Vite 5.0: What is New in it?
On December 9, 2022, Vite 4.0 was released with numerous advancements. Within less than a year, this front-end tool has come up with its new version, i.e., Vite 5.0. Released on November 16, 2023, Vite 5.0 is all set to take front-end development to the next level. If you are wondering what is new in Vite 5.0, this blog post is for you. Overview of Vite For those who do not know, Vite is a renowned front-end web development tool. It helps users build and optimize web applications. Evan You is the key person behind the launch of this lightning-fast web development tool. It was first released in 2020, and since then, it has witnessed several upgrades. Let us learn about the changes we can see in its new version, which is Vite 5.0. Upgrades in Vite 5.0 Node.js Support With this new version, Vite 5.0 has started supporting the latest versions of Node.js. If you were working with previous versions of Node.js, this might disappoint you. Vite 5.0 does not support Note.js 14, 16, 17, and 19 anymore. You will be required to have Node.js 18/ 20+. Rollup 4 Vite 5.0 adopts Rollup 4, which brings various improvements in terms of performance. For instance, users can experience improvement in tree-shaking capabilities that naturally minimize the bundle size. Additionally, you can notice the following changes as well. The assertions prop is now named as attributes prop. You cannot use Acorn plugins anymore. For Vite plugins, this.resolve skipSelf option is now true by default. Deprecate CJS Node API Another crucial change you notice in Vite 5.0 is the deprecated CJS Node API. If you call require(‘vite’), it projects a deprecation warning. Users will need to update their frameworks or files to import the ESM build of Vite. Performance In Vite 5.0, you also get a new feature server.warmup. It improves the startup time several times. It enables users to define a list of modules that should be pre-transformed as soon as the server starts. When a user uses —open or server.open, Vite 5.0 warms up the entry point of their applications. Improved Type-Script Support TypeScript has turned into a crucial part of the JavaScript ecosystem. Vite 5.0 provides full support for this statically typed language. Now users can experience improved TypeScript integration. In addition to this, they can do better type-checking and get enhanced tooling support now. Worker. Plugins Acts as a Function Now In the previous version of Vite, worker.plugins used to accept an array of plugins ((Plugin | Plugin[ ])[ ]). However, in Vite 5.0, it has to be configured as a function that returns an array of plugins (() => (Plugin | Plugin[ ])[] ). It is an imperative change; therefore, parallel worker builds run steadily. Browser Compatibility Cross-browser compatibility is vital in the contemporary web development landscape. Vite 5.0 recognizes this need. This new version supports enhanced browser compatibility features. Users can do cross-browser testing without any issues now. It helps web developers build and test applications across different browser environments. Support for Manifest Files in .vite Directory Earlier the manifest files used to be generated in the root of build.outDir by default. With Vite 5, the user can produce them in the .vite directory in the build.outDir by default. This change aids in avoiding conflicts among public files having the same manifest file names during their transfer to the build.outDir. Developer Tooling Vite 5.0 has given more emphasis on improving the overall web development experience. Users can notice improvement in the errors handling capabilities. Developers can identify and resolve issues more quickly than before. In addition to this, Vite 5.0 streamlines the development workflow by adopting a productive coding environment. Press an Additional Enter for CLI Shortcuts Users using CLI shortcuts will need to press an additional Enter to prompt the shortcut command. For instance, earlier users had to press only r to restart the dev server. Now they will press r + Enter to restart the dev server. Conclusion Vite 5.0 introduces an array of new features while also eliminating several components. Some of the primary changes you can notice in this current version of Vite are improved TypeScript integration, Rollup 4, Cleaned up API, and Deprecated CJS Node API. It would not be wrong to mention that Vite is a next-generation front-end web development tool. This new version brings a lot of revolutionary changes and simplifies the developing experience.
