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Use the database as the authority for transactional state and a message queue or event log to distribute committed changes to independent consumers. The difficult part is ensuring a database commit and its corresponding message cannot silently get out of sync. A transactional outbox or change data capture (CDC) can close that gap; retries and duplicate-safe consumers handle the delivery that follows.
Why can a database and queue get out of sync?
A common but fragile design performs two separate writes: the application commits a business change to the database, then publishes a message. If the process fails between those operations, the database may contain the change while the queue has no corresponding event. Reversing the order creates the opposite risk: a message can be delivered for a database change that never commits.
This is the dual-write problem. A database transaction generally cannot atomically commit a change in the database and a publish to a separate broker unless the systems participate in a shared transaction protocol. Rather than treating that gap as impossible, design the pipeline so publication can be retried and consumers can safely handle repeated delivery.
How does a transactional outbox keep the intent to publish?
In an outbox design, the application writes the business change and a corresponding event record to an outbox table in the same database transaction. If the transaction rolls back, neither is committed. If it commits, both the state change and the durable intent to publish are available for a separate relay to process. AWS Prescriptive Guidance describes the outbox as a way to resolve dual writes involving a database update and an event notification.
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- Write state and event together. The application’s transaction updates the business row and inserts an event with a stable identifier and the information consumers need.
- Relay committed events. A separate process either polls for unpublished outbox rows or captures their committed changes through CDC, then publishes them to the broker.
- Record progress and retry safely. The relay must recover after a crash without assuming a publish happened just because it started. If it republishes an event after an uncertain outcome, the consumer must tolerate that duplicate.
The outbox makes the database update and the intent to publish atomic; it does not make the later broker delivery and every consumer’s side effect one indivisible transaction. That distinction is why stable event IDs and idempotent handling matter.
Polling relay or CDC relay?
| Approach | What it does | Main trade-off |
|---|---|---|
| Polling outbox | A relay queries the outbox table for records to publish. | Conceptually straightforward, but repeated queries and cleanup or progress tracking add work on the application database. |
| CDC outbox relay | A connector reads committed outbox changes from the database log and routes them to the broker. | Can avoid polling queries, but requires operating the connector and managing database log retention and recovery. |
Neither approach is universally better. Choose against the required freshness, expected load, recovery plan, and the team’s ability to operate polling infrastructure or CDC connectors. The available documentation does not establish a general latency or throughput advantage for either approach.
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Should consumers receive domain events or raw table changes?
An outbox is useful when the application needs to publish an intentional contract such as “order accepted” or “account closed.” The event shape and meaning remain under application control, rather than being inferred from each table mutation. Debezium’s outbox event router captures changes from a deliberately structured outbox table and routes them as events; its routing can be configured around aggregate fields. The documented router applies to supported connectors and is not compatible with the MongoDB connector.
Raw CDC has a different purpose: it streams database row changes, which can be useful for replicas, analytical stores, and integrations that need to track table state. But an insert, update, or delete is not automatically a meaningful business event. Consumers built directly on table changes may become coupled to internal schema changes and database-specific semantics.
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- Choose an outbox contract when downstream services should react to named business events with a stable, application-owned shape.
- Choose raw CDC when consumers need row-level changes to copy or process database state.
- Use both deliberately if separate consumers need business events and a fuller change stream; document their different meanings and ownership.
How can PostgreSQL changes reach Kafka?
PostgreSQL logical replication begins with an initial snapshot of existing data and then streams subsequent changes. PostgreSQL 15 documentation states that changes within a subscription are applied in publisher commit order; that is not a promise of global ordering across unrelated subscriptions or every stage of a wider pipeline.
For a Kafka-based CDC path, Debezium’s PostgreSQL connector takes a consistent initial snapshot on first connection, then emits row-level insert, update, and delete records to Kafka topics. It reads PostgreSQL logical decoding and WAL changes through Kafka Connect. This is a way to stream table changes; if consumers need domain events, route an outbox instead of assuming every row mutation expresses business intent.
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Operationally, account for the connector’s continuity and the database’s WAL resources. PostgreSQL can purge WAL segments, so a connector that falls behind or loses the changes it needs may not be able to continue from its prior position without recovery work. Monitor connector progress and replication-slot/WAL usage, set a recovery and resnapshot plan, and ensure retention choices match how long a consumer or connector may be unavailable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does “exactly once” mean in this pipeline?
Delivery guarantees apply to particular legs, not automatically to the whole system. Kafka documentation distinguishes at-most-once delivery, which can lose records but does not redeliver them; at-least-once delivery, which can redeliver; and exactly-once flows with a defined transactional boundary. Kafka transactions can atomically commit records to Kafka output topics together with consumed offsets in supported processing flows.
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That Kafka transaction does not by itself make an update to an external database exactly once. The destination must participate in the guarantee—for example, by storing the output and consumed offset together—or the consumer must make repeated effects harmless through idempotency or deduplication. AWS also documents that standard SQS queues use at-least-once delivery and may deliver the same event more than once; consumers of those queues need the same duplicate-aware discipline.
For a database sink, a practical pattern is to include a stable event ID and use it to make the write idempotent: apply an upsert keyed by the business identity where appropriate, or store processed event IDs in an inbox/deduplication table. Acknowledge or advance the broker offset only after the sink effect is durable. Retain retries with backoff, define what happens to poison messages that repeatedly fail, and make ordering expectations explicit—often per key or aggregate rather than global.
How should a team choose and operate the pipeline?
Start from the consumers and recovery requirements, then select the mechanism. A queue or log can buffer work and distribute it independently, but retention, partitioning, ordering, and the sink’s ability to catch up determine how useful that buffer is during an outage.
- Freshness: State the actual freshness objective and validate the end-to-end path against it. There is no workload-independent latency number established for polling, CDC, or managed hosting.
- Ordering: Specify the scope consumers require—per aggregate, key, partition, or database transaction. Do not infer global ordering from PostgreSQL’s subscription-level commit ordering.
- Replay and rebuild: Decide how much broker retention is needed, how far a consumer may fall behind, and how to rebuild a sink from a snapshot or retained events if it cannot resume.
- Schema and ownership: Version and own event contracts intentionally. A domain event can insulate consumers from table layout changes; a raw CDC consumer must account for evolving schemas and row semantics.
- Operations: Include broker and connector monitoring, database WAL or replication capacity, failure recovery, and the expertise needed to run the chosen infrastructure. Managed Kafka hosting such as Amazon MSK is an option to assess against self-managed Kafka, not a default answer.
- Consumer safety: Test duplicate delivery, retry after a sink failure, offset recovery, and poison-message handling before relying on the pipeline for critical state.
A sound design states what is authoritative, where the event becomes durable, what can be replayed, how duplicates are contained, and which component owns recovery. Those boundaries—not the label “real time” or “exactly once”—determine whether the pipeline behaves predictably when a process or service fails.
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