirm the production database family and version against Django’s QuerySet reference and database backend notes before depending on a specific option.
Django also warns that evaluating select_for_update() in autocommit mode raises TransactionManagementError on a backend that supports row locking. Put the evaluation inside the transaction that should protect the workflow.
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Test the transaction behavior you intend to deploy
Django’s TestCase wraps each test in a transaction. That can make code appear to work even when the explicit transaction boundary required by select_for_update() is missing. Use TransactionTestCase when testing the intended transaction behavior, and run concurrency checks against the same database family as production. A SQLite test cannot validate PostgreSQL row-locking semantics.
- Test the invariant, not only whether the endpoint returns a success response.
- Exercise overlapping operations against the production database family and version.
- For conditional updates, verify the affected-row result and the response when the condition no longer matches.
- For Serializable transactions, verify that serialization failures trigger safe retries rather than lost work or duplicate side effects.
Locks can make competing work wait. Django notes that per-request transactions have overhead whose impact depends on query patterns and database locking. Measure latency and throughput on the actual workload before claiming one approach performs better; no workload-specific benchmark establishes a universally fastest fix.
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