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Benchmarking Apache Kafka: Performance-per-price

বেঞ্চমার্কিং অ্যাপাচি কাফকা: পারফরম্যান্স-প্রতি-মূল্য

Benchmarking von Apache Kafka: Leistung pro Preis

Evaluación comparativa de Apache Kafka: rendimiento por precio

Analyse comparative d'Apache Kafka : performances par prix

अपाचे काफ्का की बेंचमार्किंग: प्रदर्शन-प्रति-मूल्य

Apache Kafka のベンチマーク: 価格に対するパフォーマンス

Apache Kafka 벤치마킹: 가격 대비 성능

Comparativo de mercado do Apache Kafka: desempenho por preço

Сравнительный анализ Apache Kafka: соотношение производительности и цены

Apache Kafka'nın Karşılaştırması: Fiyat başına performans

Đo điểm chuẩn Apache Kafka: Hiệu suất trên mỗi giá

Apache Kafka 基准测试：性价比

How Useful are Computational Models to Traders in Their Decision Making?

“Just like there’s a possibility and time for loss, there’s always time for recovery” - Jenny Brown, head of the Recovery Technical Team at BCA. ALPHARETTA, USA, November 7th, 2023/Chainwire/-- Celebrates 3 Remarkable Years of Helping Victims of Crypto Scams Recover Their Lost Crypto. Broker Complaint Alert (BCA) celebrates 3 years of offering one of the best and most remarkable crypto recovery experiences. Since BCA began operating in December 2020, it has helped over twelve thousand scam victims to recover their lost funds from scammers. Broker Complaint Alert (BCA) BCA introduced a solution that is focused on ensuring that every single scam victim who reports the scam to their platform stands a 94% chance of recovering lost funds. This innovative approach has literally proven itself to be 94% accurate. In other words, roughly 94% of all cases reported to BCA so far were successful. BCA combines the most brilliant minds within the crypto space and the best in terms of blockchain technology to help crypto scam victims recover their lost crypto assets. The BCA solution completely takes the stress off scam victims, especially victims of crypto scams, and puts smiles on their faces once again. How Broker Complaint Alert (BCA) Helps Crypto Scam Victims With Crypto Recovery Users who have lost their crypto assets or other financial assets to any form of scam can relax and put their minds at ease by allowing Broker Complaint Alert (BCA) to lead them on a journey of successfully recovering their lost funds. Broker Complaint Alert (BCA) is a prominent organization dedicated to helping and supporting victims of crypto scams, investment scams, forex scams, or any other form of financial scams to recover their stolen funds from scammers. BCA has a dedicated team of committed online consultants that offer personalized guidance, expert consultation and access to the ebay and most reputable crypto recovery companies and service providers. 5 FAQs About Crypto Recovery Answered There are many questions relating to crypto recovery, and BCA is providing answers to 5 of the most Frequently Asked Questions here; 1.) Are crypto asset recovery companies legit? Yes, crypto asset recovery companies are legit. However, not all crypto asset recovery companies are legit. Just like in every other field, there are some bad players in crypto asset recovery. That is why it is important to conduct proper research before hiring any crypto asset recovery company. 2.) Has anyone ever recovered stolen crypto? Of course, yes! Many people have been able to recover their stolen crypto. Broker Complaint Alert (BCA) has successfully helped over 12,350 people recover their stolen crypto in the last 3 years. 3.) Can a user recover his scammed crypto? Yes, a user can recover his scammed crypto. When it comes to crypto recovery, however, the user has to go about it the right way to be able to achieve success in recovering his scammed crypto. 4.) How can a user find the best crypto recovery service provider? Finding the best crypto recovery company can seem like a daunting task. And it could be, especially if a user is not searching in the right direction. The good news is that BCA saves the stress of having to waste time trying to find the best crypto recovery company alone. Individuals seeking a way to recover stolen crypto can find all the necessary information in this publication. To initiate a successful recovery of their stolen crypto, they simply need to make a report . here How to Recover Stolen Crypto Using Broker Complaint Alert (BCA) Relief Funds The relief process offered by Broker Complaint Alert (BCA) provides hope for victims of crypto scams. With up to $9 billion available for compensation, this initiative aims to bring justice and restitution to those who have suffered financial loss due to fraudulent practices in the crypto world. By understanding the relief process, victims can navigate the application process with confidence, ensuring their eligibility for financial reimbursement. BCA’s commitment to transparency and fairness further establishes them as a trusted entity within the crypto industry. Recover your scammed crypto by reporting any crypto scam to Broker Complaint Alert (BCA). BCA is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or endorsement. ABOUT Broker Complaint Alert (BCA) helps victims of investment fraud, forex scams, crypto scams, and any type of fraud that happened to you. BCA has helped and guided thousands of people over the past 3 years. 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Broker Complaint Alert(BCA) Celebrates 3 Years Of Helping Victims Of Crypto Scams

ব্রোকার কমপ্লেইন্ট অ্যালার্ট (বিসিএ) ক্রিপ্টো স্ক্যামগুলির শিকারদের সাহায্য করার 3 বছর উদযাপন করে

Broker Complaint Alert (BCA) feiert 3 Jahre Hilfe für Opfer von Krypto-Betrug

Broker Complaint Alert (BCA) celebra 3 años ayudando a las víctimas de estafas criptográficas

Broker Complaint Alert (BCA) célèbre 3 ans d'aide aux victimes d'escroqueries cryptographiques

ब्रोकर कंप्लेंट अलर्ट (बीसीए) क्रिप्टो घोटाले के पीड़ितों की मदद के 3 साल का जश्न मनाता है

Broker Complaint Alert (BCA) が仮想通貨詐欺の被害者支援 3 周年を祝う

Broker Complaint Alert(BCA)는 암호화폐 사기 피해자를 돕는 3주년을 기념합니다.

Alerta de reclamação de corretor (BCA) comemora 3 anos ajudando vítimas de fraudes criptográficas

Оповещение о жалобах брокеров (BCA) отмечает 3-летие оказания помощи жертвам крипто-мошенничества

Broker Şikayet Uyarısı (BCA), Kripto Dolandırıcılığı Mağdurlarına Yardımın 3. Yılını Kutluyor

Cảnh báo Khiếu nại Nhà môi giới (BCA) kỷ niệm 3 năm giúp đỡ nạn nhân của các vụ lừa đảo tiền điện tử

经纪商投诉警报 (BCA) 庆祝帮助加密诈骗受害者 3 周年

While Vite has been gaining attention in the Vue.js community as a JavaScript build tool, I personally favor Parcel for building my applications. Although most tutorials and documentation tend to emphasize Vite or other popular build tools, this article aims to provide guidance on setting up Parcel for Vue.js projects. Now, let's explore the reasons why you might consider using Parcel. Vue.js Why Use Parcel? While Vite works great and builds applications fast, it focuses on building things fast. That sounds great to a lot of people probably, but for me, that is an anti-pattern. Yes, I want my dev tools to be fast, but if that is the primary focus, then often times the developer experience is often a secondary concern. Instead, I want developer tools that are primarily focused on developer experience and also fast. I think that Parcel delivers great on this promise. Most Javascript build tools like end up being wrappers around existing tools but with the configuration obscured away and pre-configured for you. While this is great to get started fast, you eventually hit configuration roadblocks and complexity as your app grows. Parcel has a zero-configuration philosophy and is not a wrapper around older tools. Once you get past setting it up, you encounter far fewer configuration roadblocks as your app grows. Vite The only downside with using Parcel, especially with Vue, is that the setup is not documented as much as I would like. Additionally, with the release of Parcel 2 and Vue 3, there are a few quirks with the setup. Hopefully, they will get worked out in the future, but for now, the instructions below will show you how to set up a Vue app with Parcel. Building a Vue 3 App with Parcel To get started, you need a Vue 3 app. If you need to start one, the is a good place to get a skeleton app to begin with. If you use just those instructions though, you'll probably get some errors or warnings we need to take care of. Parcel documentation Fix 1: package.json - Vue Bundle In your you'll want to add an alias section. This will let Parcel know the correct version of Vue to use when bundling your application. package.json "alias": {
  "vue": "vue/dist/vue.esm-browser.js"
}, Special Note for Production Builds Right now there is when you switch to production builds with Vue 3 and Parcel. So when you do a production build you need to switch the alias to . I just use a pre-build command in my CI/CD to switch that out for production builds: a bug vue.esm-browser.prod.js sed -i 's/vue.esm-browser.js/vue.esm-browser.prod.js/g' package.json Fix 2: package.json - Build type (may be needed) Depending on your build target and your output context you may want to add and also to your . It doesn't hurt to add them and to be explicit about what you're building. I also like to use often because I want Parcel to build multiple things. type targets package.json targets "type": "module",
"targets": {
  "myapp": {
    "source": "src/index.js",
    "distDir": "dist",
    "context": "browser",
    "outputFormat": "global"
  }
}, Fix 3: Main App JS - Silence More Warnings and Include Vue Options API In your main app JS, you are probably getting additional warnings or you may want to include the Vue Options API. Put these variables in your initial app JS file to silence the dev tool warnings and explicitly set up the Vue Options API or not. You probably want to put them toward the top of the file. __VUE_OPTIONS_API__ = true;

if (process.env.NODE_ENV !== 'production') {
  __VUE_PROD_DEVTOOLS__ = true;
} else {
  __VUE_PROD_DEVTOOLS__ = false;
} Have fun with your new Vue 3 app development and throw out your aspirin as you'll now be on easy street and have far fewer configuration errors. Parcel JS will handle most other setups, even installing the transformers you'll need to compile things like Vue Single File Components and Typescript.

