Configurable Services in Loklak Search

Loklak search being an angular application has a concept of wiring down the code in the special form of classes called Services. These serviced have important characteristics, which make them a powerful feature of angular.

  • Services are shared common object wired together by Dependency Injection.
  • Services are lazily instantiated at the runtime.


The DI and the instantiation part of a service are handled by angular itself so we don’t have to bother about it. The parts of the services we are always concerned about is the logical part of the service. As the services are the sharable code at the time of writing a service we have to be 100% sure that this is the part of the code which we want to share with our components, else this can lead to the bad implementation of architecture which makes application harder to debug.

Now, the next question which arises is how services are different from something like redux state? Well, the difference lies in the word itself, services don’t have a persistent state of themselves. They are just a set of methods to separate a common piece of code from all the components into one class. These services have functions which take an input, processes them and spit an output.

Services in Loklak Search

So in loklak search, the main services are the ones which on request, fetch data from the backend API and return the data to the requester. All the services in loklak search have a fixed well-defined task, i.e. to use the API and get the data. This is how all the services must be designed, with a specific set of goals. A service should never try to do what is not necessary, or in other words, each service should have one and only one aim and it should do it nicely.

In loklak search, the services are classified by the API endpoints they hit to retrieve data. These services receive the query to be searched from the requested and they send the AJAX request to correct API endpoint and return the fetched data. This is the common structure of all the Loklak services, they all have a fetchQuery() method which takes a string argument query and requests the API for that query and after completion, it either returns the correct response from the API or throws an error if something goes wrong.

class SearchService() {
public fetchQuery( query: string ) {  }
private extractData( response ) {  }
private handleError( error ) {  }

Problems faced in this structure

This simple structure was good enough for the application in the basic levels, but as the number of features in the application increase, our simple service becomes less and less flexible as the fetchQuery() method takes only a query string as an argument and requests the API for that query, along with some query parameters. These query parameters are the additional information given to the server to process and respond to our request in a particular way, like a number of results to be fetched, aggregations to be carried out, and much more. In the current implementation, the setting up these parameters were solely done by the service itself, so these parameters were fixed inside the service and there was no easy way to modify them. This reduced the flexibility of the service as all the requesters were bound to a fixed set of parameters, thus lacking the usability of the service in other places of the application.


Solution – Service Configs

The solution to this problem of service customizability is the Service Config classes. The objects of these classes contain the information about the query parameters which various requesters can configure according to their specific needs, and our services will simply configure the query params accordingly. This idea of having a shared structure for the service configurations plays very nicely with our scenario where we want extra control over the parameters which our service is configuring.

class SearchService() {
public fetchQuery( query: string, config: SearchServiceConfig ) {  }
private extractData( response ) {  }
private handleError( error ) {  }

This small modification to our service structure enables us to have the amount of control which we required. The config class is a fairly simple one.

export class SearchServiceConfig {
private count: number;
private source: Source;
private fields: Set<AggregationFields>;
private aggregationLimit: number;
private maximumRecords: number;
private startRecord: number;
private timezoneOffset: string;
private filters: Set<Filter>;

// Other methods to get/set these attributes

Now any requester will instantiate a new object of this class and will set the attributes according to his needs then this object is passed to the fetchQuery() method of our function. Which designs the request to be sent accordingly.


In conclusion, i would like to mention the how these attributes are chosen to be a part of the Config and not as a query string. Our API endpoints accept the query string along with some attributes which filter out the results or run aggregations in various fields. So we should have all these attributes in our config as these all properties may vary according to the requesters need. Therefore, this idea of configurable services makes us not only better reuse the existing models and services in multiple situations but also make us write better predictable code.

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Simplifying Scrapers using BaseScraper

Loklak Server‘s main function is to scrape data from websites and other sources and output in different formats like JSON, xml and rss. There are many scrapers in the project that scrape data and output them, but are implemented with different design and libraries which makes them different from each other and a difficult to fix changes.

Due to variation in scrapers’ design, it is difficult to modify them and fix the same issue (any issue, if it appears) in each of them. This issue signals fault in design. To solve this problem, Inheritance can be brought into application. Thus, I created BaseScraper abstract class so that scrapers are more concentrated on fetching data from HTML and all supportive tasks like creating connection with the help of url are defined in BaseScraper.

The concept is pretty easy to implement, but for a perfect implementation, there is a need to go through the complete list of tasks a scraper does.

These are the following tasks with descriptions and how they are implemented using BaseScraper:

  1. Endpoint that triggers the scraper

Every search scraper inherits class AbstractAPIHandler. This is used to fetch get parameters from the endpoint according to which data is scraped from the scraper. The arguments from serviceImpl method is used to generate output and is returned to it as JSONObject.

