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.


Setting Loklak Server with SSL

Loklak Server is based on embedded Jetty Server which can work both with or without SSL encryption. Lately, there was need to setup Loklak Server with SSL. Though the need was satisfied by CloudFlare. Alternatively, there are 2 ways to set up Loklak Server with SSL. They are:-

1) Default Jetty Implementation

There is pre-existing implementation of Jetty libraries. The http mode can be set in configuration file. There are 4 modes on which Loklak Server can work: http mode, https mode, only https mode and redirect to https mode. Loklak Server listens to port 9000 when in http mode and to port 9443 when in https mode.

There is also a need of SSL certificate which is to be added in configuration file.

2) Getting SSL certificate with Kube-Lego on Kubernetes Deployment

I got to know about Kube-Lego by @niranjan94. It is implemented in Open-Event-Orga-Server. The approach is to use:

a) Nginx as ingress controller

For setting up Nginx ingress controller, a yml file is needed which downloads and configures the server.

The configurations for data requests and response are:

proxy-connect-timeout: "15"
 proxy-read-timeout: "600"
 proxy-send-imeout: "600"
 hsts-include-subdomains: "false"
 body-size: "64m"
 server-name-hash-bucket-size: "256"
 server-tokens: "false"

Nginx is configured to work on both http and https ports in service.yml

- port: 80
  name: http
- port: 443
  name: https


b) Kube-Lego for fetching SSL certificates from Let’s Encrypt

Kube-Lego was set up with default values in yml. It uses the host-name, email address and secretname of the deployment to validate url and fetch SSL certificate from Let’s Encrypt.

c) Setup configurations related to TLS and no-TLS connection

These configuration files mentions the path and service ports for Nginx Server through which requests are forwarded to backend Loklak Server. Here for no-TLS and TLS requests, the requests are directly forwarded to localhost at port 80 of Loklak Server container.

- host:
  - path: /
    serviceName: server
    servicePort: 80

For TLS requests, the secret name is also mentioned. Kube-Lego fetches host-name and secret-name from here for the certificate

- hosts:
  secretName: loklak-api-tls

d) Loklak Server, ElasticSearch and Mosquitto at backend

These containers work at backend. ElasticSearch and Mosquitto are only accessible to Loklak Server. Loklak Server can be accessed through Nginx server. Loklak Server is configured to work at http mode and is exposed at port 80.

- port: 80
  protocol: TCP
  targetPort: 80

To deploy the Loklak Server, all these are deployed in separate pods and they interact through service ports. To deploy, we use deploy script:

# For elasticsearch, accessible only to api-server
kubectl create -R -f ${path-to-config-file}/elasticsearch/

# For mqtt, accessible only to api-server
kubectl create -R -f ${path-to-config-file}/mosquitto/

# Start KubeLego deployment for TLS certificates
kubectl create -R -f ${path-to-config-file}/lego/
kubectl create -R -f ${path-to-config-file}/nginx/

# Create web namespace, this acts as bridge to Loklak Server
kubectl create -R -f ${path-to-config-file}/web/

# Create API server deployment and expose the services
kubectl create -R -f ${path-to-config-file}/api-server/

# Get the IP address of the deployment to be used
kubectl get services --namespace=nginx-ingress


Introducing Stream Servlet in loklak Server

A major part of my GSoC proposal was adding stream API to loklak server. In a previous blog post, I discussed the addition of Mosquitto as a message broker for MQTT streaming. After testing this service for a few days and some minor improvements, I was in a position to expose the stream to outside users using a simple API.

In this blog post, I will be discussing the addition of /api/stream.json endpoint to loklak server.

HTTP Server-Sent Events

Server-sent events (SSE) is a technology where a browser receives automatic updates from a server via HTTP connection. The Server-Sent Events EventSource API is standardized as part of HTML5 by the W3C.


This API is supported by all major browsers except Microsoft Edge. For loklak, the plan was to use this event system to send messages, as they arrive, to the connected users. Apart from browser support, EventSource API can also be used with many other technologies too.

Jetty Eventsource Plugin

For Java, we can use Jetty’s EventSource plugin to send events to clients. It is similar to other Jetty servlets when it comes to processing the arguments, handling requests, etc. But it provides a simple interface to send events as they occur to connected users.

