Autolinker Component in Loklak Search

In Loklak Search the post items contain links, which are either internal or external. These links include the hashtags, mentions, and URLs. From the backend server we just received the message in the plain text format, and thus there is need to parse the plain text and render it as clickable links. These clickable links can be either internal or external. Thus we need an auto-linker component, which takes the text and render it as links.

The API of the Component

The component takes as the input the plain text, then four arrays of strings. Each containing the text to be linked. These are hashtags, mentions, links and the unshorten attribute which is used to unshorten the shortened URLs in the post. These attributes are used by the component to render the text in the appropriate format.

export class FeedLinkerComponent implements OnInit {
@Input() text: string;
@Input() hashtags: string[] = new Array<string>();
@Input() mentions: string[] = new Array<string>();
@Input() links: string[] = new Array<string>();
@Input() unshorten: Object = {};

The Logic of the Component

The basic logic of the component works as the following, we divide the text into chunks known as shards, we have three basic data structures for the component to work

  • The ShardType which is the type of the chunk it specifies whether it is plain, hashtags, mentions, and links.
  • The Shard which is the simple object containing the text to show, its type and the link it refers to

The StringIndexdChunks, they are utilized to index the chunks in the order in which they appear in the text.

const enum ShardType {
plain, // 0
link, // 1
hashtag, // 2
mention // 3

class Shard {
constructor (
public type: ShardType = ShardType.plain,
public text: String = '',
public linkTo: any = null,
public queryParams: any = null
) { }

interface StringIndexedChunks {
index: number;
str: string;
type: ShardType;

First we have a private method of the component which searches for all the elements (strings) in the text. Here we have an array which maintains the index of those chunks in the text.

private generateShards() {
const indexedChunks: StringIndexedChunks[] = [];

this.hashtags.forEach(hashtag => {
const indices = getIndicesOf(this.text, `#${hashtag}`, false);
indices.forEach(idx => {
indexedChunks.push({index: idx, str: `#${hashtag}`, type: ShardType.hashtag});

this.mentions.forEach(mention => {
const indices = getIndicesOf(this.text, `@${mention}`, false);
indices.forEach(idx => {
indexedChunks.push({index: idx, str: `@${mention}`, type: ShardType.mention});

Then we sort the chunks according to their indexes in the text. This gives us sorted array which consists of all the chunks sorted according to the indexes as they appear in the text.

indexedChunks.sort((a, b) => { return (a.index > b.index) ? 1 : (a.index < b.index) ? -1 : 0; });

The next part of the logic is to generate the shard array, an array which contains each chunk, once. To do this we iterate over the Sorted Indexed array created in the previous step and use it split the text into chunks. We iterate over the text and take substrings using the indexes of each element.

let startIndex = 0;
const endIndex = this.text.length;

indexedChunks.forEach(element => {
if (startIndex !== element.index) {
const shard = new Shard(ShardType.plain, this.text.substring(startIndex, element.index));
startIndex = element.index;
if (startIndex === element.index) {
const str = this.text.substring(startIndex, element.index + element.str.length);
const shard = new Shard(element.type, str);
switch (element.type) {
case {
if (this.unshorten[element.str]) {
shard.linkTo = str;
shard.text = this.unshorten[element.str];
else {
shard.linkTo = str;

case ShardType.hashtag: {
shard.linkTo = ['/search'];
shard.queryParams = { query : str };

case ShardType.mention: {
shard.linkTo = ['/search'];
shard.queryParams = { query : `from:${str.substring(1)}` };
startIndex += element.str.length;

if (startIndex !== endIndex) {
const shard = new Shard(ShardType.plain, this.text.substring(startIndex));

After this we have generated the chunks of the text, now the only task is to write the view of the component which uses this Shard Array to render the linked elements.

<div class="textWrapper">
<span *ngFor="let shard of shardArray">
<span *ngIf="shard.type === 0"> <!-- Plain -->
<span *ngIf="shard.type === 1"> <!-- URL Links -->
<span *ngIf="shard.type === 2"> <!-- Hashtag -->
<a [routerLink]="shard.linkTo" [queryParams]="shard.queryParams">{{shard.text}}</a>
<span *ngIf="shard.type === 3"> <!-- Mention -->
<a [routerLink]="shard.linkTo" [queryParams]="shard.queryParams">{{shard.text}}</a>
  • This renders the chunks and handles the links of both internal and external type.
  • It also also makes sure that the links get unshortened properly using the unshorten API property.
  • Uses routerLink, angular property to link in application URLs, for asynchronous reloading while clicking links.

Resources and Links

This component is inspired from the two main open source libraries.

Earlier these libraries were used in the project, but as the need of unshortening and asynchronous linking appeared in the application, a custom implementation was needed to be implemented.

