Community Blog From RxJS to Flink: Processing Data Streams

From RxJS to Flink: Processing Data Streams

This article describes how to use reactive programming in frontend development with a news website as an example.


1. What Is Frontend Development?

A question may be posed in the process of frontend development, "What is frontend development?" In my opinion, the essence of frontend development is to realize the correct response of the web page view to related events. There are three keywords, web page view, correct response, and related events.

Related events may include page clicking, mouse moving, timer, and server request. Correct responding means some statuses need to be modified according to related events. Web page view is the most familiar part during frontend development.

From this point of view, the formula is listed below:

View = reactionFn(Event)

In frontend development, events that need to be processed can be classified into the following three types:

  • An action performed on the page by users, such as clicking and mouse moving
  • Data interaction between a remote server and a local device, such as fetching and WebSocket
  • The local asynchronous events, such as setTimeout and setInterval async_event


In this way, the formula is listed below:

View = reactionFn(UserEvent | Timer | Remote API)

2. Logic Processing in Applications

The section below uses a new website to understand the relationship between the formula and frontend development. The website has three requirements:

  1. Click Refreshing: Click the Button to refresh the data
  2. Checkbox Refreshing: Select the Checkbox for automatic refreshing, or it will be stopped.
  3. Pulldown Refreshing: Refresh the data when the user pulls down the page

From the frontend perspective, the three requirements respectively correspond to:

  1. Click Refreshing: click → fetch
  2. Checkbox Refreshing: change → (setInterval + clearInterval) → fetch
  3. Pulldown Refreshing: (touchstart + touchmove + touchend) → fetch news_app



In the MVVM mode, the preceding response function (reactionFn) is executed between the Model and the ViewModel or between the View and the ViewModel. The Event is processed between the View and the ViewModel.


MVVM mode can effectively abstract the View layer and the data layer, but the response function (reactionFn) will be scattered in different conversion processes. This makes it difficult to track the data empowerment and collection processes accurately. In addition, since the processing of the Event is closely related to the View in the model, it is difficult to reuse the logic of Event processing between the View and the ViewModel.

2. Redux

In the simplest Redux model, a combination of several events corresponds to an Action. The reducer function can directly correspond to the reactionFn.


However, in the Redux model:

  • The State can only be used to describe intermediate states instead of intermediate processes.
  • The Action and Event do not have one-on-one correspondence. Therefore, it is difficult to track the change source of the State.

3. Reactive Programming and RxJS

Wikipedia describes reactive programming as "…a declarative programming paradigm oriented to the data stream and change transmission. This means that static or dynamic data streams can be easily expressed in programming languages. The relevant computing model automatically transmits changed values through the data stream."

From the perspective of data stream, the process of using the application is listed below:

  • Click the button → trigger the refreshing event → send a request → update the view
  • Select "Auto Refresh"
  • Touch the screen
  • Reach the automatic refreshing time point → trigger the refreshing event → send a request → update the view
  • Scroll on the screen
  • Reach the automatic refreshing time point → trigger the refreshing event → send a request → update the view
  • Stop scrolling on the screen → trigger the pulldown refreshing event → send a request → update the view
  • Reach the automatic refreshing time point → trigger the refreshing event → send a request → update the view
  • Disable automatic refreshing

The process is listed on the following Marble diagram:


After splitting the logic shown in the preceding diagram, there will be three steps in the development of a news application by using reactive programming:

  1. Define source data streams
  2. Combine and convert data streams
  3. Consume data streams and update views

Detailed descriptions are listed below:

  • Define source data streams
  • A variety of Event data streams can be easily defined by using RxJS.

1) Click

This includes "click" data streams.

click$ = fromEvent<MouseEvent>(document.querySelector('button'), 'click');

2) Select

This includes "change" data streams.

change$ = fromEvent(document.querySelector('input'), 'change');

3) Pull Down

This includes three data streams: touchstart, touchmove, and touchend.

touchstart$ = fromEvent<TouchEvent>(document, 'touchstart');
touchend$ = fromEvent<TouchEvent>(document, 'touchend');
touchmove$ = fromEvent<TouchEvent>(document, 'touchmove');

4) Scheduled Refreshing

interval$ = interval(5000);

5) Server Request

fetch$ = fromFetch('https://randomapi.azurewebsites.net/api/users');

Combine and Convert Data Streams

1) Click to refresh event stream

When clicking to refresh, multiple clicks will only refresh the last event stream for a short period. This can be achieved through the debounceTime operator of RxJS.


clickRefresh$ = this.click$.pipe(debounceTime(300));

