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Back to Learn
KnowledgeLayer 1Communication Patterns

Message Queues

Your e-commerce site just processed 50 orders in 2 minutes.

Your email system crashed trying to send all the confirmations at once.

Now half your customers are staring at "Order Placed" but no email.

Your systems weren't talking. They were shouting over each other.

8 min read
intermediate
Relevant If You're
Handling traffic spikes without crashing
Processing tasks that can wait a few seconds
Making sure nothing gets lost when systems fail

ESSENTIAL - The glue between systems that need to work together but not at the same time.

Where This Sits

Category 1.5: Communication Patterns

1
Layer 1

Data Infrastructure

Message QueuesEvent Buses/Pub-SubSync vs Async HandlingBatch vs Real-TimeStreamingPolling Mechanisms
Explore all of Layer 1
What It Is

A waiting room for tasks that don't need to happen right now

When you click 'Buy Now,' a lot happens behind the scenes. Charge the card. Update inventory. Send a confirmation email. Generate an invoice. Notify the warehouse. If you try to do all of this before showing the customer their receipt, they'll wait 15 seconds staring at a spinner.

Message queues let you say 'done' to the customer immediately, then handle everything else in order, at a pace your systems can handle. The order goes into a queue. The email service picks it up when it's ready. The warehouse gets notified when the previous messages clear.

If your email service crashes? The message waits in the queue. When the service comes back, it picks up where it left off. Nothing lost. No angry customers wondering where their confirmation went.

Synchronous means 'everything waits for everything.' Asynchronous means 'take a number, we'll get to you.' Message queues turn chaos into an orderly line.

The Lego Block Principle

Producers create messages. Consumers process them. The queue stores them in between. This decoupling means producers and consumers don't need to know about each other.

The core pattern:

A producer pushes a message to a named queue. The message waits until a consumer is ready. The consumer pulls the message, processes it, and acknowledges completion. If the consumer crashes before acknowledging, the message goes back in the queue.

Where else this applies:

Order processing - Checkout pushes to queue. Background workers handle inventory, emails, and invoicing.
Image processing - Upload pushes to queue. Workers resize, compress, and generate thumbnails at their own pace.
Report generation - User requests report. Queue holds the job. Worker generates PDF and emails when done.
Webhook delivery - Events push to queue. Workers retry failed webhook deliveries with backoff.
🎮 Interactive: Watch the Queue Handle Traffic

Send messages. Watch them queue. See the pattern.

Click rapidly to simulate a traffic spike. In sync mode, watch it crash. In queue mode, watch it stay calm.

Mode:
1000ms per message
0
Total Sent
0
In Queue
0
Processing
0
Completed

Message Queue

Queue is empty. Add some messages!

Try it: Click "Add Message" a few times, then hit "Traffic Burst" to simulate a spike. Watch the queue absorb the load. Then switch to Sync mode and try the same thing.
How It Works

Three queue patterns for different needs

Point-to-Point

One message, one consumer

Each message is processed exactly once by one consumer. Think of it like a to-do list where checking off an item removes it. Great for tasks where you don't want duplicate processing (like charging a credit card).

Pro: Guaranteed single processing
Con: Only one system can handle each message

Publish/Subscribe

One message, many consumers

Every subscriber gets every message. When an order is placed, the inventory system, email system, and analytics all get a copy. Each processes independently. If analytics is slow, it doesn't affect email delivery.

Pro: Multiple systems react to same event
Con: More infrastructure complexity

Work Queue

Load balancing across workers

Multiple workers pull from the same queue, each taking the next available message. Scales horizontally. If you need to process more messages, add more workers. The queue distributes work automatically.

Pro: Easy horizontal scaling
Con: Need to handle worker failures
Connection Explorer

"Handle order confirmations without crashing email"

Your flash sale generated 50 orders in 2 minutes. With message queues, the checkout completes instantly for every customer. Order confirmations flow into a queue. The email service processes them at its own pace. Zero crashes. Every customer gets their email within 30 seconds.

Hover over any component to see what it does and why it's neededTap any component to see what it does and why it's needed

Relational DB
Event Triggers
Message Queues
You Are Here
Event Bus
Workflow Orchestrator
Confirmation Sent
Outcome
React Flow
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Press enter or space to select an edge. You can then press delete to remove it or escape to cancel.
Foundation
Data Infrastructure
Delivery
Outcome

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Upstream (Requires)

Triggers (Event-based)Databases (Relational)

Downstream (Enables)

Event Buses/Pub-SubWorkflow Orchestrators
Common Mistakes

What breaks when message queues go wrong

Don't process before acknowledging

You pull a message, process it, then it crashes before you acknowledge. The message goes back in the queue. Another worker picks it up. The credit card gets charged twice. Your customer gets two emails. Now you have a support ticket.

Instead: Design for idempotency. If the same message is processed twice, the result should be the same. Use unique IDs. Check before charging.

Don't ignore dead letter queues

A malformed message fails processing. It goes back in the queue. Fails again. Goes back. Fails again. Forever. Meanwhile, good messages pile up behind it, waiting.

Instead: Configure a dead letter queue. After N retries, bad messages go to a separate queue for investigation. The main queue keeps flowing.

Don't make queues your database

You start storing data in queue messages instead of your database. Now you can't query it. You can't update it. You can only read it once. When the queue empties, the data is gone.

Instead: Queues are for tasks, not storage. Persist data in your database. Use messages to trigger actions on that data.

What's Next

Now that you understand message queues

You've learned how to decouple systems with message queues. The next step is broadcasting events to multiple subscribers with pub/sub patterns.

Recommended Next

Event Buses/Pub-Sub

Broadcasting events to multiple subscribers for loosely coupled system communication

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