#Apache Thrift

[market-spy]

The Ins and Outs of the Used Construction Equipment Market: A Witty Dive

Welcome to the wild world of used construction equipment, where bulldozers have more stories to tell than a seasoned detective and excavators have seen more dirt than your garden shovel. In this blog, we're going to take a jaunty stroll through the bustling market of pre-loved machinery, exploring its quirks, challenges, and triumphs. So, buckle up and let's dig in!

The Scoop on the Market:

Picture this: a dynamic sector where giant machines find new homes faster than you can say "Jackhammer." With a market size valued at a whopping USD 115.32 billion in 2022, the used construction equipment market isn't just big; it's colossal. And guess what? It's only getting bigger, poised to reach a staggering USD 191.55 billion by 2031. Talk about growth spurts!

What Drives the Madness:

Why the craze, you ask? Well, imagine scoring a perfectly functional crane at a fraction of the cost. It's like finding a vintage treasure in a thrift store, but with more steel and hydraulic fluid. Plus, with emerging economies clamoring for shiny new infrastructure, the demand for used equipment is skyrocketing faster than a faulty elevator.

Regional Rumbles:

North America leads the charge, waving its flag of developed infrastructure and construction galore. But don't count Europe out; it's hot on America's heels with its well-oiled building sector. And let's not forget the underdog, Asia Pacific, rising like a phoenix with its urbanization frenzy and tech-savvy construction wizards.

The Dance of Equipment Types:

Excavators and loaders take center stage, stealing the limelight with their versatility and charm. But hold onto your hard hats; cranes are waltzing their way to the top as the fastest-growing segment. It's a battle of the titans, and everyone's invited to the spectacle.

The Game of Applications:

From infrastructure development to residential and commercial construction, there's a piece of the pie for everyone. Earthmoving equipment reigns supreme, digging its claws into every construction project like an eager puppy in a pile of dirt. And with technology playing cupid, the marriage between construction and innovation is a match made in heaven.

For More Information:

https://www.skyquestt.com/report/used-construction-equipment-market

The Nitty-Gritty of Regional Insights:

North America flaunts its construction prowess, Europe brings its A-game with a dash of environmental consciousness, and Asia Pacific is the new kid on the block, ready to shake things up. It's a global dance-off, and every region's got its own moves.

The Heroes and Villains:

In this epic saga, our heroes Caterpillar, Komatsu, and Volvo Construction Equipment fight tooth and nail for market supremacy. But watch out for the villainous depreciation and maintenance costs lurking in the shadows, ready to spoil the party.

The Rising Stars:

Rental and used equipment are the rising stars of the show, offering budget-friendly alternatives in a world of shiny new toys. And with technological advancements turning used machinery into high-tech marvels, it's a win-win for buyers and sellers alike.

Conclusion:

So there you have it, folks, a whirlwind tour of the used construction equipment market, where every machine has a story to tell and every deal is a dance of supply and demand. Whether you're a seasoned veteran or a wide-eyed rookie, there's never a dull moment in this bustling marketplace. So, grab your hard hat, roll up your sleeves, and join the fray. After all, in the world of construction, the only way to go is up!

About Us-

SkyQuest Technology Group is a Global Market Intelligence, Innovation Management & Commercialization organization that connects innovation to new markets, networks & collaborators for achieving Sustainable Development Goals.

Contact Us-

SkyQuest Technology Consulting Pvt. Ltd.

