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Use a separate SQLAlchemy session for each FastAPI request, then protect donation rules in PostgreSQL with a transaction and the narrowest suitable constraint or lock. A request-scoped session prevents unsafe sharing of ORM state; it does not, by itself, prevent duplicate donations. For retries after a timeout, define an idempotency rule and return a stable result for the same key. Keep payment-provider calls outside the database transaction and coordinate them through an explicit, idempotent workflow.
Separate request lifecycle from database correctness
Two different problems arise when donation requests overlap. First, each request needs its own SQLAlchemy Session or AsyncSession: these are mutable, stateful objects that represent transaction state and must not be used concurrently by multiple threads or asyncio tasks. Second, PostgreSQL must enforce the rules that keep persisted donation data correct when transactions race.
A fresh session per request addresses the first problem, not the second. Two independent sessions can still both read a state that appears to permit a donation and then attempt conflicting writes. Use database constraints, row locks, or an isolation level chosen for the actual business invariant.
Yield and close one session per request
In FastAPI, provide a session through a dependency that yields it and closes it during dependency cleanup. FastAPI’s SQL database tutorial demonstrates this lifecycle pattern with SQLModel and SQLite; it is useful as a session-lifecycle example, not as evidence of PostgreSQL concurrency protection.
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def get_session():
with Session(engine) as session:
yield session
Keep the yielded session within that request or unit of work. Do not store a shared session globally, pass one session to concurrent tasks, or reuse it as though it were a thread-safe connection pool. The same one-task-at-a-time rule applies to SQLAlchemy’s AsyncSession.
Put related database writes in one short transaction
Define the transaction around the donation row and any related database changes that must succeed or fail together. A SQLAlchemy transaction context commits when its block completes successfully and rolls back when an exception escapes; an outer session context closes the session.
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def record_donation(session, donation_data):
with session.begin():
donation = Donation(**donation_data)
session.add(donation)
# Add other database records that must commit atomically here.
return donationAdapt the pattern to the application’s sync or async SQLAlchemy setup. The key is the boundary: perform the database writes that form one logical unit inside the transaction, commit promptly on success, and allow failures to roll back the unit rather than leaving only part of it persisted. If a database error is caught and handled by the application, ensure the transaction has been rolled back before trying further work with that session.
Choose the PostgreSQL protection that matches the race
PostgreSQL’s default isolation level is READ COMMITTED: each statement sees rows committed before that statement began. A later statement in the same transaction can therefore see newer committed state than an earlier statement did. Do not treat an earlier read alone as protection for a later write.
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| Mechanism | Use it when | Trade-off |
|---|---|---|
| Unique constraint, including an idempotency-key constraint | The invariant is uniqueness, such as one logical record per key or one record per business-defined reference. | Define key scope, retention, and repeat-request response semantics for the application. A conflicting insert must be translated into the endpoint’s documented duplicate outcome. |
SELECT ... FOR UPDATE |
The decision depends on inspecting and changing the same existing row. | Conflicting updates, deletes, and row-locking operations on the selected rows wait until the transaction ends. Keep the lock window short and check the relevant condition again after acquiring it. |
SERIALIZABLE |
A multi-row or predicate-based invariant cannot be protected safely by an appropriate constraint or targeted lock. | PostgreSQL can abort a transaction whose concurrent read/write pattern cannot be serialized. The application must retry the complete database unit of work within a bounded policy. |
READ COMMITTED |
A straightforward transaction is adequately protected by constraints or targeted locking. | Statements may observe different committed states over the course of a transaction, so an earlier read is not, by itself, a concurrency guarantee. |
Use uniqueness for duplicate logical submissions
If the rule is “this idempotency key can create only one logical donation,” enforce it with a PostgreSQL unique constraint. Application code that first checks whether the key exists and only then inserts is not enough: two overlapping transactions can both pass the check. Let the database arbitrate the conflicting insert, then map the conflict to the stable behavior the endpoint promises.