Adding Dependency

To use this plugin, we can add the following line to Gradle dependencies –

compile group: 'org.eclipse.jetty', name: 'jetty-eventsource-servlet', version: '1.0.0'


The Event Source

An EventSource is the object which is required for EventSourceServlet to send events. All the logics for emitting events needs to be defined in the related class. To link a servlet with an EventSource, we need to override the newEventSource method –

public class StreamServlet extends EventSourceServlet {
    protected EventSource newEventSource(HttpServletRequest request) {
        String channel = request.getParameter("channel");
        if (channel == null) {
            return null;
        if (channel.isEmpty()) {
            return null;
        return new MqttEventSource(channel);


If no channel is provided, the EventSource object will be null and the request will be rejected. Here, the MqttEventSource would be used to handle the stream of Tweets as they arrive from the Mosquitto message broker.

Cross Site Requests

Since the requests to this endpoint can’t be of JSONP type, it is necessary to allow cross site requests on this endpoint. This can be done by overriding the doGet method of the servlet –

protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException {
     response.setHeader("Access-Control-Allow-Origin", "*");
    super.doGet(request, response);


Adding MQTT Subscriber

When a request for events arrives, the constructor to MqttEventSource is called. At this stage, we need to connect to the stream from Mosquitto for the channel. To achieve this, we can set the class as MqttCallback using appropriate client configurations –

public class MqttEventSource implements MqttCallback {
    MqttEventSource(String channel) { = channel;
    this.mqttClient = new MqttClient(address, "loklak_server_subscriber");


By setting the callback to this, we can override the messageArrived method to handle the arrival of a new message on the channel. Just to mention, the client library used here is Eclipse Paho.

Connecting MQTT Stream to SSE Stream

Now that we have subscribed to the channel we wish to send events from, we can use the Emitter to send events from our EventSource by implementing it –

public class MqttEventSource implements EventSource, MqttCallback {
    private Emitter emitter;

    public void onOpen(Emitter emitter) throws IOException {
        this.emitter = emitter;

    public void messageArrived(String topic, MqttMessage message) throws Exception {;


Closing Stream on Disconnecting from User

When a client disconnects from the stream, it doesn’t makes sense to stay connected to the server. We can use the onClose method to disconnect the subscriber from the MQTT broker –

public void onClose() {
    try {
    } catch (MqttException e) {
        // Log some warning 



In this blog post, I discussed connecting the MQTT stream to SSE stream using Jetty’s EventSource plugin. Once in place, this event system would save us from making too many requests to collect and visualize data. The possibilities of applications of such feature are huge.

This feature can be seen in action at the World Mood Tracker app.

The changes were introduced in pull request loklak/loklak_server#1474 by @singhpratyush (me).


Backend Scraping in Loklak Server

Loklak Server is a peer-to-peer Distributed Scraping System. It scrapes data from websites and also maintain other sources like peers, storage and a backend server to scrape data. Maintaining different sources has it’s benefits of not engaging in costly requests to the websites, no scraping of data and no cleaning of data.

Loklak Server can maintain a secondary Loklak Server (or a backend server) tuned for storing large amount of data. This enables the primary Loklak Server fetch data in return of pushing all scraped data to the backend.

Lately there was a bug in backend search as a new feature of filtering tweets was added to scraping and indexing, but not for backend search. To fix this issue, I had backtracked the backend search codebase and fix it.

Let us discuss how Backend Search works:-

1) When query is made from search endpoint with:

a) source=all

When source is set to all. The first TwitterScraper and Messages from local search server is preferred. If the messages scraped are not enough or no output has been returned for a specific amount of time, then, backend search is initiated

b) source=backend

SearchServlet specifically scrapes directly from backend server.

2) Fetching data from Backend Server

The input parameters fetched from the client is feeded into DAO.searchBackend method. The list of backend servers fetched from config file. Now using these input parameters and backend servers, the required data is scraped and output to the client.

In DAO.searchOnOtherPeers method. the request is sent to multiple servers and they are arranged in order of better response rates. This method invokes method for sending request to the mentioned servers.

List<String> remote = getBackendPeers();
if (remote.size() > 0) {
    // condition deactivated because we need always at least one peer
    Timeline tt = searchOnOtherPeers(remote, q, filterList, order, count, timezoneOffset, where, SearchServlet.backend_hash, timeout);
    if (tt != null) tt.writeToIndex();
    return tt;


3) Creation of request url and sending requests

The request url is created according to the input parameters passed to method. Here the search url is created according to input parameters and request is sent to the respective servers to fetch the required messages.