2) Automatically refresh streams

RxJS switchMap is used with the defined interval$ data stream.


autoRefresh$ = change$.pipe(
  switchMap(enabled => (enabled ? interval$ : EMPTY))

3) Pull down to refresh streams

Use the previously defined touchstart$, touchmove$, and touchend$ data streams.


pullRefresh$ = touchstart$.pipe(
  switchMap(touchStartEvent =>
      map(touchMoveEvent => touchMoveEvent.touches[0].pageY - touchStartEvent.touches[0].pageY),
  filter(position => position >= 300),

Finally, the merge function is used to merge the defined clickRefresh$, autoRefresh$, and pullRefresh$ to obtain the refreshed data stream.


refresh$ = merge(clickRefresh$, autoRefresh$, pullRefresh$));

Consume Data Streams and Update Views

The refreshed data stream is directly tied to the fetch$ defined in the first step through switchMap. Then, the view data stream is obtained.


The view stream can be mapped to a view through an async pipe in the Angular framework:

<div *ngFor="let user of view$ | async">

In other frameworks, "subscribe" can be used to obtain real data in the data stream and update the view.

At this point, the development of the news application is completed by using reactive programming. The sample code [1] is developed by Angular, with no more than 160 rows.

Here is a summary of the relationship between the three steps of using reactive programming to develop frontend applications and the formula in the first section:

View = reactionFn(UserEvent | Timer | Remote API)

1) Define source data streams

This corresponds to "UserEvent | Timer | Remote API" and the corresponding functions in RxJS are listed below:

  • UserEvent: fromEvent
  • Timer: interval, timer
  • Remote API: fromFetch, WebSocket

2) Combine and convert data streams

This corresponds to the "reactionFn" and the corresponding methods in RxJS are listed below:

  • COMBINING: merge, combineLatest, zip
  • MAPPING: map
  • FILTERING: filter
  • REDUCING: reduce, max, count, scan
  • TAKING: take, takeWhile
  • SKIPPING: skip, skipWhile, takeLast, last
  • TIME: delay, debounceTime, throttleTime

3) Consume data streams and update views

This corresponds to "View." The following can be used in RxJS and Angular:

  • subscribe
  • async pipe

What are the advantages of reactive programming over MVVM or Redux?

  • It describes the event itself rather than the computing process or intermediate state.
  • A method of data stream combination and conversion is provided, which means a method of reusing continuously changing data is obtained.
  • All data streams are obtained through layer-by-layer combinations and conversions.

This makes it possible to track the sources of events and data changes accurately. If the timeline of RxJS's Marble diagram is blurred and a longitudinal aspect is added every time the view is updated, there will be two interesting things:


  • Action is the simplified EventStream
  • State corresponds to Stream at some time point

There is a sentence on the Redux official website, "If you have used RxJS, it is likely that you no longer need Redux."

The question is, "Do you need Redux if you already use Rx?" The simple answer says probably not. It's not hard to re-implement Redux in Rx. Some say it's a two-liner using theRx.scan() method.

At this point, can we further abstract the saying that the web page view can correctly respond to related events?

All events -- Find → Related events -- Make → Response

Events that occur in chronological order are essentially data streams. So, the above process can be transformed as the following:

Source data stream -- Convert → Intermediate data stream -- Subscribe → Consume data stream

This is the basic idea that reactive programming can work perfectly at the frontend. Is this idea only applied in frontend development?

The answer is no. This idea can also be widely applied in backend development and real-time computing.

3. Breaking the Information Wall

Between the frontend and backend developers, there is a wall of information called REST API. The REST API isolates the responsibilities of the frontend and backend developers to improve the development efficiency. However, this wall has also separated the horizon of the frontend and backend developers. Let's break this wall of information and view the application of the same idea in real-time computing.

1. Real-Time Computing and Apache Flink

Let's take a look at Flink first. Apache Flink is an open-source streaming processing framework developed by the Apache Software Foundation. It is used for stateful computing over bounded and unbounded data streams. Its programming model of data streams can process a single event (event-at-a-time) on finite and infinite data sets.


In practices, Flink is typically used to develop the following three types of applications:

  • Event-Driven Applications: An event-driven application extracts data from one or more event streams and triggers calculations, status updates, or other external actions based on incoming events. Application scenarios include rule-based alerting, exception detection, and anti-fraud.
  • Data Analysis Applications: Data analysis tasks need to extract valuable information and metrics from raw data, for example, the calculation of turnover during Double 11 and network quality monitoring.
  • Data Pipeline (ETL) Applications: Extract-transform-load (ETL) is a common method for data conversion and migration between storage systems. ETL jobs are triggered periodically to copy data from a transactional database to an analytical database or data warehouse.