TeamCity vs. Jenkins An In-depth Comparison
While several CI/CD tools are available, the two most preferred options are TeamCity and Jenkins. Both are powerful tools that help web developers streamline their workflow. Nevertheless, they differ in various aspects. This blog post highlights the differences between TeamCity vs. Jenkins based on several factors. Understanding these dissimilarities will help you choose the best option for your next project. So without any delay, let us get started. TeamCity vs. Jenkins: Overview What is TeamCity? Developed by JetBrains, TeamCity is a renowned continuous integration and delivery server system. It was first released in 2006. However, it has come a long way since then. The software enables users to relish flexible workflows, collaborate with other developers, and do unlimited development practices. This Java-based platform supports various programming languages and can integrate with Visual Studio, Eclipse, and more. What is Jenkins? Jenkins is a server-based, open-source CI/CD tool that allows users to build, test, and deploy web codes. It operates within servlet containers like Apache Tomcat. Jenkins was initially released on the 2nd of February 2011 by Kohsuke Kawaguchi. Jenkins operates in Java language. As a result, users can install it on UNIX, LINUX, and Windows. TeamCity vs. Jenkins: Comparison of Different Factors User-friendliness What’s the benefit of making a powerful software or tool that users can’t understand? Ease of use is one of the crucial factors to assess when choosing a CI/CD platform. You can’t leverage the platform if you cannot navigate through its interface. TeamCity features a clean web-based interface that simplifies navigation. Users often praise its straightforward setup and configuration. You get an easy-to-use dashboard with detailed build logs. In comparison to TeamCity, Jenkins can be a little difficult to navigate for beginners. However, overall it is good. The platform offers various customization options. It also supports custom CSS and Javascript files. Jenkins requires more configuration initially. Nevertheless, it won’t be that tough for you to understand it. Installation It is convenient to install TeamCity. Download the TeamCity server and read the documentation. It mentions all the instructions that you have to follow step-by-step. To install Jenkins, you need to have Java in your system. However, the installation is as smooth as that of TeamCity. As it is an open-source, you will set up the server and accomplish dependencies manually. Community Support Problem-solving approach, user engagement, and knowledge sharing are primary parts of an excellent community support system. TeamCity has community support available on its website. Users can contact their team with details of their issues. The support team might ask for information related to system or logs to fix the problem. Users can also share troubleshooting information for different issues in the knowledge base. Jenkins has the upper hand in terms of community support. Since it is an open-source platform, users share their troubleshooting experiences. Beginners can learn and resolve their issues from the experiences of previous customers. Scalability Scalability is one of the imperative considerations when deciding between TeamCity vs. Jenkins. TeamCity is capable of handling large-scale developments and deployments. Users get built-in support for distributed forms. TeamCity can distribute workload through different build agents. As a result, the platform can manage large projects efficiently. Jenkins won’t disappoint you in terms of scalability as well. With additional setup and configuration, Jenkins can manage large deployments and projects. You will have to set up distributed build agents to improve scalability. Moreover, you can also use external tools such as Kubernetes. Plugin Support TeamCity offers you almost all the basic required integrations. It has a wide range of built-in features for different technologies. For example, you can integrate it with the Visual Studio Team service. Jenkins is slightly better when it comes to integrations. You can choose from hundreds of free plugins based on your requirements. Key Integration Both platforms allow integration with different version control systems, such as Git, Mercurial, SVN, and more. TeamCity prioritizes native integration for a seamless developer experience. You can quickly integrate with JetBrains’ suite of IDEs. On the other hand, Jenkins is an open-source platform. As a result, it supports a broad range of integration with third-party tools. Security Security is another vital aspect you cannot compromise on. TeamCity supports integration with the Snyk security plugin. The plugin is capable of doing vulnerability scanning in the build pipeline. The plugin can identify and resolve various threats and risks. Jenkins also comes with various security features. Users can enable features like user authentication and more. Furthermore, Jenkins also allows integration with external authentication systems, such as LDAP, Active Directory, etc. Pricing TeamCity has three versions: TeamCity Professional, TeamCity Enterprise (from $1,999 per year), and TeamCity Cloud ( from $45 per month). Out of three, TeamCity Professional provides free access. You can even use it for commercial use. However, it comes with certain limitations. Note: Since the price keeps changing, visit the official website to check the current price. Jenkins is free to use as it is an open-source platform. You can download it and start using it. Conclusion: What Should I Choose? This comparison of TeamCity vs. Jenkins makes you aware of the ins and outs of both platforms. Now you can determine which one fulfills your needs better. Choosing between TeamCity and Jenkins depends on your specific requirements. If you are looking for user-friendly yet a robust CI/CD, go for TeamCity. On the other hand, users looking for extensive customization can choose Jenkins. However, you will have to spend more time on configurations and maintenance of this platform. Evaluate your priorities and make decisions accordingly.
MongoDB Sharding: Everything You Need to Know About it
Data sharding is the breakthrough technique of the modern data-driven era. It eliminates the limitation that a single database server can store and process only a certain amount of data. Data sharding enables users to store the data across different database servers. Therefore, it improves response time and supports scalability. This blog post gives you in-depth knowledge about MongoDB sharding. It also provides you guidance on setting up this sharding. MongoDB Overview Before we learn about MongoDB sharding, let’s get to know MongoDB. MongoDB is an open-source document-oriented NoSQL database. It has been used by several prominent companies, including Facebook, Google, and eBay. MongoDB lets users store data in flexible, JSON-like documents. Since this database features schema design, it can handle unstructured data. Consequently, it has become the first choice for most modern web applications. Unlike conventional relational database systems, MongoDB can manage a large amount of data. The best thing is that users can integrate MongoDB with all prominent programming languages, such as PHP, Python, Java, Node.js, C, C++, and more. What is MongoDB Sharding? Most companies use NoSQL databases to deal with storage and computing requirements of high-volume data. Sharding in MongoDB is a method that helps individuals distribute a database horizontally through different nodes. These nodes or servers are called “Shards”. Each shard can handle a portion of the data, creating a sharded cluster. It helps MongoDB manage the large volume of data and user concurrency efficiently. Each shard works as a unique database in a sharded cluster. They are accountable for storing and handling data. As soon as the data is added to the cluster, the balancer of MongoDB reallocates it across the shards. Thus it makes sure the workload is equally distributed. The architecture of sharding consists of the following elements: Shard Servers These servers are separate nodes within the sharded cluster. Each node collects a subset of the data and acts as an independent database. MongoDB circulates data across the shard servers to ensure even distribution. Config Servers These servers keep the metadata and configuration information for the sharded cluster. Query Routers Also referred to as “Mongos”, these routers work as the interface between the sharded cluster and applications. Query routers receive requests and queries to the suitable shards and combine results as required. As Mongos processes hide the underlying sharding complexity from the application, it looks like a single logical database. Shard key The shard key is a field that determines how data will be distributed in different shards. For even data distribution and efficient querying, it is necessary to choose the right shard key. Chunk A chunk constitutes a data range housed within a shard. It is decided by the shard key. Chunks are data migration units between shards. Balancer Balancer assures the uniform distribution of data amid the shards. Once data is added or eliminated, the balancer distributes data chunks between shards. Steps to Set up MongoDB Sharding You will require three servers to set up sharding in MongoDB. One for the config server and another for the Mongos instance. The last one is required for the shards. Here are step-by-step instructions to set up sharding in MongoDB. 