Using Vue 3 with Parcel JS

This is our final post in our three-part series demonstrating how to use the Salesforce Mobile SDK to build an Android app that works with the Salesforce platform. , we showed you how to connect to your org. showed you how to edit and add data to your org from your app. This post will show you how to synchronize data from your Salesforce org to your mobile device and handle scenarios such as network loss. Let’s get right into it! In our first post Our second post Working with Mobile Sync One of the hardest aspects of mobile development is dealing with data synchronization. How do you handle the situation when you need to add a new broker, but you’re offline? Or what if two agents are updating the same broker—how can you handle the merging of those two changes? With the Salesforce Mobile SDK, these real-world issues are handled for you by a system called . Mobile Sync has you map your phone’s local data to the data in Salesforce; it also requires you to define operations for fetching and pushing data—what it calls and . Mobile Sync syncDown syncUp Define the shape of data to be synced To get started with Mobile Sync, create a file in called : res/raw brokerstore.json {
 "soups": [
 {
 "soupName": "brokers",
 "indexes": [
 { "path": "Id", "type": "string"},
 { "path": "Name", "type": "string"},
 { "path": "Title__c", "type": "string"},
 { "path": "Phone__c", "type": "string"},
 { "path": "Mobile_Phone__c", "type": "string"},
 { "path": "Email__c", "type": "string"},
 { "path": "Picture__c", "type": "string"},
 { "path": "__local__", "type": "string"},
 { "path": "__locally_created__", "type": "string"},
 { "path": "__locally_updated__", "type": "string"},
 { "path": "__locally_deleted__", "type": "string"},
 { "path": "__sync_id__", "type": "integer"}
 ]
 }
 ]
} This file defines the shape of the data on your phone, as well as some additional metadata that’s necessary for the sync. Next, create a file called : brokersync.json {
 "syncs": [
 {
 "syncName": "syncDownBrokers",
 "syncType": "syncDown",
 "soupName": "brokers",
 "target": {"type":"soql", "query":"SELECT Name, Title__c, Phone__c, Mobile_Phone__c, Email__c, Picture__c FROM Broker__c LIMIT 10000"},
 "options": {"mergeMode":"OVERWRITE"}
 },
 {
 "syncName": "syncUpBrokers",
 "syncType": "syncUp",
 "soupName": "brokers",
 "target": {"createFieldlist":["Name", "Title__c", "Phone__c", "Mobile_Phone__c", "Email__c", "Picture__c"]},
 "options": {"fieldlist":["Id", "Name", "Title__c", "Phone__c", "Mobile_Phone__c", "Email__c", "Picture__c"], "mergeMode":"LEAVE_IF_CHANGED"}
 }
 ]
} These are the operations that Mobile Sync will use when syncing data down and up. The code to complete the Mobile Sync process depends on several factors, such as when you want to perform a synchronization, as well as hooking into the Android event cycle when a device loses (and regains) connectivity. The following code samples will show you a complete example of what needs to happen to get synchronization working, but they should be considered high-level concepts, and not necessarily production-ready enterprise-grade code. Set up periodic sync With that said, let’s take a look at how to implement synchronization. First, add this line to the end of our method in : onResume(client: RestClient) MainActivity.kt setupPeriodicSync(); Next, we’ll add a new variable and a new function to the class: MainActivity private val SYNC_CONTENT_AUTHORITY =
 "com.salesforce.samples.mobilesyncexplorer.sync.brokersyncadapter"

private fun setupPeriodicSync() {
 val account = MobileSyncSDKManager.getInstance().userAccountManager.currentAccount

 ContentResolver.setSyncAutomatically(account, SYNC_CONTENT_AUTHORITY, true)
 ContentResolver.addPeriodicSync(
 account, SYNC_CONTENT_AUTHORITY,
 Bundle.EMPTY, 10
 )
} Since we’re using in our function, let’s make sure to import it by adding this line alongside the other import statements near the top of : ContentResolver MainActivity.kt import.android.content.ContentResolver We have defined two methods that trigger synchronization. will run a sync every seconds. This is much too frequent for a production environment, but we’ll set it this way for demonstration purposes. setupPeriodicSync 10 Mapping sync operations to our data and UI We will show the next few code samples all at once, and discuss what they’re doing afterward. In , create a new file called and paste these lines into it: app/java/com.example.sfdc BrokerSyncAdapter.kt package com.example.sfdc

import android.accounts.Account
import android.content.AbstractThreadedSyncAdapter
import android.content.ContentProviderClient
import android.content.Context
import android.content.SyncResult
import android.os.Bundle
import com.salesforce.androidsdk.accounts.UserAccount
import com.salesforce.androidsdk.accounts.UserAccountManager
import com.salesforce.androidsdk.app.SalesforceSDKManager
import com.example.sfdc.BrokerListLoader

class BrokerSyncAdapter
 (
 context: Context?, autoInitialize: Boolean,
 allowParallelSyncs: Boolean
) :
 AbstractThreadedSyncAdapter(context, autoInitialize, allowParallelSyncs) {
 override fun onPerformSync(
 account: Account, extras: Bundle, authority: String,
 provider: ContentProviderClient, syncResult: SyncResult
 ) {
 val syncDownOnly = extras.getBoolean(SYNC_DOWN_ONLY, false)
 val sdkManager = SalesforceSDKManager.getInstance()
 val accManager = sdkManager.userAccountManager
 if (sdkManager.isLoggingOut || accManager.authenticatedUsers == null) {
 return
 }
 if (account != null) {
 val user = sdkManager.userAccountManager.buildUserAccount(account)
 val contactLoader = BrokerListLoader(context, user)
 if (syncDownOnly) {
 contactLoader.syncDown()
 } else {
 contactLoader.syncUp() // does a sync up followed by a sync down
 }
 }
 }

 companion object {
 // Key for extras bundle
 const val SYNC_DOWN_ONLY = "syncDownOnly"
 }
} Now, in that same folder, create with these lines: BrokerListLoader.kt package com.example.sfdc

import android.content.AsyncTaskLoader
import android.content.Context
import android.content.Intent
import android.util.Log
import com.salesforce.androidsdk.accounts.UserAccount
import com.salesforce.androidsdk.app.SalesforceSDKManager
import com.salesforce.androidsdk.mobilesync.app.MobileSyncSDKManager
import com.salesforce.androidsdk.mobilesync.manager.SyncManager
import com.salesforce.androidsdk.mobilesync.manager.SyncManager.MobileSyncException
import com.salesforce.androidsdk.mobilesync.manager.SyncManager.SyncUpdateCallback
import com.salesforce.androidsdk.mobilesync.util.SyncState
import com.salesforce.androidsdk.smartstore.store.QuerySpec
import com.salesforce.androidsdk.smartstore.store.SmartSqlHelper.SmartSqlException
import com.salesforce.androidsdk.smartstore.store.SmartStore
import org.json.JSONArray
import org.json.JSONException
import java.util.ArrayList

class BrokerListLoader(context: Context?, account: UserAccount?) :
 AsyncTaskLoader<List<String>?>(context) {
 private val smartStore: SmartStore
 private val syncMgr: SyncManager
 override fun loadInBackground(): List<String>? {
 if (!smartStore.hasSoup(BROKER_SOUP)) {
 return null
 }
 val querySpec = QuerySpec.buildAllQuerySpec(
 BROKER_SOUP,
 "Name", QuerySpec.Order.ascending, LIMIT
 )
 val results: JSONArray
 val brokers: MutableList<String> = ArrayList<String>()
 try {
 results = smartStore.query(querySpec, 0)
 for (i in 0 until results.length()) {
 brokers.add(results.getJSONObject(i).getString("Name"))
 }
 } catch (e: JSONException) {
 Log.e(TAG, "JSONException occurred while parsing", e)
 } catch (e: SmartSqlException) {
 Log.e(TAG, "SmartSqlException occurred while fetching data", e)
 }
 return brokers
 }

 @Synchronized
 fun syncUp() {
 try {
 syncMgr.reSync(
 SYNC_UP_NAME
 ) { sync ->
 if (SyncState.Status.DONE == sync.status) {
 syncDown()
 }
 }
 } catch (e: JSONException) {
 Log.e(TAG, "JSONException occurred while parsing", e)
 } catch (e: MobileSyncException) {
 Log.e(TAG, "MobileSyncException occurred while attempting to sync up", e)
 }
 }

 /**
 * Pulls the latest records from the server.
 */
 @Synchronized
 fun syncDown() {
 try {
 syncMgr.reSync(
 SYNC_DOWN_NAME
 ) { sync ->
 if (SyncState.Status.DONE == sync.status) {
 fireLoadCompleteIntent()
 }
 }
 } catch (e: JSONException) {
 Log.e(TAG, "JSONException occurred while parsing", e)
 } catch (e: MobileSyncException) {
 Log.e(TAG, "MobileSyncException occurred while attempting to sync down", e)
 }
 }

 private fun fireLoadCompleteIntent() {
 val intent = Intent(LOAD_COMPLETE_INTENT_ACTION)
 SalesforceSDKManager.getInstance().appContext.sendBroadcast(intent)
 }

 companion object {
 const val BROKER_SOUP = "brokers"
 const val LOAD_COMPLETE_INTENT_ACTION =
 "com.salesforce.samples.mobilesyncexplorer.loaders.LIST_LOAD_COMPLETE"
 private const val TAG = "BrokerListLoader"
 private const val SYNC_DOWN_NAME = "syncDownBrokers"
 private const val SYNC_UP_NAME = "syncUpBrokers"
 private const val LIMIT = 10000
 }