   // URL creation
    urlstring = protocolhostportstub + "/api/search.json?q="
           + URLEncoder.encode(query.replace(' ', '+'), "UTF-8") + "&timezoneOffset="
           + timezoneOffset + "&maximumRecords=" + count + "&source="
           + (source == null ? "all" : source) + "&minified=true&shortlink=false&timeout="
           + timeout;
    if(!"".equals(filterString = String.join(", ", filterList))) {
       urlstring = urlstring + "&filter=" + filterString;
    // Download data
    byte[] jsonb = ClientConnection.downloadPeer(urlstring);
    if (jsonb == null || jsonb.length == 0) throw new IOException("empty content from " + protocolhostportstub);
    String jsons = UTF8.String(jsonb);
    JSONObject json = new JSONObject(jsons);
    if (json == null || json.length() == 0) return tl;
    // Final data fetched to be returned
    JSONArray statuses = json.getJSONArray("statuses");


Configuring Youtube Scraper with Search Endpoint in Loklak Server

Youtube Scraper is one of the interesting web scrapers of Loklak Server with unique implementation of its data scraping and data key creation (using RDF). It couldn’t be accessed as it didn’t have any url endpoint. I configured it to use both as separate endpoint (api/youtubescraper) and search endpoint (/api/search.json).


  1. YoutubeScraper Endpoint: /api/youtubescraperExample:
  2. SearchServlet Endpoint: /api/search.json


The configurations added in Loklak Server are:-

1) Endpoint

We can access YoutubeScraper using endpoint /api/youtubescraper endpoint. Like other scrapers, I have used BaseScraper class as superclass for this functionality .

2) PrepareSearchUrl

The prepareSearchUrl method creates youtube search url that is used to scrape Youtube webpage. YoutubeScraper takes url as input. But youtube link could also be a shortened link. That is why, the video id is stored as query. This approach optimizes the scraper and adds the capability to add more scrapers to it.

Currently YoutubeScraper scrapes the video webpages of Youtube, but scrapers for search webpage and channel webpages can also be added.

URIBuilder url = null;
String midUrl = "search/";
    try {
       switch(type) {
           case "search":
               midUrl = "search/";
               url = new URIBuilder(this.baseUrl + midUrl);
               url.addParameter("search_query", this.query);
           case "video":
               midUrl = "watch/";
               url = new URIBuilder(this.baseUrl + midUrl);
               url.addParameter("v", this.query);
           case "user":
               midUrl = "channel/";
               url = new URIBuilder(this.baseUrl + midUrl + this.query);
               url = new URIBuilder("");
    } catch (URISyntaxException e) {
       DAO.log("Invalid Url: baseUrl = " + this.baseUrl + ", mid-URL = " + midUrl + "query = " + this.query + "type = " + type);
       return "";


3) Get-Data-From-Connection

The getDataFromConnection method is used to fetch Bufferedreader object and input it to scrape method. In YoutubeScraper, this method has been overrided to prevent using default method implementation i.e. use type=all

public Post getDataFromConnection() throws IOException {
    String url = this.prepareSearchUrl(this.type);
    return getDataFromConnection(url, this.type);


4) Set scraper parameters input as get-parameters

The Map data-structure of get-parameters fetched by scraper fetches type and query. For URL, the video hash-code is separated from url and then used as query.

this.query = this.getExtraValue("query");
this.query = this.query.substring(this.query.length() - 11);


5) Scrape Method

Scrape method runs the different scraper methods (in YoutubeScraper, there is only one), iterate it using PostTimeline and wraps in Post object to the output. This simple function can improve flexibility of scraper to scrape different pages concurrently.

Post out = new Post(true);
Timeline2 postList = new Timeline2(this.order);
postList.addPost(this.parseVideo(br, type, url));
out.put("videos", postList.toArray());



Optimising Docker Images for loklak Server

The loklak server is in a process of moving to Kubernetes. In order to do so, we needed to have different Docker images that suit these deployments. In this blog post, I will be discussing the process through which I optimised the size of Docker image for these deployments.

