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The dependable fix for the dual-write problem is to save the business change and an event record in the same database transaction, then publish that record asynchronously through a relay. This transactional outbox pattern closes the gap where a database commit succeeds but publishing fails. It does not make delivery exactly once: relays can publish duplicates, so consumers must be idempotent, and ordering needs its own design.
How do I keep my database and event bus consistent?
Put an event in a transactional outbox as part of the same local transaction that updates the business data. A separate relay reads committed events and publishes them to the broker. If the transaction commits, both the business change and the outbox record exist; if it rolls back, neither does. This is the key guarantee of the transactional outbox pattern.
The outbox does not make the database and broker participants in one atomic transaction. Instead, it makes the database transaction the reliable handoff point: the service durably records what it needs to publish before attempting publication. A relay can retry after a temporary failure without losing the event.
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Store enough information to publish a stable, meaningful event without guessing from a later version of the business record. A typical outbox entry includes:
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- A unique, stable event ID for deduplication.
- An aggregate or entity ID, such as an order ID or account ID.
- An event type and, where appropriate, a schema version.
- The event payload, or a reference to the data needed to construct it.
- A creation timestamp and a sequence number or entity version when ordering matters.
In a relational database, the outbox is commonly a table. In a document database, event data may be stored as a property of the same item as the business state, subject to that database’s transaction boundaries.
What does the workflow look like?
- Begin a local transaction. Validate the command and apply the business change.
- Insert the event record. Use a stable event ID and include the entity identity, type, payload, and any required ordering information.
- Commit once. Commit the business record and outbox entry together.
- Run a relay. Discover committed entries by polling, transaction-log tailing, or a database change feed.
- Publish and record progress. Send each event to the broker. Only after broker acknowledgement should the relay mark it complete or delete it.
- Deduplicate at consumers. Before applying an event’s effect, check whether its ID has already been processed, or use another durable idempotency mechanism.
In simplified form:
BEGIN
update business_entity
insert outbox_event(event_id, aggregate_id, event_type, sequence, payload)
COMMIT
relay:
read committed outbox events
publish event
after broker acknowledgement, record progress or delete the row
consumer:
if event_id already processed: acknowledge or safely ignore
otherwise apply the effect and record event_idThe sender, database, outbox, and relay are distinct roles in the pattern. They may run in different processes, but the atomic step is the write of business state plus event record inside the database transaction.
What happens if the app crashes after the database commit but before publishing?
With a naive database-then-broker sequence, the business change remains committed while downstream services never receive its event. With an outbox, the event record remains in the database, so a relay can discover and publish it after recovery.
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Reversing the order does not solve the problem. If the application publishes first and the database transaction then fails, consumers may act on a phantom event for a change that never committed. AWS describes both inconsistent outcomes in its transactional outbox guidance.
A short retry loop or a highly available broker can reduce the chance of a failed publish, but neither makes the two operations atomic. The durable fix changes the boundary: persist the intent to publish in the same transaction as the business change, then retry delivery from that durable record.
How do I fix the dual-write problem?
Start by deciding which service owns the business change and which database transaction is authoritative. Put the outbox in that service’s database, write the event alongside the state change, and assign a relay responsibility for delivery. Then design explicitly for retries, duplicate delivery, ordering, and cleanup.
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1. Define the event contract
Choose event names and payloads that describe a business fact consumers can act on. Include a stable ID and the aggregate ID. Add schema versioning when consumers need to distinguish payload formats, and include a sequence or version if they must detect stale or out-of-order changes. Avoid relying on a mutable database row as the only source of event meaning: a relay that reads the row later may see a newer state than the one that caused the original event.
2. Make the transaction boundary real
Verify that the business write and outbox insert use the same database transaction and the same database resource. If one write is outside the transaction, the outbox cannot protect it. For a document database, verify its partition or item-level transaction scope; the phrase “same database” alone does not prove the writes are atomic together.
3. Choose the relay mechanism
For a modest event volume or a team seeking a straightforward implementation, a polling publisher is often the clearest starting point. For an environment already equipped to operate a transaction log or native change feed, CDC may fit better. The trade-offs are discussed below.
4. Specify acknowledgement and retry behavior
Define what counts as successful publication: typically, the broker’s acknowledgement. Retain or retry an event if publication fails or the relay cannot confirm success. Decide how retries are delayed and bounded, and how a message that repeatedly fails is quarantined or dead-lettered. Deleting or marking an entry complete before acknowledgement risks loss; leaving it unmarked after successful publish can lead to a duplicate, which is safer if consumers are idempotent.