1. Make a Directory Using Config Server First, you need to create a directory for the config server data. You can do this by running the below command on the 1st server. mkdir /data/configdb 2. Begin MongoDB in Config Mode Once you have created the directory, your next step will be starting MongoDB in config mode on the 1st server. Use this command in order to do it. mongod –configsvr –dbpath /data/configdb –port 27019 The command will begin the config server on port 27019 and store all the data in the /data/configdb directory. Note: We are employing the –configsvr flag to specify this server’s role as a config server. 3. Start Mongos Instance Next, start the process of Mongos instance. It will route queries to the accurate shards depending on the sharding key. Use the following command to begin this process. mongos –configdb <config server>:27019 Remember, you have to replace <config server> with the hostname/IP address of the machine where the config server is running. 4. Connect to Mongos Instance Use MongoDB shell to connect to Mongos instance. Below is the command to perform this. mongo –host <mongos-server> –port 27017 In the above command, replace <mongos-server> with the IP address or hostname of the server running the mongos instance. The command will open up the MongoDB shell and let you interact with Mongo’s instance, and add servers to the cluster. 5. Add Servers to Clusters Once you are connected to the mangos instance, add servers to the cluster. Use the following command: sh.addShard(“<shard-server>:27017”) In the above command, you need to replace <shard-server> with the hostname or IP address of the server running the shard. It will add the shard to the cluster so that it is available for utilization. Repeat this step for each shard you need to add to the cluster. 6. Permit Sharding for Database In the end, you need to enable sharding for a database. Here is the command for this process: sh.enableSharding(“<database>”) Replace <database> with the name of the database you want to shard. It will allow sharding for the particular database, enabling you to share its data across different shards. Congratulations! Now you have a fully functional MongoDB sharded cluster to handle high-traffic loads. Final Words Undoubtedly, MongoDB sharding is a robust technique supporting the horizontal scaling of databases. It allows a database to handle extensive data loads without compromising on performance. We hope this post gives you adequate information on MongoDB sharding. You can leverage the power of sharding and make the best use of your hardware resources.
Astro 3.0: Everything You Need to Know about it
Astro is an open-source web framework that enables users to design fast, efficient, and high-performance websites. It released its new avatar on August 30, 2023, known as Astro 3.0. According to the team, it is several times faster and feature-rich than previous versions. In this post, we will take an in-depth look at the revolutionary features of Astro 3.0. Before that, let us have a quick look at an Astro overview. Overview of Astro This open-source web framework is used to design heavy websites, such as landing pages, blogs, technical documentation, etc. Launched in June 2021, this open-source project provides support to more than 10,000 users. Similar to platforms like Next.js, Nuxt.js, and SolidStart, Astro features single-file components. The framework comes with a wide range of features, including partial hydration, zero-config development, incremental static regeneration, and static site generation. Let’s find out how Astro 3.0 is different from the previous version. What is new with Astro 3.0? Astro 3.0 is becoming popular as a prominent web framework to support the View Transitions API. Furthermore, it provides functionality for new browser APIs. Astro 3.0 features several exciting features, such as image optimization, SSR enhancements, performant HTML output, faster rendering, and more. Get to know more about these features in detail below. Astro 3.0: Exploring the New Features and Enhancements Full support for View Transitions API One of the outstanding features of Astro 3.0 is its support for View Transitions API. This application programming interface allows developers to create seamless transitions between different website components. You can fade, persist, morph, and slide stateful elements across page navigation. Furthermore, it enables native browser transition effects between pages. Earlier, only single-page applications could do transitions. With Astro 3.0, native page transition is possible. This feature makes it easier for developers to enhance user experience. Now you can: Transform persistent elements from one page to another Fade content on and off Slide content on and off Persist regular UI across pages without a refresh. Image Optimization (stable) Astro 3.0 features stable image optimization. Like Next.js, it also has a built-in <image> component that manages everything about rendering images on the web. This new version of Astro allows developers to compress or resize images. It automatically minimizes the page load times. As a result, images load faster in the browsers of users. This built-in feature has a significant role in enhancing website speed and performance. You can follow the below steps to use image optimization in Astro 3.0. Configure astro assets in your astro.config.mjs. Import images from the relative path from the existing .astro file. Use the image’s src and other properties in the <img> tag. Some other worth describing updates about image optimization in Astro 3.0 include: Support for Vercel’s built-in image service. You can optimize images from CMS tools and remote workflows. Astro utilizes Sharp as the default optimization library Faster Rendering Performance Astro is renowned for its rendering performance. However, with version 3.0, it has taken one step further. Compared to Astro 2.9, this new version renders components about 30% faster. This significant performance improvement is due to the less amount of JavaScript transported to the client by improving the rendering path. The developing team at Astro removed as much superfluous code as possible. SSR Enhancements for Serverless Astro 3.0 has brought numerous improvements to Server Side Rendering (SSR) for serverless applications. SSR technique generates web pages on the server before transporting them to the clients. Astro 3.0 lets you create responsive and dynamic web applications using serverless functions. Therefore, you get new ways to connect to the hosting platforms. HMR Enhancements for JSX Updating code was a big challenge with previous versions. Fortunately, with Astro 3.0, you can update codes instantly without refreshing the entire page. The framework brings HMR enhancements to JSX components. As a result, developers see changes in real-time. Not only does it minimize the development time, but also speeds up the debugging process. Astro 3.0 provides fast refresh support to React and Preact users. They can make changes without the fear of losing the component’s state. In addition, it also supports the component hierarchy. Consequently, the application maintains its structure after code changes. Optimized Build Output Astro 3.0 comes up with several changes in optimized build outputs. This latest version naturally minifies HTML outputs, reducing the payload and improving the overall response time. In addition, the team at Astro has replaced the messy astro-xxxx class names with a specific HTML attribute. Conclusion Astro 3.0 improvements make this framework ideal for web developers looking to create user-friendly yet powerful websites. It puts more stress on developer experience, performance, and user satisfaction. With features like support for the View Transitions API, improved rendering performance, and Image Optimization, we can say that Astro 3.0 leaps forward in the web development world.
