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Prevent double-booking by claiming each seat with a conditional write against authoritative inventory, then treat payment as a separate, durable workflow—not part of one transaction. A hold needs an application-enforced deadline, and every payment, expiry, retry, and duplicate message must move the reservation through guarded state transitions. DynamoDB TTL can clean up old records later; it cannot release a seat precisely when a hold expires.
How do you stop two customers from booking the same seat?
Make the seat claim conditional in the database. A read-then-write check is unsafe: two requests can both read “available” before either writes. Instead, let DynamoDB accept an update only if the authoritative seat record is still in the expected state. The request that wins the condition owns the transition; a competing request fails and should be told the seat is no longer available.
For example, an UpdateItem can change a seat from AVAILABLE to HELD only when its condition expression says the current status is AVAILABLE. Store the reservation or order ID, hold owner, attempt or idempotency key, event version, and UTC holdExpiresAt with the claim. On subsequent changes, require the expected prior state and version as well as the correct owner. This prevents a delayed payment result or cleanup worker from overwriting a newer decision.
If claiming the seat also requires durable changes—such as creating a reservation record and recording an event to publish—use a DynamoDB transaction to make those local changes all-or-nothing. DynamoDB transactions support coordinated writes across items, but keep them small: AWS documents a limit of 100 unique items and 4 MB of transaction data, prohibits multiple actions against the same item in one transaction, and limits transactional ACID guarantees to a Region. Transactional work has additional prepare-and-commit operations, so contention and throughput still need planning. A transaction cannot include the external payment processor.
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Which states should a hold and payment move through?
Define application states before designing endpoints or retry logic. These are suggested names, not states imposed by AWS or Stripe.
| Inventory state | Meaning |
|---|---|
AVAILABLE |
No active reservation owns the seat. |
HELD |
A specific reservation owns the seat until its application-visible deadline. |
PAYMENT_PENDING |
The payment workflow is in progress; inventory must not be claimed by another buyer. |
CONFIRMED |
The reservation has been committed under the product’s payment policy. |
RELEASED |
The reservation no longer owns the seat; a later buyer may claim it. |
| Payment state | Meaning |
|---|---|
NOT_STARTED |
No provider operation has begun. |
AUTHORIZING |
An authorization request or customer authentication is in progress. |
AUTHORIZED |
The provider reports funds authorized, but not necessarily captured. |
CAPTURE_PENDING |
A capture request has been made and its final result may not yet be known. |
PAID |
The provider confirms the payment was captured or otherwise completed. |
FAILED or CANCELED |
The provider reports a terminal failure or cancellation. |
UNKNOWN_NEEDS_RECONCILIATION |
A request or response was ambiguous; provider state must be checked before taking another consequential action. |
Keep inventory and payment state distinct. A reservation record should retain provider object IDs, business idempotency keys, timestamps, and a version or sequence that lets handlers reject stale events. Define which transitions are valid, which service owns them, and what compensation follows a terminal failure. The hold duration and performance targets are product and workload choices; the available service documentation does not establish a universal flash-sale benchmark or recommended interval.
Can DynamoDB TTL release a seat on time?
No. Set holdExpiresAt when the hold is created and check it in application logic whenever payment completion, cancellation, reclaim, or confirmation attempts to change the reservation. DynamoDB TTL accepts a numeric Unix epoch timestamp in seconds, but deletes expired items asynchronously—typically within a few days, according to AWS’s TTL documentation. It is eventual cleanup, not a deadline timer.
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If payment succeeds after the hold is no longer valid, do not silently confirm the expired reservation. Apply an explicit late-success policy: for example, conditionally try to reacquire the seat if it remains available; otherwise void or refund as appropriate, notify the customer, and reconcile the provider result. The product and payment method determine the permitted compensation.
Should you authorize first and capture after confirming the seat?
When the provider and payment method support separate authorization and capture, authorizing after the hold and capturing only after the seat reservation is committed can reduce the chance of capturing for inventory the system failed to secure. It does not eliminate uncertainty: an authorization may succeed while its response is lost, customer authentication may outlast the hold, or a capture request may time out after the provider processed it.
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Stripe’s PaymentIntent documentation describes a PaymentIntent as tracking a customer’s payment session and recommends one PaymentIntent per order or customer session. With manual capture, a successful attempt can reach requires_capture; Stripe’s capture API accepts an intent in that state. Stripe says uncaptured PaymentIntents are canceled after a set number of days, seven by default. That provider default is not a suitable seat-hold duration by itself, and the actual payment method’s constraints must be checked.
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Use a stable business idempotency key for each order or payment attempt, persist the provider ID and each known outcome, and use provider events or retrieval to resolve uncertain results. Do not infer that payment succeeded merely because the customer returned to a success page. If the result is ambiguous, keep inventory protected according to the expiry and product policy while reconciling; do not release the seat or submit another charge based only on a timeout.