For this task, the method serviceImpl has been defined in BaseScraper and method getData is implemented to return the output. This method is the driver method of the scraper.

public JSONObject serviceImpl(Query call, HttpServletResponse response, Authorization rights, JSONObjectWithDefault permissions) throws APIException {
    return this.getData().toJSON(false, "metadata", "posts");


  1. Constructor

The constructor of Scraper defines the base URL of the website to be scraped, name of the scraper and data structure to fetch all get parameters input to the scraper. For get parameters, the Map data structure is used to fetch them from Query object.

Since every scraper has it’s own different base URL, scraper name and get parameters used, so it is implemented in respective Scrapers. QuoraProfileScraper is an example which has these variables defined.

  1. Get all input variables

To get all input variables, there are setters and getters defined for fetching them as Map from Query object in BaseScraper. There is also an abstract method getParam(). It is defined in respective scrapers to fetch the useful parameters for scraper and set them to the scraper’s class variables.

// Setter for get parameters from call object
protected void setExtra(Query call) {
    this.extra = call.getMap();
    this.query = call.get("query", "");

// Getter for get parameter wrt to its key
public String getExtraValue(String key) {
    String value = "";
    if(this.extra.get(key) != null) {
        value = this.extra.get(key).trim();
    return value;

// Defination in QuoraProfileScraper
protected void setParam() {
    if(!"".equals(this.getExtraValue("type"))) {
        this.typeList = Arrays.asList(this.getExtraValue("type").trim().split("\\s*,\\s*"));
    } else {
        this.typeList = new ArrayList<String>();
        this.setExtraValue("type", String.join(",", this.typeList));


  1.  URL creation for web scraper

The URL creation shall be implemented in a separate method as in TwitterScraper. The following is the rough implementation adapted from one of my pull request:

protected String prepareSearchUrl(String type) {
    URIBuilder url = null;
    String midUrl = "search/";

    try {
        switch(type) {
            case "question":
                url = new URIBuilder(this.baseUrl + midUrl);
                url.addParameter("q", this.query);
                url.addParameter("type", "question");
    return url.toString();


  1. Get BufferedReader object from InputStream

getDataFromConnection method fetches the BufferedReader object from ClientConnection. This object reads the web page line by line by the scrape method to fetch data. See here.

ClientConnection connection = new ClientConnection(url);
BufferedReader br = getHtml(connection);
public BufferedReader getHtml(ClientConnection connection) {

    if (connection.inputStream == null) {
        return null;

    BufferedReader br = new BufferedReader(new InputStreamReader(connection.inputStream, StandardCharsets.UTF_8));
    return br;


  1. Scraping of data from HTML

The Scraper method for scraping data is declared abstract in BaseScraper and defined in the scraper. This can be a perfect example of implementation for BaseScraper (See code the here) and scraper (here).

  1. Output of data

The output of scrape method is fetched in Post data objects that are implemented for the respective scraper. These Post objects are added to Timeline iterator and which outputs data as JSONArray. Later the objects are output in enclosed Post object wrapper.

This data can be directly output as Post object, but adding it to iterator makes the Post Objects capable to be sorted in an order and be indexed to ElasticSearch.



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Read more about the article Iterating the Loklak Server data
Iterating the Loklak Server data

Iterating the Loklak Server data

Loklak Server is amazing for what it does, but it is more impressive how it does the tasks. Iterators are used for and how to use them, but this project has a customized iterator that iterates Twitter data objects. This iterator is .

Timeline implements an interface iterable (isn’t it iterator?). This interface helps in using Timeline as an iterator and add methods to modify, use or create the data objects. At present, it only iterates Twitter data objects. I am working on it to modify it to iterate data objects from all web scrapers.

The following is a simple example of how an iterator is used.

// Initializing arraylist
List<String> stringsList = Arrays.asList("foo", "bar", "baz");

// Using iterator to display contents of stringsList
System.out.print("Contents of stringsList: ");

Iterator iter = al.iterator();
while(iter.hasNext()) {
    System.out.print( + " ");


This iterator can only iterate data the way array does. (Then why do we need it?) It does the task of iterating objects perfectly, but we can add more functionality to the iterator.


Timeline iterator iterates the MessageEntry objects i.e. superclass of TwitterTweet objects. According to Javadocs, “Timeline is a structure which holds tweet for the purpose of presentation, There is no tweet retrieval method here, just an iterator which returns the tweets in reverse appearing order.”

Following are some of the tasks it does:

  1. As an iterator:

This basic use of Timeline is to iterate the MessageEntry objects. It not only iterates the data objects, but also fetches them (See here).

