The Synchronization Trap
In simple apps, the code does one thing, waits, then does the next. This is fine until you need to process 1,000 images per minute. If your web server waits for each image to be uploaded and resized before responding, your users will leave.
The Producer-Consumer Logic
This pattern decouples the Request (Producer) from the Work (Consumer) using a Buffer (usually a Queue).
- Producer: Accepts the task and drops it into a queue. It then returns to the user immediately.
- Buffer: A safe storage area (like Redis or RabbitMQ) that holds the tasks.
- Consumer: Takes tasks from the queue as fast as it can and processes them in the background.
Why It Scales
By separating these roles, you can scale them independently. If the queue is growing too fast, just spin up more Consumers. Your web server (Producer) stays responsive no matter how slow the actual processing takes.
Real-World Hero: Logging
Almost every high-performance logging system (like Logstash or Kafka) uses this pattern. Your app “produces” logs to a memory buffer, and a “consumer” thread writes them to disk without slowing down your main execution.
References & Further Reading
- Concurrency in Practice: Java Producer-Consumer
- Microsoft Learn: Producer-Consumer Queue
- System Design Primer: Queueing Basics