Indexing for multiscrapers in Loklak Server

I recently added multiscraper system which can scrape data from web-scrapers like YoutubeScraper, QuoraScraper, GithubScraper, etc. As scraping is a costly task, it is important to improve it’s efficiency. One of the approach is to index data in cache. TwitterScraper uses multiple sources to optimize the efficiency.

This system uses Post message holder object to store data and PostTimeline (a specialized iterator) to iterate the data objects. This difference in data structures from TwitterScraper leads to the need of different approach to implement indexing of data to ElasticSearch (currently in review process).

These are the following changes I made while implementing ‘indexing of data’ in the project.

1) Writing of data is invoked only using PostTimeline iterator

In TwitterScraper, the data is written in message holder TwitterTweet. So all the tweets are written to index as they are created. Here, when the data is scraped, Writing of the posts is initiated. Scraping of data is considered a heavy process. This approach keeps lower resource usage in average traffic on the server.

protected Post putData(Post typeArray, String key, Timeline2 postList) {
   if(!"cache".equals(this.source)) {
   return this.putData(typeArray, key, postList.toArray());

2) One object for holding a message

During the implementation, I kept the same message holder Post and post-iterator PostTimeline from scraping to indexing of data. This helps to keep the structure uniform. Earlier approach involves different types of message wrappers in the way. This approach cuts the processes for looping and transitioning of data structures.

3) Index a list, not a message

In TwitterScraper, as the messages are enqueued in the bulk to be indexed. But in this approach, I have enqueued the complete lists. This approach delays the indexing till the scraper is done with processing the html.

Creating the queue of postlists:

// Add post-lists to queue to be indexed
try {
} catch (InterruptedException e) {


Indexing of the posts in postlists:

// Start indexing of data in post-lists
for (Timeline2 postList: postBulk) {
    if (postList.size() < 1) continue;
    if(postList.dump) {
        // Dumping of data in a file
    // Indexing of data to ElasticSearch


4) Categorizing the input parameters

While searching the index, I have divided the query parameters from scraper into 3 categories. The input parameters are added to those categories (implemented using map data structure) and thus data fetched are according to them. These categories are:

// Declaring the QueryBuilder
BoolQueryBuilder query = new BoolQueryBuilder();


a) Get the parameter– Get the results for the input fields in map getMap.

// Result must have these fields. Acts as AND operator
if(getMap != null) {
    for(Map.Entry<String, String> field : getMap.entrySet()) {
field.getKey(), field.getValue()));


b) Don’t get the parameter- Don’t get the results for the input fields in map notGetMap.

// Result must not have these fields.
if(notGetMap != null) {
    for(Map.Entry<String, String> field : notGetMap.entrySet()) {
                field.getKey(), field.getValue()));


c) Get if possible- Get the results with the input fields if they are present in the index.

// Result may preferably also get these fields. Acts as OR operator
if(mayAlsoGetMap != null) {
    for(Map.Entry<String, String> field : mayAlsoGetMap.entrySet()) {
                field.getKey(), field.getValue()));



By applying these changes, the scrapers are shifted from a message indexing to list of messages indexing. This way we are keeping load on RAM low, but the aggregation of latest scraped data may be affected. So there will be a need to workaround to solve this issue while scraping itself.


Restoring State after Orientation Change in Loklak Wok Android

During orientation change i.e. from portrait to landscape mode in Android, the current activity restarts again. As the activity restarts again, all the defined variables loose their previous value, for example the scroll position of a RecyclerView, or the data in the rows of RecyclerView etc. Just imagine a user searched some tweets in Loklak Wok Android, and as the user’s phone is in “Auto rotation” mode, the orientation changes from portrait to landscape. As a result of this, the user loses the search result and has to do the search again. This leads to a bad UX.

Saving state in onSavedInstanceState

The state of the app can be saved by inserting values in a Bundle object in onSavedInstanceState callback. Inserting values is same as adding elements to a Map in Java. Methods like putDouble, putFloat, putChar etc. are used where the first parameter is a key and the second parameter is the value we want to insert.

public void onSaveInstanceState(Bundle outState) {
   if (mLatitude != null && mLongitude != null) {
       outState.putDouble(PARCELABLE_LATITUDE, mLatitude);
       outState.putDouble(PARCELABLE_LONGITUDE, mLongitude);


The values can be retrieved back when onCreate or onCreateView of the Activity or Fragment is called. Bundle object in the callback parameter is checked, whether it is null or not, if not the values are retrieved back using the keys provided at the time of inserting. The latitude and longitude of a location in TweetPostingFragment are retrieved in the same fashion

public void onViewCreated(View view, @Nullable Bundle savedInstanceState) {
   if (savedInstanceState != null) { // checking if bundle is null
       // extracting from bundle
       mLatitude = savedInstanceState.getDouble(PARCELABLE_LATITUDE);
       mLongitude = savedInstanceState.getDouble(PARCELABLE_LONGITUDE);
       // use extracted value

Restoring Custom Objects, using Parcelable

But what if we want to restore custom object(s). A simple option can be serializing the objects using the native Java Serialization or libraries like Gson. The problem in these cases is performance, they are quite slow. Parcelable can be used, which leads the pack in performance and moreover it is provided by Android SDK, on top of that, it is simple to use.