Initial Image

The image that I started with used Ubuntu as base. It installed all the components needed and then modified the configurations as required –

FROM ubuntu:latest

# Env Vars
ENV DEBIAN_FRONTEND noninteractive

WORKDIR /loklak_server

RUN apt-get update
RUN apt-get upgrade -y
RUN apt-get install -y git openjdk-8-jdk
RUN git clone /loklak_server
RUN git checkout development
RUN ./gradlew build -x test -x checkstyleTest -x checkstyleMain -x jacocoTestReport
RUN sed -i.bak 's/^\(port.http=\).*/\180/' conf/
... # More configurations
RUN echo "while true; do sleep 10;done" >> bin/

# Start
CMD ["bin/", "-Idn"]

The size of images built using this Dockerfile was quite huge –

REPOSITORY          TAG                 IMAGE ID            CREATED              SIZE

loklak_server       latest              a92f506b360d        About a minute ago   1.114 GB

ubuntu              latest              ccc7a11d65b1        3 days ago           120.1 MB

But since this size is not acceptable, we needed to reduce it.

Moving to Apline

Alpine Linux is an extremely lightweight Linux distro, built mainly for the container environment. Its size is so tiny that it hardly puts any impact on the overall size of images. So, I replaced Ubuntu with Alpine –

FROM alpine:latest

RUN apk update
RUN apk add git openjdk8 bash

And now we had much smaller images –

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE

loklak_server       latest              54b507ee9187        17 seconds ago      668.8 MB

alpine              latest              7328f6f8b418        6 weeks ago         3.966 MB

As we can see that due to no caching and small size of Alpine, the image size is reduced to almost half the original.

Reducing Content Size

There are many things in a project which are no longer needed while running the project, like the .git folder (which is huge in case of loklak) –

$ du -sh loklak_server/.git
236M loklak_server/.git

We can remove such files from the Docker image and save a lot of space –

rm -rf .[^.] .??*

Optimizing Number of Layers

The number of layers also affect the size of the image. More the number of layers, more will be the size of image. In the Dockerfile, we can club together the RUN commands for lower number of images.

RUN apk update && apk add openjdk8 git bash && \
  git clone /loklak_server && \

After this, the effective size is again reduced by a major factor –

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE

loklak_server       latest              54b507ee9187        17 seconds ago      422.3 MB

alpine              latest              7328f6f8b418        6 weeks ago         3.966 MB


In this blog post, I discussed the process of optimising the size of Dockerfile for Kubernetes deployments of loklak server. The size was reduced to 426 MB from 1.234 GB and this provided much faster push/pull time for Docker images, and therefore, faster updates for Kubernetes deployments.


Persistently Storing loklak Server Dumps on Kubernetes

In an earlier blog post, I discussed loklak setup on Kubernetes. The deployment mentioned in the post was to test the development branch. Next, we needed to have a deployment where all the messages are collected and dumped in text files that can be reused.

In this blog post, I will be discussing the challenges with such deployment and the approach to tackle them.

Volatile Disk in Kubernetes

The pods that hold deployments in Kubernetes have disk storage. Any data that gets written by the application stays only until the same version of deployment is running. As soon as the deployment is updated/relocated, the data stored during the application is cleaned up.

Due to this, dumps are written when loklak is running but they get wiped out when the deployment image is updated. In other words, all dumps are lost when the image updates. We needed to find a solution to this as we needed a permanent storage when collecting dumps.

Persistent Disk

In order to have a storage which can hold data permanently, we can mount persistent disk(s) on a pod at the appropriate location. This ensures that the data that is important to us stays with us, even
when the deployment goes down.

In order to add persistent disks, we first need to create a persistent disk. On Google Cloud Platform, we can use the gcloud CLI to create disks in a given region –

gcloud compute disks create --size=<required size> --zone=<same as cluster zone> <unique disk name>

After this, we can mount it on a Docker volume defined in Kubernetes configurations –

        - mountPath: /path/to/mount
          name: volume-name
    - name: volume-name
        pdName: disk-name
        fsType: fileSystemType

But this setup can’t be used for storing loklak dumps. Let’s see “why” in the next section.

Rolling Updates and Persistent Disk

The Kubernetes deployment needs to be updated when the master branch of loklak server is updated. This update of master deployment would create a new pod and try to start loklak server on it. During all this, the older deployment would also be running and serving the requests.

The control will not be transferred to the newer pod until it is ready and all the probes are passing. The newer deployment will now try to mount the disk which is mentioned in the configuration, but it would fail to do so. This would happen because the older pod has already mounted the disk.

Therefore, all new deployments would simply fail to start due to insufficient resources. To overcome such issues, Kubernetes allows persistent volume claims. Let’s see how we used them for loklak deployment.