1 Apache Way,

Westford,

Massachusetts 01886

USA (+1) 617–230–0741

Email- info@skyquestt.com

Website: https://www.skyquestt.com

[craigbrownphd-blog-blog]

If you did not already know

Apache Thrift The Apache Thrift software framework, for scalable cross-language services development, combines a software stack with a code generation engine to build services that work efficiently and seamlessly between C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, JavaScript, Node.js, Smalltalk, OCaml and Delphi and other languages. … IBN-Net Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. Unlike existing works that designed CNN architectures to improve performance on a single task of a single domain and not generalizable, we present IBN-Net, a novel convolutional architecture, which remarkably enhances a CNN’s modeling ability on one domain (e.g. Cityscapes) as well as its generalization capacity on another domain (e.g. GTA5) without finetuning. IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances. This work has three key contributions. (1) By delving into IN and BN, we disclose that IN learns features that are invariant to appearance changes, such as colors, styles, and virtuality/reality, while BN is essential for preserving content related information. (2) IBN-Net can be applied to many advanced deep architectures, such as DenseNet, ResNet, ResNeXt, and SENet, and consistently improve their performance without increasing computational cost. (3) When applying the trained networks to new domains, e.g. from GTA5 to Cityscapes, IBN-Net achieves comparable improvements as domain adaptation methods, even without using data from the target domain. With IBN-Net, we won the 1st place on the WAD 2018 Challenge Drivable Area track, with an mIoU of 86.18%. … UNet++ In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively. … Alpha Model-Agnostic Meta-Learning (Alpha MAML) Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability. We address this shortcoming by introducing an extension to MAML, called Alpha Model-agnostic meta-learning, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates. Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice. … https://analytixon.com/2023/01/27/if-you-did-not-already-know-1950/?utm_source=dlvr.it&utm_medium=tumblr

[craigbrownphd]

If you did not already know

Apache Thrift The Apache Thrift software framework, for scalable cross-language services development, combines a software stack with a code generation engine to build services that work efficiently and seamlessly between C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, JavaScript, Node.js, Smalltalk, OCaml and Delphi and other languages. … IBN-Net Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. Unlike existing works that designed CNN architectures to improve performance on a single task of a single domain and not generalizable, we present IBN-Net, a novel convolutional architecture, which remarkably enhances a CNN’s modeling ability on one domain (e.g. Cityscapes) as well as its generalization capacity on another domain (e.g. GTA5) without finetuning. IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances. This work has three key contributions. (1) By delving into IN and BN, we disclose that IN learns features that are invariant to appearance changes, such as colors, styles, and virtuality/reality, while BN is essential for preserving content related information. (2) IBN-Net can be applied to many advanced deep architectures, such as DenseNet, ResNet, ResNeXt, and SENet, and consistently improve their performance without increasing computational cost. (3) When applying the trained networks to new domains, e.g. from GTA5 to Cityscapes, IBN-Net achieves comparable improvements as domain adaptation methods, even without using data from the target domain. With IBN-Net, we won the 1st place on the WAD 2018 Challenge Drivable Area track, with an mIoU of 86.18%. … UNet++ In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively. … Alpha Model-Agnostic Meta-Learning (Alpha MAML) Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability. We address this shortcoming by introducing an extension to MAML, called Alpha Model-agnostic meta-learning, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates. Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice. … https://analytixon.com/2023/01/27/if-you-did-not-already-know-1950/?utm_source=dlvr.it&utm_medium=tumblr

[commiehook]

Spotted this old Apache in town today while the wife and I were out thrifting. Looks pretty solid. I don't know much about Chevys, but this pickup has to be mid 50's, yes?

[thriftrescue]

buncha interesting books at thrift

[pizza-soup]

A neat thing happened to me today. I was looking around the thrift store and found a huge bag of embroidery thread but a lady's basket was in the way. I said Excuse Me, and she moved, I said Thank You.... In Apache. I’ve been listening to my dad’s mom in the phone and it’s something I picked up. I’ve been wanting to learn more from her tbh.

The older lady in front of me squinted and I was half scared she’d think I said something offensive to her in another language. Her eyes lit up suddenly, smiled and said “ Are you Mescarelo? I'm from Ruidoso!”

“ No, Coyotero. I live in Los Alamos.”

So that whole time in the store we were talking about our families, where they live, who are our grandmothers or aunts, and if we have any links as distant relatives. We don’t, at least none that we know of. I was a bit suspicious of all her questions since a lot of older ladies like her act as matchmakers, and yeah..she wasted no time whipping out her phone to show me her grandsons. Her very single and available grandsons. XD Why are all the grannies like this?

