How PostgreSQL Solves a Race Condition
How PostgreSQL Solves a Race Condition
Introduction: The Busy Kitchen
Think of a modern application like a busy restaurant. The waiter (your main app) takes orders, but the real work happens in the kitchen—sending emails, generating reports, processing notifications. These background tasks don’t need to run instantly, so we push them to a job queue.
Instead of adding extra infrastructure like RabbitMQ or Kafka, imagine using something we already have: PostgreSQL. Our job queue is simply a jobs table with columns like:
task_type
payload
status (pending, processing, completed)
A group of background programs—our workers—continuously scan this table, pick a pending job, process it, and mark it complete.
Simple enough… until concurrency enters the picture.
1. The Problem: A Race for the Same Job
Now imagine five workers starting at the exact same moment. Each one runs:
“Give me a job where status = 'pending'.”
PostgreSQL happily returns the same row—say, Job #31—to all of them. None of the workers knows the others exist yet. The result?
Worker 1 processes Job #31
Worker 2 processes Job #31
Worker 3 processes Job #31
…and so on
This is a classic race condition: multiple processes accessing and modifying the same shared resource at the same time. Instead of parallelism, we get duplicated work, wasted resources, and potentially dangerous side effects (like sending the same email five times).
Clearly, our workers need coordination.
2. The Solution: PostgreSQL Row-Level Locking
PostgreSQL already knows how to solve this problem. The key lies in row-level locking, specifically:
FOR UPDATE SKIP LOCKED
This small addition changes everything.
What it does
FOR UPDATE
Tells PostgreSQL:
“When I select this row, lock it immediately. I’m claiming it.”
SKIP LOCKED
Tells PostgreSQL:
“If a row is already locked by another worker, don’t wait—skip it and move on.”
Together, these clauses turn chaos into order.
Each worker runs a query that:
Finds a pending job
Locks it instantly
Updates its status to processing
Safely works on it without interference
All within a single transaction.
3. The Victory: Order Restored
Now when five workers start simultaneously, here’s what happens:
WorkerJob ProcessedWorker 1Job #37Worker 2Job #38Worker 3Job #39Worker 4Job #40Worker 5Job #41
The first worker locks Job #37.
The others see it’s locked and skip it.
Each worker claims a different job.
No duplication. No waiting. No coordination service. Just PostgreSQL doing what it does best.
Locked jobs are effectively invisible to other workers until the transaction completes, guaranteeing exactly-once processing per job.
4. The Moral of the Story
The lesson is simple but powerful:
You don’t always need new infrastructure to solve concurrency problems.
By understanding and using PostgreSQL’s built-in locking features, we built a robust, production-ready job queue without adding Kafka, RabbitMQ, or extra operational complexity.
The benefits are real:
Fewer moving parts
Lower infrastructure cost
Easier maintenance
Strong consistency guarantees
Sometimes, the best solution isn’t a new tool—it’s a deeper understanding of the tools you already have.
If you want, I can:
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Add a real SQL example
Compare this approach with Kafka / RabbitMQ
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