Persistent Volume Claims

Kubernetes provides Persistent Volume Claims which claim resources (storage) from a Persistent Volume (just like a pod does from a node). The higher level APIs are provided by Kubernetes (configurations and kubectl command line). In the loklak deployment, the persistent volume is a Google Compute Engine disk –

apiVersion: v1
kind: PersistentVolume
  name: dump
  namespace: web
    storage: 100Gi
    - ReadWriteOnce
  persistentVolumeReclaimPolicy: Retain
  storageClassName: slow
    pdName: "data-dump-disk"
    fsType: "ext4"


It must be noted here that a persistent disk by the name of data-dump-index is already created in the same region.

The storage class defines the way in which the PV should be handled, along with the provisioner for the service –

kind: StorageClass
  name: slow
  namespace: web
  type: pd-standard
  zone: us-central1-a


After having the StorageClass and PersistentVolume, we can create a claim for the volume by using appropriate configurations –

apiVersion: v1
kind: PersistentVolumeClaim
  name: dump
  namespace: web
    - ReadWriteOnce
      storage: 100Gi
  storageClassName: slow


After this, we can mount this claim on our Deployment –

    - name: dump
      mountPath: /loklak_server/data
  - name: dump
      claimName: dump


Verifying persistence of Dumps

To verify this, we can redeploy the cluster using the same persistent disk and check if the earlier dumps are still present there –

$ http
HTTP/1.1 200 OK
Cache-Control: public, max-age=60
Content-Type: text/html;charset=utf-8


<h1>Index of /dump</h1>
<pre>      Name 
[gz ] <a href="messages_20170802_71562040.txt.gz">messages_20170802_71562040.txt.gz</a>   Thu Aug 03 00:07:21 GMT 2017   132M
[gz ] <a href="messages_20170803_69925009.txt.gz">messages_20170803_69925009.txt.gz</a>   Mon Aug 07 15:40:04 GMT 2017   532M
[gz ] <a href="messages_20170807_36357603.txt.gz">messages_20170807_36357603.txt.gz</a>   Wed Aug 09 10:26:24 GMT 2017   377M
[txt] <a href="messages_20170809_27974404.txt">messages_20170809_27974404.txt</a>      Thu Aug 10 08:51:49 GMT 2017  1564M


In this blog post, I discussed the process of deployment of loklak with persistent dumps on Kubernetes. This deployment is intended to work as in near future. The changes were proposed in loklak/loklak_server#1377 by @singhpratyush (me).


Using Mosquitto as a Message Broker for MQTT in loklak Server

In loklak server, messages are collected from various sources and indexed using Elasticsearch. To know when a message of interest arrives, users can poll the search endpoint. But this method would require a lot of HTTP requests, most of them being redundant. Also, if a user would like to collect messages for a particular topic, he would need to make a lot of requests over a period of time to get enough data.

For GSoC 2017, my proposal was to introduce stream API in the loklak server so that we could save ourselves from making too many requests and also add many use cases.

Mosquitto is Eclipse’s project which acts as a message broker for the popular MQTT protocol. MQTT, based on the pub-sub model, is a lightweight and IOT friendly protocol. In this blog post, I will discuss the basic setup of Mosquitto in the loklak server.

Installation and Dependency for Mosquitto

The installation process of Mosquitto is very simple. For Ubuntu, it is available from the pre installed PPAs –

sudo apt-get install mosquitto

Once the message broker is up and running, we can use the clients to connect to it and publish/subscribe to channels. To add MQTT client as a project dependency, we can introduce following line in Gradle dependencies file –

compile group: 'net.sf.xenqtt', name: 'xenqtt', version: '0.9.5'


After this, we can use the client libraries in the server code base.

The MQTTPublisher Class

The MQTTPublisher class in loklak would provide an interface to perform basic operations in MQTT. The implementation uses AsyncClientListener to connect to Mosquitto broker –

AsyncClientListener listener = new AsyncClientListener() {
    // Override methods according to needs


The publish method for the class can be used by other components of the project to publish messages on the desired channel –

public void publish(String channel, String message) {
    this.mqttClient.publish(new PublishMessage(channel, QoS.AT_LEAST_ONCE, message));


We also have methods which allow publishing of multiple messages to multiple channels in order to increase the functionality of the class.

Starting Publisher with Server

The flags which signal using of streaming service in loklak are located in conf/ These configurations are referred while initializing the Data Access Object and an MQTTPublisher is created if needed –

String mqttAddress = getConfig("stream.mqtt.address", "tcp://");
streamEnabled = getConfig("stream.enabled", false);
if (streamEnabled) {
    mqttPublisher = new MQTTPublisher(mqttAddress);


The mqttPublisher can now be used by other components of loklak to publish messages to the channel they want.