// Declare Timeline object according to order the data object has been created
Timeline tline = new Timeline(Timeline.parseOrder("created_at"));

// Adding data objects to the timeline
// Outputing all data objects as array of JSON objects
for (MessageEntry me: tline) {
    JSONArray postArray = new JSONArray();
    for (MessageEntry post : this) {


  1. The order of iterating the data objects

Timeline can arrange and iterate the data objects according to the date of creation of the twitter post, number of retweets or number of favourite counts. For this there is an Enum declaration of Order in the Timeline class which is initialized during creation of Timeline object. [link]

    Timeline tline = new Timeline(Timeline.parseOrder("created_at"));


  1. Pagination of data objects

There is an object cursor, some methods, including getter and setters to support pagination of the data objects. It is only internally implemented, but can also be used to return a section of the result.

  1. writeToIndex method

This method can be used to write all data fetched by Timeline iterator to ElasticSearch for indexing and to dump that can be used for testing. Thus, indexing of data can concurrently be done while it is iterated. It is implemented here.

  1. Other methods

It also has methods to output all data as JSON and customized method to add data to Timeline keeping user object and Data separate, etc. There are a bit more things in this iterable class which shall be explored instead.



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Multithreading implementation in Loklak Server

Loklak Server is a near-realtime system. It performs a large number of tasks and are very costly in terms of resources. Its basic function is to scrape all data from websites and output it at the endpoint. In addition to scraping data, there is also a need to perform other tasks like refining and cleaning of data. That is why, multiple threads are instantiated. They perform other tasks like:

  1. Refining of data and extract more data

The data fetched needs to be cleaned and refined before outputting it. Some of the examples are:

a) Removal of html tags from tweet text:

After extracting text from html data and feeding to TwitterTweet object, it concurrently runs threads to remove all html from text.

b) Unshortening of url links:

The url links embedded in the tweet text may track the users with the help of shortened urls. To prevent this issue, a thread is instantiated to unshorten the url links concurrently while cleaning of tweet text.

  1. Indexing all JSON output data to ElasticSearch

While extracting JSON data as output, there is a method here in that indexes data to ElasticSearch.

Managing multithreading

To manage multithreading, Loklak Server applies following objects:

1. ExecutorService

To deal with large numbers of threads ExecutorService object is used to handle threads as it helps JVM to prevent any resource overflow. Thread’s lifecycle can be controlled and its creation cost can be optimized. This is the best example of ExecutorService application is here:

public class TwitterScraper {
    // Creation of at max 40 threads. This sets max number of threads to 40 at a time
    public static final ExecutorService executor = Executors.newFixedThreadPool(40);
    // Feeding of TwitterTweet object with data
    TwitterTweet tweet = new TwitterTweet(
        imgs, vids, place_name, place_id,
        user, writeToIndex,  writeToBackend
    // Starting thread to refine TwitterTweet data
    if (tweet.willBeTimeConsuming()) {
    }    .


2. basic Thread class

Thread class can also be used instead of ExecutorService in cases where there is no resource crunch. But it is always suggested to use ExecutorService due to its benefits. Thread implementation can be used as an anonymous class like here.

3. Runnable interface

Runnable interface can be used to create an anonymous class or classes which does more task than just a task concurrently. In Loklak Server, TwitterScraper concurrently indexes the data to ElasticSearch, unshortens link and cleans data. Have a look at implementation here.


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Unifying Data from Different Scrapers of loklak server using Post

Loklak Server project is a software that scrapes data from different websites through different endpoints. It is difficult to create a single endpoint. For a single endpoint, there is a need of a decent design for using multiple scrapers. For such a task, multiple changes are needed. That is why one of the changes I introduced was Post class that acts as both wrapper and an interface for data objects of search scrapers (though implementation in scrapers is in progress).

Post is a subclass of JSONObject that helps in working with JSON data in Java. In other words, Post is a JSONObject with an identity (we call it postId) and and a timestamp of the data scraped. It is used to capture data fetched by the web-scrapers. Benefit of JSONObject as superclass is that it provides methods to capture and access data efficiently.

Why Post?

At present there is a Class MessageEntry which is the superclass of TwitterTweet (data object of TwitterScraper). It has numerous methods that can be used by data objects to clean and analyse data. But it has a disadvantage, it is a specialized for social websites like Twitter, but will become redundant for different types websites like Quora, Github, etc.

Whereas Post object is a small but powerful and flexible object with its ability to deal with data like JSONObject. It contains getter and setter methods, identity members used to provide each Post object a unique identity. It doesn’t have any methods for analysis and cleaning of data, but MessageEntry class’ methods can be used for this purpose.