How should the slow path handle success, failure, and timeout?
Payment is a workflow across independent systems. DynamoDB cannot atomically commit with a processor, so success in one system can coincide with a timeout or failure in the other. Persist each local outcome and use forward recovery or a compensating action; a refund, void, cancellation, or seat release is a new operation, not a rollback that erases external history.
- Conditionally claim the seat and persist the hold, reservation/idempotency record, and any outbox record that must be created with the claim.
- Create or reuse the provider payment object using the stable business idempotency key; persist its identifier and current local state.
- Allow authentication and provider processing to run asynchronously. The hold deadline remains enforced by application transitions even while this work is in progress.
- Persist authorization or failure results idempotently. If the response is missing or ambiguous, record an unknown state and retrieve or reconcile the provider’s status.
- Before confirmation or capture, re-check that the reservation still owns the seat, the expected version is current, and the hold has not expired.
- If the required conditions pass, commit the reservation and initiate capture according to the chosen provider flow. If capture fails definitively, apply the documented failure policy; if capture is uncertain, keep it in reconciliation rather than assuming either success or failure.
- Release only through a conditional transition. If payment succeeds after the reservation has expired or lost the seat, follow the late-success policy instead of overwriting current inventory.
The exact ordering depends on the provider and payment method. For example, if the reservation is committed before capture, define how a definitive capture failure changes that committed reservation. If capture happens earlier, define how to compensate when the seat cannot be committed. In either order, an unknown outcome needs reconciliation before another charge or a potentially conflicting inventory decision.
How do you make duplicate webhooks and queue messages safe?
Assume events can be delivered more than once and can arrive out of order. Standard Amazon SQS queues provide at-least-once delivery, so a consumer may receive a duplicate. FIFO queues can help with ordered handling, but neither queue choice replaces business-level idempotency. A consumer should record or otherwise recognize the event ID, verify the reservation ID and aggregate version, and perform a side effect only if the current state permits it.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteA practical event envelope includes an event ID, reservation or order ID, aggregate version, event type, creation time, and correlation or trace ID. Keep processed-event or idempotency records for an application-chosen retention period. For stale events, define whether to ignore, retry, or reconcile; do not let an old success event reverse a newer cancellation simply because it arrived later.
Avoid a dual write that commits the reservation and separately publishes an event: either operation could succeed alone. The AWS transactional outbox guidance describes saving the domain change and outbox record together, then relaying the event, or using change-data capture such as DynamoDB Streams. The relay and consumer must still tolerate repeats.
| Choice | What it provides | What the application still must handle |
|---|---|---|
| Transactional outbox | The domain update and intent to publish are stored together; a separate relay publishes the outbox record. | Relay retries, duplicate publication, delivery monitoring, and idempotent consumers. |
| Change-data capture, such as DynamoDB Streams | Changes can drive downstream event processing without a separate application dual write. | Consumer retries, duplicate or stale effects, and recovery when downstream processing fails. |
When does saga orchestration help?
Use an explicit workflow when the sequence has several durable steps, timeouts, retries, and compensations that need visible ownership. AWS defines a saga as a sequence of local transactions; on failure, the workflow can retry or continue with forward recovery, or run compensating transactions for earlier effects. The AWS saga guidance recommends the pattern for long-lived transactions across services and notes that complexity and debugging effort grow with participating services.
Step Functions is one possible orchestrator; a queue-driven state machine or another workflow mechanism may suit different latency and operational requirements. Whichever approach is chosen, compensations should be idempotent and observable, with bounded retries, timeouts, dead-letter handling, and a route to reconcile unresolved outcomes. AWS’s event-driven payment-system guidance illustrates components including DynamoDB, Streams/EventBridge Pipes, Lambda, Step Functions, and SQS. It is an architectural example, not a seat-reservation implementation or throughput guarantee.
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Capacity planning must account for contention on the hottest seat or inventory keys; on-demand capacity does not remove application-level hot-key contention. Load-test the actual event traffic profile and key patterns, and keep admission control or waiting-room behavior outside the seat claim so a surge does not become uncontrolled retries against a small set of seats.
- Conditional-check failures, transaction conflicts or cancellations, and throttles.
- Queue depth and age, workflow age, and payment states that remain unresolved too long.
- Expired holds not yet released and the size and age of the reconciliation backlog.
- Retries, dead-letter volume, and the outcomes of compensation actions such as voids, refunds, and releases.
Alert on age and backlog as well as raw counts: a small number of old unknown payment outcomes can require more urgent attention than a high volume of quickly resolved retries. Set thresholds from the system’s own workload and customer commitments; the cited AWS and Stripe material supplies no universal requests-per-second target for this design.
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