I found out she does quilting and has really beautiful work, that’s something I’ve never tried to do, and I mentioned needle felting and she has heard of it but thinks it looks too complicated. It can be, it depends. I showed her a photo of something I made and she thought it was cute, she suggested I do some southwestern animals. That’s actually a good idea, I've never really seen that done before. We exchanged emails - I don’t have social media -, and I went on my way to another thrift store before going home to start dinner. No idea what I want to make though. I’m lazy. I honestly could go for chili dogs.

[northxnisha-blog]

Service Management in Cloud  Environment

https://www.linkedin.com/post/edit/6351339103073734656

http://tech.rithanya.com/tech/publication-calendar

[computingpostcom]

Application Performance Monitoring (APM) can be defined as the process of discovering, tracing, and performing diagnoses on cloud software applications in production. These tools enable better analysis of network topologies with improved metrics and user experiences. Pinpoint is an open-source Application Performance Management(APM) with trusted millions of users around the world. Pinpoint, inspired by Google Dapper is written in Java, PHP, and Python programming languages. This project was started in July 2012 and later released to the public in January 2015. Since then, it has served as the best solution to analyze the structure as well as the interconnection between components across distributed applications. Features of Pinpoint APM Offers Cloud and server Monitoring. Distributed transaction tracing to trace messages across distributed applications Overview of the application topology – traces transactions between all components to identify potentially problematic issues. Lightweight – has a minimal performance impact on the system. Provides code-level visibility to easily identify points of failure and bottlenecks Software as a Service. Offers the ability to add a new functionality without code modifications by using the bytecode instrumentation technique Automatically detection of the application topology that helps understand the configurations of an application Real-time monitoring – observe active threads in real-time. Horizontal scalability to support large-scale server group Transaction code-level visibility – response patterns and request counts. This guide aims to help you deploy Pinpoint APM (Application Performance Management) in Docker Containers. Pinpoint APM Supported Modules Below is a list of modules supported by Pinpoint APM (Application Performance Management): ActiveMQ, RabbitMQ, Kafka, RocketMQ Arcus, Memcached, Redis(Jedis, Lettuce), CASSANDRA, MongoDB, Hbase, Elasticsearch MySQL, Oracle, MSSQL(jtds), CUBRID, POSTGRESQL, MARIA Apache HTTP Client 3.x/4.x, JDK HttpConnector, GoogleHttpClient, OkHttpClient, NingAsyncHttpClient, Akka-http, Apache CXF JDK 7 and above Apache Tomcat 6/7/8/9, Jetty 8/9, JBoss EAP 6/7, Resin 4, Websphere 6/7/8, Vertx 3.3/3.4/3.5, Weblogic 10/11g/12c, Undertow Spring, Spring Boot (Embedded Tomcat, Jetty, Undertow), Spring asynchronous communication Thrift Client, Thrift Service, DUBBO PROVIDER, DUBBO CONSUMER, GRPC iBATIS, MyBatis log4j, Logback, log4j2 DBCP, DBCP2, HIKARICP, DRUID gson, Jackson, Json Lib, Fastjson Deploy Pinpoint APM (Application Performance Management) in Docker Containers Deploying the PInpoint APM docker container can be achieved using the below steps: Step 1 – Install Docker and Docker-Compose on Linux. Pinpoint APM requires a Docker version 18.02.0 and above. The latest available version of Docker can be installed with the aid of the guide below: How To Install Docker CE on Linux Systems Once installed, ensure that the service is started and enabled as below. sudo systemctl start docker && sudo systemctl enable docker Check the status of the service. $ systemctl status docker ● docker.service - Docker Application Container Engine Loaded: loaded (/usr/lib/systemd/system/docker.service; enabled; vendor preset: disabled) Active: active (running) since Wed 2022-01-19 02:51:04 EST; 1min 4s ago Docs: https://docs.docker.com Main PID: 34147 (dockerd) Tasks: 8 Memory: 31.3M CGroup: /system.slice/docker.service └─34147 /usr/bin/dockerd -H fd:// --containerd=/run/containerd/containerd.sock Verify the installed Docker version. $ docker version Client: Docker Engine - Community Version: 20.10.12 API version: 1.41 Go version: go1.16.12 Git commit: e91ed57 Built: Mon Dec 13 11:45:22 2021 OS/Arch: linux/amd64 Context: default Experimental: true