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Store processed event IDs durably, often in a deduplication table with a uniqueness constraint, and apply the event’s business effect in a transaction that also records the ID. If a consumer crashes, this arrangement prevents it from applying the same event effect twice. Where possible, make operations naturally idempotent—for example, setting a status to a specified value rather than blindly incrementing a counter.
6. Operate the outbox as production data
Monitor the age of the oldest unpublished event, relay lag, retry counts, duplicate rates, and broker queue depth. Set retention, cleanup, archival, and replay procedures. Keep enough information to investigate and recover failures without retaining event data indefinitely by accident.
Exercise failure points deliberately in system-level tests: after the local commit, after broker publication but before progress is recorded, and during consumer retries. Those cases expose whether the system loses events, produces duplicates safely, or violates an ordering rule.
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Should I use a polling publisher or CDC?
Polling reads pending rows from an outbox table; change data capture (CDC) reads committed database changes from a transaction log or native change feed. Both can relay durable changes, but they differ in latency, database load, event modeling, and operational demands.
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| Approach | How it finds events | Strengths | Trade-offs |
|---|---|---|---|
| Polling publisher | Periodically queries committed, unprocessed outbox rows. | Easy to understand; works with ordinary relational transactions; uncompleted rows remain durable and can be retried. | Latency depends on polling interval and batch size; queries and cleanup use database capacity; ordering needs explicit handling. |
| Transaction-log tailing or CDC | Reads changes from a database log or native change feed. | Can avoid repeated table scans and may use database-native change delivery. | Requires database-specific capabilities and operational knowledge; event meaning may need reconstruction; offsets, schemas, streams, replication slots, or equivalent components need management. |
A polling relay usually provides a visible application-level event record. Teams can inspect what is waiting, retain or replay selected rows, and define the event contract directly. Its costs are polling load, cleanup work, and latency introduced by its polling schedule.
CDC can be useful when database changes themselves are the integration contract or when a native feed is already part of the platform. It is not automatically an application-event system: a row update may not say clearly whether a business event occurred, and reconstructing event meaning from database changes can couple consumers to schema details.
PostgreSQL logical replication, for example, uses a publisher-subscriber model: it sends an initial snapshot and then changes as they occur, applying changes in publisher order within a subscription. PostgreSQL also documents possible conflicts when multiple subscribers write to the same replicated tables. The linked documentation is for PostgreSQL 13, which is marked unsupported; check documentation for the PostgreSQL version actually deployed before relying on version-specific behavior.
Choose based on the event contract and operational capacity, not the assumption that CDC eliminates reliability work. Either route still needs a clear acknowledgement boundary, recovery behavior, and consumer handling for repeated delivery.
How do I prevent duplicate events?
Assume that a relay may publish an event more than once. If it crashes after the broker accepts a message but before the relay records success, it may retry the same event after restarting. The outbox makes the source change and event record atomic; it does not make the broker publish and the relay’s progress update one atomic operation.
The usual honest guarantee is at-least-once delivery: an event remains retryable until the relay knows it was accepted, but a retry can create a duplicate. The pattern’s description calls out this crash scenario and the need for idempotent consumers. AWS also notes that standard SQS delivery can deliver the same message more than once in its guidance.
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- Assign each logical event a unique ID once, when creating the outbox record. Do not generate a new ID for every relay attempt.
- Record processed IDs durably at the consumer and make the deduplication record atomic with the consumer’s business effect.
- Use idempotent state-setting operations where appropriate, while recognizing that not every business action can be made idempotent by changing its shape.
- Use aggregate IDs and sequence numbers to identify stale or repeated updates when the domain needs them.
- Include retries, broker acknowledgements, and dead-letter or quarantine handling in the documented delivery contract.
“Exactly once” needs a narrowly stated scope. A broker may offer exactly-once behavior for a specific operation or boundary, but that does not by itself guarantee that a consumer’s database effect happens exactly once across independent systems and failures. The outbox should not be described as an end-to-end exactly-once guarantee.
How should I preserve event order?
Ordering is a separate design requirement from atomic persistence. If one order changes in transactions T1 and T2, consumers may need its events E1 and E2 in that same order, but a general-purpose outbox does not automatically guarantee delivery order across all entities or all workers.
Define the scope first: per aggregate, per broker partition, per session, per subscription, or global. Global order is a much stronger requirement than most business workflows need. A common design preserves order for one entity while allowing unrelated entities to progress concurrently.
- Store an aggregate version or monotonically increasing sequence with each event.
- Partition messages by aggregate ID, or use the broker’s session mechanism where available.
- Ensure relay workers do not advance the same aggregate out of order; use safe coordination or a single ordered worker for that scope.
