What sets Observables (Rx.js) apart from ES2015 generators?

Question

What sets Observables (Rx.js) apart from ES2015 generators?

From what I've gathered, there are various techniques used for solving asynchronous programming workflows:

Callbacks (CSP)
Promises

Newer methods include:

Rx.js Observables (or mostjs, bacon.js, xstream etc)
ES6 generators
Async/Await

The trend seems to be shifting away from callbacks and promises towards these newer approaches. Currently, my understanding is that Async/Await serves as a cleaner abstraction on top of ES2015 generators.

One thing that puzzles me is the conceptual distinction between Observables and Generators. Although I have experience using both, I struggle with comprehending their use cases.

It appears to me that both Observables and Generators tackle the same issue - asynchronicity. The main difference I notice is that generators inherently offer imperative code semantics while Observables, particularly those in Rxjs, lean towards a reactive paradigm. But is this the only differentiator?

Should the decision between Observable and Generator rely on this criterion? What are the advantages and disadvantages of each?

Could it be that I'm missing the bigger picture?

Also, considering that Observables are expected to become part of future ECMAScript versions, will Promises (with cancelable tokens), Observables, and Generators end up competing against each other?

javascript ecmascript-6 generator reactive-programming rxjs5

Answer 1

Answer №1

When it comes to observables, changes are pushed rather than being pulled by the function reacting to them. In contrast, generators require values to be pulled out of them, giving the function control over when it is ready for a new value.

I faced backpressure issues with observables, but generators allow for values to be released at any desired pace.

Regarding promises and observables, promises act as single-emission observables, suggesting that the real competition lies between async/await and observables. Async/await has already gained traction in languages like C# and Node.js, but there's a growing interest in observables due to their reactive feel and alignment with functional programming principles.

In an insightful article dated 2018-01-31, André Staltz discusses the relationship between generators and Observables, highlighting how they share a common base class and can serve as either Listeners or Pullers depending on the context.

The distinction between reactive programming and iterable programming becomes blurred when considering Callbags - a callback spec introduced by Staltz for handling both types of data transformations between producers and consumers. This approach simplifies building operators that work seamlessly across different modes of programming.

To learn more, I recommend reading the full article here. The concept of Callbags opens up possibilities for crafting libraries capable of supporting both reactive and iterable programming paradigms.

Examples from a compact library based on the Callbag spec illustrate its versatility:

Reactive programming example

Selecting and observing the first 5 odd numbers from a clock ticking every second:

const {forEach, interval, map, filter, take, pipe} = require('callbag-basics');

pipe(
  interval(1000),
  map(x => x + 1),
  filter(x => x % 2),
  take(5),
  forEach(x => console.log(x))
);
// Output:
// 1
// 3
// 5
// 7
// 9

Iterable programming example

Choosing 5 numbers from a range and dividing them by 4 through iterative pulling:

const {forEach, fromIter, take, map, pipe} = require('callbag-basics');

function* range(from, to) {
  let i = from;
  while (i <= to) {
    yield i;
    i++;
  }
}

pipe(
  fromIter(range(40, 99)), 
  take(5), 
  map(x => x / 4), 
  forEach(x => console.log(x))
);
// Output:
// 10
// 10.25
// 10.5
// 10.75
// 11

Answer 2

When it comes to observables, changes are pushed rather than being pulled by the function reacting to them. In contrast, generators require values to be pulled out of them, giving the function control over when it is ready for a new value.

I faced backpressure issues with observables, but generators allow for values to be released at any desired pace.

Regarding promises and observables, promises act as single-emission observables, suggesting that the real competition lies between async/await and observables. Async/await has already gained traction in languages like C# and Node.js, but there's a growing interest in observables due to their reactive feel and alignment with functional programming principles.

In an insightful article dated 2018-01-31, André Staltz discusses the relationship between generators and Observables, highlighting how they share a common base class and can serve as either Listeners or Pullers depending on the context.

The distinction between reactive programming and iterable programming becomes blurred when considering Callbags - a callback spec introduced by Staltz for handling both types of data transformations between producers and consumers. This approach simplifies building operators that work seamlessly across different modes of programming.

To learn more, I recommend reading the full article here. The concept of Callbags opens up possibilities for crafting libraries capable of supporting both reactive and iterable programming paradigms.