 init {
 val sdkManager = MobileSyncSDKManager.getInstance()
 smartStore = sdkManager.getSmartStore(account)
 syncMgr = SyncManager.getInstance(account)
 // Setup schema if needed
 sdkManager.setupUserStoreFromDefaultConfig()
 // Setup syncs if needed
 sdkManager.setupUserSyncsFromDefaultConfig()
 }
} What did we just do? Each file has a specific role, and while the objects and fields will certainly be different for your app, the functions and philosophies of these classes are the same: is responsible for mapping the sync operations you defined in with the Kotlin code that will actually perform the work. Notice that there are and methods that use the Mobile SDK’s to load the JSON files and communicate back and forth with Salesforce. BrokerListLoader brokersync.json syncUp syncDown SyncManager can perhaps best be thought of as the code that’s responsible for scheduling the synchronization. That is, it’s the entry point for , and can be called by UI elements (such as during a refresh button click) or Android system events (such as connectivity loss). BrokerSyncAdapter BrokerListLoader Lastly, we need to add one line to our file (in ). Our new sync functionality will need special Android permissions, which we ask the user to allow upon app installation at runtime. Add the following line with to the end of your manifest: AndroidManifest.xml app/manifests WRITE_SYNC_SETTINGS <uses-permission android:name="com.example.sfdc.C2D_MESSAGE" />
 <uses-permission android:name="android.permission.WRITE_SYNC_SETTINGS" />
</manifest> Testing sync Our last step is to test that mobile sync works. With the emulator running, you should be logged in and see a list of brokers. This list should mirror the broker list that you see in your web browser on your local machine. In your web browser, edit one of the broker names, and save that change. Then, in your emulator, you can power off the phone or switch to another application and then switch back to your . You should see your list of broker names updated with the change you made in your browser: sfdc-mobile-app And that’s all! Again, this is just a foundational building block for you to learn from. In just about 150 lines of code, we’ve devised a solution for a rather complicated series of moving parts: Mapping Salesforce custom objects to JSON Setting up a timer to periodically sync data back and forth between a Salesforce org and a mobile device Handling errors (and recovering from issues) around network connectivity Conclusion The Salesforce Mobile SDK makes it tremendously easy to connect mobile devices with Salesforce data. In this post, you learned how to query and manipulate data from your phone, and saw the results reflected instantaneously on Salesforce. You also learned about Mobile Sync and its role in anticipating issues with connectivity. And yet, with all of this information, there’s still that the Salesforce Mobile SDK can do. Take a look at the on working with the SDK. Or, if you prefer to do more coding, there are to keep you busy. We can’t wait to see what you build! so much more complete documentation plenty of Trailhead tutorials

Building a Kotlin Mobile App with the Salesforce SDK: Synchronizing Data

When developing a new product, the first thing that comes to mind is how to structure code. There is a battle going on in this regard: monolithic vs microservices. It has been going on for a while since software developers and company owners are still trying to figure out which architectural style is ideal for their applications. The scalability, efficiency, and competitiveness of the product are determined by the strategy used – monolithic vs microservices. While monolithic systems have been used for a while, microservices are a comparatively modern form of software system structure. Indeed, a slew of technologies emerged under the DevOps mindset, allowing us to design scalable, distributed systems based on microservices. One of the main advantages of a microservice architecture is that it makes it simpler to select the technological stack (programming languages, databases, etc.) that is most suited for the desired functionality (service) rather than being forced to take a more conventional, one-size-fits-all approach. Even though such an innovational approach has its benefits, it doesn’t mean that it would be the best solution for each and every application. So let’s sort out in which cases deploying microservice architecture would be the right decision. Microservices Use Cases The difficulties that this technique overcomes are frequently not present while building the first version of an application. Furthermore, implementing a complex, distributed design will stymie progress. This may be a significant issue for startups, whose main task is frequently how to fast change the business model and associated application. However, when it comes to scaling and you need to employ functional deconstruction, the knotted interactions may make it tough to break down your monolithic application into a group of services. On the other side, when your aim is to develop a sophisticated and scalable application – microservice architecture is what you need. Adopting new sophisticated tech stacks will be your greatest answer in the future if you want to add new functionality to your application and modernize it. The microservices strategy will come to your aid in this regard, particularly in gaining a competitive advantage. Read also: Detailed Tutorial on How to Build Micro Frontend Based on Angular How does Microservices Communication work? The microservices design focuses on categorizing potentially huge and unwieldy programs. Every microservice is meant to solve a certain feature and function of an application, such as logging, data search, etc. Several of these microservices work together to produce a single efficient application. Because it is typical for each service instance to operate as a distinct process, services must communicate via a process communication protocol. The strategies could be HTTP, AMQP, or a binary TCP protocol. It depends on the nature of your app. Here is the division in a nutshell: Depending on the protocol type : stranded sub-processes, the system involves isolating each microservice as much as feasible. Synchronous : unlocked sub-processes and protocols that are interoperable with a wide range of operating systems and cloud settings are employed. Asynchronous Depending on the receiver's number – each request will be processed by a separate receiver or service. Single – the structure of event-driven microservices is used. Many But this is all theory, let’s move to the practice. Below we shared our insights on how to apply microservices communication with the use of Apache Kafka. Practical Application of Microservices Communication using Apache Kafka Apache Kafka is a considerably different choice since it is a message broker, it supports asynchronous message-based communication. Rather than performing synchronous HTTP requests and waiting for a response, we could simply consume an Apache Kafka. Running Apache Kafka instance locally (Using Docker images) In order to start an Apache Kafka server, first, we will need to start a Zookeeper server. But there is one way to make it simpler, for that we should create a simple Docker composition. Let’s create a folder called Apache Kafka with a single docker-compose.yml in it. Here is the code sample, that you need to write inside the file ‘docker-compose.yml’. version: '2'
services:
 zookeeper:
 image: confluentinc/cp-zookeeper:latest
 environment:
 ZOOKEEPER_CLIENT_PORT: 2181
 ZOOKEEPER_TICK_TIME: 2000
 ports:
 - 22181:2181
 
 kafka:
 image: confluentinc/cp-kafka:latest
 depends_on:
 - zookeeper
 ports:
 - 29092:29092
 environment:
 KAFKA_BROKER_ID: 1
 KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
 KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092,PLAINTEXT_HOST://localhost:29092
 KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
 KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 For making it run, you should have Docker installed. Find the installation link . here The next step is to go to the folder with the file and run the composition using the command ‘docker compose up’. After images are installed we have an Apache Kafka server running on the specified port: Creating a microservice to produce Kafka events Let’s start with generating a Nest application using the CLI: `nest new event-producer-service` Now we have a simple Nest application. It looks like that: At this stage, let’s add a package for Apache Kafka called ‘kafkajs’, and also ‘@nestjs/microservices’ to support microservices with Nest. After that you need to run on the terminal command: yarn add kafkajs
yarn add @nestjs/microservices Let’s add Kafka events producer to the specific module where we need to send Kafka messages. Note: In our example, we are adding to the app module, but this can be implemented in any other module you need. We should add a provider to support Kafka messages. The code below is entered in the file ‘kafka-producer.provider.ts’. import { Transport, ClientProxyFactory } from "@nestjs/microservices";
import { Producer } from "kafkajs";
const brokerURLs = ['localhost:29092'];

export const KafkaProducerProvider = {
 provide: "KafkaProducer",
 useFactory: (): Promise<Producer> => {
 const kafkaClient = ClientProxyFactory.create({
 transport: Transport.KAFKA,
 options: {
 client: {
 brokers: brokerURLs,
 },
 producer: {
 allowAutoTopicCreation: true,
 },
 },
 });

 return kafkaClient.connect();
 },
}; For now, we have hard-coded brokerUrls. But also, we used useFactory. This way in the future we will be able to inject a Config Service instance, for example, in order to provide currently hard-coded data. Let’s add the provider to the app module. Here is the code for that: import { Inject, Module, OnModuleDestroy } from '@nestjs/common';
import { AppController } from './app.controller';
import { AppService } from './app.service';
import { KafkaProducerProvider } from './providers/kafka-producer.provider';
import { Producer } from "kafkajs";

@Module({
 imports: [],
 controllers: [AppController],
 providers: [
 AppService,
 KafkaProducerProvider,
 ],
})
export class AppModule implements OnModuleDestroy {
 constructor(
 @Inject("KafkaProducer")
 private readonly kafkaProducer: Producer,
 ) {}

 async onModuleDestroy(): Promise<void> {
 await this.kafkaProducer.disconnect();
 }
} After that, we can add a method to the service to send Kafka events and an endpoint for it. To do so, enter the ‘app.service.ts’ file to write some code. import { Inject, Injectable } from '@nestjs/common';
import { Producer } from "kafkajs";

@Injectable()
export class AppService {
 constructor(
 @Inject("KafkaProducer")
 private readonly kafkaProducer: Producer,
 ) {

 }
 getHello(): string {
 return 'Hello World!';
 }

 async sendMessage(topic, data, key?) {
 return this.kafkaProducer.send({
 topic,
 messages: [
 {
 value: JSON.stringify(data),
 key,
 },
 ],
 });
 }
} Then move to the ‘app.controller.ts’ file. import { Body, Controller, Get, Post } from '@nestjs/common';
import { AppService } from './app.service';

@Controller()
export class AppController {
 constructor(private readonly appService: AppService) {}

 @Get()
 getHello(): string {
 return this.appService.getHello();
 }

 @Post('message')
 async sendMessage(@Body() body) {
 const { topic, data, key } = body;

 return this.appService.sendMessage(topic, data, key);
 }
} Creating a microservice to consume Kafka events Let’s generate a new app. For that, just use the command and install the same packages as for producer service. nest new event-consumer-service Now we can modify our ‘main.ts’ and connect our broker. So, go to the ‘main.ts’ file and apply such a piece of code. import { NestFactory } from '@nestjs/core';
import { MicroserviceOptions, Transport } from '@nestjs/microservices';
import { AppModule } from './app.module';

async function bootstrap() {
 const app = await NestFactory.createMicroservice<MicroserviceOptions>(AppModule, {
 transport: Transport.KAFKA,
 options: {
 client: {
 brokers: ['localhost:29092'],
 }
 }
 });
 app.listen(() => { console.log('Microservices started.'); })
}
bootstrap(); Now we are going to create a handler for the specific event (test-topic). Open up an ‘app.controller.ts’ file for that. Below you’ll find the code sample: import { Controller, Get } from '@nestjs/common';
import { MessagePattern, Payload } from '@nestjs/microservices';
import { AppService } from './app.service';

@Controller()
export class AppController {
 constructor(private readonly appService: AppService) {}