The objects of class which needs to be restored implements Parcelable interface and the class must provide a static final object called CREATOR which implements Parcelable.Creator interface.

writeToParcel and describeContents method need to be override to implement Parcelable interface. In writeToParcel method the member variables are put inside the parcel, in our case describeContents method is not used, so, simply 0 is returned. Status class which stores the data of a searched tweet implements parcelable.

public int describeContents() {
   return 0;

public void writeToParcel(Parcel dest, int flags) {
   dest.writeParcelable(mUser, flags);


NOTE: The order in which variables are pushed into Parcel needs to be maintained while variables are extracted from the parcel to recreate the object. This is the reason why no “key” is required to push data into a parcel as we do in bundle.

The CREATOR object implements the creation of object from a Parcel. The CREATOR object overrides two methods createFromParcel and newArray. createFromParcel is the method in which we implement the way an object is created from a parcel.

public static final Parcelable.Creator<Status> CREATOR = new Creator<Status>() {
   public Status createFromParcel(Parcel source) {
       return new Status(source); // a private constructor to create object from parcel

   public Status[] newArray(int size) {
       return new Status[size];


The private constructor, note that the order in which variables were pushed is maintained while retrieving the values.

private Status(Parcel source) {
   mText = source.readString();
   mRetweetCount = source.readInt();
   mFavouritesCount = source.readInt();
   mImages = source.createStringArrayList();
   mUser = source.readParcelable(User.class.getClassLoader());


The status objects are restored the same way, latitude and longitude were restored. putParcelableArrayList in onSaveInstaceState and getParcelableArrayList in onCreateView methods are used to push into Bundle object and retrieve from Bundle object respectively.

public void onSaveInstanceState(Bundle outState) {
   ArrayList<Status> searchedTweets = mSearchCategoryAdapter.getStatuses();
   outState.putParcelableArrayList(PARCELABLE_SEARCHED_TWEETS, searchedTweets);

// retrieval of the pushed values in bundle
public View onCreateView(LayoutInflater inflater, ViewGroup container,
                            Bundle savedInstanceState) {
   if (savedInstanceState != null) {
       List<Status> searchedTweets =
   return view;


Testing Presenter of MVP in Loklak Wok Android

Imagine working on a large source code, and as a new developer you are not sure whether the available source code works properly or not, you are surrounded by questions like, Are all these methods invoked properly or the number of times they need to be invoked? Being new to source code and checking manually already written code is a pain. For cases like these unit-tests are written. Unit-tests check whether the implemented code works as expected or not. This blog post explains about implementation of unit-tests of Presenter in a Model-View-Presenter (MVP) architecture in Loklak Wok Android.

Adding Dependencies to project

In app/build.gradle file

defaultConfig {
   testInstrumentationRunner ""

dependencies {
   androidTestCompile 'org.mockito:mockito-android:2.8.47'
   androidTestCompile ''
   androidTestCompile ''

Setup for Unit-Tests

The presenter needs a realm database and an implementation of LoklakAPI interface. Along with that a mock of the View is required, so as to check whether the methods of View are called or not.

The LoklakAPI interface can be mocked easily using Mockito, but the realm database can’t be mocked. For this reason an in-memory realm database is created, which will be destroyed once all unit-test are executed. As the presenter is required for each unit-test method we instantiate the in-memory database before all the tests start i.e. by annotating a public static method with @BeforeClass, e.g. setDb method.

public static void setDb() {
   RealmConfiguration testConfig = new RealmConfiguration.Builder()
   mDb = Realm.getInstance(testConfig);


NOTE: The in-memory database should be closed once all unit-tests are executed. So, for closing the databasse we create a public static method annotated with @AfterClass, e.g. closeDb method.

public static void closeDb() {


Now, before each unit-test is executed we need to do some setup work like instantiating the presenter, a mock instance of API interface generated  by using mock static method and pushing in some sample data into the database. Our presenter uses RxJava and RxAndroid which depend on IO scheduler and MainThread scheduler to perform tasks asynchronously and these schedulers are not present in testing environment. So, we override RxJava and RxAndroid to use trampoline scheduler in place of IO and MainThread so that our test don’t encounter NullPointerException. All this is done in a public method annotated with @Before e.g. setUp.