Bun 1.0: Unveiling the Ultimate Development Tool
Since its launch, Bun 1.0 has become the talk of town in the web development community. It is gaining popularity as an all-in-one tool for JavaScript and TypeScript development. If you haven’t used it yet and want to explore Bun 1.0 features, this post is for you. Before we jump into the Bun 1.0 features, let’s learn about it briefly. Overview of Bun and its Significance Bun is a renowned open-source bundler for JavaScript and TypeScript. Jarred Sumner is the key person behind the foundation of this JavaScript runtime. Unlike Node.js and Deno, the bundler uses JavaScriptCore as the JavaScript engine. Bun 1.0 was launched on September 8, 2023. It is a versatile tool to build, test, debug, and run JavaScript and TypeScript applications. Bun 1.0 is quite fast in comparison to Node.js and Deno. Let us uncover all the Bun 1.0 features one by one. Features of Bun 1.0 Universal Tool Bun 1.0 meets the requirements of both JavaScript and TypeScript developers. Whether you are working on a single-file project or developing a full-stack application, Bun provides an efficient development environment. Below are some features that make Bun 1.0 worth using: Bun supports quick command execution, thanks to npx. NPX is part of Bun. It eliminates the requirement of nodemon as it features a built-in watch mode. It is an ideal replacement for Node.js. Bun is capable of reading .env files. It means you don’t need any 3rd party configuration. You get support for various file formats such as .js, .ts, .cjs, .mjs., tsx, etc. Bun provides you with an integrated bundling solution. It replaces web pack, parcel, rollup, rebuild, etc. Bun 1.0 also features testing libraries that remove the requirement for jest and similar tools. Bun is an npm-compatible package manager. Therefore, it naturally reduces the need for yarn and npm. High Speed and Performance Speed and performance are other aspects of Bun impressing JavaScript developers. It lets you run your code at an excellent speed. Bun 1.0 is several times faster. You won’t need to use tools like yarn, npm, and pnpm. Bun takes about 0.36 seconds to compile a code. In the case of pnpm, compilation may take up to 6.44 seconds. With npm, code compilation takes 10.58 seconds, while Yarn takes 12.08 seconds for the same task. Compared to Node.js, Bun is about four times faster. Bun 1.0 provides top-notch performance, thanks to its advanced optimization technology and efficient code bundling. In addition, it minimizes the load times for web applications. As a result, it provides a better user experience. Built-in Support for JavaScript and TypeScript Bun 1.0 provides complete support for JavaScript and TypeScript. Developers can work with both languages without using any third-party transpilers. Bun makes it easy to set up your development process. You do not need to struggle with various tools. Bun handles everything so that you can focus on coding entirely. Hot Reloading Bun 1.0 allows you to see instant updates in applications as you make changes. All credit goes to hot reloading. Bun features built-in hot reloading that enhances the development process by providing real-time updates to code and configurations. As a result, you can quickly spot all issues and fix them. With Bun, you do not need Nodemon. It automatically refreshes the server when developers run TypeScript or JavaScript code. If you have been using npm rum, you can replace it with bun run. It will reduce command execution time by at least 150 milliseconds on every run. Installation Speed It can be frustrating and time-consuming to install development tools. Fortunately, Bun 1.0 supports lightning-fast installation that reduces the setup hassle. Bun uses a global module cache system to avoid redundant downloads from the npm registry. Consequently, it uses quick system calls, available in different operating systems. Compatibility Adaptability is one of the primary Bun 1.0 features that users appreciate. This open-source bundler can effortlessly integrate with well-known server frameworks such as Hono, Koa, and Express. Web developers also get support for applications built using full-stack frameworks, including Next.js, Nuxt, Astro, Vite, Remix, etc. In addition to this, Bun 1.0 is also compatible with ESM and CommonJS. It means you can use both of them together in the same file. This feature was missing in Node.js. Conclusion Bun 1.0 is an ideal choice for developers working on JavaScript and TypeScript. It has numerous built-in features, making it a game-changer in the ever-evolving web development era. The bundle ends your dependency on complex and slow fragmented tool chains. It won’t be wrong to mention that Bun has brought revolutionary changes in the development of JavaScript projects. So these are a few worth mentioning Bun 1.0 features.