Uses of Post Object

When I started working on Post Object, it could be used as marker interface for data objects. Following are the advantages I came up with it:

1) Accessing the data object of any scraper using its variable. And yes, this is the primary reason it is an interface.

2) But in addition to accessing the data objects, one can also directly use it to fetch, modify or use data without knowing the scraper it belongs. This feature is useful in Timeline iterator.

This is an example how Post interface is used to append two lists of Posts (maybe carrying different type of data) into one.

public void mergePost(PostTimeline list) {
    for (Post post: list) {


Post as a wrapper object

While working on Post object, I converted it into a class to also use it as a wrapper. But why a wrapper? Wrapper can be used to wrap a list of Post objects into one object. It doesn’t have any identity or timestamp. It is just a utility to dump a pack of data objects with homogeneous attributes.

This is an example implementation of Post object as wrapper. typeArray is a wrapper which is used to store 2 arrays of data objects in it. These data object arrays are timeline objects that are saved as JSONArray objects in the Post wrapper.

    Post typeArray = new Post(true);
    switch(type) {
        case "users":
            typeArray.put("users", scrapeProfile(br, url).toArray());
        case "question":
            typeArray.put("question", scrapeQues(br, url).toArray());




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Query Model Structure of Loklak Search

Need to restructure

The earlier versions of loklak search applications had the issues of breaking changes whenever any new feature was added in the application. The main reason for these unintended bugs was identified to be the existing query structure. The query structure which was used in the earlier versions of the application only comprised of the single entity a string named as queryString.

export interface Query {
 queryString: string;

This simple query string property was good enough for simple string based searches which were the goals of the application in the initial phases, but as the application progressed we realized this simple string based implementation is not going to be feasible for the long term. As there are only a limited things we can do with strings. It becomes extremely difficult to set and reset the portions of the string according to the requirements. This was the main vision for the alternate architecture design scheme was to the ease of enabling and disabling the features on the fly.

Application Structure

Therefore, to overcome the difficulties faced with the simple string based structure we introduced the concept of an attribute based structure for queries. The attribute based structure is simpler to understand and thus easier to maintain the state of the query in the application.

export interface Query {
 displayString: string;
 queryString: string;
 routerString: string;
 filter: FilterList;
 location: string;
 timeBound: TimeBound;
 from: boolean;

The reason this is called an attribute based structure is that here each property of an interface is independent of one another. Thus each one can be thought of as a separate little key placed on the query, but each of these keys are different and are mutually exclusive. What this means is, if I want to write an attribute query, then it does not matter to me which other attributes are already present on the query. The query will eventually be processed and sent to the server and the corresponding valid results if exists will be shown to the user.

Now the question arises how do we modify the values of these attributes? Now before answering this I would like to mention that this interface is actually instantiated in the the Redux state, so now our question automatically gets answered, the modification to redux state corresponding to the query structure will be done by specific reducers meant for modification of each attribute. These reducers are again triggered by corresponding actions.

export const ActionTypes = {
 VALUE_CHANGE: '[Query] Value Change',
 FILTER_CHANGE: '[Query] Filter Change',
 LOCATION_CHANGE: '[Query] Location Change',
 TIME_BOUND_CHANGE: '[Query] Time Bound Change',

This ActionTypes object contains the the corresponding actions which are used to trigger the reducers. These actions can be dispatched in response to any user interaction by any of the components, thus modifying a particular state attribute via the reducer.

Converting from object to string

Now for our API endpoint to understand our query we need to send the proper string in API accepted format. For this there is need to convert dynamically from query state to query string, for this we need a simple function which take in query state as an input return the query string as output.

export function parseQueryToQueryString(query: Query): string {
 let qs: string;
 qs = query.displayString;
 if (query.location) {
qs += ` near:${query.location);

 if (query.timeBound.since) {
   qs += ` since:${parseDateToApiAcceptedFormat(query.timeBound.since)}`;

 if (query.timeBound.until) {
   qs += ` until:${parseDateToApiAcceptedFormat(query.timeBound.until)}`;

 return qs;

In this function we are just checking and updating the query string according to the various attributes set in the structure, and then returning the query string. So if eventually we have to convert to the string, then what is the advantage of this approach? The main advantage of this approach is that we know the query structure beforehand and we use the structure to build the string not just randomly selecting and removing pieces of information from a string. Whenever we update any of the attribute of the query state, the query is generated fresh, and not modifying the old string.


