The Trap of Database Triggers in Event-Driven Architecture

In distributed systems, there is a constant tension between reliability and autonomy. When we need to synchronize data between a transactional microservice and a downstream data warehouse, the "easy" answer is often the database trigger.

It starts innocently enough. A product manager asks, "Can we make sure updates to the Order table are reflected in the reporting dashboard immediately?" The engineering lead thinks, "Well, if we rely on application code to send events, developers might forget to add the code, or the process might crash after the database commit but before the event is sent. Let's just put a trigger on the table. It’s guaranteed to run."

It feels convenient. It feels safe. But in practice, database triggers are a siren song that leads to brittle, unscalable architectures.

The Illusion of "Immediate" Consistency

The desire for triggers often stems from a misunderstanding of the CAP and PACELC theorems. Stakeholders want immediate consistency (C) without sacrificing latency (L). But in a networked environment, you cannot have it all.

Triggers enforce what looks like strong consistency by coupling the reporting logic to the primary transaction. The result? Everything slows down.

The Performance Penalty

Database triggers execute within the same transaction context as your INSERT, UPDATE, or DELETE statements. This means your primary business transaction—the one the user is waiting for—cannot commit until the trigger finishes.

Benchmarks on PostgreSQL show that even a simple join-based trigger can reduce write throughput (TPS) by over 12% while increasing latency. This isn't just about speed; it's about coupling.

• Lock Contention: Triggers extend the duration of transaction locks. In high-concurrency environments, this leads to MultiXact overhead in MVCC databases like PostgreSQL, where the engine burns CPU cycles just tracking shared locks.
• Recursive Failure: If your trigger fails (e.g., the audit table is full), your business transaction rolls back. A reporting glitch effectively takes down your checkout process.

Meaning vs. Mutation: The Semantic Gap

The biggest architectural argument against triggers isn't performance—it's meaning.

A trigger captures a mutation: Row 123 in Table 'Orders' changed column 'Status' to 3. An event captures intent: Order 123 was Shipped.

When you rely on triggers to feed a data warehouse, you are forcing the warehouse to reverse-engineer business logic from raw data changes. This is a leaky abstraction. You are exposing your private internal schema to the outside world.

• Scenario: You decide to refactor your 'Orders' table for performance.
• Result: You silently break the warehouse pipeline because it was depending on the specific shape of that table.

With Domain Events, you define a contract (e.g., using Avro or Protobuf). The internal schema can change wildly, but as long as you emit the OrderShipped event with the agreed-upon fields, downstream consumers are safe.

Solving the "Forgetful Engineer" Problem

The primary defense for triggers is usually human error: "What if an engineer forgets to write the code to emit the event?"

This is a valid concern, but the solution isn't to bury logic in the database (gatekeeping); it's to build better tools (guardrails).

1. The Transactional Outbox Pattern

Instead of dual-writing (updating the DB and trying to publish to a message broker), write the event to an Outbox table in the same transaction as your business data. This guarantees atomicity. A separate background process picks up the outbox messages and sends them. You get the reliability of a trigger without the synchronous blocking penalty.

2. Leverage Modern Libraries (Wolverine & Rebus)

You don't need to implement the Outbox pattern from scratch. Modern .NET libraries solve the "Forgetful Engineer" problem by baking reliability into the infrastructure:

• Wolverine: Integrates the outbox deep into the command handling pipeline. When you call PublishAsync, Wolverine automatically enlists that message into the current database transaction (EF Core or Marten). The message is stored as an "envelope" alongside your entity changes and is only sent to the broker after the commit succeeds.
• Rebus: Uses a transaction-scope approach. It allows you to configure an outbox that hooks into your database transaction. Messages published during the request are stored in a local table first, ensuring that if the transaction rolls back, the message is never sent.

By adopting these tools, you turn "reliability" into a configuration setting rather than a repetitive coding task.

3. The Warehouse as a First-Class Consumer

The most robust architectural decision is to stop treating the data warehouse as a special case that requires "database synchronization." Instead, treat it like any other microservice.

Publish Domain Events for Everyone When your application publishes an OrderShipped event, it shouldn't care who is listening. It could be the Email Service, the Inventory Service, or the Data Warehouse.

By having the warehouse subscribe to the same public Domain Events as your other services:

• Decoupling: The warehouse depends on the contract (the event schema), not the implementation (the database tables).
• Resilience: You can refactor your internal database schema completely without breaking the warehouse reports, as long as you continue to publish the standard events.
• Consistency: Your business logic is the single source of truth for all downstream systems.

Use the Outbox as an Internal Detail To ensure these events are never lost (to solve the "Dual Write" problem), we use the Transactional Outbox pattern mentioned above. But crucially, the Outbox is an internal implementation detail of the publisher. The outside world—including the data warehouse—doesn't hook into your database or your outbox table directly. They simply subscribe to the message bus and receive clean, versioned events.

Conclusion: Guardrails Over Gatekeepers

Triggers are a relic of monolithic database design. In a modern distributed system, we need to treat data storage and data integration as separate concerns.

• Storage is for state persistence.
• Integration is for communicating behavior.

By moving from triggers to the Transactional Outbox pattern and Domain Events, we gain:

1. Safety: We still guarantee data delivery (at-least-once).
2. Performance: We stop blocking user transactions for reporting tasks.
3. Clarity: We communicate "Business Facts," not just "Row Updates."

Don't let the fear of forgetting code drive you into the trap of database coupling. Build the guardrails in your application platform, and let your database do what it does best: store data efficiently.