Every Outbox Needs an Inbox

At-Least-Once + Idempotency using Outbox and Inbox patterns.

Why Message Delivery Still Fails

You've just deployed a feature that triggers an email confirmation after a user places an order.

• The user gets charged.

• The order shows up in the database.

• But the email never arrives.

The logs show that the service meant to publish an event... but never did.

You sigh, knowing this isn't your first lost message and won't be the last unless you fix your architecture.

The problem? We often focus only on sending messages reliably (Outbox), but forget to handle receiving messages reliably (Inbox).

Both sides matter. And that's why every Outbox needs an Inbox.

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Understanding Delivery Guarantees

Before diving deeper, it's worth clarifying the three major delivery guarantees in distributed systems:

• At-most-once: Messages are delivered at most once. Some may be lost. No duplicates.

• At-least-once: Messages are retried until acknowledged. No loss, but duplicates are possible.

• Exactly-once: Messages are delivered once and only once. No loss, no duplicates. Very hard to achieve reliably across distributed components.

Most production systems settle on at-least-once + idempotency, as it balances complexity and reliability.

The Outbox and Inbox patterns are what make this delivery guarantee practical.

The Outbox Pattern: Reliable Send

The Outbox pattern ensures that domain events are sent only if the related business operation is committed.

How it works:

1. Begin DB Transaction
2. Save order to `orders` table
3. Insert event to `outbox` table
4. Commit transaction

A background process then polls the outbox table and sends messages to the message broker.

This ensures:

• No message is sent if the DB write fails

• No DB write is done without queuing the message

But here's the catch: It only covers your half of the contract.

The Inbox Pattern: Reliable Receive

Now, imagine your order service emits an event, but the inventory service crashes just before processing it. Or processes it twice due to retries.

That's where the Inbox pattern comes in.

How it works:

1. Receive message from broker
2. Start DB transaction
3. Check if `message_id` exists in `inbox` table
4. If not, insert into `inbox`, then process
5. Commit

This ensures:

• Messages are processed only once (idempotency)

• Messages are never lost due to crashes

The combination of Outbox + Inbox gives you at-least-once delivery with idempotent processing, the practical sweet spot.

Putting All Together

The following sequence diagram ties everything together. It shows the entire lifecycle of a message from the moment a user places an order, through reliable event emission via the Outbox pattern, to safe and idempotent processing on the receiver's side via the Inbox pattern.

After the event is saved to the inbox table, the same service that inserted it (e.g., InventoryService) proceeds to execute the corresponding business logic, like updating inventory stock or initiating follow-up actions. This execution is part of the same transaction that saves the inbox entry to ensure consistency.

Step-by-step breakdown:

• The user places an order.

• OrderService wraps the operation in a transaction, writing to both orders and outbox_events tables.

• A background dispatcher picks up new events from the Outbox and publishes them to a message broker.

• InventoryService receives the event, checks its inbox table to avoid duplicates, and processes it if not alr’t already been handled.

This flow ensures that no message is lost and each message is processed exactly once, achieving at-least-once semantics with idempotency.

Here's how the full flow looks as a sequence diagram:

Real-World Example

Stripe uses a variation of this pattern. Events are stored and replayable. Every event includes an idempotency_key and event delivery is tracked for success.

Many teams roll their own simpler version using:

• Postgres tables for outbox/inbox

• Kafka or RabbitMQ for transport

• A cron job or queue-based worker for dispatch

Trade-offs and Failure Modes

• Delayed processing: Outbox dispatcher or Inbox consumer can lag

• Duplication: At-least-once delivery means processing logic must be idempotent

• Storage growth: Outbox/Inbox tables can grow; need purging/archiving strategy

• Delivery not immediate: Adds latency due to polling or background workers

Despite these, it's the most robust design for mission-critical messaging between services.

Anti-Patterns and Pitfalls

While the Outbox and Inbox patterns are powerful for reliable messaging, several pitfalls can undermine their effectiveness if not addressed. These patterns should not be treated as fire-and-forget solutions; they require thoughtful integration into your system’s lifecycle, operations, and failure management strategies:

• Unbounded Table Growth: Without a strategy to purge or archive old entries, outbox and inbox tables can grow indefinitely, impacting performance and storage costs. You need to implement regular cleanup or archival processes.

• Database as Bottleneck: Relying heavily on the database for both business and messaging operations can reduce system elasticity. If high throughput is a priority, you must consider alternatives or optimizations.

• Immediate Delivery Expectations: These patterns introduce latency, as events are processed asynchronously. Don’t assume immediate delivery; design your system to tolerate some delay.

Wrapping up

• Sending reliably isn't enough. You must also receive reliably.

• Outbox ensures events are emitted only after successful business ops

• Inbox ensures events are processed once, even if duplicated

• Exactly-once delivery is hard. Aim for at-least-once + idempotency

• Both patterns rely on transactional writes and retryable processing

To understand why the Outbox and Inbox patterns matter, consider this well-defined comparison of delivery guarantees:

Build like the network is unreliable—because it is.

Every outbox needs an inbox.

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