Message Queue
intermediateDefinition
A waiting line for tasks. One part of the app drops jobs into the queue; another part picks them up and works through them at its own pace. This means slow work doesn't make people stand around waiting, and a sudden rush of jobs doesn't crash anything.
In the wild
When you upload a video to a website, the site doesn't make you wait while it converts the file. It puts 'convert this video' into a queue and immediately tells you 'we'll let you know when it's ready.' A worker pulls the job from the queue minutes later, finishes the conversion, and emails you the link.
More from Architecture
Caching
Saving the result of slow work somewhere fast so the next person who needs it gets it instantly. The first time something is calculated or fetched it's slow; every time after that, the saved copy is handed out until it gets too old to trust.
CAP Theorem
A rule about systems that store data on many computers at once. It says you can have at most two of three things: everyone always sees the same data, the system always responds, and the system keeps working even when the computers can't talk to each other. You always have to give one up.
CDN (Content Delivery Network)
A worldwide network of computers that keep copies of a website's images, videos, and other files in many cities at once. When you visit the site, you're served from the nearest copy. So the page loads faster and the original server isn't overwhelmed.
Client-Server Architecture
The basic shape of most apps and websites. The client is the program in front of you, your browser, your phone app, and the server is a powerful computer somewhere else that does the heavy lifting and stores the data. The client asks for things, the server answers.
Event-Driven Architecture
A way of building software where different parts don't call each other directly. Instead, one part announces 'something happened' and any other part that cares can react. This keeps the pieces loosely connected, so adding new reactions is easy.
Eventual Consistency
When data is stored in many places at once, an update doesn't reach all of them at the exact same moment. For a brief time, different copies might disagree. But if you wait a beat, they all catch up and end up matching.