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In a reactive context, when you make a call to a reactive data source to read/get data, Meteor sets up callbacks to rerun the computation when you or someone else writes/sets a change to that data.

It is the data source that stores and runs these callbacks. If a reactive data source’s method is called outside of a reactive context, it returns a result but does not store a callback.

Without a computation to register, there is no way for it to know what code to rerun, as the only time we know both sides of the data source-consumer relationship is when we from the data source.

A benefit to this behavior is that you can use reactive data sources normally in non-reactive code with no side-effects.

A reactive data source is any provider of data that follows Meteor’s contract for providing reactivity.

The data source is responsible for making reactivity work.

Have you ever tried to add something to a Meteor template and been frustrated that it didn’t update the way you expected (or at all)?

Meteor is often so “magical” that we rely on it without understanding its fundamentals.

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As we do this, we will touch on some common misconceptions.

When we advance beyond simple use cases, we may wonder why the magic stopped, and it can be difficult to know where to begin looking.

Meteor is designed to allow beginners to be productive quickly while making it easy for more advanced users to access more powerful and deeper features.

This is most visible when Meteor updates templates in real-time, but you can run any code reactively.

Meteor has “full stack reactivity” as one of its Seven Principles, but it’s not a single monolithic system. It would be easy to picture reactivity as a single watcher keeping track of all data structures and checking dependencies whenever a change is detected.