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A separate availability check cannot guarantee a booking is still available when the later write runs: two requests can both see an empty interval and then both try to reserve it. For time-range reservations in PostgreSQL, enforce the rule in the database with a range column and an exclusion constraint; treat a rejected write as a normal booking conflict. Use Serializable transactions and retries when the business rule spans broader reads and writes that a direct constraint cannot express.
Why does check then write fail under concurrent traffic?
Under PostgreSQL’s default Read Committed isolation, each command starts with a new snapshot. If two transactions check availability before either has inserted its reservation, each can see no conflict and proceed. The read has not locked or otherwise reserved the empty interval. Both requests may therefore pass the check even though the later writes would create an invalid state.
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This is a time-of-check/time-of-use race: the result of the first read is only a view of availability at that moment, not a promise that the resource remains available. A user-facing availability check can still be useful, but it should not be the integrity guard.
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For interval reservations, represent the booked interval with a PostgreSQL range type and enforce non-overlap with an exclusion constraint. PostgreSQL’s range-types documentation demonstrates this pattern: an equality condition identifies the resource, and the range overlap operator && prohibits intersecting intervals for that resource. The documentation describes exclusion constraints as a way to specify constraints such as “non-overlapping” on a range type.
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Define the range and constraint
For independently bookable rooms, a representative schema is:
CREATE EXTENSION btree_gist;CREATE TABLE room_reservation (
room text NOT NULL,
during tsrange NOT NULL,
EXCLUDE USING gist (room WITH =, during WITH &&)
);The range-types example uses tsrange. Choose tsrange or tstzrange based on whether your application models local timestamps or absolute instants. Decide explicitly how timezone conversion works and whether interval endpoints are inclusive or exclusive; the example alone does not establish the right policy for a particular application. Check the deployed PostgreSQL major version and confirm that btree_gist is available under your database host’s extension policy before applying the schema.
The combined equality-and-overlap pattern uses btree_gist; GiST backs the exclusion constraint. PostgreSQL’s example rejects an overlapping reservation for the same room while permitting an interval for a different room. The database checks the invariant on the write path, so concurrent requests cannot both persist conflicting intervals.
Handle a rejected booking as a conflict
Keep a preliminary availability query if it improves the interface, but let the insert or update determine whether the booking is valid. When the exclusion constraint rejects a conflicting interval, translate that specific database violation into the application’s booking-conflict result or API response. Keep it distinguishable from unrelated database failures; the appropriate status code or domain result depends on your application.
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When is INSERT … ON CONFLICT the right tool?
INSERT ... ON CONFLICT is for a conflict rule represented by a unique or exclusion arbiter when the desired behavior is an insert-or-update or no-op outcome. PostgreSQL’s INSERT documentation describes that mechanism. It is not a general substitute for defining an arbitrary overlapping-interval rule, nor does it make a separate read followed by a write safe. Model the invariant first, then choose the write behavior that matches the application.
When should I use Serializable isolation and retries?
An exclusion constraint is a direct fit for the rule “these intervals must not overlap for this resource.” Other invariants may depend on predicates or related rows in ways that cannot be represented by one such constraint. For those broader multi-row read/write rules, Serializable isolation may be appropriate. PostgreSQL’s transaction-isolation documentation explains that Serializable transactions can be aborted when concurrent activity would make the result inconsistent with serial execution.
Retry the complete transaction
Applications using Serializable must be ready to restart transactions after serialization failures. PostgreSQL identifies SQLSTATE 40001 for serialization failure; retry the whole transaction, including its reads and decisions, rather than only repeating the final statement with stale values from the aborted attempt. PostgreSQL also notes that exact conflicting dependencies can be difficult to predict, and documented unique violations can still occur in some Serializable cases.
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| Approach | Best fit | How correctness is enforced | Typical conflict outcome | Operational consideration |
|---|---|---|---|---|
| Unique constraint | Duplicate-key rule | Constraint on the write | Constraint violation; handle according to the application’s insert/update policy | Index and constraint costs; contention depends on workload |
| Exclusion constraint | Non-overlapping intervals for a resource | Constraint on the write using range overlap | Conflicting booking is rejected | GiST-backed constraint and any resulting contention; verify extension availability |
| Serializable transaction | Broader predicate- or multi-row invariant not directly captured by a constraint | Database detects unsafe concurrent transaction dependencies | Serialization abort; restart the complete transaction | Retry frequency and monitoring/restart costs vary with the workload |
There is no universal throughput winner. PostgreSQL notes Serializable can perform best in some environments depending on the cost of monitoring and restarting transactions relative to explicit locking and blocking. The relevant trade-off must be evaluated against the actual workload; the documentation does not supply a benchmark for a particular booking system.
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What if the booking rule is not just an interval?
The range-and-exclusion design addresses time-range reservations: no overlapping booking for the same resource. Another capacity or inventory invariant may require a different schema and constraint. For example, “never exceed a quantity across a group of related rows” is not automatically equivalent to a non-overlapping interval rule. Identify the invariant precisely, then encode it directly where possible; consider Serializable transactions for broader read/write dependencies that a direct constraint does not capture.
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