This approach makes the application to be able to modify the search queries sent to server in a streamlined and logical way, just by using simple data structure. This query model has provided us with a lot of advantages which are visible in the aspect of application stability and performance. This model has cuts out dirty regex matching, of typed queries and thus again help us to make simpler queries.

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Using JS Profiler to Solve App Slowness in Loklak Search

The loklak search web application had an issue of application slowdown in the development environment. There was a highly prominent lag and a slowdown in the application while typing a search query in the search box. This issue was being faced from but the reason for the lad and slowdown was unknown. So this blog explains how the issue was identified, what were the reasons behind it, and how the issue was solved.

The most important aspect of fixing any bug in the application is to first identify it. Here in our case what helped us to identify the issue was the proper use of JS profiler to find the hidden issue underneath.

Reporting of the issue

The issue was observed and reported in the issue #365. On discussion it was observed that the issue was not being observed by all the users but only some of them, this made it really interesting as the application, on the whole, was being shipped uniformly to all the users but still only fractions of them were facing this issue, and all the people who were experiencing this were the developers of the application


For the detection of the issue, it was important to have a starting point to search issue. Here the only thing which we knew about the issue was that it was being faced only when typing in the application search box. So this was our starting ground. The most important tool while solving such issues is the browser itself. The dev tools in the most modern browsers have a built in Javascript Profiler. A profiler is a tool which dynamically at the run time records the activities happening in the various threads of the application, most importantly main thread. So we can find the JS profiler in browser dev tools, in Chrome 59 it is in Performance tab.

In every profiler there is a record button (marked above) pressing this will start monitoring all the activities on various JS threads and other pipelines. So this profiler and understanding its logs is the key to solve any performance related issue in any web application.

So we started by hitting the record button and then typing in the search box. And this is the output which we saw for profiling. This output contains a lot of numbers and to make sense of all of them it is important to understand the important bits of it as the output, on the whole, can be a lot overwhelming.


We will go piece by piece and will try to make sense of the output and if possible identify the reason for our original issue.


  • This is the timeline strip which represents the overall view of the main thread of JS. The yellow portions show the percentage of CPU utilization with respect to time, here we can clearly see the consistent blocking CPU utilization by the main thread over a span of 10 seconds (during which we typed). The high CPU utilization starts with our first keystroke.
  • The red rectangles at the top represent the dropped frames, the frames which are never rendered due to heavy load, and the green line at the bottom represent the frames per second which you can see is practically zero. This implies that the CPU is entirely hogged up by the main thread and there is no room left for rendering frames.
  • Now, this is the Interactions panel this indicates all the user generated events which are triggered. It clearly shows that the key up and key character events are getting triggered but are taking a lot of processing time. The first key press, key character and key up events run normally. But then each subsequent event hogs up the CPU.
  • The above observation indicates what ever is the issue in the application it is in between the registering of an event and its final processing.
  • So the natural question arises what we do during this processing. On looking at the code it seems like we dispatch a Redux Action that the query string has updated, according to the action reducer updates the state. Now, this implies that our reducer is taking the time to update the state. But this does not make sense, as reducers are the pure functions, i.e. they don’t make any blocking calls. So they can’t hog up all the CPU cycles for 10 seconds.
  • So we need to investigate further to identify what is happening after the action is dispatched, to the store. This is the function call stack trace on the main thread. What we see here is each nested function call as we move down the tree.
  • The above trace shows that what all is happening when a key is pressed. There is again a key point to observe that the time to process the events is increasing per keystroke, for the first key stroke it starts from 112ms and jumps to 400ms in for the subsequent key presses, which causes the slowdown.
  • Now we can see in the above stack trace and find out the Rate determining step for each event processing. The RDS of a step is the slowest step which causes the whole process to execute slowly.
  • Now as we move down the tree we find that all the steps are taking very nominal time but some function down in the tree is causing the issues. So we move down the tree to identify the rate determining function call.
  • As we move down the tree we first find our action dispatch call as expected, the processing of this call is not taking much time but some of the functions in that process are causing our issue, thus we continue to move down the tree.
  • Here we find our culprit which is causing our issue the slowest step is a method which is making a call to our StoreDevtools module and notifying it about the latest store update. This notifying action is taking the most time.
  • As we move down the tree we first find our action dispatch call as expected, the processing of this call is not taking much time but some of the functions in that process are causing our issue, thus we continue to move down the tree.
  • Here we find our culprit which is causing our issue the slowest step is a method which is making a call to our StoreDevtools module and notifying it about the latest store update. This notifying action is taking the most time.
  • So the issue was caused by the import of dev-tools module in our app module
  • Now another important observation here is that why only a few people were facing this issue
    • The only people who faced this issue were the developers working on the project because as the import statement says “instrumentOnlyWithExtension” it specifies that this module will only run when there is Devtools Extension installed.
    • And this is the case with the application developers only.