..... Now proceed and install Docker-compose using the dedicated guide below: How To Install Docker Compose on Linux Add your system user to the Docker group to be able to run docker commands without sudo sudo usermod -aG docker $USER newgrp docker Step 2 – Deploy the Pinpoint APM (Application Performance Management) The Pinpoint docker container can be deployed by pulling the official docker image as below. Ensure that git is installed on your system before you proceed. git clone https://github.com/naver/pinpoint-docker.git Once the image has been pulled, navigate into the directory. cd pinpoint-docker Now we will run the Pinpoint container that will have the following containers joined to the same network: The Pinpoint-Web Server Pinpoint-Agent Pinpoint-Collector Pinpoint-QuickStart(a sample application, 1.8.1+) Pinpoint-Mysql(to support certain feature) This may take several minutes to download all necessary images. Pinpoint-Flink(to support certain feature) Pinpoint-Hbase Pinpoint-Zookeeper All these components and their configurations are defined in the docker-compose YAML file that can be viewed below. cat docker-compose.yml Now start the container as below. docker-compose pull docker-compose up -d Sample output: ....... [+] Running 14/14 ⠿ Network pinpoint-docker_pinpoint Created 0.3s ⠿ Volume "pinpoint-docker_mysql_data" Created 0.0s ⠿ Volume "pinpoint-docker_data-volume" Created 0.0s ⠿ Container pinpoint-docker-zoo3-1 Started 3.7s ⠿ Container pinpoint-docker-zoo1-1 Started 3.0s ⠿ Container pinpoint-docker-zoo2-1 Started 3.4s ⠿ Container pinpoint-mysql Sta... 3.8s ⠿ Container pinpoint-flink-jobmanager Started 3.4s ⠿ Container pinpoint-hbase Sta... 4.0s ⠿ Container pinpoint-flink-taskmanager Started 5.4s ⠿ Container pinpoint-collector Started 6.5s ⠿ Container pinpoint-web Start... 5.6s ⠿ Container pinpoint-agent Sta... 7.9s ⠿ Container pinpoint-quickstart Started 9.1s Once the process is complete, check the status of the containers. $ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES cb17fe18e96d pinpointdocker/pinpoint-quickstart "catalina.sh run" 54 seconds ago Up 44 seconds 0.0.0.0:8000->8080/tcp, :::8000->8080/tcp pinpoint-quickstart 732e5d6c2e9b pinpointdocker/pinpoint-agent:2.3.3 "/usr/local/bin/conf…" 54 seconds ago Up 46 seconds pinpoint-agent 4ece1d8294f9 pinpointdocker/pinpoint-web:2.3.3 "sh /pinpoint/script…" 55 seconds ago Up 48 seconds 0.0.0.0:8079->8079/tcp, :::8079->8079/tcp, 0.0.0.0:9997->9997/tcp, :::9997->9997/tcp pinpoint-web 79f3bd0e9638 pinpointdocker/pinpoint-collector:2.3.3 "sh /pinpoint/script…" 55 seconds ago Up 47 seconds 0.0.0.0:9991-9996->9991-9996/tcp, :::9991-9996->9991-9996/tcp, 0.0.0.0:9995-9996->9995-9996/udp,