public void setUp() throws Exception {
   // mocking view and api
   mMockView = mock(SuggestContract.View.class);
   mApi = mock(LoklakAPI.class);

   mPresenter = new SuggestPresenter(mApi, mDb);

   queries = getFakeQueries();
   // overriding rxjava and rxandroid
   RxJavaPlugins.setIoSchedulerHandler(scheduler -> Schedulers.trampoline());
   RxAndroidPlugins.setMainThreadSchedulerHandler(scheduler -> Schedulers.trampoline());



Some fake suggestion queries are created which will be returned as observable when API interface is mocked. For this, simply two query objects are created and added to a List after their query parameter is set. This is implemented in getFakeQueries method.

private List<Query> getFakeQueries() {
   List<Query> queryList = new ArrayList<>();

   Query linux = new Query();

   Query india = new Query();

   return queryList;


After that, a method is created which provides the created fake data wrapped inside an Observable as implemented in getFakeSuggestionsMethod method.

private Observable<SuggestData> getFakeSuggestions() {
   SuggestData suggestData = new SuggestData();
   return Observable.just(suggestData);


Lastly, the mocking part is implemented using Mockito. This is really simple, when and thenReturn static methods of mockito are used for this. The method which would provide the fake data is invoked inside when and the fake data is passed as a parameter to thenReturn. For example, stubSuggestionsFromApi method

private void stubSuggestionsFromApi(Observable observable) {

Finally, Unit-Tests

All the tests methods must be annotated with @Test.

Firstly, we test for a successful API request i.e. we get some suggestions from the Loklak Server. For this, getSuggestions method of LoklakAPI is mocked using stubSuggestionFromApi method and the observable to be returned is obtained using getFakeSuggestions method. Then, loadSuggestionFromAPI method is called, the one that we need to test. Once loadSuggestionFromAPI method is invoked, we then check whether the method of the View are invoked inside loadSuggestionFromAPI method, this is done using verify static method. The unit-test is implemented in testLoadSuggestionsFromApi method.

public void testLoadSuggestionsFromApi() {

   mPresenter.loadSuggestionsFromAPI("", true);



Similarly, a failed network request for obtaining is suggestions is tested using testLoadSuggestionsFromApiFail method. Here, we pass an IOException throwable – wrapped inside an Observable – as parameter to stubSuggestionsFromApi.

public void testLoadSuggestionsFromApiFail() {
   Throwable throwable = new IOException();

   mPresenter.loadSuggestionsFromAPI("", true);


Lastly, we test if our suggestions are saved in the database by counting the number of saved suggestions and asserting that, in testSaveSuggestions method.

public void testSaveSuggestions() {
   int count = mDb.where(Query.class).findAll().size();
  // queries is the List that contains the fake suggestions
   assertEquals(queries.size(), count);


Service Workers in Loklak Search

Loklak search is a web application which is built on latest web technologies and is aiming to be a progressive web application. A PWA is a web application which has a rich, reliable, fast, and engaging web experience, and web API which enables us to get these are Service Workers. This blog post describes the basics of service workers and their usage in the Loklak Search application to act as a Network Proxy to and the programmatical cache controller for static resources.

What are Service Workers?

In the very formal definition, Matt Gaunt describes service workers to be a script that the browser runs in the background, and help us enable all the modern web features. Most these features include intercepting network requests and caching and responding from the cache in a more programmatical way, and independent from native browser based caching. To register a service worker in the application is a really simple task, there is just one thing which should be kept in mind, that service workers need the HTTPS connection, to work, and this is the web standard made around the secure protocol. To register a service worker

if ('serviceWorker' in navigator) {
window.addEventListener('load', function() {
navigator.serviceWorker.register('/sw.js').then(function(registration) {
// Registration was successful
console.log('ServiceWorker registration successful with scope: ', registration.scope);
}, function(err) {
// registration failed 🙁
console.log('ServiceWorker registration failed: ', err);

This piece of javascript, if the browser supports, registers the service worker defined by sw.js. The service worker then goes through its lifecycle, and gets installed and then it takes control of the page it gets registered with.

What does service workers solve in Loklak Search?

In loklak search, service workers currently work as a, network proxy to work as a caching mechanism for static resources. These static resources include the all the bundled js files and images. These bundled chunks are cached in the service workers cache and are responded with from the cache when requested. The chunking of assets have an advantage in this caching strategy, as the cache misses only happen for the chunks which are modified, and the parts of the application which are unmodified are served from the cache making it possible for lesser download of assets to be served.

Service workers and Angular

As the loklak search is an angular application we, have used the @angular/service-worker library to implement the service workers. This is simple to integrate library and works with the, CLI, there are two steps to enable this, first is to download the Service Worker package

npm install --save @angular/service-worker

And the second step is to enable the service worker flag in .angular-cli.json

"apps": [
      // Other Configurations
      serviceWorker: true

Now when we generate the production build from the CLI, along with all the application chunks we get, The three files related to the service workers as well

  • sw-register.bundle.js : This is a simple register script which is included in the index page to register the service worker.
  • worker-basic.js : This is the main service worker logic, which handles all the caching strategies.
  • ngsw-manifest.json : This is a simple manifest which contains the all the assets to be cached along with their version hashes for cache busting.