 @Get()
 getHello(): string {
 return this.appService.getHello();
 }

 @MessagePattern('test-topic')
 respondToTestTopic(@Payload() message) {
 console.log(message.value);
 return 'Hello World';
 }
} Smoke test Now let’s run our services. If everything is done well when we send POST requests to the producer app using the topic , we’ll see the logs with our data in the consumer app. test-topic To try how do they communicate, we send a request to Postman. And then the request will appear to the producer service. Hence, if your requests are running, you are successfully set up the communication between microservices. Just like that. Concluding Knowing the benefits and drawbacks of multiple architectures isn't enough. It is critical to understand when to employ certain tools. Surely, there are a whole lot more options for microservices communication, both synchronous and asynchronous. Our experience in implementing such a process with Apache Kafka is just one method among many others. Nevertheless, here we shared our personal insights with the code samples, that we hope might be useful for your development team. Also published on Incora Blog

Microservices Communication with the use of Apache Kafka

I am pretty sure you are aware that every time you trade you lose money? With each typical online trade, 3 sneaky little money thieves dip into your pocket and snatch a little profit: you will pay a commission to the brokeryou will need to pay a spread on the entry and exit price of each tradeyou will also pay a spread on your margin interest Damn, this trading is costly isn’t it? So, what I am about to reveal is a seriously amazing bit of advice that will save you the most precious things known to man. Time, Money and one more thing, unnecessary Stress! If you already trade the markets, you will know that an instant way to lose money is to open and close positions using a high-frequency trading (HFT) strategy, (as fast as your fingers and systems can work that is). Sure there are professional and experienced traders who have successful strategies, though it only takes a little poking around to see that they have worked for a long time to get to that enviable position, often with a lot of hair loss, lost money and near nervous breakdowns to boot! Any experienced trader will tell you that each closed position allows a sneaky little money thief to take a bit of your profit. And when the stats show that about 75% of all trades are on the losing side….you can start to see why so many people struggle to make manual FX trading work for their investment needs. So, is this the time to give up on Forex trading? One of the strategies that certain traders like to perfect is the HFT (high-frequency trading) system, but done manually, this system is pretty much impossible, and if attempted means you are working more like a robot. And let’s be honest why would anyone take pleasure from doing the job of a boring old robot…open, analyse, close, open, analyse, close, open, analyse, close…repeat at lighting speed….no chance! RSI and brain explosion coming soon! So playing the high-frequency trading game (as fast as any human can) is a strategy that you are always at an instant loss from the very start and if you can stand the heat, stress, and potential for RSI you may make it work for you, in the long run. But be aware that on every one of the many trades you make you’re going to take a number of losses before you make your profit, since you will pay a fixed spread on the entry and exit price and then an agreed commission, so those fees will triple before you even see your profit. It’s clear then that the losses we incur as traders will increase in relation to the number of trades we make.So, put simply the more often you trade, the larger the profits you will need just to break-even and ensure those pesky money thieves who are there to snipe at your gains are not impacting your investment growth goals. There is no wonder that, so many beginner traders give up after sometimes heavy losses. Sure, some stick it out and create elaborate systems to help them ensure that they are playing the long game and beating the markets more than losing, the thing is anyone in the professional trading game will know that getting to that stage of consistent profits takes serious money, time, energy, commitment and lots of learning and months, if not years of testing. Do you really have the time and deep pockets to do all that work? What if you could just add your desired investment amount into an automated AI-based high-frequency trading software that had audited ROI of 2-4% per month (24-48% per year)…and did all the hard work for you! Have I just given you a big sigh of relief and some hope that you can trade the markets without all the hassle? Good, because what I am about to make you aware of is at the cutting edge of AI, FinTech and machine-based learning, and directly helps people like you invest in the FX markets with a system that offers a stable ROI without all the hassle. Who is behind this trading software revolution? Introducing 8topuz (pronounced Octopuz), a European, award-winning FinTech company that has developed a 10 year back-tested, AI-based trading software with a proven 3-year audited ROI of between 2-4% monthly average. Get E-Book And Performance Data: https://8topuz.com/8topuz-ai-algorithm-performance/ Did someone just say ditch the banks!? So how can you tap into such a system? Do you need to be a trader? Do you need to know the markets?Are you able to manage the software yourself? We know there are many questions surrounding new tools and ideas so here are a few questions that are so often asked about this automated trading solution. 1) Do you need to be a trader? Not at all, our clients include traders, institutions and portfolio managers who see the huge value of an audited AI-based trading software solution. You do however need to open a broker account where your segregated funds will be kept safe with one of our trusted, regulated partners so we can integrate our plugin (software) to your MT4 trading account allowing us to trade on your behalf. Do not worry we will handhold you all the way if any of that sounds new or a little alien to you. 2) What is the minimum deposit amount to get started? Typically, we only allow a minimum deposit of $10k USD as the system needs to see volume in trading to be effective due to the way our AI-based machine-learned algorithm works, but we also have a PAMM option which allows you to work with other investors in a collective pool. 3) Can I get a demo account? Yes, we offer rolling 30-day access to the system so you can easily see what is happening with the AI-based algorithm within a monthly period and get a true feel of how it all works. 4) I am still a bit confused by the value of the 8topuz service and have more questions No worries, it’s not always that easy to understand, your simple solution to start your alternative investment journey is to sign up for our E-Book on AI investing and get our audited monthly performance data for the past 3 years and schedule a call to speak to one of our trained FinTech AI specialists who will guide you on all aspects of the service. For the Techies: The 8topuz AI Algorithm, has been running for the last 3 years, using a proprietary neural network that analyses market’s depth and looks for patterns of pre-set mathematical models (such as fractals, chaos and waves) that allows it to understand and forecast market’s trends on a real-time basis (in fact it is a trending program). The neural network is supervised, multi-layered and composed by variable nodes, meaning: By feeding the network with pre-set data, the system creates its own real-time strategies to achieve the established objectives. In only milliseconds, our system can choose the most appropriate strategy among more than 30,000 options in every single market  condition. The network’s main tasks are as follows: Pattern recognition.Process optimization, and Signal validation and information processing. Usage of analysed market intelligence and troubleshooting: If the system identifies a trend which is not followed by the market, our software applies a counter-strategy to solve the situation  on a real-time basis, adapting to the new scenario. During this non-stop analysis, research and optimization process, all new movements and patterns are stored to be used in up-coming  scenarios, helping solve future market situations. Hence, it learns from its own mistakes and successfully solves the problems posed. Ready to join the AI trading revolution? Get E-Book And Performance Data: https://8topuz.com/8topuz-ai-algorithm-performance/ #think8topuz I am pretty sure you are aware that every time you trade you lose money? I am pretty sure you are aware that every time you trade you lose money? With each typical online trade, 3 sneaky little money thieves dip into your pocket and snatch a little profit: you will pay a commission to the broker you will need to pay a spread on the entry and exit price of each trade you will also pay a spread on your margin interest you will pay a commission to the broker you will need to pay a spread on the entry and exit price of each trade you will also pay a spread on your margin interest Damn, this trading is costly isn’t it? Damn, this trading is costly isn’t it? So, what I am about to reveal is a seriously amazing bit of advice that will save you the most precious things known to man. Time, Money and one more thing, unnecessary Stress! Time, Money and one more thing, unnecessary Stress! If you already trade the markets, you will know that an instant way to lose money is to open and close positions using a high-frequency trading (HFT) strategy, (as fast as your fingers and systems can work that is). Sure there are professional and experienced traders who have successful strategies, though it only takes a little poking around to see that they have worked for a long time to get to that enviable position, often with a lot of hair loss, lost money and near nervous breakdowns to boot! Any experienced trader will tell you that each closed position allows a sneaky little money thief to take a bit of your profit. And when the stats show that about 75% of all trades are on the losing side….you can start to see why so many people struggle to make manual FX trading work for their investment needs. So, is this the time to give up on Forex trading? So, is this the time to give up on Forex trading? One of the strategies that certain traders like to perfect is the HFT (high-frequency trading) system, but done manually, this system is pretty much impossible, and if attempted means you are working more like a robot. And let’s be honest why would anyone take pleasure from doing the job of a boring old robot…open, analyse, close, open, analyse, close, open, analyse, close…repeat at lighting speed….no chance! RSI and brain explosion coming soon! RSI and brain explosion coming soon! So playing the high-frequency trading game (as fast as any human can) is a strategy that you are always at an instant loss from the very start and if you can stand the heat, stress, and potential for RSI you may make it work for you, in the long run. But be aware that on every one of the many trades you make you’re going to take a number of losses before you make your profit, since you will pay a fixed spread on the entry and exit price and then an agreed commission, so those fees will triple before you even see your profit. It’s clear then that the losses we incur as traders will increase in relation to the number of trades we make. So, put simply the more often you trade, the larger the profits you will need just to break-even and ensure those pesky money thieves who are there to snipe at your gains are not impacting your investment growth goals. There is no wonder that, so many beginner traders give up after sometimes heavy losses. There is no wonder that, so many beginner traders give up after sometimes heavy losses. Sure, some stick it out and create elaborate systems to help them ensure that they are playing the long game and beating the markets more than losing, the thing is anyone in the professional trading game will know that getting to that stage of consistent profits takes serious money, time, energy, commitment and lots of learning and months, if not years of testing. Do you really have the time and deep pockets to do all that work? Do you really have the time and deep pockets to do all that work? What if you could just add your desired investment amount into an automated AI-based high-frequency trading software that had audited ROI of 2-4% per month (24-48% per year)…and did all the hard work for you! Have I just given you a big sigh of relief and some hope that you can trade the markets without all the hassle? Good, because what I am about to make you aware of is at the cutting edge of AI, FinTech and machine-based learning, and directly helps people like you invest in the FX markets with a system that offers a stable ROI without all the hassle. Who is behind this trading software revolution? Who is behind this trading software revolution? Introducing 8topuz (pronounced Octopuz), a European, award-winning FinTech company that has developed a 10 year back-tested, AI-based trading software with a proven 3-year audited ROI of between 2-4% monthly average. 8topuz AI-based trading software Get E-Book And Performance Data: Get E-Book And Performance Data: https://8topuz.com/8topuz-ai-algorithm-performance/ https://8topuz.com/8topuz-ai-algorithm-performance/ Did someone just say ditch the banks!? Did someone just say ditch the banks!? So how can you tap into such a system? So how can you tap into such a system? Do you need to be a trader? Do you need to know the markets? Are you able to manage the software yourself? Do you need to be a trader? Do you need to know the markets? Are you able to manage the software yourself? We know there are many questions surrounding new tools and ideas so here are a few questions that are so often asked about this automated trading solution. 1) Do you need to be a trader? 1) Do you need to be a trader? Not at all, our clients include traders, institutions and portfolio managers who see the huge value of an audited AI-based trading software solution. You do however need to open a broker account where your segregated funds will be kept safe with one of our trusted, regulated partners so we can integrate our plugin (software) to your MT4 trading account allowing us to trade on your behalf. Do not worry we will handhold you all the way if any of that sounds new or a little alien to you. 2) What is the minimum deposit amount to get started? 2) What is the minimum deposit amount to get started? Typically, we only allow a minimum deposit of $10k USD as the system needs to see volume in trading to be effective due to the way our AI-based machine-learned algorithm works, but we also have a PAMM option which allows you to work with other investors in a collective pool. 3) Can I get a demo account? 3) Can I get a demo account? Yes, we offer rolling 30-day access to the system so you can easily see what is happening with the AI-based algorithm within a monthly period and get a true feel of how it all works. 4) I am still a bit confused by the value of the 8topuz service and have more questions 4) I am still a bit confused by the value of the 8topuz service and have more questions No worries, it’s not always that easy to understand, your simple solution to start your alternative investment journey is to sign up for our E-Book on AI investing and get our audited monthly performance data for the past 3 years and schedule a call to speak to one of our trained FinTech AI specialists who will guide you on all aspects of the service. For the Techies: For the Techies: The 8topuz AI Algorithm, has been running for the last 3 years, using a proprietary neural network that analyses market’s depth and looks for patterns of pre-set mathematical models (such as fractals, chaos and waves) that allows it to understand and forecast market’s trends on a real-time basis (in fact it is a trending program). The neural network is supervised, multi-layered and composed by variable nodes, meaning: The neural network is supervised, multi-layered and composed by variable nodes, meaning: By feeding the network with pre-set data, the system creates its own real-time strategies to achieve the established objectives. In only milliseconds, our system can choose the most appropriate strategy among more than 30,000 options in every single market  condition. The network’s main tasks are as follows: The network’s main tasks are as follows: Pattern recognition. Process optimization, and Signal validation and information processing. Pattern recognition. Process optimization, and Signal validation and information processing. Usage of analysed market intelligence and troubleshooting: Usage of analysed market intelligence and troubleshooting: If the system identifies a trend which is not followed by the market, our software applies a counter-strategy to solve the situation  on a real-time basis, adapting to the new scenario. During this non-stop analysis, research and optimization process, all new movements and patterns are stored to be used in up-coming  scenarios, helping solve future market situations. Hence, it learns from its own mistakes and successfully solves the problems posed. Ready to join the AI trading revolution? Ready to join the AI trading revolution? Get E-Book And Performance Data: Get E-Book And Performance Data: https://8topuz.com/8topuz-ai-algorithm-performance/ https://8topuz.com/8topuz-ai-algorithm-performance/ #think8topuz

