Beyond the Tutorials: What Really Happens with the Outbox Pattern
The outbox pattern promises atomic consistency in distributed systems, but the implementation reality is messier than most tutorials admit. Here's what actually happens when you combine Go, Postgres, and the complexities of real-world event delivery.
The outbox pattern is supposed to save us from distributed systems hell. Write your event to the database, commit the transaction, and let a background process handle the messy details of actually sending it to your message broker. Simple, elegant, bulletproof, or so they tell you.
Here’s what’s actually happening in production: Your outbox table is growing exponentially, your background processor is choking on FOR UPDATE SKIP LOCKED contention, and that “at-least-once delivery” guarantee is starting to feel like a cruel joke when consumers are drowning in duplicate messages.
The Promise That Keeps Breaking
The core idea sounds brilliant: Instead of the classic dual-write problem where you update your database and pray your message broker call succeeds, you write both the business data and the event to Postgres in a single transaction. One commit, guaranteed consistency. The message relay will pick it up later. What could go wrong?
Everything, it turns out.
The pattern’s elegance masks a fundamental truth: you’re trading one complex distributed systems problem for several smaller, but equally nasty, ones. Alex Pliutau’s recent implementation ↗ shows the basic mechanics, but smooths over the operational nightmares that keep infrastructure engineers awake at night.
The Postgres Lock Contention Horror Show
Let’s talk about that FOR UPDATE SKIP LOCKED query that’s supposed to prevent multiple relay instances from processing the same message:
Looks innocent, right? Under load, this becomes a database murder weapon. Each relay instance is constantly polling, creating a thundering herd effect. Postgres spends more time managing locks than actually processing your business logic. The SKIP LOCKED helps, but at scale, you’re essentially DDoSing your own database.
One team I worked with saw their outbox table hit 10 million pending messages during a Black Friday sale. The relay workers were consuming 80% of database CPU just fighting over locks. Their “solution”? Add more relay instances, which made the problem exponentially worse.
The WAL Position Time Bomb
Some smart engineers skip the polling approach entirely and tap into Postgres’s Write-Ahead Log (WAL) using logical replication. The pglogrepl ↗ library lets you stream changes directly, eliminating the polling overhead.
Sounds perfect, until you realize you’ve traded simple polling complexity for the joy of managing WAL positions, replication slots, and the inevitable moment when your consumer falls so far behind that Postgres starts complaining about WAL retention. Miss a single heartbeat message and you’re rebuilding your entire event stream from scratch.
Teams using this approach often discover their “simple” outbox pattern requires a dedicated Postgres DBA just to keep the replication slots healthy. The complexity doesn’t disappear, it just gets redistributed.
The Idempotency Trap
“Just make your consumers idempotent”, they say, as if it’s as simple as adding a unique ID check. The reality is messier. Consider this scenario:
- Order service writes order and outbox message in transaction
- Relay publishes message to broker
- Relay crashes before updating message as “processed”
- Relay restarts, finds same message, republishes
- Consumer receives duplicate, checks ID, processes anyway because the business logic spans multiple tables and microservices
Suddenly your “idempotent” consumer needs to understand the entire distributed state of your system. The outbox pattern didn’t eliminate distributed systems complexity, it just pushed it downstream.
The Observability Black Hole
Traditional monitoring breaks down with outbox patterns. Your application metrics show healthy request-response times, while events are actually arriving minutes late because the relay is silently choking. By the time you notice the lag, you’re already in crisis mode.
Worse, the pattern creates a new failure mode: messages that are technically “delivered” but never actually processed because the consumer is overwhelmed by duplicates. Your dashboards show green lights while your business grinds to a halt.
Smart teams track end-to-end latency from database commit to actual business outcome, not just broker delivery. They monitor relay batch sizes, Postgres lock wait times, and consumer backpressure. It requires infrastructure that most teams don’t realize they need until it’s too late.
The Performance Paradox
Here’s where it gets spicy: The outbox pattern is supposed to improve reliability, but it can actually reduce performance in ways that seem counterintuitive. Every transaction now requires an additional insert into the outbox table. Under high load, this seemingly minor overhead compounds dramatically.
One benchmarking exercise showed a 40% throughput decrease when adding outbox logic to a payment processing service. The additional I/O from the outbox insert, combined with increased lock contention, meant the system could handle significantly fewer transactions per second.
The team’s “solution” was to batch multiple events into single outbox inserts, which introduced its own complexity around partial failures and message ordering. They were literally trading throughput for reliability while claiming victory.
The Vendor Lock-In Accelerant
Perhaps the most ironic outcome: teams adopt outbox patterns to avoid vendor lock-in with specific message brokers, then end up deeply coupled to specific database behaviors.
Your Postgres-specific SKIP LOCKED syntax, WAL configuration, and replication slot management become critical infrastructure components. Moving to a different database means rewriting substantial portions of your event delivery logic.
When Outbox Actually Works
After all this criticism, you might wonder why anyone uses the outbox pattern at all. The truth is, it works beautifully, under specific conditions:
- Moderate throughput: Below ~1000 events/second, polling solutions work fine
- Tolerant consumers: When occasional duplicates or minor delays aren’t business-critical
- Simple topologies: Systems with just a few services and straightforward event flows
- Mature observability: When you have the monitoring infrastructure to detect problems early
The pattern shines when you’re migrating from a monolith to microservices and need gradual, reliable event extraction. It’s a pragmatic bridge from synchronous to asynchronous architectures, not a permanent solution for high-scale systems.
The Uncomfortable Truth
The outbox pattern isn’t fundamentally broken, it’s just that most implementations pretend the hard parts don’t exist. Real distributed systems require embracing complexity, not hiding it behind simple patterns.
The teams that succeed with outbox patterns invest heavily in operational tooling: custom dashboards for relay health, automated WAL position monitoring, sophisticated idempotency schemes, and circuit breakers that actually understand business context.
They also recognize when to abandon the pattern entirely. Some eventually migrate to event sourcing, others implement dual writes with sophisticated reconciliation systems, and some simply accept that perfect consistency isn’t worth the operational overhead.
The Path Forward
Before you reach for that outbox pattern tutorial, ask yourself: Are you solving the right problem? Sometimes the answer is simpler database design. Sometimes it’s embracing eventual consistency more aggressively. Sometimes it’s investing in better reconciliation tools rather than trying to prevent every failure mode.
The outbox pattern is a tool, not a silver bullet. Use it with eyes wide open to the operational complexity you’re signing up for. Your future self debugging a message relay at 3 AM will thank you for the honesty.
Because in distributed systems, the only thing more expensive than acknowledging complexity is pretending it doesn’t exist.