- Define consumer behavior for a missing sequence, a stale event, or an earlier event that is retried, dead-lettered, or rejected.
Azure Dataverse guidance says Service Bus sessions provide ordering within a session, not globally, and recommends application-level sequence numbers when missing or out-of-sequence events matter. See the Dataverse transactional outbox architecture. PostgreSQL’s publisher-order behavior is likewise scoped to a subscription, not a universal ordering promise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When is an outbox not enough?
An outbox solves reliable outbound publication from one service-owned transaction. It does not make several independently owned databases one ACID transaction. If a business workflow requires multiple services to change their own data, coordinate those steps as a distributed workflow—commonly a saga—instead of stretching one local outbox beyond its boundary.
A saga makes progress through explicit steps and needs defined behavior for retries, timeouts, failures, and compensating actions. Each participating service can still use an outbox to publish its own commands or events reliably; the saga coordinates the larger workflow and tracks its state.
Two-phase commit may be available in a narrow environment, but it depends on support from the participating database and broker and couples them to one distributed transaction. The outbox pattern guidance treats this as a specialized alternative, not a default repair. AWS recommends sagas or transaction-level handling when work spans multiple services in its architecture guidance.
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What changes for Cosmos DB, PostgreSQL, and Dataverse?
The pattern is general, but the atomicity boundary and relay options depend on the database and platform. Check the supported transaction scope before choosing where the business object and event will live.
Azure Cosmos DB
Microsoft’s Cosmos DB example writes the business object and event in the same transactional batch, then uses Change Feed and Service Bus for background publication. A Cosmos DB transactional batch is atomic only within one logical partition; it cannot write documents in different containers or logical partitions as one batch. The partition key is therefore part of the consistency design, not only a scaling decision. The Microsoft Cosmos DB architecture describes reliable messaging, ordering, deduplication support, and TTL-based cleanup, while noting that its sample is not production-ready and has multithreading limitations. Treat the sample as an architectural illustration rather than a drop-in production implementation.
PostgreSQL
Logical replication is a database-change delivery option: it begins with a snapshot and streams later changes. It may suit integrations that need replicated row changes, while an application-level outbox is better suited to an explicit business-event contract. The PostgreSQL 13 logical replication documentation is for an unsupported release, so confirm details in documentation for the supported version in use.
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Dataverse and Azure Service Bus
Microsoft’s Dataverse reference architecture writes an outbox record in the same transaction as the source record and uses an asynchronous plug-in to send messages to Azure Service Bus. Its operational recommendations include Azure Monitor metrics, Application Insights traces, retries, dead-letter handling, and application-level sequence numbers where needed. See the Dataverse reference architecture.
What should I check before putting an outbox into production?
Use this checklist to validate the whole delivery path, not only the database insert:
- Ownership: Is there one clearly identified source of truth and service responsible for the business transaction?
- Atomicity: Are the state change and outbox entry committed in one local transaction, within the database’s actual transaction scope?
- Event identity: Does every event have a stable ID, aggregate ID, type, timestamp, and schema version? Is a sequence included when required?
- Relay recovery: Can the relay safely restart, retry, and resume from durable progress without losing unacknowledged events?
- Consumer behavior: Is deduplication durable and coordinated with the business effect? Can the consumer tolerate retries?
- Ordering: Is the promised scope explicit, and does it match the relay, broker, and consumer behavior?
- Poison events: Is there a quarantine or dead-letter path, with a procedure to inspect and recover rejected messages?
- Observability: Are outbox age, relay lag, retries, duplicates, and queue depth visible to operators?
- Retention and replay: Are cleanup, archival, retention, and replay rules defined before old entries accumulate?
- Failure exercises: Have you verified crashes after commit, after broker publication but before recording success, and during consumer retries?
- Workflow boundary: If multiple services must change state, is a saga or another explicit cross-service workflow coordinating them?
Frequently Asked Questions
Should an outbox event contain a full snapshot or just an entity ID?
There is no universal payload shape established by the pattern. A self-contained event can reduce consumers’ dependence on later reads, while a reference keeps messages smaller but requires the referenced data to remain available and suitable for the consumer’s purpose.
Can I use the outbox to replay events after a consumer outage?
Potentially, if the event records or an archive are retained and the consumer can safely process them again. Replay needs an explicit retention and offset strategy, because cleanup rules that are safe for routine delivery may remove data needed for recovery.
Should every database table change create an outbox event?
No. Emit events for meaningful facts or changes that other components need to act on, rather than treating every low-level row mutation as a public contract. This keeps consumers less coupled to internal schema details.