Can You Consistently Beat the FX Markets using AI Trading Software, Without Actually Trading?

Open-Source Web UI for Apache Kafka As a messaging platform, Kafka needs no introduction. Since its inception, it has virtually rewritten the book on event streaming and has catalyzed the adoption of the now household design patterns — microservices, event-sourcing, and CQRS. Being such a godsend, it gets away with its notorious lack of tooling. You’d be hard-pressed to find a developer who hasn’t at one time looked at the built-in CLI tools, cupped their face and uttered: almost “Is this it? Are you kidding me?” What’s Out There With the popularity of Kafka, it’s no surprise that several commercial vendors have jumped on the opportunity to monetize Kafka’s apparent lack of tooling by offering their own. , and are some examples, but they’re all for personal use only unless you’re willing to pay. (And it’s not to say that you shouldn’t, , but that’s rather beside the point.) Kafka Tool Landoop, KaDeck au contraire Any non-trivial use in a commercial setting would be a violation of their licensing terms. It’s one thing using them at home for tutorials or personal projects; when you’re using a commercial tool without the appropriate license, you are putting your employer at risk of litigation and playing Russian Roulette with your career. But what about ? open-source When it comes to Kafka topic viewers and web UIs, the go-to tool is . With 800K Docker pulls at the time of writing, and growing at a rate of 10K pulls/day, there aren’t many Kafka tools that have enjoyed this level of adoption. And there’s a reason behind that: Kafdrop does an amazing job of filling the apparent gaps in the observability tooling of Kafka, solving problems that the community has been pointing out for too long. open-source Kafdrop Kafdrop is an Apache 2.0 licensed project, like Apache Kafka itself. So it’s won’t cost you a penny. If you haven’t used it yet, you probably ought to. So let’s take a deeper look. What Can It Do? — topic and partition assignments, and controller status View Kafka brokers — partition count, replication status, and custom configuration View topics — JSON, plain text and Avro encoding Browse messages — per-partition parked offsets, combined and per-partition lag View consumer groups Create new topics View ACLs Getting Started The Kafdrop web UI project is hosted on GitHub at . obsidiandynamics/kafdrop There’s a couple of options at your disposal. You could show a little bravery by and building from source. It’s a Java (JDK 11) Spring Boot project, and you can build it with a single Maven command, providing you have the JDK installed. cloning the repository If you want to go down this path, the repo’s file will guide you through the steps. For now, let's take the easy way — Docker. (I sure would.) README.md Launching Kafdrop Docker images are . Images are tagged with the . The tag points to the latest stable release. hosted on DockerHub Kafdrop release number latest To launch the container in the foreground, run the following command: docker run -it --rm -p 9000:9000 \
 -e KAFKA_BROKERCONNECT=<host:port,host:port> \
 obsidiandynamics/kafdrop The environment variable must be set to the bootstrap list of brokers. KAFKA_BROKERCONNECT That’s it. We should be up and running. Once it starts, you can launch the Kafka web UI by navigating to . localhost:9000 Note: The above example assumes an authenticated connection over a plaintext TCP socket. If your cluster is configured to use authentication and/or transport-level encryption, consult the README.md for connection options. It's actually surprisingly easy to configure Kafdrop for a SASL/SSL locked-down cluster. Running in a Kafka sandbox Don’t have a Kafka broker running? No worries. Just use the following file to bring up a stack: docker-compose.yaml Kafka + Kafdrop version: "2" services: kafdrop: image: obsidiandynamics/kafdrop restart: "no" ports: - "9000:9000" environment: KAFKA_BROKERCONNECT: "kafka:29092" depends_on: - "kafka" kafka: image: obsidiandynamics/kafka restart: "no" ports: - "2181:2181" - "9092:9092" environment: KAFKA_LISTENERS: "INTERNAL://:29092,EXTERNAL://:9092" KAFKA_ADVERTISED_LISTENERS: "INTERNAL://kafka:29092,EXTERNAL://localhost:9092" KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: "INTERNAL:PLAINTEXT,EXTERNAL:PLAINTEXT" KAFKA_INTER_BROKER_LISTENER_NAME: "INTERNAL" Now launch the stack with docker-compose up. Once started, browse to . localhost:9000 Navigating the Kafka Web UI Browsing the Cluster The screen is the landing page of the web UI. Cluster Overview You get to see the overall layout of the cluster — the individual brokers that make it up, their addresses and some key broker stats — whether they are a controller and the number of partitions each broker owns. The latter is quite important — as your cluster size and the number of topics (and therefore partitions) grows, you generally want to see an approximately level distribution of partitions across the cluster. Next is the Topics List, which in most cases is what you’re really here for. Any reasonably-sized microservices-based ecosystem might have hundreds, if not thousands of topics. As you’d expect, the list is searchable. The stats displayed alongside each topic are fairly ho-hum. The one worth noting is the column. under-replicated Essentially, it’s telling us the number of partition replicas that have fallen behind the primary. Zero is a good figure. Anything else is indicative of either a broker or a network issue that requires immediate attention. Note: Kafdrop is a discovery exploration tool; it is not a real-time monitoring tool. You should instrument your brokers and raise alerts when things go awry. Listing Topics Click on a topic on the list to get to the screen. Topic Overview The screen is subdivided into four sections. On the top-left, there is a summary of the topic stats — a handy view, not dissimilar to what you would have seen in the cluster overview. On the top-right, you can view the custom configuration. In the example above, the topic runs a stock-standard config, so there’s nothing to see. Had the configuration been overridden, you’d see a set of custom values like in the example below. The bottom-left section enumerates over the partitions. The partition indexes are links — clicking through will reveal the first 100 messages in the topic. There are several interesting parameters displayed in this section: : The zero-based index of the partition within its encompassing topic. Partition ID : The offset of the earliest message in the partition. If the partition is empty, the first offset is the same as the high-water mark. First offset : The high-watermark of the partition, is the offset that will be assigned to the next published message. Last offset : The number of messages in the partition. Size : The ID of the broker node that is presently acting as the leader. Leader Node : The IDs of all broker nodes that hold a replica of the partition. This includes the leader ID. Replica Nodes : The IDs of replica nodes that are in-sync, inclusive of the leader node. In-sync Replica Nodes : The IDs of replica nodes that are currently offline. Under healthy conditions, this should be an empty set. Offline Replica Nodes : Whether the current leader node happens to be the preferred one. Preferred Leader : Whether the partition is under-replicated, i.e. there is at least one replica that is not in sync with the primary. Under-replicated The consumers' section on the bottom-right lists the consumer group names as well as their aggregate lag (the sum of all individual partition lags). Viewing Consumer Groups Clicking on the consumer group on the Topic Overview gets you into the . This screen provides a comprehensive breakdown of a single consumer group. Consumer View The view is sectioned by topic. For each topic, a separate table lists the underlying partitions. Against each partition, we see the committed offset, which we can compare against the first and last offsets to see how our consumer is tracking. Conveniently, Kafdrop displays the computed lag for each partition, which is aggregated at the footer of each topic table. Note: Some amount of lag is unavoidable. For every message published, there will invariably be a quantum of time between the point of publishing and the point of consumption. In Kafka, this period is usually in the order of tens or hundreds of milliseconds, depending on both the producer and consumer client options, network configuration, broker I/O capabilities, the size of the pagecache and a myriad of other factors. growing lag What you need to look out for is — suggesting that the consumer is either unable to keep up or has stalled altogether. In the latter case, you’ll also notice that the lag isn’t being depleted even when the producer is idling. This is when you’ll need to ALT-TAB away from Kafdrop into your favourite debugger. Viewing Messages The screen is the coveted topic viewer that has in all likelihood brought you here. You can get to the message view in one of two ways: Message View Click the button on the Topic Overview screen. View Messages Click the individual partition link in the Topic Overview. It’s exactly what you’d expect — a chronologically-ordered list of messages (or , in Kafka parlance) for a chosen partition. records Each entry conveniently displays the offset, the record key (if one is set), the timestamp of publication, and any headers that may have been appended by the producer. There’s another little trick up Kafdrop’s sleeve. If the message happens to be a valid JSON document, the topic viewer can nicely format it. Click on the green arrow on the left of the message to expand it. In Conclusion The more you use Kafka, the more you come to discover and appreciate its true potential — not just as a versatile event streaming platform, but as a general-purpose messaging middleware that lets you assemble complex business systems from asynchronous, loosely coupled services. You’ll invariably experience the frustrations that are to be expected from a technology that has only recently entered the mainstream, by comparison with the more mature MQ brokers of yore. What’s reassuring is that the Open Source community hasn’t stood still, producing an evolving ecosystem, complete with the documentation and tooling necessary for us to get on with our jobs. The least we can do in return is raise a pull request once in a while or maybe answer a StackOverflow question or three. Was this article useful to you? I’d love to hear your feedback, so don’t hold back! If you are interested in Kafka or event streaming, or just have any questions, follow me on Twitter .