:::9995-9996->9995-9996/udp pinpoint-collector 4c4b5954a92f pinpointdocker/pinpoint-flink:2.3.3 "/docker-bin/docker-…" 55 seconds ago Up 49 seconds 6123/tcp, 0.0.0.0:6121-6122->6121-6122/tcp, :::6121-6122->6121-6122/tcp, 0.0.0.0:19994->19994/tcp, :::19994->19994/tcp, 8081/tcp pinpoint-flink-taskmanager 86ca75331b14 pinpointdocker/pinpoint-flink:2.3.3 "/docker-bin/docker-…" 55 seconds ago Up 51 seconds 6123/tcp, 0.0.0.0:8081->8081/tcp, :::8081->8081/tcp pinpoint-flink-jobmanager e88a13155ce8 pinpointdocker/pinpoint-hbase:2.3.3 "/bin/sh -c '/usr/lo…" 55 seconds ago Up 50 seconds 0.0.0.0:16010->16010/tcp, :::16010->16010/tcp, 0.0.0.0:16030->16030/tcp, :::16030->16030/tcp, 0.0.0.0:60000->60000/tcp, :::60000->60000/tcp, 0.0.0.0:60020->60020/tcp, :::60020->60020/tcp pinpoint-hbase 4a2b7dc72e95 zookeeper:3.4 "/docker-entrypoint.…" 56 seconds ago Up 52 seconds 2888/tcp, 3888/tcp, 0.0.0.0:49154->2181/tcp, :::49154->2181/tcp pinpoint-docker-zoo2-1 3ae74b297e0f zookeeper:3.4 "/docker-entrypoint.…" 56 seconds ago Up 52 seconds 2888/tcp, 3888/tcp, 0.0.0.0:49155->2181/tcp, :::49155->2181/tcp pinpoint-docker-zoo3-1 06a09c0e7760 zookeeper:3.4 "/docker-entrypoint.…" 56 seconds ago Up 52 seconds 2888/tcp, 3888/tcp, 0.0.0.0:49153->2181/tcp, :::49153->2181/tcp pinpoint-docker-zoo1-1 91464a430c48 pinpointdocker/pinpoint-mysql:2.3.3 "docker-entrypoint.s…" 56 seconds ago Up 52 seconds 0.0.0.0:3306->3306/tcp, :::3306->3306/tcp, 33060/tcp pinpoint-mysql Access the Pinpoint APM (Application Performance Management) Web UI The Pinpoint Web run on the default port 8079 and can be accessed using the URL http://IP_address:8079. You will be granted the below page. Select the desired application to analyze. For this case, we will analyze our deployed Quickapp. Select the application and proceed. Here, click on inspector to view the detailed metrics. Here select the app-in-docker You can also make settings to Pinpoint such as setting user groups, alarms, themes e.t.c. Under administration, you can view agent statistics for your application Manage your applications under the agent management tab To set an alarm, you first need to have a user group created. you also need to create a pinpoint user and add them to the user group as below. With the user group, an alarm for your application can be created, a rule and notification methods to the group members added as shown. Now you will have your alarm configured as below. You can also switch to the dark theme which appears as below. View the Apache Flink Task manager page using the URL http://IP_address:8081. Voila! We have triumphantly deployed Pinpoint APM (Application Performance Management) in Docker Containers. Now you can discover, trace, and perform diagnoses on your applications.

[lolmains]

Iconsole application

#Iconsole application code

There are several maven plugins for this, but I will be using protoc-jar-maven-plugin, that wraps protoc executable as a jar so it can run in any OS and compile your.

