Reactive programming with tracker

Meteor Deep Dive – Reactive Programming With Tracker

By on August 23, 2016

FYI: This is one of three topics of our third Meteor Meetup on August 22th, 2016. The author is Khang Nguyen, a young talent member of Designveloper. This article is based on his writing, you can read the original one here.

Introduction

As a Meteor developer, I believe that everyone who has worked with Meteor all had experience with Tracker, or at least used it through some kinds of interfaces like getMeteordata or createContainer which come with the react-meteor-data package.

However, not all people know how Tracker works its magic. In this post, I’m going to dig into every facet of this case as well as bring to you a little bit of background knowledge.

What Is Tracker?

Tracker is a Meteor’s core package, it is small but incredibly powerful library for transparent reactive programming in Meteor.

Using Tracker you have much of the power of Functional Reactive Programming FRP system without following FRP’s principles when implementing your application. Combined with Tracker-aware libraries, like Session/ReactiveVar/ReactiveDict, this lets you build complex event-driven programs without writing a lot of boilerplate event-handling code.

What Make It Great?

In a nutshell, it is reactivity. In my opinion, it is the strongest aspect of Meteor. Tracker helps us make our system work reactively both on client and server with ease. We do not have to learn any extra stuffs about reactive programming or functional programming to get started.

Just read the Tracker api and do the work then the magic happens. Or even some Meteor-novice who do not know a thing about Tracker, their code still work reactively. Do you know what am I talking about? It is good old Blaze (I say it’s old because I already moved to React for all new projects).

Blaze’s helpers are reactive natively because they use Tracker inside, if you put inside them a reactive data source then whenever that source changes those helpers will be recomputed and you get new data on the screen.

Let’s read some code and behold what I am talking about, if you are already familiar with Tracker, skip this part and move to the next section to inspect the magic.

// Set up the reactive code
const counter1 = new ReactiveVar(0);
const counter2 = new ReactiveVar(0);

const observeCounter = function() {
  Tracker.autorun(function() {
    const text = `Counter1 is now: ${counter1.get()}`;
    console.warn(text);
  });
  console.warn(`Counter2 is now: ${counter2.get()}`);
};

const computation = Tracker.autorun(observeCounter);

/*
Message on the console:
Counter1 is now: 0
Counter2 is now: 0
*/

// and now change the counter1's value
counter1.set(1);
/*
Message on the console:
Counter1 is now: 1
*/

counter2.set(3);
/*
Message on the console:
Counter1 is now: 1
Counter2 is now: 3
*/

counter1.set(7);
/*
Message on the console:
Counter1 is now: 7
*/

In reality, it happens as shown below:

How Does Tracker Work?

Basically Tracker is a simple interface that lets reactive data sources (like counter in the example above) talk to reactive data consumers (the observeCounter function). Below is the flow of Tracker (I ignore some good parts to make it as simple as possible)

  • Call Tracker.autorun with function F
  • If inside F, there is a reactive data source named R, then that source will add F to its dependence list
  • Then whenever the value of R changes, R will retrieve all its dependence from the dependence list and run them.
  • Everything is easier said than done. So, let’s take a look at the code:

    const counter = new ReactiveVar(1);
    
      const f = function() {
        console.log(counter.get());
      };
    
      Tracker.autorun(f);

    In the above code: counter is our reactive data source. It raises a question is that how can it know what function used inside to add that function as its dependence?

    This is where the Meteor team does their trick to make this flow transparent. In fact, Tracker is an implementation of the Observer pattern or at least an Observer-liked pattern. Observer pattern can be used to create a reactive library like Tracker.

    In an traditional implementation of Observer, we can think of F as an observer and R as a subject. R must have an interface to add F as its observer and notify/run F when its value changes. Something like this:

    const counter = new Source();
    
      const f = function(val) {
        console.log(val);
      };
    
      counter.addObserver(f);

    To imitate this, Tracker provides us these interface:

  • Tracker.autorun
  • Tracker.Dependency
  • Tracker.Dependency.prototype.depend
  • Tracker.Dependency.prototype.changed
  • Tracker.Dependency is the implementation of Subject in traditional Observer. All of reactive data source to use with Tracker use this object inside.

