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Why a timeout can make a retry dangerous
A timeout tells the planner that it did not receive a timely response. It does not establish whether the operation failed. The database or external service may have completed the action and lost the acknowledgment on its way back. If the planner submits the action again as a new operation, the business logic may run twice.
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Google Cloud’s guidance on transaction retries warns that retrying after a network timeout without deduplication risks executing business logic twice. The practical distinction is between retrying delivery of the same logical action and creating a second action. A safe retry must preserve the identity of the first action and let the system find its existing state or result.
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What the transactional outbox does—and does not do
A transactional outbox stores a pending action in a durable record alongside related application state. Both writes commit in one local database transaction. A separate worker reads committed records and delivers them to a tool, queue, or service.
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- If the transaction rolls back, neither the relevant local state nor its tool intent commits, so a worker has nothing to deliver.
- If the transaction commits but the planner loses the acknowledgment or stops, the intent remains available for recovery.
- If delivery succeeds but the worker crashes before recording completion, it may deliver the same intent again.
That last case is why an outbox is not an exactly-once mechanism. Relays commonly provide at-least-once delivery: they keep trying to deliver a committed intent, and duplicate deliveries are possible. The consumer must make repeated processing harmless, deduplicate by a stable identity, or provide a way to reconcile uncertain outcomes. AWS Prescriptive Guidance, Microsoft Learn, and Confluent all describe duplicate handling as part of the design.
Design the planner around one stable intent ID
- Choose the logical identity at request acceptance. Associate the requested action with an identifier that remains the same when the planner retries that action. Do not assign a fresh identity merely because a transport attempt or worker run is new. The sources do not prescribe a planner-specific ID format.
- Persist state and intent atomically. In one database transaction, write the planner’s relevant state and an outbox record. A practical record can include the intent ID, tool or action name, validated arguments or a safe reference to them, creation time, and delivery status. This is an implementation shape, not a required schema from the cited guidance.
- Enforce uniqueness at the write boundary. Use a unique constraint or equivalent conditional write on the logical intent identity. If the same request arrives again, return or look up the existing intent rather than inserting a second one. The identity check and state change must be coordinated so concurrent retries cannot both create a new logical action.
- Have a relay process committed pending records. The worker should claim pending work, record enough attempt and outcome information to recover after a restart, and avoid treating an uncommitted row as deliverable. Use bounded retries and backoff appropriate to the workflow; there is no universal retry interval established by the cited guidance.
- Carry the identity downstream. If the tool or API accepts an idempotency key, send the intent ID as that key. If it does not, deduplicate at a receiver you control or define reconciliation and human-review behavior for an uncertain result.
- Return the existing operation on a repeated planner request. Report whether that intent is pending, completed, or outcome unknown. Do not turn an unanswered request into a second logical action, and do not label an unknown outcome as a confirmed failure.
What should happen in each failure case?
| Failure point | What may have happened | Safe response |
|---|---|---|
| Database transaction fails | Neither the related state nor the outbox intent commits. | Do not invoke the tool. The request can be retried as the same logical action. |
| Commit succeeds, but the planner misses the acknowledgment | The intent exists even though the caller may believe the request failed. Google Cloud documents this unknown-commit situation. | Retry with the same identity and look up the existing intent. Do not create a new intent just because the acknowledgment was lost. |
| Worker stops before the external call | The committed intent remains pending or recoverable. | Resume delivery from the durable record. |
| External call succeeds, then worker stops before marking completion | The external effect may have happened although the outbox still appears unfinished. | Retry only if the downstream boundary deduplicates the same identity; otherwise reconcile the result before risking another effect. |
| External service is unavailable | The intent may still be valid even though this delivery attempt failed. | Retain it for bounded, observable retry or route it for recovery. Do not delete the intent merely because one attempt failed. |
Choose a relay and transaction boundary that fit the system
The outbox pattern is not one particular broker or database feature. The important question is whether the application can atomically store its local state and the intent, then reliably relay committed records.
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| Approach | How it relays intent | Boundary and trade-off |
|---|---|---|
| Relational outbox with AWS messaging | A database transaction writes the business row and outbox record; a processor relays the record to Amazon SQS. AWS also describes DynamoDB Streams as a change-data-capture option. | Standard SQS may deliver a message more than once, so AWS calls for idempotent processing. Queue delivery does not make an arbitrary external side effect transactional. |
| Azure Cosmos DB change feed | A transactional batch writes the business object and event; the change feed relays the event. Microsoft describes using the event document ID as the Azure Service Bus MessageId for duplicate detection. | Cosmos DB transactional batches operate within one logical partition. Microsoft’s example has multithreading limitations and is explicitly not production-ready. |
| Kafka with an outbox relay | A separate process, connector, or change-data-capture path publishes outbox records to Kafka. | Confluent describes at-least-once delivery, so downstream duplicate handling remains necessary. Frequent outbox inserts and deletes can also add resource pressure. |
Choose polling, a change feed, or another CDC relay based on the database and operational model. Preserve per-aggregate or workflow order only where the domain requires it; event-sourcing workflows are one case where order can matter. If the database restricts transactions to a partition or equivalent boundary, ensure the state and intent can be written within that boundary.
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Account for the external tool boundary
A database transaction cannot generally include an unrelated API call. Calling a tool inside a transaction that may be retried can therefore repeat the external call even if the database rolls back. An outbox moves that call out of the local transaction, but it cannot make an arbitrary external service participate in the database commit.
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Before relying on automatic retries, determine whether the destination supports an idempotency key, exposes operation status, or offers a reconciliation path. If none is available and repeating the action could cause harm, an ambiguous result needs a deliberate recovery path—possibly pausing for human review—instead of blind retry. Broker-level duplicate detection can reduce repeated messages, but it does not by itself make the tool’s side effect atomic or remove the need to protect the application boundary.
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- Durable storage and cleanup: Outbox rows consume storage. Define retention and cleanup so records are preserved long enough for recovery and audit needs without growing indefinitely.
- Relay operations: Monitor pending work, delivery failures, retry attempts, and backlog age; establish a way to resume or inspect work after a worker restart.
- Delivery delay: A relay adds a step between accepting an action and invoking the tool, so the workflow must tolerate that delay or communicate pending status.
- Write load and contention: Persisting an additional record adds database work. Confluent notes that frequent inserts and deletes can create resource pressure; measure the effect in the actual workload rather than assuming a universal cost.
AWS Durable Execution documentation discusses at-least-once and at-most-once retry semantics for that specific AWS feature. Those settings can clarify what that workflow retries, but neither should be read as a general guarantee of exactly-once execution across a planner, database, relay, and external tool.
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