Kafdrop

Introduction to Event Streaming with Kafka and Kafdrop

Kafka itself comes with command line tools that can do all the administration tasks, but those tools aren’t very convenient because they are not integrated into one tool and you need to run a different tool for different tasks. Moreover, it is getting difficult to work with them when your clusters grow large or when you have several clusters. So today we will cover some GUI alternatives. Kafka Tool First is . It is a windows program that can connect to a Kafka cluster and do all basic tasks. It can list brokers, topics or consumers and their properties. It allows you to create new topics or update existing ones and you can even look at the messages in a topic or partition. Although it is very useful, its UI seems somewhat old and it lacks some monitoring features like topic lag. Also, it is . Kafka Tool not free for commercial use So you can’t really use it at work unless you pay for it. (Technically you can, but that would violate the licensing terms and put you and your employer at risk.) Kafka Manager is a web based management system for Kafka developed at Yahoo. It is capable of administrating multiple clusters, it can show statistics on individual brokers or topics such as messages per second, lag and so on. But it’s more of an administrative tool — unfortunately you can’t use it to browse messages. It also requires access to the ZooKeeper nodes, so you might not be able to use it in some production environments, where ZooKeeper nodes are typically firewalled. Kafka Manager Here are the steps to install it: First, download the zip distribution from the link above and unzip it. Edit and change to one or more of your ZooKeeper hosts, for example application.conf kafka-manager.zkhosts kafka-manager. = . zkhosts "cloudera2:2181" Now you should build Kafka Manager. It uses play framework but it is installed and configured automatically (unlike Kafka web console that is discussed later). In the directory where you unzipped it, run: ./sbt clean dist This can take long time to complete (about 30 minutes on the first build, as it has to download a bunch of dependencies). This will create a distribution file ./ /universal/kafka-manager- . . . .zip target x x x x Replace x.x.x.x with the version number. Unzip it to a directory of your choice. After it’s done starting, you can access it with you browser at port 9000 (this is the default, but you can change it by adding startup parameter). -Dhttp.port= Kafdrop is a web UI for viewing Kafka topics and browsing consumer groups. The tool displays information such as brokers, topics, partitions, consumers, and lets you view messages. Kafdrop seems modern and very impressive and its capabilities are very similar to those of Kafka Manager, but with more focus on the letting you view the contents of the brokers. Its features include: Kafdrop — topic and partition assignments, and controller status View Kafka brokers — partition count, replication status, and custom configuration View topics — JSON, plain text and Avro encoding Browse messages — per-partition parked offsets, combined and per-partition lag View consumer groups Create new topics View ACLs To get started, just use Docker: docker run -it -p 9000:9000 \
 -e KAFKA_BROKERCONNECT=<host:port,host:port> \
 obsidiandynamics/kafdrop When it starts, open your browser to : localhost:9000 Burrow Another tool worth mentioning is from LinkedIn. We will not cover it in detail this time because it does not fall into the same category as the previous tools mentioned here. It does not have a graphical user interface and it does not have any cluster management capabilities. Burrow It’s strength is in its ability to monitor consumer groups for lags and general health in a simple way. It can also automatically notify an administrator by email or http about any problems. I covered it in more detail in this post: . Monitoring Kafka consumer lag with Burrow About the author Guy Shilo is a Data engineer from Israel. Guy’s been dealing with data and IT for many years as a DBA, mostly on Oracle platforms. In the last few years, Guy had witnessed the rise of new, distributed data storage and processing platforms such as Hadoop and Kafka with all its sub-projects, and a plethora of specialized NoSQL databases, search engines and distributed computing frameworks.

Kafka Administration and Monitoring UI Tools

How to Improve the Parallelization of Torch Dataloaders Using Torch.multiprocessing

কিভাবে Torch.multiprocessing ব্যবহার করে টর্চ ডেটালোডারের সমান্তরালকরণ উন্নত করা যায়

So verbessern Sie die Parallelisierung von Torch-Dataloadern mit Torch.multiprocessing

Cómo mejorar la paralelización de los cargadores de datos de Torch mediante el multiprocesamiento de Torch.

Comment améliorer la parallélisation des chargeurs de données Torch à l'aide de Torch.multiprocessing

Torch.multiprocessing का उपयोग करके Torch डेटालोडर्स के समानांतरीकरण में सुधार कैसे करें

Torch.multiprocessing を使用して Torch データローダーの並列化を改善する方法

Torch.multiprocessing을 사용하여 Torch 데이터 로더의 병렬화를 개선하는 방법

Como melhorar a paralelização de dataloaders Torch usando Torch.multiprocessing

Как улучшить распараллеливание загрузчиков данных Torch с помощью Torch.multiprocessing

Torch.multiprocessing Kullanılarak Torch Veri Yükleyicilerinin Paralelleştirilmesi Nasıl Geliştirilir?

Cách cải thiện tính năng song song hóa của bộ tải dữ liệu Torch bằng Torch.multiprocessing

如何使用 Torch.multiprocessing 提高 Torch 数据加载器的并行化

Load Balancing Strategies for Applications: From Infrastructure to Code

: Introduction Zero-knowledge proofs enable private, secure transactions. With zkSNARKs and zkSTARKs, a prover can prove possession of some information to a verifier without revealing the actual data. This has huge potential for anonymity and confidentiality. But zkSTARKs and zkSNARKs are complex. Risc0 makes them more accessible. Let’s see it’s very basic implementation: This tutorial will cover: Installation of Risc0 Writing your first zero-knowledge proof program using rust Prerequisites: Copy pasting shortcuts Installations required: Rust language Cargo crate Some basic coding knowledge in rust Code Editor (VSCODE if possible) By the end, you'll have practical experience with zero-knowledge proofs in Risc0. No advanced math or cryptography background required. We'll focus on the coding essentials to start building real-world solutions. This hands-on introduction aims to make powerful privacy tech understandable for any developer. : Installation (For MacOS) To install you can run the below command in the terminal: rust and cargo, curl [https://sh.rustup.rs](https://sh.rustup.rs/) -sSf | sh To install Risc0, you run the below command after installation of rust and restart the terminal: cargo install cargo-risczero For the above commands to build successfully, you will need to have installed the required dependencies. brew install openssl brew install pkgconf Next, we'll need to install the toolchain with: risc0 cargo risczero install That’s all we need. So, let’s head to the code editor. Writing Some Magical Lines: Now that we are done with the installation you can read and follow along these abra kadabra 🪄 steps: Open the code editor and head to the location where you want to create your project in the editor’s terminal. Create a new instance of the project using the below command in the terminal: . cargo risczero new multiply You should be seeing a folder created called multiply. cd into it. cd multiply The folder structure is very simple. We have a host folder and a methods folder. The host folder contains the host program which we call the guest program. It also has the ability to verify if you want. The methods folder contains the guest program which contains the portion of the zkvm application that gets proven. It receives the input params from the host, then based on the logic, it generates results, commits them to the journal, and sends them to the host as a receipt. The rest of the files will be explained on the go as required. Let's start with the guest program. Let’s change the name of the files . main.rs —> multiply.rs Create a folder named in folder and move to it. Your folder structure should look something like this: bin src mutiply.rs Open and change the update to the . Cargo.toml name = "method_name” —> name = "multiply” Add the below code in . Cargo.toml [[bin]]
name = "multiply"
path = "src/bin/multiply.rs" So your final will look like this: Cargo.toml Now, open . Here we will edit the main function. This is the function that will be executed in zkvm. multiply.rs In the below code, we are taking the input from the host program. Then we are making sure that the input is not trivial factors that is 1. Then we calculate the product and finally commit it back to the host of the program. // We will get the values for these variables from host program
 let a:u64 = env::read();
 let b:u64 = env::read();

 // To avoid trivial factors like multiplication by 1
 if a == 1 || b == 1 {
 panic!("Trivial factors !!") // The panic! macro in Rust is used to intentionally crash a program when an unrecoverable error occurs
 }

 // Caculate the product of the two numbers
 let product = a.checked_mul(b).expect("Integer Overflow");