Examples from a compact library based on the Callbag spec illustrate its versatility:

Reactive programming example

Selecting and observing the first 5 odd numbers from a clock ticking every second:

const {forEach, interval, map, filter, take, pipe} = require('callbag-basics');

pipe(
  interval(1000),
  map(x => x + 1),
  filter(x => x % 2),
  take(5),
  forEach(x => console.log(x))
);
// Output:
// 1
// 3
// 5
// 7
// 9

Iterable programming example

Choosing 5 numbers from a range and dividing them by 4 through iterative pulling:

const {forEach, fromIter, take, map, pipe} = require('callbag-basics');

function* range(from, to) {
  let i = from;
  while (i <= to) {
    yield i;
    i++;
  }
}

pipe(
  fromIter(range(40, 99)), 
  take(5), 
  map(x => x / 4), 
  forEach(x => console.log(x))
);
// Output:
// 10
// 10.25
// 10.5
// 10.75
// 11

Answer 3

Answer №2

Back in 2016, the question was raised when synchronous generators were still commonly used.

But today, a more intriguing inquiry would be about the distinctions between async generators, reactive observables, and reactive programming in general. Let me elaborate further.

In the discussion that follows:

A will denote concepts like async/await and promises.
B will represent async generators.
C stands for reactive observables.

All of these mechanisms are crafted to handle asynchronous data, but each with its own nuances.

For finite and small asynchronous data sources, such as a single HTTP request or a few KBs of disk data, implementing A is straightforward and effective.

However, as the data source becomes infinite (e.g., user interactions) or too large to store in memory, the limitations of A become evident due to complexities arising from backpressure issues. Here, both B and C excel by offering backpressure solutions in various forms.

When tasks like buffering, de-duplication, debouncing, or merging with other data sources come into play, C takes the lead with its array of generic operators designed to address common stream challenges. For instance, preventing users from spamming comments within a short time frame (e.g., 5 seconds).

Does this signify that C surpasses B overall?

In my view, no. Despite the versatility of C, B prevails with its simplicity, flexibility, and seamless integration with the language (e.g., for await ... of). It does not necessitate a major shift in programming paradigm, unlike what incorporating C might entail.

Answer 4

Back in 2016, the question was raised when synchronous generators were still commonly used.

But today, a more intriguing inquiry would be about the distinctions between async generators, reactive observables, and reactive programming in general. Let me elaborate further.

In the discussion that follows:

A will denote concepts like async/await and promises.
B will represent async generators.
C stands for reactive observables.

All of these mechanisms are crafted to handle asynchronous data, but each with its own nuances.

For finite and small asynchronous data sources, such as a single HTTP request or a few KBs of disk data, implementing A is straightforward and effective.

However, as the data source becomes infinite (e.g., user interactions) or too large to store in memory, the limitations of A become evident due to complexities arising from backpressure issues. Here, both B and C excel by offering backpressure solutions in various forms.

When tasks like buffering, de-duplication, debouncing, or merging with other data sources come into play, C takes the lead with its array of generic operators designed to address common stream challenges. For instance, preventing users from spamming comments within a short time frame (e.g., 5 seconds).

Does this signify that C surpasses B overall?

In my view, no. Despite the versatility of C, B prevails with its simplicity, flexibility, and seamless integration with the language (e.g., for await ... of). It does not necessitate a major shift in programming paradigm, unlike what incorporating C might entail.

Answer 5

Answer №3

If we view rxjs observables as asynchronous generators, it's like having generators that yield promises. The difference is that the content may not be ready when we call .next, unlike regular generators.

The subscriber continuously consumes the generator's content by calling .next in an infinite loop
You can perform actions once the returned promise is resolved (or rejected)
An observable created with an async generator will complete when the generator finishes its execution.

For more information, check out this article

Learn about the proposal for asynchronous iterators here

Answer 6

If we view rxjs observables as asynchronous generators, it's like having generators that yield promises. The difference is that the content may not be ready when we call .next, unlike regular generators.

The subscriber continuously consumes the generator's content by calling .next in an infinite loop
You can perform actions once the returned promise is resolved (or rejected)
An observable created with an async generator will complete when the generator finishes its execution.

For more information, check out this article

Learn about the proposal for asynchronous iterators here

What sets Observables (Rx.js) apart from ES2015 generators?

Answer №1

Answer №2

Answer №3

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