#Iconsole application code

Automatically, by adding protoc code generation to your maven project build.If you wish to proceed with this method read protoc installation guide here Manually, by downloading protoc in your machine and running it.proto files targeting the desired output language. Ok, protobuf contracts created, now, to generate code (java classes) from this contract, we need to use protoc executable to compile. Open IntelliJ IDEA CE and choose to create a new project.For this example I'll be using OpenJDK 15, IntelliJ IDEA CE and Maven as build tool to do so. That said, let us start by creating the Java console application. The ideal for big and complex projects is to have a separate repository as a neutral ground for projects. proto file will be a common resource in C# and Java but for simplicity sake, I'll recreate it in both projects repository. Well Known Types here or Google Common Types here. There are other scalar types, you can read the documentation here. You have to copy these types definitions file from google repository and paste it in your project whatever the language you are using. Date and Money types are a "Google Common Type", differently than "Well Known Type" you are not able to use it only by importing.Timestamp type is a " Well Known Type" introduced in proto3, you can use these types by importing in the first lines of proto file.First follow instructions of the README file of the Java project repository: If you just want to read some code and figure it out on your own, I've setup these two applications repository. A C# console application that will read the hard coded customer file generated by Java console application, and display the data in console.proto specification to generate a file with a hard coded customer A Java console application that will use the customer.In this example we will be creating two projects: Protobuf is similar to Apache Thrift used by Facebook, Ion by Amazon or Microsoft Bonds Protocol. Protocol buffers can serve as basis for a remote procedure call (RPC) which are widely used for inter-machine communication, most commonly used with gRPC. A program from any language supported is able to generate source code from that description in order to create or parse a stream of bytes that represents the structured data. This protocol involves a specific interface description language to describe the data structure defined in a. Protobuf messages are serialized into a binary wire format which is very compact and boosts performance. It was designed by Google early 2001 (but only publicly released in 2008) to be smaller and faster than XML. Protocol Buffers (protobuf) is a method of serializing structured data which is particulary useful to communication between services or storing data.

[techhelpnotes]

What is the current choice for doing RPC in Python

XML-RPC is part of the Python standard library:

Python 2: xmlrpclib and SimpleXMLRPCServer

Python 3: xmlrpc (both client and server)

Apache Thrift is a cross-language RPC option developed at Facebook. Works over sockets, function signatures are defined in text files in a language-independent way.

[nearmesblog]

We typically deliver a message and accumulate a proper away response without cleaning a web page. However, earlier, allowing real-time functionality modified right proper into a big mission for app developers after the present way HTTP extended polling and AJAP, the network of developers has the long run placed a choice to develop real-time packages.

WebSockets WebSockets are upgraded HTTP connections that preserve until the consumer or the server kills the connection through the WebSocket connection. It’s miles viable to perform duplex communication that is an extravagant way to say communication is viable from and to the server the clients the use of this single connection this as an opportunity lowers the amount of network overhead needed to enlarge the real-time apps with the use of WebSockets because of the that there may be non-prevent polling of HTTP endpoints needed.

📷

Build a WebSockets in Golang

Step-1: Setting up a handshakeAt first, you need to form an HTTP handler using a WebSocket endpoint: Next, run the WebSocket structure. The consumer typically sends the number one handshake request when the server authenticated a Web Socket request, it has to reply with a response of a handshake.Step-2: Transferring statistics framesOnce you whole the handshake, your software program can have a look at and write to and from the Specification of the WebSockets describes a specific frame format that is finished amongst a server and consumer.Step-3: Closing a handshakeWhen one of the customer sports activities sends a near to border along with a reputation because of the payload, a handshake is closed spontaneously, the third-party sends to the frame can also deliver a close to motive with the payload in case the consumer initiates the closing, the server needs to deliver an equal close to frame in turn.

List of WebSockets in Golang libraries

1. STDLIBThis Web Socket library belongs to the equal vintage library. It applies a server and consumer for the Web Socket protocol you don’t need to install it and it includes reliable documentation. However, on the other hand, it doesn’t have some talents that can be located in a single in each of a kind WebSocket libraries. In the STDLIB (x/internet/WebSocket) package deal, Golang WebSocket packages don’t permit clients to use I/O buffers yet again amongst connections.2. GorillaIn the Gorilla toolkit, the WebSocket package deal talents as an examined and whole software program of the WebSocket protocol and a regular package deal API this Web Socket package deal is easy to use and nicely documented and you can discover its documentation on the reliable web page of Gorilla.