 // Commit back the output to the host to save it as receipt
 env::commit(&product); After the above changes, your should look like this. multiply.rs There is one more final change in the of folder. Cargo.toml methods Open and update the value of . name = "multiply-methods” Your final will look like below. Cargo.toml Our work here is done. Now, let’s go to the host program. Your host folder must be looking like this now. We want to split the into two files that are and . main.rs prover.rs verify.rs Create a new folder under and name it as . src bin Remove . Create files and name them as & . main.rs verify.rs prove.rs Your folder structure should look something like this now. Open , and let’s start coding: prove.rs Add the below code. These are the imports that we will require. use multiply_methods::MULTIPLY_ELF; // It is a binary file of multiply_method
use risc0_zkvm::{
 default_prover,
 serde::{from_slice, to_vec},
 ExecutorEnv,
 }; Let’s make changes to the main function. fn main() {

		// Declaring our secret input params
 let a: u64 = 17;
 let b: u64 = 23;

 // First, we construct an executor environment
 let env = ExecutorEnv::builder()
 .add_input(&to_vec(&a).unwrap()) // Passing the input params to environment so it can be used by gues proggram
 .add_input(&to_vec(&b).unwrap())
 .build()
 .unwrap();

 // Obtain the default prover.
 let prover = default_prover();

 // Produce a receipt by proving the specified ELF binary.
 let receipt = prover.prove_elf(env, MULTIPLY_ELF).unwrap();

 // Extract journal of receipt (i.e. output c, where c = a * b)
 let c: u64 = from_slice(&receipt.journal).unwrap();

 // Print an assertion
 println!("Hello, world! I know the factors of {}, and I can prove it!", c);

		// Let's serialize the receipt so we can save it to an file for verifier program to verify.
 let serialized = bincode::serialize(&receipt).unwrap();

		// Writing the serialized contect to receipt.bin file
 let _saved_file = match std::fs::write("./receipt.bin", serialized){
 Ok(()) => println!("Receipt saved and serialized as receipt.bin"),
 Err(_) => println!("Something went wrong !!"),
 };

} Your final should look like this. prove.rs Let’s open and add code to our . verify.rs Here, we will import the guest program image ID and some basic imports. use multiply_methods::MULTIPLY_ID;
use risc0_zkvm::Receipt; Let’s make changes to the main function. fn main(){
 // Let's impor the receipt that was generated by prove
 let receipt_path ="./receipt.bin".to_string();
 let receipt_file = std::fs::read(receipt_path).unwrap();
 
 // As we has serialized the receipt we need to desrialize it
 let receipt = bincode::deserialize::<Receipt>(&receipt_file).unwrap();
 
	// Let's verify if the receipt that was generated was not created tampered with
 let _verification = match receipt.verify(MULTIPLY_ID){
 Ok(()) => println!("Proof is Valid"),
 Err(_) => println!("Something went wrong !!"),
 };
} Your final should look something like this. verify.rs I promise these are the final changes. Now, we are almost done. Open the in folder, and make the below changes under dependencies. Cargo.toml host multiply-methods = { path = "../methods" } Your Cargo.toml will look like this. It’s finally time to see if our code works. Fire up your console in the root directory of the project, and run the below command in console cargo run --release --bin prove This command will generate the proof and receipt for the verifier. When running first time it will take a lot of time. So don’t worry grab a coffee until it completes. Once this is done, if you want, you can verify the receipt that you have generated. For that, run this command cargo run --release --bin verify When running first time it will take a lot of time. So don’t worry grab a coffee until it completes. Congratulations !!! on writing your first ZK app using Risc0. If you need any help, please drop me a comment, and I will get back to you.

Creating Your First ZK Project Using Risc0: A Guide for Beginners

Risc0 ব্যবহার করে আপনার প্রথম ZK প্রকল্প তৈরি করা: নতুনদের জন্য একটি গাইড

Erstellen Sie Ihr erstes ZK-Projekt mit Risc0: Ein Leitfaden für Anfänger

Creando su primer proyecto ZK usando Risc0: una guía para principiantes

Création de votre premier projet ZK à l'aide de Risc0 : un guide pour les débutants

Risc0 का उपयोग करके अपना पहला ZK प्रोजेक्ट बनाना: शुरुआती लोगों के लिए एक गाइड

Risc0 を使用して最初の ZK プロジェクトを作成する: 初心者向けガイド

Risc0을 사용하여 첫 번째 ZK 프로젝트 만들기: 초보자를 위한 가이드

Criando seu primeiro projeto ZK usando Risc0: um guia para iniciantes

Создание вашего первого проекта ZK с использованием Risc0: руководство для начинающих

Risc0 Kullanarak İlk ZK Projenizi Oluşturma: Yeni Başlayanlar İçin Bir Kılavuz

Tạo dự án ZK đầu tiên của bạn bằng Risc0: Hướng dẫn cho người mới bắt đầu

使用 Risc0 创建您的第一个 ZK 项目：初学者指南

In a recent project, I was working on, I had to create a data pipeline that reads flight data from a (Opensky Network API) and sends the data continuously to an S3 bucket. To accomplish this, I decided to use Kafka, a powerful and scalable streaming platform. This choice not only helped me achieve my project goals but also provided an excellent opportunity to learn Kafka and enhance my skill set. REST API Streaming from an API By default, the API did not offer any or streaming services through web sockets, so I had to find a way to get the data into Kafka and simulate a streaming behavior. Polling the API was the only option available, the idea is to send requests repeatedly to the API given a certain frequency and then check if the data has been changed or not. webhooks Two options existed to implement API polling and send the results to a Kafka topic. The first option was to incorporate the polling logic directly into the Kafka producer application. However, this approach lacked scalability, and fault tolerance, and would require significant coding effort. The second approach involved utilizing Kafka Connect, which offered scalability, and fault tolerance, and minimized the amount of code we needed to write. Introducing Kafka Connect Kafka Connect is a library that provides integration with different data sources and sinks, it runs different workers in a distributed fashion as a cluster or in a standalone mode for testing and small applications. Each worker can run different tasks defined by connectors, these connectors define how to get the data from a given source or how to send it to a destination (sink connector), it also defines a set of transformations and converters. We will not talk about how connectors work in detail as this is out of the scope of this article, and Confluent has excellent and in-depth tutorials on Kafka Connect. Multiple connectors for common data sources and destinations have already been developed and shared with other developers for their use, You can find a ready-to-use connector in Confluent Hub or on GitHub. In this tutorial, we will use a REST API connector developed by Lenny Löfberg and available on GitHub, This connector can poll data from a REST API and send the results to a Kafka topic. We will look at how to install and configure this connector to stream data about flights over a given region using the public and free OpenSky Network API. This tutorial assumes that you have already installed Java 11 and the latest version of Kafka (3.5.0 at the time of writing this). Installing the REST Connector Before using Kafka Connect or installing a connector, let’s create a “connectors” folder inside the Kafka directory that will hold all the connector binaries and dependencies as well as other connectors that you might use in the future, we also need to create another folder inside the config folder of Kafka that will contain the connector configuration file and the worker configuration. Your Kafka folder structure should resemble the following : kafka_3.5.0/
 - bin/
 - connectors/
 - config/
      - ...
      - connectors/
 - libs/
 - licences
 - logs/
   ... Once the structure is in place, we can proceed to install the REST Kafka Connector, navigate to the connectors folder ( ) and clone the : kafka/connectors/ GitHub repository git clone https://github.com/llofberg/kafka-connect-rest.git You should have the folder with all the code and dependencies required to install the connector. To use a Kafka Connector we have to build the main JARs and the other dependencies (as JAR files also) and link them to our Kafka Connect cluster using a configuration file. kafka-connect-rest Let’s start with building the connector using Maven, to do so run the following commands: mvn clean install
mkdir jars
cp kafka-connect-rest-plugin/target/kafka-connect-rest-plugin-*-shaded.jar jars/ 
cp kafka-connect-transform-from-json/kafka-connect-transform-from-json-plugin/target/kafka-connect-transform-from-json-plugin-*-shaded.jar jars/
cp kafka-connect-transform-add-headers/target/kafka-connect-transform-add-headers-*-shaded.jar jars/
cp kafka-connect-transform-velocity-eval/target/kafka-connect-transform-velocity-eval-*-shaded.jar jars/ These commands will build the whole project using maven based on the file which generates the JARs for each component, then it  creates a folder in the connector directory and copies all the built JAR files into that folder. From now on, we only need to point the Kafka Connect worker to these JAR files. pom.xml jars Configuring the Connector Now that the connector is installed, we will have to configure the worker instance and the connector itself. Let’s start with configuring the worker through the configuration file in the folder. worker.properties config/connectors bootstrap.servers=localhost:9092
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=true
value.converter.schemas.enable=true
offset.storage.file.filename=/tmp/connect.offsets
offset.flush.interval.ms=10000
plugin.path=$KAFKA_HOME/connectors/
max.request.size=11085880 You will need to change these values based on your own Kafka environment, more precisely, you will need to edit the bootstrap server address and the option with the absolute path to the connector folder (replace ). The option is used by the worker to look for the connector JARs, it expects a directory of directories. plugin.path $KAFKA_HOME plugin.path Another extra step you might need is to export the connector JAR files to the CLASSPATH of your Java runtime so they can be found and used by Kafka Connect worker : sudo nano /etc/environment And then edit the CLASSPATH variable in the opened file : CLASSPATH=".:/usr/local/bin/kafka/connectors/kafka-connect-rest/jars"
export CLASSPATH Make sure to modify it with the right path depending on your installation. We are almost done with configuring the Kafka Connector, now we only need to edit the connector properties configuration file, to do so, create a configuration file in the folder. In this file, put the following configuration : rest-source-connector.properties config/connector name=rest-source
key.converter = org.apache.kafka.connect.storage.StringConverter
value.converter = org.apache.kafka.connect.storage.StringConverter
connector.class = com.tm.kafka.connect.rest.RestSourceConnector
tasks.max = 1
rest.source.poll.interval.ms = 10000
rest.source.method = GET
rest.source.url = https://opensky-network.org/api/states/all
rest.source.headers = Content-Type:application/json,Accept:application/json
rest.source.topic.selector = com.tm.kafka.connect.rest.selector.SimpleTopicSelector
rest.source.destination.topics = flights You will need to change the following options based on your needs : : The polling frequency in milliseconds, in this example, a request will be sent each 10 seconds (10k ms). rest.source.poll.interval.ms : The HTTP method used to query the API. rest.source.method : The endpoint you wish to call, make sure to include all the parameters in case of a GET request. rest.source.url The topic where records will be sent to. rest.source.destination.topics You are also free to change the headers and the number of required tasks in case you are running Kafka Connect with the distributed mode. Running the Connector Before running the connector, make sure that Zookeeper server and Kafka server are running, then run Kafka Connect in standalone mode using the command available in Kafka : connect-standalone.sh ./bin/connect-standalone.sh config/connectors/worker.properties config/connectors/rest-source.properties The command takes as parameters the worker configuration file and the different connector configurations, once this is running, it will poll the API endpoint with a given frequency and will send the records to the specified Kafka topic. Conclusion In this tutorial we have seen how to stream data from an API into a Kafka topic which is quite common, you can learn more here about how you can use and how to to set up a cloud-based data streaming pipeline. Kafka Connect is a very powerful library and offers both scalability and fault tolerance for your different data integrations. Kafka Connect with Docker install Kafka on EC2