The calculation of hourly turnover on the e-commerce platform during Double 11 is an example here. Let's see if the solution mentioned above still works.

In this scenario, the user's ordering data should be obtained first. Then, the hourly transaction data is calculated and stored in the database for caching by Redis. Finally, the data is obtained through an API and displayed on the webpage.

The logic of data stream processing in this process is listed below:

Data stream of user orders - convert → Hourly transaction data stream - Subscribe → Writing to the database

It is consistent with the idea described in the previous section:

Source data stream -- Convert → Intermediate data stream -- Subscribe → Consume data stream

If the Marble diagram is used to describe this process, it may seem like the following figure. It seems that the window operator of RxJS can also be used to complete the same function. Is it really the case?


2. Hidden Complexity

Real-time computing is much more complex than reactive programming in the frontend. Here are some examples:

  • Event Disorder

Event disorders occur in the frontend development process. In the most typical case, the earlier request receives the response later than the following requests, as shown in the following Marble diagram. There are many ways to deal with this situation at the frontend, but they will not be introduced in detail in this article.


This part introduces the time disorder during data processing. In frontend development, there is a very important premise that reduces the complexity of frontend application development. The premise says frontend events are triggered and processed at the same time.


Let's imagine the user actions on the page, such as clicking and mouse moving, have become asynchronous events with an unknown response time. How complex will the frontend development be?

However, different event triggering and processing time is an important premise for real-time computing. Take the calculation of hourly turnover calculation as an example as well. When the original data stream is transmitted through layers, the sequence of data on the computing node may have been out of order.


If the window is still divided according to the arrival time of the data, the final calculation result will be wrong:


The calculation should be completed after the late event arrived to obtain the correct calculation result of window2. However, there is a dilemma:

  • Endless Waiting: The late event may be lost during transmission, and no data is generated in window2.
  • Short Waiting Time: The calculation is performed before the late event arrives, so the calculation result is incorrect.

Flink introduced the Watermark mechanism to solve this problem. The Watermark mechanism decides when it is unnecessary to wait for the late event. This mechanism essentially provides a compromise solution for the accuracy and timeliness of real-time computing.

There is a vivid metaphor for Watermark. When studying at school, the teacher will close the door of the class and say, "Students that arrive later than the current time are considered late for class, and they will be punished." In Flink, the Watermark mechanism is the same as the teacher closing the door.


  • Data Back Pressure

When RxJS is used in a browser, I wonder if you have considered a situation where the speed of generating observable data is faster than consuming by operator or observer. A large amount of unconsumed data is cached in the memory. This kind of situation is called back pressure. Fortunately, the data back pressure on the frontend will only occupy a large amount of browser memory, and there will be no more serious consequences.

However, in real-time computing, what is the possible solution if the issue above occurs?


For many streaming applications, data loss is unacceptable. Flink has designed a mechanism to avoid this:

  • Ideally, data is buffered in a persistent channel.
  • When the data back pressure occurs, the receiver with a lower speed will decrease the transmitter's speed as soon as the queue buffer is exhausted. In other words, when data stream slows down, the channel presses the data back from the stream tank to the stream source. Then, the stream source is throttled to adjust the speed to the lowest value to achieve a stable state.


  • Checkpoint

In real-time computing, billions of data records can be processed per second. Such data cannot be processed by a single server independently. In Flink, the operator's computing logic is executed with different subtasks on different task managers. However, when a machine fails, how can it handle the overall computing logic and state to ensure the correctness of the final calculation results?


Flink introduces the checkpoint mechanism to restore the state and computing position of tasks. Checkpoint enables good fault tolerance. Flink uses a variant of the Chandy-Lamport algorithm, which is called asynchronous barrier snapshotting.

When Checkpoint is enabled, the offsets of all sources will be recorded, and numbered checkpoint barriers will be inserted into their streams. When passing through each operator, barriers will mark the stream part before and after each checkpoint.


When an error occurs, Flink restores the state based on the state stored in the checkpoint to ensure the correct final result.

Tip of the Iceberg

Due to the limited space, this article only introduces a small part. However, the model is common in both reactive programming and real-time computing. We hope this article can give you some insight into data streams.

Source data stream -- Convert → Intermediate data stream -- Subscribe → Consume data stream


  1. https://github.com/vthinkxie/ng-pull-refresh
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