Future enhancements in Loklak Search with Service Workers

The service workers are fresh in loklak search and are currently just used for caching the static resources. We will be using service workers for more sophisticated caching strategies like

  • Dynamically caching the results and resources received from the API
  • Using IndexedDB interface with service workers for storing the API response in a structured manner.
  • Using service workers, and app manifest to provide the app like experience to the user.


Resources and Links

Implementing Direct URL in loklak Media Wall

Direct URL is a web address which redirects the user to the preset customized media wall so that the media wall can directly be used to be displayed on the screen. Loklak media wall provides direct URL which has information related to customizations set by the user included in the web address. These customizations, as the query parameters are detected when the page is initialized and actions are dispatched to make changes in the state properties, and hence, the UI properties and the expected behaviour of media wall.

In this blog, I would be explaining how I implemented direct URL in loklak media wall and how customizations are detected to build on initialization of component, a customized media wall.

Flow Chart


Media Wall Direct URL effect

This effect detects when the WALL_GENERATE_DIRECT_URL action is dispatched and creates a direct URL string from all the customization state properties and dispatches a side action WallShortenDirectUrlAction() and stores direct URL string as a state property. For this, we need to get individual wall customization state properties and create an object for it and supply it as a parameter to the generateDirectUrl() function. Direct URL string is returned from the function and now, the action is dispatched to store this string as a state property.

generateDirectUrl$: Observable<Action>
= this.actions$
.map(([action, state]) => {
return {
query: state.mediaWallQuery.query,
wallBackground: state.mediaWallCustom.wallBackground
.map(queryObject => {
const configSet = {
queryString: queryObject.query.displayString,
wallBackgroundColor: queryObject.wallBackground.backgroundColor
const shortenedUrl = generateDirectUrl(configSet);
return new mediaWallDirectUrlAction.WallShortenDirectUrlAction(shortenedUrl);

Generate Direct URL function

This function generates Direct URL string from all the current customization options value. Now,  keys of the object are separated out and for each element of the object, it checks if there is some current value for the elements and it then first parses the value of the element into URI format and then, adds it to the direct URL string. In such a way, we are creating a direct URL string with these customizations provided as the query parameters.

export function generateDirectUrl(customization: any): string {
const shortenedUrl = ;const activeFilterArray: string[] = new Array<string>();
let qs = ;
Object.keys(customization).forEach(config => {
if (customization[config] !== undefined && customization[config] !== null) {
if (config !== ‘blockedUser’ && config !== ‘hiddenFeedId’) {
qs += `${config}=${encodeURIComponent(customization[config])}&`;
else {
if (customization[config].length > 0) {
qs += `${config}= ${encodeURIComponent(customization[config].join(‘,’))}&`;
qs += `ref=share`;
return qs;

Creating a customized media wall

Whenever the user searches for the URL link on the web, a customized media wall must be created on initialization. The media wall component detects and subscribes to the URL query parameters using the queryParams API of the ActivatedRoute. Now, the values are parsed to a required format of payload and the respective actions are dispatched according to the value of the parameters. Now, when all the actions are dispatched, state properties changes accordingly. This creates a unidirectional flow of the state properties from the URL parameters to the template. Now, the state properties that are supplied to the template are detected and a customized media wall is created.

private queryFromURL(): void {
.subscribe((params: Params) => {
const config = {
queryString: params[‘queryString’] || ,
imageFilter: params[‘imageFilter’] || ,
profanityCheck: params[‘profanityCheck’] || ,
removeDuplicate: params[‘removeDuplicate’] || ,
wallHeaderBackgroundColor: params[‘wallHeaderBackgroundColor’] || ,
wallCardBackgroundColor: params[‘wallCardBackgroundColor’] || ,
wallBackgroundColor: params[‘wallBackgroundColor’] ||
}public setConfig(configSet: any) {
if (configSet[‘displayHeader’]) {
const isTrueSet = (configSet[‘displayHeader’] === ‘true’); mediaWallDesignAction.WallDisplayHeaderAction(isTrueSet));
if (configSet[‘queryString’] || configSet[‘imageFilter’] || configSet[‘location’]) {
if (configSet[‘location’] === ‘null’) {
configSet[‘location’] = null;
const isTrueSet = (configSet[‘imageFilter’] === ‘true’);
const query = {
displayString: configSet[‘queryString’],
queryString: ,
routerString: configSet[‘queryString’],
filter: {
video: false,
image: isTrueSet
location: configSet[‘location’],
timeBound: {
since: null,
until: null
from: false
} mediaWallAction.WallQueryChangeAction(query));

Now, the state properties are rendered accordingly and a customized media wall is created. This saves a lot of effort by the user to change the customization options whenever uses the loklak media wall.