What’s New in Vue 3.3? Explore the Differences
Vue.js is a renowned JavaScript framework that helps web developers build UIs (User interfaces) and SPAs (Single-page applications). This open-source JavaScript library undergoes updates from time to time. It has released various versions so far. Lately, on May 11, 2023, Vue announced its new version, i.e., Vue 3.3. Numerous developers are keen to learn what’s new in Vue 3.3. If you’re one of them, here is the comprehensive guide. Vue 3.3 Updates Vue is evolving fast, and with each new version, it brings a lot of improvements. In version Vue 3.3, you will notice the following features and enhancements. 1. Improvements in TypeScript TypeSupport Improvement in TypeScript is one of the significant improvements in Vue.js 3.3. It helps users write type-safe in Vue applications. Earlier, they could use only local types like type literals and interfaces in the type parameter position of the defineProps and defineEmits compiler macros. With Vue 3.3 updates, this issue has been resolved. The Vue compiler is now capable of handling both imported types and a limited set of complex types. The type interface for reactive properties is more accurate in this new version of Vue. It naturally minimizes the possibility of type-related errors. <script setup lang="ts"> import type { Props } from './foo' // imported + intersection type defineProps<Props & { extraProp?: string }>() </script> 2. Support for Different Data Types Vue 3.3 features a plethora of improvements. One significant among them is support for different data types. It gives you support for generic components. Now users can easily make reusable components that work with various data types smoothly. This feature is highly beneficial for those developing components that deal with varying data types. What makes it better is users can do this without compromising on safety. 3. Suspense Vue 3.3 has introduced the Suspense feature, allowing the users to handle asynchronous operation seamlessly in the components. The user can define fallback content to display while waiting for data to load. The suspense feature of Vue 3.3 can significantly improve your user experience, especially when your component needs to fetch data from an API. <template> <Suspense> <template #default> <AsyncComponent /> </template> <template #fallback> <LoadingSpinner /> </template> </Suspense> </template> 4. Improved Syntax for defineEmits Another notable improvement you can see in this latest version of Vue.js is enhanced syntax for the defineEmits function. It enables you to declare the events that a component releases. The function improves the readability of code and lets you define Emits with an object notation. It helps users to make a better representation of the emitted events inside the component. <script> import { defineEmits } from 'vue'; export default { emits: defineEmits(['click', 'input']), }; </script> In the above code snippet, the ‘emits’ property utilizes the ‘defineEmits’ function in conjunction with an array having event names. This approach guarantees a concise declaration of the component’s emitted events. Therefore, it enhances the readability of the code. 5. defineModel: Streamlining Two-way Binding Components The innovative defineModel function introduced in Vue 3.3 makes it easy to create two-way binding components. It provides a user-friendly method for defining the modelValue prop and update:modelValue event. Generally, it is used in v-model bindings. 6. Easy Access to Reactive Props Vue 3.3 has simplified the access to reactive props inside a component’s setup function. This improvement streamlines the process of managing props, supporting easy writing and reading of code and conciseness. <script> import { reactive } from 'vue'; export default { props: { user: Object, }, setup(props) { const { user } = props; // Destructuring reactive props // Utilizing the destructured user object console.log(user.name); // … }, }; </script> The provided code snippet destructures the user prop within the setup function. It allows direct access to its properties. It helps in simplifying the code readability. 7. Improvements in Devtools Devtools in Vue 3.3 have undergone various improvements. Some of the major updates are as per below. Event Inspector The Event Inspector in Vue 3.3 gives better insights into the event system of applications. It lets you inspect event listeners and find out which components are listening to particular events. Pinning Components You can now “pin” components in Vue. As a result, it is easier to keep track of a particular component while steering through an application’s component tree. 8. Type Slots with defineSlots Vue 3.3 features an innovative function named defineSlots. As the name indicates, it encourages precise specification of slot types within a component. This feature boosts type safety in components. Furthermore, it improves IDE support for slot content. <template> <div> <slot name="header" :data="headerData" /> <slot :data="defaultData" /> </div> </template> <script> import { defineSlots } from 'vue'; export default { slots: defineSlots({ header: { data: { type: Object, required: true, }, }, default: { data: { type: String, required: false, default: 'Default Slot Content', }, }, }), }; </script> In the above code snippet, the slots use the defineSlots function to define the type of slots used in the component. It helps developers to do type-checking and relish autocompletion during leveraging slots. Conclusion In the ever-involving world of web development, it is necessary to keep pace with cutting-edge innovations. What’s new in Vue 3.3 is worth exploring for web developers. Vue.js has been constantly improving to empower users and provide them with a better experience. The Vue 3.3 updates have brought substantial improvement in TypeScript support and APIs. You can definitely consider using Vue 3.3 for your next project. Unlock the boundless opportunities with Vue 3.3. These are a few major updates in Vue 3.3. For complete details, you can refer to Vue 3.3 release notes.
