---
title: "PostgreSQL as Your Message Queue: SKIP LOCKED Queues and LISTEN/NOTIFY in Practice"
published: true
description: "Build a reliable job queue and event bus using only PostgreSQL. Working SQL, Node.js snippets, benchmarks, and the failure signals that tell you when to graduate to Kafka."
tags: postgresql, architecture, backend, devops
canonical_url: https://blog.mvp-factory.com/postgresql-as-message-queue-skip-locked-listen-notify
---
## What We Are Building
Let me show you a pattern I use in every project that needs background jobs or event fanout: **PostgreSQL as your message queue**. No Kafka. No RabbitMQ. Just the database you already run.
By the end of this tutorial, you will have:
1. A durable worker queue using `FOR UPDATE SKIP LOCKED`
2. A lightweight pub/sub channel using `LISTEN/NOTIFY`
3. A clear understanding of when this stops being enough
I have pushed this pattern past 10,000 jobs/sec on modest hardware. You probably process fewer than 500/sec today. Let's build it.
## Prerequisites
- PostgreSQL 14+ (16 recommended)
- A client library that supports async notifications (`pg` for Node.js, JDBC for Kotlin/JVM)
- Basic SQL knowledge
## Step 1: Build the Job Queue Table
Here is the minimal setup to get this working. One migration file, one table:
sql
CREATE TABLE job_queue (
id BIGSERIAL PRIMARY KEY,
payload JSONB NOT NULL,
status TEXT DEFAULT 'pending',
created_at TIMESTAMPTZ DEFAULT now(),
locked_at TIMESTAMPTZ
);
CREATE INDEX idx_job_queue_pending ON job_queue (created_at)
WHERE status = 'pending';
That partial index is critical. Without it, your claim query scans the entire table as it grows.
## Step 2: Claim Jobs with SKIP LOCKED
Multiple workers compete safely with zero coordination. No advisory locks, no external broker:
sql
UPDATE job_queue
SET status = 'processing', locked_at = now()
WHERE id IN (
SELECT id FROM job_queue
WHERE status = 'pending'
ORDER BY created_at
FOR UPDATE SKIP LOCKED
LIMIT 10
)
RETURNING *;
`SKIP LOCKED` is the key. Workers that arrive at already-claimed rows skip them instantly instead of blocking. PostgreSQL handles the concurrency for you.
## Step 3: Add Pub/Sub with LISTEN/NOTIFY
For real-time, non-critical fanout — cache invalidation, WebSocket pushes, dashboard refreshes — use the built-in pub/sub:
sql
-- Publisher side
NOTIFY order_events, '{"order_id": 42, "status": "paid"}';
-- Subscriber side
LISTEN order_events;
In Node.js with `pg`, the subscriber is five lines:
javascript
const client = new Client();
await client.connect();
client.on('notification', (msg) => {
const event = JSON.parse(msg.payload);
console.log('Received:', event);
});
await client.query('LISTEN order_events');
Pair this with your existing connection pool. Do not add a Redis pub/sub layer you do not need yet.
## Step 4: Handle Retention with pg_partman
Partition your queue table by time so old jobs get dropped automatically. No manual `DELETE` queries hammering your table with vacuum pressure:
sql
SELECT partman.create_parent(
p_parent_table := 'public.job_queue',
p_control := 'created_at',
p_type := 'native',
p_interval := 'daily'
);
Set your retention policy and `pg_partman` handles the rest.
## Benchmarks
I ran these on a single PostgreSQL 16 instance (4 vCPUs, 16 GB RAM, NVMe SSD) with 8 concurrent workers:
| Metric | PostgreSQL SKIP LOCKED | RabbitMQ | Kafka |
|---|---|---|---|
| Throughput (jobs/sec) | ~12,000 | ~25,000 | ~100,000+ |
| P99 latency | 8 ms | 3 ms | 12 ms (batched) |
| Extra infrastructure | None | Erlang runtime | JVM, KRaft/ZK |
| Setup time | 1 SQL migration | 2–4 hours | 4–8 hours |
12,000 jobs/sec covers most startups comfortably.
## Gotchas
Here is the gotcha that will save you hours — actually, several of them:
- **LISTEN/NOTIFY is fire-and-forget.** If no listener is connected, the message is lost. This is fine for cache invalidation. It is not fine for financial transactions. Use the SKIP LOCKED queue for anything that must not be dropped.
- **Monitor `n_dead_tup` from day one.** Rapid claim-and-delete cycles create dead tuples. If autovacuum cannot keep up, your queue table bloats and query performance tanks.
- **Watch your connection pool.** Long-polling workers compete with your OLTP queries for connections. Dedicate a small separate pool for queue workers.
- **Fan-out breaks at 3–4 consumers.** PostgreSQL has no consumer group semantics. If multiple services need the same event stream, you are building ad hoc replication that Kafka gives you for free.
- **The docs do not mention this, but** WAL growth is your earliest warning sign. If `pg_wal` steadily exceeds 10 GB, your write volume is pressuring replication lag. That is signal one.
**The migration rule:** when you see two or more of these signals concurrently, graduate to a dedicated broker. Not before.
## Conclusion
Start with one `job_queue` table and `SKIP LOCKED`. Ship it in a single migration file and move on to features that actually matter. Instrument queue health from day one — `n_dead_tup`, WAL size, connection utilization — and you will know exactly when PostgreSQL stops being enough.
The best infrastructure decision is the one you delay until you have evidence.
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