MVP in Loklak Wok Android using Dagger2

MVP stands for Model-View-Presenter, one of the most popular and commonly used design pattern in android apps. Where “Model” refers to data source, it can be a SharedPreference, Database or data from a Network call. Going by the word, “View” is the user interface and finally “Presenter”, it’s a mediator between model and view. Whatever events occur in a view are passed to presenter and the presenter fetches the data from the model and finally passes it back to the view, where the data is populated in ViewGroups. Now, the main question, why it is so widely used? One of the obvious reason is the simplicity to implement it and it completely separates the business logic, so, easy to write unit-tests. Though it is easy to implement, its implementation requires a lot of boilerplate code, which is one of its downpoints. But, using Dagger2 the boilerplate code can be reduced to a great extent. Let’s see how Dagger2 is used in Loklak Wok Android to implement MVP architecture.

Adding Dagger2 to the project

In app/build.gradle file

dependencies {
   compile ''
    annotationProcessor ''



First a contract is created which defines the behaviour or say the functionality of View and Presenter. Like showing a progress bar when data is being fetched, or the view when the network request is successful or it failed. The contract should be easy to read and going by the names of the method one should be able to know the functionality of methods. For tweet search suggestions, the contract is defined in SuggestContract interface.

public interface SuggestContract {

   interface View {

       void showProgressBar(boolean show);

       void onSuggestionFetchSuccessful(List<Query> queries);

       void onSuggestionFetchError(Throwable throwable);

   interface Presenter {

       void attachView(View view);

       void createCompositeDisposable();

       void loadSuggestionsFromAPI(String query, boolean showProgressBar);

       void loadSuggestionsFromDatabase();

       void saveSuggestions(List<Query> queries);

       void suggestionQueryChanged(Observable<CharSequence> observable);

       void detachView();


A SuggestPresenter class is created which implements the SuggestContract.Presenter interface. I will not be explaining how each methods in SuggestPresenter class is implemented as this blog solely deals with implementing MVP. If you are interested you can go through the source code of SuggestPresenter. Similarly, the view i.e. SuggestFragment implements SuggestContract.View interface.

So, till this point we have our presenter and view ready. The presenter needs to access the model and the view requires to have an instance of presenter. One way could be instantiating an instance of model inside presenter and an instance of presenter inside view. But, this way model, view and presenter would be coupled and that defeats our purpose. So, we just INJECT model into presenter and presenter into view using Dagger2. Injecting here means Dagger2 instantiates model and presenter and provides wherever they are requested.

ApplicationModule provides the required dependencies for accessing the “Model” i.e. a Loklak API client and realm database instance. When we want Dagger2 to provide a dependency we create a method annotated with @Provides as providesLoklakAPI and providesRealm.

LoklakAPI providesLoklakAPI(Retrofit retrofit) {
   return retrofit.create(LoklakAPI.class);

Realm providesRealm() {
   return Realm.getDefaultInstance();


If we look closely providesLoklakAPI method requires a Retrofit instance i.e. a to create an instance of LoklakAPI the required dependency is Retrofit, which is fulfilled by providesRetrofit method. Always remember that whenever a dependency is required, it should not be instantiated at the required place, rather it should be injected by Dagger2.

Retrofit providesRetrofit() {
   Gson gson = Utility.getGsonForPrivateVariableClass();
   return new Retrofit.Builder()


As the ApplicationModule class provides these dependencies the class is annotated with @Module.

public class ApplicationModule {

   private String mBaseUrl;

   public ApplicationModule(String baseUrl) {
       this.mBaseUrl = baseUrl;
   // retrofit, LoklakAPI, realm @Provides methods

After preparing the source to provide the dependencies, it’s time we request the dependencies.

Dependencies are requested simply by using @Inject annotation e.g. in the constructor of SuggestPresenter @Inject is used, due to which Dagger2 provides instance of LoklakAPI and Realm for constructing an object of SuggestPresenter.

public class SuggestPresenter implements SuggestContract.Presenter {

   private final Realm mRealm;
   private LoklakAPI mLoklakAPI;
   private SuggestContract.View mView;

   public SuggestPresenter(LoklakAPI loklakAPI, Realm realm) {
       this.mLoklakAPI = loklakAPI;
       this.mRealm = realm;
   // implementation of methods defined in contract

@Inject can be used on the fields also. When @Inject is used with a constructor the class also becomes a dependency provider, this way creating a method with @Provides is not required in a Module class.

Now, it’s time to connect the dependency providers and dependency requesters. This is done by creating a Component interface, here ApplicationComponent. The component interface defines where are the dependencies required. This is only for those cases where dependencies are injected by using @Inject for the member variables. So, we define a method inject with a single parameter of type SuggestFragment, as the Presenter needs to be injected in SuggestFragment.