The Ultimate Guide to GitLab CI/CD: Along with Example of Building CI/CD Pipeline for Python
No one can deny the significance of CI (Continuous Integration) and CD (Continuous Deployment) in software development. They enable a coder to integrate and deploy software codes and identify possible issues simultaneously. Consequently, the process naturally saves the time and effort of a developer. While several platforms support CI/CD, GitLab has grown in popularity. It automates software development in several aspects. This guide makes you aware of the features of GitLab CI/CD. In addition, you will learn to build CI/CD pipelines on GitLab. So let us get started. What is GitLab CI/CD? CI stands for Continuous Integration, while CD for Continuous Deployment/Delivery. CI supports the continuous integration of code changes from various contributors into a shared repository. On the other hand, CD allows code deployment while being developed. GitLab CI/CD is a set of tools and techniques automating software development. It enables users to create, test and deploy code changes inside the GitLab to the end users. The platform aims to support consistent workflow and improve the speed and quality of code. Features of GitLab CI/CD GitLab has several benefits over conventional software development methods. Some key benefits are as per below: ⦁ GitLab keeps CI/CD and code management in the same place. ⦁ It’s a cloud-hosted platform. You do not need to worry about setting up and managing databases or servers. ⦁ You can sign up for the subscription plan that suits your budget. ⦁ You can run different types of tests, such as unit tests, integration tests, or end-to-end tests. ⦁ GitLab automatically builds and tests your code changes as they are pushed to the repository. ⦁ Since GitLab CI/CD is built-in, there is no need for plugin installation. ⦁ The platform supports continuous code collaboration and version control. The Architecture of GitLab CI/CD GitLab CI/CD architecture consists of the following components: GitLab Server Like every online platform, GitLab works on a server. The GitLab server is accountable for hosting all your Git repositories. It helps you keep your data on the server for your client and team. The GitLab server hosts your applications and configures the pipeline. It also manages the pipeline execution and assigns jobs to the runners available. GitLab.com is run by a GitLab instance that further comprises an application server, database, file storage, background workers, etc. Runners Runners are applications that run CI/CD pipelines. GitLab has several runners configured. Every user can access these runners on gitlab.com. Users are allowed to set up their own GitLab runners. Jobs Jobs are tasks performed by the GitLab pipeline. Each job has a unique name and script. Each script gets finished one after the other. A user moves on to the next one only when the previous one is complete. Stages Stages are referred to the differences between jobs. They ensure the completion of jobs in the pipeline. For instance, testing, building, and deploying. Pipeline The pipeline is a complete set of stages. Every stage comprises single or multiple jobs. You can find various types of pipelines in GitLab. These types include basic pipelines, multi-branch pipelines, merge request pipelines, parent-child pipelines, scheduled pipelines, multi-job pipelines, etc. Commit Commit is a record of changes made in the code or files. It is similar to what we see in a GitHub repository. So this is an architecture of GitLab CI/CD. Let us learn how to build a simple CI/CD pipeline with GitLab. Building a Simple CI/CD pipeline for a Python Application 1. First, create an account on GitLab. 2. Next, create a new project. You get four different options to create your project. Choose any method convenient to you. In this example, we will import the project from GitHub. 3. Once the project is set up, create a yaml file. Give it a name that is easy to remember. For example, .gitlab-ci.yml. Above is an example of tests run. Image: It is the image we intend to use to execute our script. before_script: Before script helps you install the prerequisites required to run your scripts. It also includes commands you need to run before the script command. after_script: This script outlines commands running after each job. It may also include failed jobs handling. To add the Python image, we are using images available on DockerHub. 4. Under the CI/CD tab, you will find the ‘Jobs’ tab to get detailed logs and troubleshooting. 5. Next, create an account on DockerHub. You can find the image for Docker on Dockerhub. 6. Go back to the yaml script and write a script to upload the docker image to the repository. You will need to use credentials. To ensure the safety of credentials, use another GitLab feature. Go to Settings-> CI/CD-> Variables Here you can make global variables that you can refer to in the code. If you use the masked variable option, it will prevent the visibility of variable content in logs. 7 Next, upload the image to a private repository. Tag the repository name in Dockerhub. It will help you when writing the Docker push command. The stage clause guarantees that each stage will execute one after another. You can create variables both globally and inside the jobs. You can use them as: $var1 8. In our example, we are following docker in the docker concept. It means we have to make docker available inside its container. The docker client and daemon are inside the container to execute the docker command. 9. Now it is time for the preparation of the deployment server. The process involves configuring the tools and settings to automate the deployment. You can use any remote server. In this example, we are using an Ubuntu server. 10. We used the following command to create a private key. ssh-keygen The method to create a private variable is the same as mentioned in step 6. 11. Next, add the yaml script. Before using the docker run command, stop existing containers. Especially those running on the same port. For this, we have added line 37. By default,
Keras Core 3.0 — Pioneering the Next Frontier in Deep Learning APIs