3. GOB WASThis is a small WebSocket package that includes a strong list of talents, like a low-diploma API that lets in the growth of the unusual place, custom packet dealing with and a zero-reproduction upgrade GOBWAS doesn’t need intermediate allocations within the route of I/O. Moreover, its talented high-diploma helpers and wrappers at the end of the API within the WsUtil package, allowing developers to begin speedy without analyzing the protocol’s internals.4. GOWebsocketsThis Golang device offers an in-depth arrays. It allows for putting request headers, compressing information, and controlling concurrency. It assists proxies and subprotocols for transmitting and getting binary information and text.

Conclusion This instructional has covered some of the fundamentals of WebSockets and to grow a clean WebSocket based complete app in Golang extraordinary tool besides the tool, someone in each kind of possible packages permits developers to grow strong streaming solutions they include package RPC, gRPC, Apache Thrift, and go-socket. The supply of well-documented equipment like WebSocket’s and the non-save you development of streaming technology makes it smooth for builders to build up real-time apps.

[prasoonroxtar-blog]

BIG DATA SERVICES

Coding Brains is the top software service provider company supporting clients capitalize on the transformational potential of Big Data.

Utilizing our business domain expertise, we assist clients in integrating Big Data into their over all IT architecture and implement the Big Data Solution to take your business to a whole new level. With the belief that companies will rely on Big Data for business decision making, our team has focused on the delivery and deployment of Big Data solutions to assist corporations with strategic decision making.

Our Services related to Big Data analytics support in analyzing the information to give you smart insights into new opportunities. Our data scientists consist of a unique approach to develop services that analyze each piece of information before making any critical business decision. Big Data Analytics Consulting & Strategy support you to opt for an appropriate technology that complements your data warehouse.

APACHE HADOOP

With the utilization of Apache Hadoop consulting, we help Business verticals to leverage the advantage of a large amount of information by organizing it in an appropriate way to gain smarter insights. We have a team of professionals who gain upper hand in Big Data technology and expert in offering integrated services across the Hadoop ecosystem of HDFS, Map Reduce, Sqoop, Oozie, and Flume.

APACHE HIVE

At Coding Brains, Our Hive development and integration services are designed to enable SQL developers to write Hive Query Language statements that are similar to standard SQL ones. Our objective to bring the familiarity of relational technology to big data processing utilizing HQL and other structures and processes of relational databases.

APACHE MAHOUT

We offer solutions related to Apache Mahout designed to make business intelligence apps. Our team specializes in developing scalable applications that meet user expectations. Focused on deriving smarter insights, we have expertise in Apache Mahout and proficiency in implementing Machine Learning algorithms.

APACHE PIG

Coding Brains offers services in Apache Pig, an open- source platform and a high-level data analysis language that is utilized to examine large data sets. Our Big Data Professionals use Pig to execute Hadoop jobs in MapReduce and Apache Spark. We help simplify the implementation of technology and navigate the complexities.

APACHE ZOOKEEPER

Our team delivers high-end services in Apache ZooKeeper, an open-source project dealing with managing configuration information, naming, as well as group solutions that are deployed on the Hadoop cluster to administer the infrastructure.

APACHE KAFKA

As a leading IT Company, We provide comprehensive services in Apache Kafka, a streaming platform. Our Big Data team expert in developing enterprise-grade streaming apps as well as for streaming data pipelines that present the opportunity to exploit information in context-rich scenarios to drive business results.

NOSQL DATABASE

Our services provide elements such as quality, responsiveness, and consistency to deliver development solutions for exceptional results. We understand your business-critical requirement and deliver NoSQL technologies that are most appropriate for your apps. We support enterprises to seize new market opportunities and strategically respond to threats by empowering better client experiences.