Fixing the issue

  • For fixing the issue it was decided that for best performance the StoreDevTools module will not be included in the package by default.
  • The module can be selectively imported for testing purposes as and when required by the developers
  • It will always be removed while merging into the main branch.


Now after all these efforts and fixing it is worth mentioning the speed gains which were achieved after removing store dev-tools module.

  • This the profiler output after running the profiling on similar queries as before. Without even going into detail we can see it is much cleaner now.
  • These are the main thread and frame rates achieved after the removal of the module. We can clearly see the main thread is completely free and we thus achieve high frame rates.
  • The last important observation is that the action dispatching which was taking about 350ms earlier is now taking only 2.5ms. Such a performance boost is huge at about 140x at each key press.


When a web application has slow response or lag, it makes the users leave the application altogether, and this causes the huge setback to the efforts which we developers make in increasing the user interaction. Thus, any issue on client side must be solved as quickly as possible. The most important tool with which we use to test and predict what is going wrong with the application is the browser itself. The browser and the dev-tools provide us important tools required to optimize the application for improving the performance.

Resources and links

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Adding unit tests for effects in Loklak Search

Loklak search uses @ngrx/effects to listen to actions dispatched by the user and sending API request to the loklak server. Loklak search, currently, has seven effects such as Search Effects,  Suggest Effects which runs to make the application reactive. It is important to test these effects to ensure that effects make API calls at the right time and then map the response to send it back to the reducer.

I will  explain here how I added unit tests for the effects. Surprisingly, the test coverage increased from 43% to 55% after adding these tests.

Effects to test

We are going to test effects for user search. This effect listens to the event of type USER_SEARCH and makes a call to the user-search service with the query as a parameter. After a response is received, it maps the response and passes it on the UserSearchCompleteSuccessAction action which performs the later operation. If the service fails to get a response, it makes a call to the UserSearchCompleteFailAction.


ApiUserSearchEffects is the effect which detects if the USER_SEARCH action is dispatched from some component of the application and consequently, it makes a call to the UserSearchService and handles the JSON response received from the server. The effects then, dispatch the action new UserSearchCompleteSuccessAction if response is received from server or either dispatch the action new UserSearchCompleteFailAction if no response is received. The debounce time is set to 400 so that response can be flushed if a new USER_SEARCH is dispatched within the next 400ms.

For this effect, we need to test if the effects actually runs when USER_SEARCH action is made. Further, we need to test if the correct parameters are supplied to the service and response is handled carefully. We also, need to check if the response if really flushed out within the certain debounce time limit.

export class ApiUserSearchEffects {@Effect()
search$: Observable<Action>
= this.actions$
.map((action: userApiAction.UserSearchAction) => action.payload)
.switchMap(query => {
const nextSearch$ = this.actions$.ofType(userApiAction.ActionTypes.USER_SEARCH).skip(1);const follow_count = 10;return this.apiUserService.fetchQuery(query.screen_name, follow_count)
.map(response => new userApiAction.UserSearchCompleteSuccessAction(response))
.catch(() => of(new userApiAction.UserSearchCompleteFailAction()));
private actions$: Actions,
private apiUserService: UserService
) { }


Unit test for effects

  • Configure the TestBed class before starting the unit test and add all the necessary imports (most important being the EffectsTestingModule) and providers. This step will help to isolate the effects completely from all other components and testing it independently. We also need to create spy object which spies on the method userService.fetchQuery with provider being UserService.

beforeEach(() => TestBed.configureTestingModule({
imports: [
providers: [
provide: UserService,
useValue: jasmine.createSpyObj(‘userService’, [‘fetchQuery’])
  • Now, we will be needing a function setup which takes params which are the data to be returned by the Mock User Service. We can now configure the response to returned by the service. Moreover, this function will be initializing EffectsRunner and returning ApiUserSearchEffects so that it can be used for unit testing.

function setup(params?: {userApiReturnValue: any}) {
const userService = TestBed.get(UserService);
if (params) { userService.fetchQuery.and.returnValue(params.userApiReturnValue);
}return {
runner: TestBed.get(EffectsRunner),
apiUserSearchEffects: TestBed.get(ApiUserSearchEffects)


  • Now we will be adding unit tests for the effects. In these tests, we are going to test if the effects recognise the action and return some new action based on the response we want and if it maps the response only after a certain debounce time.We have used fakeAsync() which gives us access to the tick() function. Next, We are calling the function setup and pass on the Mock Response so that whenever User Service is called it returns the Mock Response and never runs the service actually. We will now queue the action UserSearchAction in the runner and subscribe to the value returned by the effects class. We can now test the value returned using expect() block and that the value is returned only after a certain debounce time using tick() block.