@Component(modules = ApplicationModule.class)
public interface ApplicationComponent {

   void inject(SuggestFragment suggestFragment);



The component interface is instantiated in onCreate method of LoklakWokApplication class, so that it is accessible all over the project.

public class LoklakWokApplication extends Application {

   private ApplicationComponent mApplicationComponent;

   public void onCreate() {
       mApplicationComponent = DaggerApplicationComponent.builder()
               .applicationModule(new ApplicationModule(Constants.BASE_URL_LOKLAK))

   public ApplicationComponent getApplicationComponent() {
       return mApplicationComponent;

NOTE: DaggerApplicationComponent is created after building the project. So, AndroidStudio will show “Cannot resolve symbol …”, thus build the project : Build > Make Module ‘app’.

Finally, in the onCreateView callback of SuggestFragment we call inject method of DaggerApplicationComponent to tell Dagger2 that SuggestFragment is requesting dependencies.

public View onCreateView(LayoutInflater inflater, ViewGroup container,
                        Bundle savedInstanceState) {
   LoklakWokApplication application = (LoklakWokApplication) getActivity().getApplication();

   return rootView;


Animations in Loklak Wok Android

Imagine an Activity popping out of nowhere suddenly in front of the user. And even more irritating, the user doesn’t even know whether a button was clicked. Though these are very small animation implementations but these animations enhance the user experience to a new level. This blog deals with the animations in Loklak Wok Android, a peer message harvester of Loklak Server.

Activity transition animation

Activity transition is applied when we move from a current activity to a new activity or just go back to an old activity by pressing back button.

In Loklak Wok Android, when user navigates for search suggestions from TweetHarvestingActivity to SuggestActivity, the new activity i.e. SuggestActivity comes from right side of the screen and the old one i.e. TweetHarvestingActivity leaves the screen through the left side. This is an example of left-right activity transition. For implementing this, two xml files which define the animations are created, enter.xml and exit.xml are created.




NOTE: The entering activity comes from right side, that’s why android:fromXDelta parameter is set to 100% and as the activity finally stays at extreme left, android:toXDelta parameter is set to 0%.

As the current activity, in this case TweetHarvestingActivity, leaves the screen from left to the negative of left. So, in exit.xml the android:fromXDelta parameter is set to 0% and android:toXDelta parameter is set to -100%.

Now, that we are done with defining the animations in xml, it’s time we apply the animations, which is really easy. The animations are applied by invoking Activity.overridePendingTransition(enterAnim, exitAnim) just after the startActivity method. For example, in openSuggestActivity

private void openSuggestActivity() {
   Intent intent = new Intent(getActivity(), SuggestActivity.class);
   getActivity().overridePendingTransition(R.anim.enter, R.anim.exit);


Touch Selectors

Using touch selectors background color of a button or any clickable can be changed, this way a user can see that the clickable responded to the click. The background is usually light accent color or a lighter shade of the icon present in button.

There are three states involved while a clickable is touched, pressed, activated and selected. And a default state, i.e. the clickable is not clicked. The background color of each state is defined in a xml file like media_button_selector, which is present in drawable directory.

<selector xmlns:android="">

   <item android:drawable="@color/media_button_touch_selector_backgroud" android:state_pressed="true"/>
   <item android:drawable="@color/media_button_touch_selector_backgroud" android:state_activated="true"/>
   <item android:drawable="@color/media_button_touch_selector_backgroud" android:state_selected="true"/>

   <item android:drawable="@android:color/transparent"/>


The selector is applied by setting it as the background of a clickable, for example, touch selector applied on Location image button present in fragment_tweet_posting.xml .

   android:background="@drawable/media_button_selector" />


Notice the change in the background color of the buttons when clicked.


Some youtube videos for getting started:

Analyzing Production Build Size in Loklak Search

Loklak search being a web application it is critical to keep the size of the application in check to ensure that we are not transferring any non-essential bytes to the user so that application load is faster, and we are able to get the minimal first paint time. This requires a mechanism for the ability to check the size of the build files which are generated and served to the user. Alongside the ability to check sizes it is also critically important to analyze the distribution of the modules along with their sizes in various chunks. In this blog post, I discuss the analysis of the application code of loklak search and the generated build files.

Importance of Analysis

The chunk size analysis is critical to any application, as the chunk size of any application directly determines the performance of any application, at any scale. The smaller the application the lesser is the load time, thus faster it becomes usable at the user side. The time to first-paint is the most important metric to keep in mind while analyzing any web application for performance, though the first paint time consists of many critical parts from loading, parsing, layout and paint, but still the size of any chunk determines all the time it will take to render it on the screen.

Also as we use the 3rd party libraries and components it becomes crucially important to inspect the impact on the size of the application upon the inclusion of those libraries and components.

