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


Implementation of Customizable Instant Search on Susper using Local Storage

Results on Susper could be instantly displayed as user types in a query. This was a strict feature till some time before, where the user doesn’t have customizable option to choose. But now one could turn off and on this feature.

To turn on and off this feature visit ‘Search Settings’ on Susper. This will be the link to it: and you will find different options to choose from.

How did we implement this feature?


 <h4><strong>Susper Instant Predictions</strong></h4>
 <p>When should we show you results as you type?</p>
 <input name="options" [(ngModel)]="instantresults" disabled value="#" type="radio" id="op1"><label for="op1">Only when my computer is fast enough</label><br>
 <input name="options" [(ngModel)]="instantresults" [value]="true" type="radio" id="op2"><label for="op2">Always show instant results</label><br>
 <input name="options" [(ngModel)]="instantresults" [value]="false" type="radio" id="op3"><label for="op3">Never show instant results</label><br>

User is displayed with options to choose from regarding instant search.when the user selects a new option, his selection is stored in the instantresults variable in search settings component using ngModel.


Later when user clicks on save button the instantresults object is stored into localStorage of the browser

onSave() {
 if (this.instantresults) {
   localStorage.setItem('instantsearch', JSON.stringify({value: true}));
 } else {
   localStorage.setItem('instantsearch', JSON.stringify({ value: false }));
   localStorage.setItem('resultscount', JSON.stringify({ value: this.resultCount }));


Later this value is retrieved from the localStorage function whenever a user enters a query in search bar component and search is made according to user’s preference.


Later this value is retrieved from the localStorage function whenever a user enters a query in search bar component and search is made according to user’s preference.

onquery(event: any) { query.QueryAction(event));
 let instantsearch = JSON.parse(localStorage.getItem('instantsearch'));

 if (instantsearch && instantsearch.value) { queryactions.QueryServerAction({'query': event, start: this.searchdata.start, rows: this.searchdata.rows}));
   this.displayStatus = 'showbox';
 } else {
   if (event.which === 13) { queryactions.QueryServerAction({'query': event, start: this.searchdata.start, rows: this.searchdata.rows}));
     this.displayStatus = 'showbox';


Interaction of different components here:

  1. First we set the instantresults object in Local Storage from search settings component.
  2. Later this value is retrieved and used by search bar component using localstorage.get() method to decide whether to display results instantly or not.

Below, Gif shows how you could use this feature in Susper to customise the instant results in your browser.



Adding Unit Test for Reducer in loklak search

Ngrx/store components are an integral part of the loklak search. All the components are dependent on how the data is received from the reducers. Reducer is like a client-side database which stores up all the data received from the API response. It is responsible for changing the state of the application. Reducers also supplies data to the Angular components from the central Store. If correct data is not received by the components, the application would crash. Therefore, we need to test if the reducer is storing the data in a correct way and changes the state of the application as expected.

Reducer also stores the current state properties which are fetched from the APIs. We need to check if reducers store the data in a correct way and if the data is received from the reducer when called from the angular components.

In this blog, I would explain how to build up different components for unit testing reducers.

Reducer to test

This reducer is used for store the data from suggest.json API from the loklak server.The data received from the server is further classified into three properties which can be used by the components to show up auto- suggestions related to current search query.

  • metadata: – This property stores the metadata from API suggestion response.
  • entities: – This property stores the array of suggestions corresponding to the particular query received from the server.
  • valid: – This is a boolean which keeps a check if the suggestions are valid or not.

We also have two actions corresponding to this reducer. These actions, when called, changes the state properties which , further, supplies data to the components in a more classified manner. Moreover, state properties also causes change in the UI of the component according to the action dispatched.

  • SUGGEST_COMPLETE_SUCCESS: – This action is called when the data is received successfully from the server.
  • SUGGEST_COMPLETE_FAIL: – This action is called when the retrieving data from the server fails.

export interface State {
metadata: SuggestMetadata;
entities: SuggestResults[];
valid: boolean;
}export const initialState: State = {
metadata: null,
entities: [],
valid: true
};export function reducer(state: State = initialState, action: suggestAction.Actions): State {
switch (action.type) {
case suggestAction.ActionTypes.SUGGEST_COMPLETE_SUCCESS: {
const suggestResponse = action.payload;return {
metadata: suggestResponse.suggest_metadata,
entities: suggestResponse.queries,
valid: true
}case suggestAction.ActionTypes.SUGGEST_COMPLETE_FAIL: {
return Object.assign({}, state, {
valid: false
}default: {
return state;

Unit tests for reducers

  • Import all the actions, reducers and mocks

import * as fromSuggestionResponse from ‘./suggest-response’;
import * as suggestAction from ‘../actions/suggest’;
import { SuggestResponse } from ‘../models/api-suggest’;
import { MockSuggestResponse } from ‘../shared/mocks/suggestResponse.mock’;


  • Next, we are going to test if the undefined action doesn’t a cause change in the state and returns the initial state properties. We will be creating an action by const action = {} as any;  and call the reducer by const result = fromSuggestionResponse.reducer(undefined, action);. Now we will be making assertions with expect() block to check if the result is equal to initialState and all the initial state properties are returned

describe(‘SuggestReducer’, () => {
describe(‘undefined action’, () => {
it(‘should return the default state’, () => {
const action = {} as any;const result = fromSuggestionResponse.reducer(undefined, action);