it(‘should return a new userApiAction.UserSearchCompleteSuccessAction, ‘ +
‘with the response, on success, after the de-bounce’, fakeAsync(() => {
const response = MockUserResponse;const {runner, apiUserSearchEffects} = setup({userApiReturnValue: Observable.of(response)});

const expectedResult = new userApiAction.UserSearchCompleteSuccessAction(response);

runner.queue(new userApiAction.UserSearchAction(MockUserQuery));

let result = null;$.subscribe(_result => result = _result);
tick(399); // test debounce

it(‘should return a new userApiAction.UserSearchCompleteFailAction,’ +
‘if the SearchService throws’, fakeAsync(() => {
const { runner, apiUserSearchEffects } = setup({ userApiReturnValue: Observable.throw(new Error()) });

const expectedResult = new userApiAction.UserSearchCompleteFailAction();

runner.queue(new userApiAction.UserSearchAction(MockUserQuery));

let result = null;$.subscribe(_result => result = _result );

tick(399); // Test debounce


Continue ReadingAdding unit tests for effects in Loklak Search

Introducing Customization in Loklak Media Wall

My GSoC Project includes implementing media wall in loklak search . One part of the issue is to include customization options in media wall. I looked around for most important features that can be implemented in a media wall to give the user a more appealing and personalized view. One of the feature that can be implemented is enabling Full Screen Mode.  This feature can help the user to display media wall on the projector or any big display screen without compromising with space available. In one part, I would be explaining how I implemented Full screen Mode in loklak media wall using fullscreen.js library.

Secondly, it is important to include a very reactive and user-friendly setting box. The setting box should be a central container in which all the customization options will be included.In loklak media wall,  setting box is implemented as a dialog box with various classifications in form of tabs. I would also be explaining  how I designed customization menu using Angular Material.


Full Screen Mode

Since loklak search is an Angular 2 application and all the code is written in typescript, we can’t simply use the fullscreen.js library. We have to import the library into our application and create a directive that can be applied to the element to use it in the application.

  • Install fullscreen.js library in the application using Node Package Manager.

npm install save screenfull

import {Directive, HostListener, Output, EventEmitter} from ‘@angular/core’;
import * as screenfull from ‘screenfull’;@Directive({
selector: ‘[toggleFullscreen]’
export class ToggleFullscreenDirective {constructor() {}@HostListener(‘click’) onClick() {
if (screenfull.enabled) {
  • Import Directive into the module and add it to declaration. This allows directive to be used anywhere in the template.

import { ToggleFullscreenDirective } from ‘../shared//full-screen.directive’;
declarations: [
export class MediaWallModule { }
  • Now, the directive is ready to use on the template. We just have to add this attribute directive to an element.

<i toggleFullscreen mdTooltip=“Full Screen” mdTooltipPosition=“below” class=“material-icons md-36”>fullscreen</i>

Customization Menu

Customization Menu is created using the idea of central container for customization. It is created using two components of Angular Material – Dialog Box and Tabs . We will now be looking how customization menu is implemented using these two components.

  • Create a component with the pre-configured position, height and width of the dialog box. This can be done simply using updatePosition and updateSize property of the MdDialogRef class.

export class MediaWallCustomizationComponent implements OnInit {
public query: string;constructor(
private dialogRef: MdDialogRef<MediaWallCustomizationComponent>,
private store: Store<fromRoot.State>,
private location: Location) { }ngOnInit() {
this.dialogRef.updateSize(‘80%’, ‘80%’);
}public searchAction() {
if (this.query) { mediaWallAction.WallInputValueChangeAction(this.query));
this.location.go(‘/wall’, `query=${this.query}`);
  • Create a template for the Customization menu. We will be using md-tab and md-dialog to create a dialog box with options displayed using tabs. dynamicHeight should be set to true so that dialog box adjust according to the tabs. We can simply add an attribute md-dialog-close to the button which will close the dialog box. All the content should be added in the div with attribute md-dialog-content linked to it. Moreover, to make options look more user-friendly and adjustable on smaller screens, icons must be added with the Tab title.