    Let’s look at the basic implementation of ReactiveVar I use for examples above:

    ReactiveVar = function (initialValue, equalsFunc) {
        if (! (this instanceof ReactiveVar))
          // called without `new`
          return new ReactiveVar(initialValue, equalsFunc);
    
        this.curValue = initialValue;
        this.equalsFunc = equalsFunc;
        this.dep = new Tracker.Dependency;
      };
    
      ReactiveVar.prototype.get = function () {
        if (Tracker.active)
          this.dep.depend();
    
        return this.curValue;
      };
    
      ReactiveVar.prototype.set = function (newValue) {
        var oldValue = this.curValue;
    
        if ((this.equalsFunc || ReactiveVar._isEqual)(oldValue, newValue))
          // value is same as last time
          return;
    
        this.curValue = newValue;
        this.dep.changed();
      };

    So when we create a new instance of ReactiveVar, a Tracker.Dependency object will be created. This object will have two main functions: depend and changed with get call inside get and set function respectively.

    This is the Tracker’s flow with more details:

    Tracker.autorun will create a computation with the function (F) pass to it as the computation’s props.

    // https://github.com/meteor/meteor/blob/devel/packages/tracker/tracker.js#L569-L585
      Tracker.autorun = function(f, options) {
        // ...
        var c = new Tracker.Computation(f, Tracker.currentComputation, options.onError);
    
        // ...
    
        return c;
      };

    When being initiated, this computation is also set as the current computation inside a “global” variable named Tracker.currentComputation. And F will be run for the first time.

    // https://github.com/meteor/meteor/blob/devel/packages/tracker/tracker.js#L146-L208
      Tracker.Computation = function(f, parent, onError) {
        // ...
        self._func = f;
        self._onError = onError;
        self._recomputing = false;
    
        var errored = true;
        try {
          self._compute();
          errored = false;
        } finally {
          self.firstRun = false;
          if (errored)
            self.stop();
        }
      };
    
      // https://github.com/meteor/meteor/blob/devel/packages/tracker/tracker.js#L302-L316
      Tracker.Computation.prototype._compute = function() {
        var self = this;
        self.invalidated = false;
    
        var previous = Tracker.currentComputation;
        setCurrentComputation(self);
        var previousInCompute = inCompute;
        inCompute = true;
        try {
          withNoYieldsAllowed(self._func)(self);
        } finally {
          setCurrentComputation(previous);
          inCompute = previousInCompute;
        }
      };
    
      // https://github.com/meteor/meteor/blob/devel/packages/tracker/tracker.js#L29-L32
      var setCurrentComputation = function(c) {
        Tracker.currentComputation = c;
        Tracker.active = !!c;
      };

    If there is a .get operation (meaning .depend) inside body of F , this function will be run and set the current computation stored in the global var named Tracker.currentComputation as it’s dependent.

    // https://github.com/meteor/meteor/blob/devel/packages/tracker/tracker.js#L403-L420
      Tracker.Dependency.prototype.depend = function(computation) {
        if (!computation) {
          // ...
          computation = Tracker.currentComputation;
        }
        var self = this;
        var id = computation._id;
    
        if (!(id in self._dependentsById)) {
          self._dependentsById[id] = computation;
          // ...
          return true;
        }
        return false;
      };

    Then whenever .set is call (meaning .changed), F will be rerun

    // https://github.com/meteor/meteor/blob/devel/packages/tracker/tracker.js#L428-L432
      Tracker.Dependency.prototype.changed = function() {
        var self = this;
        for (var id in self._dependentsById)
          self._dependentsById[id].invalidate();
      };

    Yeah so it is the basic idea. Beyond this basic flow actually there are some other important things to be take care for to have a complete production-ready Tracker library. I am not going to write about those things, instead I will just name them. And you can go and check yourself for a deeper understanding of Tracker. They are:

  • Tracker inside Tracker (computation inside computation)
  • Clear stopped computations
  • Prevent infinite loop
  • Takeaways

    Here’s something we’ve discovered so far:

  • Tracker make it possible to do reactive programming in Meteor
  • It is an observer-liked implementation
  • A good trick: use a “global currentComputation” to implement transparent Observer
  • Those guys who wrote Tracker are awesome 😉
  • So, did you find some great information of your own in this blog? I would love to know your ideas in the comments below.

    Also, don’t forget to help this post spread by emailing it to a friend, or sharing it on Twitter or Facebook if you enjoyed it. Thank you!

    About Author

    author

    Hi, my name is Van. I’m a stubborn introvert, an iced milk tea drinker, a barefoot kitchen dancer, a singer of shower time and an enthusiastic copywriter dying to write about anything around my world.

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