  • Now, we are going to test SUGGEST_COMPLETE_SUCCESS and SUGGEST_COMPLETE_FAIL action and check if reducers change only the assigned state properties corresponding to the action in a correct way.  Here, we will be creating action as assigned to the const action variable in the code below. Our next step would be to create a new state object with expected new state properties as assigned to variable const expectedResult below. Now, we would be calling reducer and make an assertion if the individual state properties of the result returned from the reducer (by calling reducer) is equal to the state properties of the expectedResult (Mock state result created to test).

describe(‘SUGGEST_COMPLETE_SUCCESS’, () => {
it(‘should add suggest response to the state’, () => {
const ResponseAction = new suggestAction.SuggestCompleteSuccessAction(MockSuggestResponse);
const expectedResult: fromSuggestionResponse.State = {
metadata: MockSuggestResponse.suggest_metadata,
entities: MockSuggestResponse.queries,
valid: true
};const result = fromSuggestionResponse.reducer(fromSuggestionResponse.initialState, ResponseAction);
});describe(‘SUGGEST_COMPLETE_FAIL’, () => {
it(‘should set valid to true’, () => {
const action = new suggestAction.SuggestCompleteFailAction();
const result = fromSuggestionResponse.reducer(fromSuggestionResponse.initialState, action);expect(result.valid).toBe(false);


Crawl Job Feature For Susper To Index Websites

The Yacy backend provides search results for Susper using a web crawler (or) spider to crawl and index data from the internet. They also require some minimum input from the user.

As stated by Michael Christen (@Orbiter) “a web index is created by loading a lot of web pages first, then parsing the content and placing the result into a search index. The question is: how to get a large list of URLs? This is solved by a crawler: we start with a single web page, extract all links, then load these links and go on. The root of such a process is the ‘Crawl Start’.”

Yacy has a web crawler module that can be accessed from here: As we would like to have a fully supported front end for Yacy, we also introduced a crawler in Susper. Using crawler one could tell Yacy what process to do and how to crawl a URL to index search results on Yacy server. To support the indexing of web pages with the help of Yacy server, we had implemented a ‘Crawl Job’ feature in Susper.

1)Visit and give information regarding the sites you want Susper to crawl.Currently, the crawler accepts an input of URLs or a file containing URLs. You could customise crawling process by tweaking crawl parameters like crawling depth, maximum pages per domain, filters, excluding media etc.

2) Once crawl parameters are set, click on ‘Start New Crawl Job’ to start the crawling process.

3) It will raise a basic authentication pop-up. After filling, the user will receive a success alert and will be redirected back to home page.

The process of crawl job on Yacy server will get started according to crawling parameters.

Implementation of Crawler on Susper:

We have created a separate component and service in Susper for Crawler

Source code can be found at:

When the user initiates the crawl job by pressing the start button, it calls startCrawlJob() function from the component and this indeed calls the CrawlStart service.We send crawlvalues to the service and subscribe, to the return object confirming whether the crawl job has started or not.


startCrawlJob() {
 this.crawlstartservice.startCrawlJob(this.crawlvalues).subscribe(res => {
   alert('Started Crawl Job');
 }, (err) => {
   if (err === 'Unauthorized') {
     alert("Authentication Error");


After calling startCrawlJob() function from the service file, the service file creates a URLSearchParams object to create parameters for each key in input and send it to Yacy server through JSONP request.


startCrawlJob(crawlvalues) {
 let params = new URLSearchParams();
 for (let key in crawlvalues) {
   if (crawlvalues.hasOwnProperty(key)) {
     params.set(key, crawlvalues[key]);

 params.set('callback', 'JSONP_CALLBACK');

 let options = new RequestOptions({ search: params });
 return this.jsonp
   .get('', options).map(res => {



Using @Output EventEmitter to Hide Search Suggestions in Angular for Susper Web App

Problem: In Susper the suggestions box doesn’t hide when there are no suggestions. To fix this, we have used @Output to create interaction between the search bar and suggestions box.

Susper gives suggestions to the user when user types a query. These suggestions are retrieved from the suggest.json endpoint from Yacy server.

We have a separate component for searching a query and a separate component for showing suggestions (auto-complete.component.ts). The architectural link between the query box, suggestion box and the results page is a bit complicated.

The search bar and the auto-complete component doesn’t interact directly. Whenever a new query is entered, the search bar triggers an action with a payload including the query. On receiving the new query, auto-complete component calls Yacy server to get suggestions from the endpoint and display them inside the suggestion box. Whenever a user searches making a new query, the search bar implementation opens the suggestion box even if there are no results. So there should be a way to inform search bar component that suggestions box has received empty results and search bar could hide the suggestions box.

To achieve this we used @Output to emit an event

@Output() hidecomponent: EventEmitter<any> = new EventEmitter<any>();


this.autocompleteservice.getsearchresults(query).subscribe(res => {
 if (res) {
   if (res[0]) {
     this.results = res[1];
     if (this.results.length === 0) {
     } else {


Then in search bar component, this is binded to a function hidesuggestions() which takes care of hiding the suggestion box.


<app-auto-complete (hidecomponent)="hidesuggestions($event)" id="auto-box" [hidden]="!ShowAuto()"></app-auto-complete>



hidesuggestions(data: number) {
 if (data === 1) {
   this.displayStatus = 'hidebox';
 } else {
   this.displayStatus = 'showbox';
ShowAuto() {
 return (this.displayStatus === 'showbox');


Here you could see that the auto-complete component’s hidden attribute in searchbar.component.ts is binded with ShowAuto() function which takes care about the interaction and hides the suggestions box whenever there are no results.

Below a GIF shows how this suggestions feature is working on Susper

Source code related to this implementation is available at this pull