The key design is application-specific. Decide what entity or account the key is scoped to, how long it remains valid, and what a repeated request receives. The same key should represent the same logical operation; do not silently treat a changed request as a fresh donation. The sources do not prescribe a particular idempotency schema or HTTP status, so choose and document those semantics for the service.
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Lock an existing row when a decision depends on its current state
Use SELECT ... FOR UPDATE inside the transaction when requests must serialize a decision about a particular existing row—for example, a mutable record whose state determines whether a donation-related transition is allowed. After the lock is acquired, re-read or re-evaluate the condition before writing. A row lock does not block ordinary reads; it protects the locked rows from conflicting updates, deletes, and other row-locking commands until transaction end.
Locking adds waiting and deadlock risks. If a transaction needs multiple locks, acquire them in a consistent order across code paths. PostgreSQL’s explicit-locking guidance identifies consistent lock order as the general defense against deadlocks. If PostgreSQL detects a deadlock, it aborts one participant; handle that failure rather than assuming the transaction completed.
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Use SERIALIZABLE only when its broader protection is needed
REPEATABLE READ uses the snapshot from the transaction’s first query or data-modification statement. SERIALIZABLE uses a transaction snapshot and can abort one transaction when concurrent activity would make the result inconsistent with any serial execution. This makes serializable isolation useful for broader read/write invariants that a targeted constraint or lock cannot safely cover, but it requires retry handling.
When a serialization failure occurs, retry the entire transaction from its beginning so its reads and decisions are made against a fresh attempt. Retrying only the final failed statement can reuse decisions made from the aborted transaction’s snapshot. Set a bounded retry policy; if attempts are exhausted, return or surface a controlled failure rather than retrying forever. Only retry work that is safe to repeat, and never blindly repeat an external payment action as part of a database retry.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Handle overlapping requests and timeouts with a deliberate flow
- Validate before the critical transaction. Check request shape and authentication first where practical, so invalid requests do not hold database locks or keep transactions open.
- Open the transaction and establish the concurrency rule. Apply the relevant idempotency or uniqueness constraint, or lock the existing row whose state governs the decision.
- Re-check mutable conditions after locking. Make the decision using the state protected by the lock, then insert or update all database records that must commit together.
- Commit promptly. Keep the transaction limited to database work. On a serialization failure or deadlock, retry the whole database unit only if it is safe to do so and the retry policy permits it.
- Coordinate payment-provider work separately. Use explicit state transitions and an idempotent integration design, such as an outbox or equivalent workflow, rather than assuming a database transaction can include a remote charge.
- Return a stable repeat-request result. For a repeated idempotency key, return the documented outcome for that same logical operation. Translate expected uniqueness conflicts deliberately instead of exposing an unhandled database error.
Keep payment side effects outside the database transaction
A normal PostgreSQL transaction cannot make a payment processor’s remote action atomic with a local database commit. Holding a transaction open during a slow network call keeps database resources and possibly locks occupied, but rollback still cannot reverse a charge that the processor has already accepted.
Represent the workflow explicitly: persist the relevant donation/payment state, coordinate the external action through a durable mechanism such as an outbox or an equivalent idempotent process, and make provider calls safe to repeat. Define what happens when the provider succeeds but the application does not receive the response, or when the local commit and external action do not complete together. Those recovery semantics belong to the integration design, not to session scope or transaction isolation alone.
Quick Recap
Diagnose the failure by its cause
- Same session used by multiple tasks: give each request or unit of work its own session; do not share mutable ORM transaction state concurrently.
- Two requests create the same logical donation: enforce the uniqueness rule in PostgreSQL and define the duplicate response for the idempotency key or business reference.
- A state check becomes stale before the write: lock the existing row, then re-check the condition, or use a suitable stronger isolation strategy for a broader invariant.
- Serialization failure: discard the failed attempt and retry the complete transaction from the start, subject to a bounded policy.
- Deadlock: let the aborted transaction fail cleanly; review lock order so transactions acquire multiple objects consistently.
- Payment result is uncertain after timeout: reconcile through the application’s explicit idempotent payment workflow; do not infer that a database rollback reversed the processor action.
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