<h1 mddialogtitle>Customization Menu</h1>
<button class=“form-close” mddialogclose>x</button>
<span mddialogcontent>
<mdtabgroup color=“accent” dynamicHeight=“true”>
<ngtemplate mdtablabel>
Search For
<h3> Search Customization </h3>
<mdinputcontainer class=“example-full-width” color=“accent”>
<input placeholder=“Search Term” mdInput type =“text” class=“input” name=“search-term” [(ngModel)]=“query”>
<span class=“apply-button”>
<button mdraisedbutton color=“accent” mddialogclose (click)=“searchAction()”>Display</button>

The code currently shows up code for search customization. It basically, records to the input using [(ngModel)] for two-way binding and makes the call the search action whenever user clicks on Display button.

  • Add a button which would open dialog box using open property of MdDialog class. This property would provide an instance for MediaWallCustomizationComponent and the component will show up dynamically.

<i class=“material-icons md-36” (click)=“”>settings</i>
  • It is important to add MediaWallCustomizationComponent as an entry component in the module so that AOT compiler can create a ComponentFactory for it during initialization.

import { MediaWallCustomizationComponent } from ‘./media-wall-customization/media-wall-customization.component’;

entryComponents: [
export class MediaWallModule { }


This creates an appealing and user-friendly customization menu which acts a central container for customization options.


Continue ReadingIntroducing Customization in Loklak Media Wall

Adding Masonry Grid Layout to loklak Media Wall

Working on loklak media walls, I wanted to add a responsive self-adjusting grid layout for media walls. Going through the most trending media wall, I concluded that the most commonly used view for media walls is Masonry view. This view is a much similar view to the Pinterest grid layout. In fact, Masonry Desandro states that Masonry view can be one of the most responsive and most pretty ways to present cards. It is also beneficial to use masonry view as it avoids unnecessary gaps and makes full use of the display screen.

In this blog, we are going to see how I added a masonry view to the media walls without any other dependency than CSS using column-count and column-gap. We would also be looking upon how to adjust font-size using rem units to make text readable for all screen-sizes depending on number of columns.


<span class=“masonry”>
<span class=“masonry-panel” *ngFor=“let item of (apiResponseResults$ | async)”>
<span class=“masonry-content”>
<mediawallcard [feedItem]=“item”></mediawallcard>
  • The first span with class masonry is like a container in which all the cards will be embedded. This div will provide a container to adjust the number of columns in which cards will be adjusted.
  • The second span with class masonry-panel will be the column division. These panels are like the elements of a matrix. These panels are responsive and will adjust according to the screen size.
  • The third span with class masonry-content are like the content box in which all the content will be embedded. This div will create a space in the panel for the card to be adjusted.
  • The fourth element media-wall-card are the cards in which all the feed items are placed.

CSS File

  • Adjusting columns – The column-count and column-gap property was introduced in CSS3 to divide the element in a specified number of column and to keep the specified number (whole number) of column gap between the elements respectively. For different screen sizes, these are the column count that are kept. We need adjust the number of columns according to various screen sizes so that cards neither look too stretched or too bleak. Media wall is now responsive enough to adjust on smaller screens like mobiles with one column and on larger screens too with five columns.

@media only screen and (max-width: 600px) {
.masonry {
columncount: 1;
columngap: 0;
} @media only screen and (min-width: 600px) and (max-width: 900px) {
.masonry {
columncount: 2;
columngap: 0;
} @media only screen and (min-width: 1280px) and (max-width: 1500px) {
.masonry {
columncount: 3;
columngap: 0;
} @media only screen and (min-width: 1500px) and (max-width: 1920px) {
.masonry {
columncount: 4;
columngap: 0;
} @media only screen and (min-width: 1920px) {
.masonry {
columncount: 5;
columngap: 0;
  • Adjusting Font-Size – For a fixed aspect ratio of various divisions of the media wall card, we need a central unit that can be adjusted to keep this ratio fixed. Using px will rather make the sizes fixed and adjusting these sizes for various screen sizes will make it hectic and would spoil the ratio. We will be instead using rem as a font-unit to adjust the font size for different screen sizes. Firstly, we need to assign a certain font size to all the elements in the media wall card. Now, we can configure the central font-size of the root unit for all the screen sizes using @media tag.

One thing that should be kept in mind is that The root font size should be kept more than 14px always.

@media only screen and (max-width: 600px) {
:root {
font-size: 14px;
}@media only screen and (min-width: 600px) and (max-width: 800px) {
:root {
font-size: 16px;
}@media only screen and (min-width: 800px) and (max-width: 1200px) {
:root {
font-size: 17px;
}@media only screen and (min-width: 1200px) and (max-width: 1500px) {
:root {
font-size: 18px;
}@media only screen and (min-width: 1500px) {
:root {
font-size: 20px;


This will create a masonry layout and now, all the cards can be adjusted in this self-adjusting grid and will look readable on all types of screen.


Continue ReadingAdding Masonry Grid Layout to loklak Media Wall