In the dynamic landscape of artificial intelligence, where breakthroughs occur in rapid succession and the boundaries of what’s possible are constantly pushed, the Keras framework has emerged as a steadfast companion for machine learning practitioners and researchers. With the advent of Keras Core 3.0, the framework embarks on a transformative journey, poised to redefine the very essence of capabilities, performance, and adaptability, and solidify its position as a trailblazer in the realm of deep learning. This article delves into the evolution of Keras, highlights the remarkable features of version 3.0, and explores its compatibility with various backends. Understanding Keras — A Journey from Inception to Innovation Keras, born from the visionary mind of François Chollet in 2015, swiftly rose to prominence as a high-level neural networks API known for its intuitive design and unparalleled experimentation agility. Its initial incarnation and subsequent integration with TensorFlow marked a pivotal moment, propelling Keras into the limelight of machine learning tools. As the AI landscape evolved, Keras adapted in tandem, shaping itself to meet the diverse demands of an ever-expanding user community. Now, with the unveiling of Keras Core 3.0, this evolutionary saga culminates in a symphony of enhancements that not only elevate the framework’s capabilities but also redefine its role as an indispensable asset in the arsenal of AI practitioners. Redefining Possibilities — Unveiling Keras 3.0’s Game-Changing Features Embracing the Multi-Backend Landscape Keras 3.0 emerges as a trailblazer with its unprecedented support for multiple backends. While its roots are anchored in TensorFlow, this version casts a wider net, inviting frameworks like jAX and PyTorch into its fold. The result? A harmonious coexistence that empowers researchers and practitioners to wield their preferred framework without renouncing the prowess of Keras. Precision Perfected — Advanced Performance Optimization Keras Core 3.0 doubles down on performance optimization, seamlessly weaving techniques like mixed-precision training and distributed training into its fabric. The result is a turbocharged training process and maximized hardware resource utilization. These optimization strategies work behind the scenes, enabling users to focus on the art of model development and experimentation, confident that the framework is orchestrating the complex ballet beneath. Expanding the Horizons — A Flourishing Ecosystem The Keras ecosystem flourishes with renewed vigour in Keras 3.0. The framework’s enhanced support for KerasCV and KerasNLP, specialized libraries tailored for computer vision and natural language processing, empowers it to excel in these domains. This synergy doesn’t just streamline the development process; it equips users with an extensive toolkit to conquer the intricate challenges inherent in these fields. Uniting the Diverse — Cross-Framework Compatibility Keras Core 3.0 ushers in an era of harmony across deep learning frameworks. Models crafted in Keras effortlessly traverse the boundaries between TensorFlow, jAX, and PyTorch backends, reflecting a unification in an ecosystem historically divided. This seamless compatibility erases barriers, fostering an environment of collaboration and experimentation, where diverse tools coalesce to drive innovation. Evolution by Design — The Philosophy of Progressive Disclosure Keras 3.0 embodies the ethos of progressive disclosure, catering to both novices and seasoned practitioners. The API unfolds in a manner that facilitates the gentle onboarding of newcomers while gradually unveiling the advanced features craved by experts. This balanced approach ensures Keras remains accessible and indispensable, irrespective of users’ proficiency levels. A Stateless Symphony of Design — The Stateless API Paradigm The introduction of the stateless API marks a paradigm shift in Keras 3.0. Aligned with the trend of integrating functional programming concepts in deep learning, this design choice fosters modular architecture, encourages code reusability, and champions clean code organization. This leap not only elevates the development experience but also fortifies code maintenance and collaborative prowess. Navigating the Possibilities — Keras for TensorFlow, jAX, and PyTorch Embarking on the Voyage: Installation Embarking on the journey with Keras Core 3.0 is an effortless endeavour. Installation guides for each supported backend are readily available in the official documentation, providing users the freedom to opt for the backend that resonates with their ethos and project requisites. This adaptability cements Keras as an indispensable entity amid the ever-shifting currents of AI technology. For installation, $ pip install keras-core import keras_core as keras Aligning with the Core: Backend Configuration Configuring the backend is a seamless ritual, often requiring a mere few lines of code. This configuration determines the engine propelling Keras—be it TensorFlow, jAX, or PyTorch. This flexibility empowers users to fluidly transition between backends, paving the way for efficient exploration and experimentation. Run the following command for backend configuration: $ export KERAS_BACKEND="jax" $ python train.py Or $ KERAS_BACKEND=jax python train.py Mastery in Action: Integrating KerasCV and KerasNLP The integration of KerasCV and KerasNLP into Keras Core 3.0 paints a transformative landscape. KerasCV brings forth a symphony for computer vision tasks, providing dedicated APIs and pre-fabricated models for image classification, object detection, and segmentation. Meanwhile, KerasNLP empowers users to navigate the challenges of natural language processing with access to cutting-edge language models, tokenization tools, and sequence manipulation layers. And here is some KerasCV usage example: import keras_cv import keras_core as keras filepath = keras.utils.get_file(origin="https://i.imgur.com/gCNcJJI.jpg") image = np.array(keras.utils.load_img(filepath)) image_resized = ops.image.resize(image, (640, 640))[None, …] model = keras_cv.models.YOLOV8Detector.from_preset( "yolo_v8_m_pascalvoc", bounding_box_format="xywh", ) predictions = model.predict(image_resized) A Confluence of Innovation: In the ever-accelerating tapestry of deep learning, Keras Core 3.0 emerges as a beacon of innovation and adaptability. With its embrace of multiple backends, advanced performance optimization, amplified ecosystem, cross-framework harmony, philosophy of progressive disclosure, and the advent of the stateless API, Keras 3.0 redefines itself as the quintessential deep learning API. It resonates across the spectrum of users—novices venturing forth and experts charting the boundaries of possibility. As the grand symphony of deep learning unfolds, Keras Core 3.0 remains a steadfast companion, empowering developers to manifest their visions with unmatched finesse and precision.