Development Phase Checking

Angular CLI provides a clean mechanism to track and check the size of all the chunks always at the runtime, these stats simply show the size of each chunk in the application in the terminal on every successful compilation, and this provides us a broad idea about the chunks to look and address.

Deep Analysis using Webpack Bundle Analyzer

The angular cli while generating the production build provides us with an option to generates the statistics about the chunks including the size and namespaces of the each module which is part of that chunk. These stats are directly generated by the webpack at the time of bundling, code splitting, and tree shaking. These statistics thus provide us to peek into the actual deeper level of chunk creation in webpack to analyze sizes of its various components. To generate the statistics we just need to enable the –stats-json flag while building.

ng serve --prod --aot --stats-json

This will generate the statistics file for the application in the /dist directory, alongside all the bundles. Now to have the visual and graphical analysis of these statistics we can use a tool like webpack-bundle-analyzer to analyze the statistics. We can install the webpack-bundle-analyzer via npm,

npm install --save-dev webpack-bundle-analyzer

Now, to our package.json we can add a script, running this script will open up a web page which contains graphical visualization of all the chunks build in the application

// package.json

      “scripts”: {
         "analyze": "webpack-bundle-analyzer dist/stats.json"

These block diagrams also contain the information about the sub modules contained in each chunk, and thus we can easily analyze and compare the size of each component we add in the application.

Now, we can see in the above distribution, the main.bundle is of the largest size among all the other chunks. And the major part of it is being occupied by, moment.js, this analysis provides us with a deeper insight into the impact of a module like moment.js on the application size. This helps us to reason about the analyze which part of the application is worth, and which parts of the application can be replaced with lighter alternatives and which parts of the application are worth the size they are consuming, as for a 3rd party module which consumes a lot of sizes but is used in some insignificant feature, must be replaced with a lightweight alternative.


Thus being able to see the description of modules in each and every chunk provides us with a method to reason about, and compare the alternative approaches for a particular solution to a problem, in terms of the effect of those approaches on the size of the application so we are able to make the best decision.

Resources and Links

  • Analyzing the builds blog by hackernoon
  • Bundle analysis for webpack applications blog by Nimesh

Using CSS Grid in Loklak Search

CSS Grid is the latest web standard for the layouts in the web applications. This is the web standard which allows the HTML page to be viewed as 2-dimensional for laying out the elements in the page. It is thus used in parts of loklak search for layout. In this blog post, I will discuss the basic naming convention for CSS grid and its usage in Loklak Search for layout structuring and responsiveness.

CSS Grid Basics

There are some basic terminologies regarding grid few major ones are the following

Grid Container

The grid container is the container which is the wrapper of all the grid items. It is declared by display: grid, this makes all the direct children of that element to become grid items.

Grid Tracks

We define rows and columns of the grid as the lines, the area between any two lines is called a grid track. Tracks can be defined using any length unit. Grid also introduces an additional length unit to help us create flexible grid tracks. The new fr unit represents a fraction of the available space in the grid container.

Grid Cells

The area between any two horizontal and vertical lines is called a grid cell.

Grid Area

The area formed by the combination of two or more cells is called a grid area.

Using CSS grid in Loklak Search

The CSS grid is used in loklak search uses CSS grid in the feeds page to align elements in a responsive way on mobile and desktop. Earlier there was the issue that on small displays the info box of the results appeared after the feed results, and we needed to make sure that it appears on top on smaller displays. This is the outline of the structure of the feed page.

<div class=”feed-wrapper”>
<div class=”feed-results”>
<!-- Feed Results -->

<div class=”feed-info-box”>
<!-- Feed Info Box -->

Now we use the CSS grid to position the items according to the display width. First we declare the “feed-wrapper” as display:grid to make it a Grid Container, and we associate the rows and columns accordingly.

.feed-wrapper {
   display: grid;
   grid-template-columns: 150px 632px 455px 1fr;
   grid-template-rows: auto;

This defines the grid to be consisting of 4 columns of width 150px, 632px,  455px and one remaining unit i.e. 1fr. The rows are set to be auto.

Now we define the grid areas i.e. the names of the areas using the grid-area:<area> css property. This gives names to the elements in the CSS grid.

.feed-results {
   grid-area: feed-results;

.feed-info-box {
   grid-area: feed-info-box;

The last thing which remains now is to specify the position of these grid elements in the grid cells according to the display width, we use simple media queries along with simple grid area positioning property, i.e. grid-template-areas.

.feed-wrapper {
   /* Other Properties */
   @media(min-width: 1200px) {
      grid-template-areas: ". feed-results feed-info-box .";

   @media(max-width: 1199px) {
      grid-template-columns: 1fr;

This positions both the boxes according to the display width, in one column for large displays, and info box on top of results on mobile displays.

This is how it looks on the large desktop displays


This is how it looks on small mobile displays

Links and References