Adding Mosquitto to Kubernetes

Since loklak has also a nice Kubernetes setup, it was very simple to introduce a new deployment for Mosquitto to it.

Changes in Dockerfile

The Dockerfile for master deployment has to be modified to discover Mosquitto broker in the Kubernetes cluster. For this purpose, corresponding flags in have to be changed to ensure that things work fine –

sed -i.bak 's/^\(stream.enabled\).*/\1=true/' conf/ && \
sed -i.bak 's/^\(stream.mqtt.address\).*/\1=mosquitto.mqtt:1883/' conf/ && \


The Mosquitto broker would be available at mosquitto.mqtt:1883 because of the service that is created for it (explained in later section).

Mosquitto Deployment

The Docker image used in Kubernetes deployment of Mosquitto is taken from toke/docker-kubernetes. Two ports are exposed for the cluster but no volumes are needed –

apiVersion: extensions/v1beta1
kind: Deployment
  name: mosquitto
  namespace: mqtt
      - name: mosquitto
        image: toke/mosquitto
        - containerPort: 9001
        - containerPort: 8883


Exposing Mosquitto to the Cluster

Now that we have the deployment running, we need to expose the required ports to the cluster so that other components may use it. The port 9001 appears as port 80 for the service and 1883 is also exposed –

apiVersion: v1
kind: Service
  name: mosquitto
  namespace: mqtt
  - name: mosquitto
    port: 1883
  - name: mosquitto-web
    port: 80
    targetPort: 9001


After creating the service using this configuration, we will be able to connect our clients to Mosquitto at address mosquitto.mqtt:1883.


In this blog post, I discussed the process of adding Mosquitto to the loklak server project. This is the first step towards introducing the stream API for messages collected in loklak.

These changes were introduced in pull requests loklak/loklak_server#1393 and loklak/loklak_server#1398 by @singhpratyush (me).


Scraping Concurrently with Loklak Server

At Present, SearchScraper in Loklak Server uses numerous threads to scrape Twitter website. The data fetched is cleaned and more data is extracted from it. But just scraping Twitter is under-performance.

Concurrent scraping of other websites like Quora, Youtube, Github, etc can be added to diversify the application. In this way, single endpoint search.json can serve multiple services.

As this Feature is under-refinement, We will discuss only the basic structure of the system with new changes. I tried to implement more abstract way of Scraping by:-

1) Fetching the input data in SearchServlet

Instead of selecting the input get-parameters and referencing them to be used, Now complete Map object is referenced, helping to be able to add more functionality based on input get-parameters. The dataArray object (as JSONArray) is fetched from DAO.scrapeLoklak method and is embedded in output with key results

    // start a scraper
    inputMap.put("query", query);
    DAO.log(request.getServletPath() + " scraping with query: "
           + query + " scraper: " + scraper);
    dataArray = DAO.scrapeLoklak(inputMap, true, true);


2) Scraping the selected Scrapers concurrently

In, the useful get parameters of inputMap are fetched and cleaned. They are used to choose the scrapers that shall be scraped, using getScraperObjects() method.

Timeline2.Order order= getOrder(inputMap.get("order"));
Timeline2 dataSet = new Timeline2(order);
List<String> scraperList = Arrays.asList(inputMap.get("scraper").trim().split("\\s*,\\s*"));


Threads are created to fetch data from different scrapers according to size of list of scraper objects fetched. input map is passed as argument to the scrapers for further get parameters related to them and output data according to them.

List<BaseScraper> scraperObjList = getScraperObjects(scraperList, inputMap);
ExecutorService scraperRunner = Executors.newFixedThreadPool(scraperObjList.size());

    for (BaseScraper scraper : scraperObjList)
        scraperRunner.execute(() -> {


} finally {

    try {
        scraperRunner.awaitTermination(24L, TimeUnit.HOURS);
    } catch (InterruptedException e) { }


3) Fetching the selected Scraper Objects in

Here the variable of abstract class BaseScraper (SuperClass of all search scrapers) is used to create List of scrapers to be scraped. All the scrapers’ constructors are fed with input map to be scraped accordingly.

List<BaseScraper> scraperObjList = new ArrayList<BaseScraper>();
BaseScraper scraperObj = null;

if (scraperList.contains("github") || scraperList.contains("all")) {
    scraperObj = new GithubProfileScraper(inputMap);