How to Stream From a REST API Using Kafka Connect

WorkManager is ubiquitous in modern android development. We use it to run any background work which should not be bound to the application process lifecycle. It was first introduced in May 2018 and replaced several scheduling frameworks. The library unifies it all with a concise and simple API. You no longer have to orchestrate JobScheduler, GcmNetworkManager, Evernote AndroidJob, and so on. In fact, this new API is saving us quite a lot of maintenance work, but is it perfect? There are a few rough areas we’ll explore in this article. Dependency injection It’s hard to imagine a modern production-grade android application without dependency injection. In our example, we’ll use Dagger2, but the principle will remain the same for any DI framework. Consider that you have a job that runs every hour. Its purpose is to delete old telemetry records from an SQLite database. The naive approach would be to create a component inside the worker and call : inject class CleanTelemetryWorker(context: Context, params: WorkerParameters) : Worker(context, params) {

 @Inject
 lateinit var telemetryRepository: TelemetryRepostory

 init {
 CleanTelemetryWorkerComponent.Factory.create(context).inject()
 }

 override fun doWork(): ListenableWorker.Result {
 telemetryRepository.clean()
 }
} This solution is not ideal for a few reasons: The dependency inversion principle breaks since the component should not know how to get its dependencies This worker is hard to test because there’s no easy way to mock dependencies, but we’ll get to it later What we want is to be able to declare any worker like this: class CleanTelemetryWorker @Inject constructor(
 context: Context, 
 params: WorkerParams,
 telemetryRepository: TelemetryRepository,
 ...//any other external dependencies
) Can we do better? We can! As you may know, we can customize WorkManager using the class: Configuration class MyApplication() : Application(), Configuration.Provider {
 override fun getWorkManagerConfiguration() =
 Configuration.Builder()
 .setMinimumLoggingLevel(android.util.Log.INFO)
 .setWorkerFactory(myCustomWorkerFactory)
 .build()
} You override in your class and provide a config object. The most important part here is the method. It allows us to pass a component that will create jobs for us. It sounds like the ideal place to inject our dependencies into workers. getWorkManagerConfiguration Application setWorkerFactory has only one method we can override, and it’s called… : WorkerFactory createWorker class CustomWorkerProvider {
 override createWorker(appContext: Context, 
 workerClassName: String, 
 workerParameters: WorkerParameters): ListenableWorker? {
 //We need to provide workers here. 
 } 
} Now we can provide arguments into our worker’s constructor. Fortunately, we don't have to do it ourselves. There exists this handy tool to generate dependency provision for us called Dagger 2. Let’s try to create a simple and robust schema to inject dependencies into workers. Of course there’s plenty of ways to do this, but let’s start simple and then add some options to it as we go. So what exactly do we need to create a worker? The class has two required dependencies: and . Let’s start with the dagger component that we will init in the function. ListenableWorker Context WorkerParameters WorkerComponent createWorker @Component
interface WorkerComponent {
 @Component.Factory
 interface Factory {
 fun create(
 @BindsInstance context: Context,
 @BindsInstance workerParameters: WorkerParameters,
 ): WorkerComponent
 }
} We use annotation to tell dagger to create an implementation of which will return us the implementation. Component.Factory WorkerComponent.Factory WorkerComponent annotation in the factory’s method means that this component should be able to provision and using the objects we passed to the method. @BindsInstance create Context WorkParameters Cool, we can now provide workers by adding a method that returns a concrete worker class, like CleanTelemetryWorker @Component(dependencies=[ApplicationComponent::class])
interface WorkerComponent {

 @Component.Factory
 interface Factory {
 fun create(
 applicationComponent: ApplicationComponent,
 @BindsInstance context: Context,
 @BindsInstance workerParameters: WorkerParameters,
 ): WorkerComponent
 }

 fun cleanTelemetryWorker(): CleanTelemetryWorker
...
} Notice that we need to add dependencies to the component’s annotation because by itself can’t provide you with anything other than and . We also have to pass the dependency component as a factory argument. WorkerComponent Context WorkerParameters Let’s see how the will look like now: CustomWorkerProvider class CustomWorkerProvider(private val applicationComponent: ApplicationComponent) {
 override createWorker(appContext: Context, 
 workerClassName: String, 
 workerParameters: WorkerParameters): ListenableWorker? {
 val component = DaggerWorkerComponent.factory().create(applicationComponent, appContext, workerParameters)
 when(workeClassName) {
 ClientTelemetryWorker::class.qualifiedName -> component.cleanTelemetryWorker()
 ...
 }
 } 
} It looks much better, but every time we add a new worker, we have to provide it here and add a new function definition to the component. Can dagger2 help us with this? Dagger Multibinding! With dagger multi binding, we can declare a module for each worker which will provide a worker provider in a map, where keys are the java classes of the workers. @Module
interface CleanTelemetryWorkerModule {
 @Binds
 @IntoMap
 @ClassKey(CleanTelemetryWorker::class.java)
 fun bindCleanTelemetryWorker(cleanTelemetryWorkerProvider: Provider<CleanTelemetryWorker>): Provider<out ListenableWorker>
} Let’s add this module to the component: @Component(dependencies=[ApplicationComponent::class], 
 modules=[CleanTelemetryWorkerModule::class],)
interface WorkerComponent {
 ...
} And finally, our will look like this: CustomWorkerProvider class CustomWorkerProvider(private val applicationComponent: ApplicationComponent) {
 override createWorker(appContext: Context, 
 workerClassName: String, 
 workerParameters: WorkerParameters): ListenableWorker? {
 val component = DaggerWorkerComponent.factory().create(applicationComponent, appContext, workerParameters)
 val workerProvidersMap = component.workerProvidersMap()
 return workerProvidersMap[Class.forName(workerClassName)]!!.get()
 } 
} Every time you have to add a new job, you’ll only have to create a new module and attach it to the . Neat, right? WorkerComponent Why does it matter, after all? The jobs now look like any other class with externally declared dependencies. It allows us to enforce the dependency inversion principle of SOLID.
With the relatively small overhead, we achieve the following benefits: Testability We can mock job dependencies or pass different implementations at any time. Jobs are not aware of the dependency inversion framework. If you choose to switch from Dagger 2 to any other DI framework, your jobs will remain the same. The only thing that will change is the external glue. Ease of support Reducing mental stress on the developers is also a thing. Previously every time you created a job, you had to think about how to get your dependencies there. It’s not always trivial, especially if your dependency graph has different scopes. In the following article, we’ll reap the benefits of this setup and explore how to properly test your workers now that we have an easy way to mock the dependencies. I hope this article was helpful! Let me know what you think :)

How to Cook WorkManager with Dagger 2

I recently had an interviewer ask me about multithreading in NodeJS. I claimed to be proficient in NodeJS but the interviewer, as well as myself, quickly recognized there were a few fundamentals concepts I had not taken the time to research. After the interview I decided to read up on the NodeJS event loop and multithreading. A Node.js application runs on a single thread. In this thread an event loop listens for events and then triggers the events associated callback function upon detection. By this simple illustration we can already see that Node.js does not support multithreading because each application is run on a single thread. Once again if an interviewer asks if Node.js supports multithreading you can confidently say no. Although multithreading is not supported there is a way to harness the power of a multicore system by using processes. Node.js has a module called designed to support a multiprocessing alternative. Let’s view some code from the Node.js . cluster docs const cluster = require('cluster');const http = require('http');const numCPUs = require('os').cpus().length; if (cluster.isMaster) {// Fork workers.for (var i = 0; i < numCPUs; i++) {cluster.fork();} cluster.on('exit', (worker, code, signal) => {console.log(`worker ${worker.process.pid} died`);});} else {// Workers can share any TCP connection// In this case it is an HTTP serverhttp.createServer((req, res) => {res.writeHead(200);res.end('hello world\n');}).listen(8000);} The cluster module allows you to spawn multiple child processes that can share a common port. In this example the worker processes are listening for an http connection on port 8000. With the child processes in place the system can handle greater loads. Hooray! We’ve managed to successfully implement a multicore Node.js application however with every design decision there are trade-offs to evaluate. In this case our trade-offs are associated with the concepts of multithreading and multiprocessing. Threads share the same memory space and creating new threads do not take up much of the system resources. Processes run in separate memory allocations with a complete copy of the program increasing the memory overhead of the application. In situations where memory might be scarce, Node.js might be the least effective tool for application code. If I explained these concepts to my interviewer I would have surely impressed him. No harm done though. In the end the experience gave me an opportunity to learn something new. April 12, 2016 👋🏽Hi! I’m Steven Natera. Follow me on Twitter . I’m actively involved in the , specifically . I like writing about code, startups, React, and Kubernetes. @StevenNatera open source community GatsbyJS

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