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How can I install Apache Cassandra 4.0 on CentOS 8 | Rocky Linux 8 machine?. Apache Cassandra is a free and open-source NoSQL database management system designed to be distributed and highly available. Cassandra can handle large amounts of data across many commodity servers without any single point of failure. This guide will walk you through the installation of Cassandra on CentOS 8 | Rocky Linux 8. After installation is done, we’ll proceed to do configurations and tuning of Cassandra to work with machines having minimal resources available. Features of Cassandra Cassandra provides the Cassandra Query Language (CQL), an SQL-like language, to create and update database schema and access data. CQL allows users to organize data within a cluster of Cassandra nodes using: Keyspace: defines how a dataset is replicated, for example in which datacenters and how many copies. Keyspaces contain tables. Table: defines the typed schema for a collection of partitions. Cassandra tables have flexible addition of new columns to tables with zero downtime. Tables contain partitions, which contain partitions, which contain columns. Partition: defines the mandatory part of the primary key all rows in Cassandra must have. All performant queries supply the partition key in the query. Row: contains a collection of columns identified by a unique primary key made up of the partition key and optionally additional clustering keys. Column: A single datum with a type which belong to a row. Cassandra has support for the following client drivers: Java Python Ruby C# / .NET Nodejs PHP C++ Scala Clojure Erlang Go Haskell Rust Perl Elixir Dart Install Apache Cassandra 4.0 on CentOS 8 | Rocky Linux 8 Java is required for running Cassandra on CentOS 8 | Rocky Linux 8. As of this writing, required version of Java is 8. If you want to use cqlsh, you need the latest version of Python 2.7. Step 1: Install Java 8 and Python and cqlsh Install Python3 Pip and OpenJDK 8 on your CentOS / Rocky Linux 8: sudo yum install python3 python3-pip java-1.8.0-openjdk java-1.8.0-openjdk-devel Install cqsh using pip3 Python package manager: sudo pip3 install cqlsh tox Ensure the install is successful: .... Collecting importlib-metadata; python_version < "3.8" (from click->geomet=0.1->cassandra-driver->cqlsh) Downloading https://files.pythonhosted.org/packages/71/c2/cb1855f0b2a0ae9ccc9b69f150a7aebd4a8d815bd951e74621c4154c52a8/importlib_metadata-4.8.1-py3-none-any.whl Collecting zipp>=0.5 (from importlib-metadata; python_version < "3.8"->click->geomet=0.1->cassandra-driver->cqlsh) Downloading https://files.pythonhosted.org/packages/bd/df/d4a4974a3e3957fd1c1fa3082366d7fff6e428ddb55f074bf64876f8e8ad/zipp-3.6.0-py3-none-any.whl Collecting typing-extensions>=3.6.4; python_version < "3.8" (from importlib-metadata; python_version < "3.8"->click->geomet=0.1->cassandra-driver->cqlsh) Downloading https://files.pythonhosted.org/packages/74/60/18783336cc7fcdd95dae91d73477830aa53f5d3181ae4fe20491d7fc3199/typing_extensions-3.10.0.2-py3-none-any.whl Installing collected packages: thrift, cql, zipp, typing-extensions, importlib-metadata, click, geomet, cassandra-driver, cqlsh Running setup.py install for thrift ... done Running setup.py install for cql ... done Successfully installed cassandra-driver-3.25.0 click-8.0.3 cql-1.4.0 cqlsh-6.0.0 geomet-0.2.1.post1 importlib-metadata-4.8.1 thrift-0.15.0 typing-extensions-3.10.0.2 zipp-3.6.0 Confirm the installation of Java and cqlsh. $ java -version openjdk version "1.8.0_312" OpenJDK Runtime Environment (build 1.8.0_312-b07) OpenJDK 64-Bit Server VM (build 25.312-b07, mixed mode) $ cqlsh --version cqlsh 6.0.0 Step 2: Install Apache Cassandra 4.0 on CentOS 8 | Rocky Linux 8 Now that Java and Python are installed. Let’s now add Cassandra repository to our CentOS / Rocky system. sudo tee /etc/yum.repos.d/cassandra.repo