NetDevOps — A Comprehensive Guide with Components and Obstacles
Considering the automation through Agile development processes, the software development industry has experienced a massive shift towards NetDevOps. The credit goes to its underlying network infrastructure offering network automation to fast-paced modern businesses. Since the non-DevOps approach hovers around tools, developers may experience a lack of traceability, testing, and collaboration. Here NetDevOps can help you cop with these limitations and eliminate security vulnerabilities while ensuring expected performance. Similarly, there’s a glut of things you need to know about NetDevOps if you’re looking to incorporate it into your development process. This guide will lead you to the various NetDevOps components and obstacles for a better understanding. What is NetDevOps and Why is it Worth Using? As the term describes itself, NetDevOps is a technical blend of Networking and DevOps. It streamlines the DevOps principles for the deployment and management of network services. If we dig deeper, NetDevOps apply CI/CD DevOps concepts to networking activities for faster delivery. In addition to this, its automated workflows bolster the abstraction, codification, and Infrastructure as Code (IaC) implementation. NetDevOps also eliminate the configuration drift to embed quality and resiliency within the network. In a nutshell, it improves agility by driving clear workflows aiding auditing, governance, and troubleshooting. Challenges You May Face During NetDevOps Development Risk Aversion One of the challenges that organizations may face during NetDevOps development is risk aversion. Many companies are hesitant to adopt new technologies and practices due to the fear of potential failures or disruptions to their existing network infrastructure. This risk aversion can hinder the adoption of NetDevOps methodologies, which emphasize automation, continuous integration, and continuous delivery. To address this challenge, organizations need to focus on building trust by demonstrating the benefits and success stories of NetDevOps implementation. Technical Debt Technical debt refers to the accumulated shortcuts, workarounds, and suboptimal code or configurations that result from rushed or incomplete implementation of network automation processes. This can lead to various issues, including increased complexity, reduced maintainability, and decreased scalability. To mitigate technical debt, organizations should prioritize code quality, conduct regular code reviews, and follow established best practices and coding standards. Implementing automated testing frameworks and leveraging continuous integration and delivery pipelines can help identify and address technical debt early in the development process. Skills Shortage NetDevOps development requires a unique set of skills that combine network engineering, software development, and automation expertise. However, finding individuals with a strong skill set in these areas can be challenging due to the shortage of qualified professionals. To address this issue, organizations can invest in training and upskilling their existing network and IT teams. This can include providing access to relevant courses, certifications, and hands-on training programs. Collaboration with external training providers or universities can also help bridge the skills gap. Documentation Effective documentation plays a crucial role in NetDevOps development, as it ensures that network configurations, automation workflows, and troubleshooting processes are well-documented and accessible to the team. However, maintaining up-to-date and comprehensive documentation can be challenging, especially when changes occur rapidly in dynamic network environments. Organizations can address this challenge by adopting documentation frameworks and tools that facilitate automated documentation generation. Version control systems, wiki platforms, and collaborative document editing tools can also help streamline the documentation process. Unstandardized Data NetDevOps development relies on gathering and analyzing network data to drive automation and decision-making processes. However, network data can be highly diverse and unstandardized, making it challenging to extract meaningful insights and build reliable automation workflows. Organizations should invest in data normalization and standardization techniques to ensure consistency and compatibility across different data sources. This can include using standardized data models, implementing data transformation pipelines, and leveraging data analytics tools for data cleansing and preprocessing. Tool Limitations NetDevOps development often requires the use of various tools and technologies, including network configuration management systems, automation frameworks, and orchestration platforms. However, tool limitations can arise, such as a lack of integration capabilities, limited scalability, or inadequate support for specific network devices or protocols. To overcome these challenges, organizations should thoroughly evaluate and choose tools that align with their specific requirements and network environment. They should also consider open-source solutions that offer flexibility and community support. Top NetDevOps Components Modularity Modularity is a key component of NetDevOps, enabling the creation of flexible and scalable network architectures. By breaking down network systems into modular components, organizations can easily adapt and scale their networks as per evolving requirements. Modularity facilitates the deployment of microservices, allowing for the independent development and deployment of specific network functionalities. This approach not only enhances agility but also simplifies troubleshooting and maintenance, as issues can be isolated to specific modules. For instance, using containerization technologies like Docker, network functions can be encapsulated within lightweight, portable containers, ensuring consistent behavior across different environments. Example 1 – Multiple applications in a single VPC network architecture Example 2 – Single application per VPC network architecture Cultural Changes Cultural changes play a crucial role in successfully implementing NetDevOps. Traditionally, network and operations teams operated in silos, with limited collaboration between them. However, NetDevOps encourages a cultural shift towards increased collaboration, communication, and shared responsibility. By fostering a DevOps culture, organizations can break down barriers between different teams, promoting a collaborative approach to network management. This cultural shift involves embracing shared goals, establishing cross-functional teams, and encouraging continuous learning and skill development. Automation and Infrastructure as Code Automation and Infrastructure as Code (IaC) are pivotal components of NetDevOps, enabling organizations to achieve faster and more efficient network deployments. Automation eliminates manual, error-prone tasks and accelerates the provisioning and configuration of network devices. Tools like Ansible, Puppet, or Chef enable the automation of network device configurations, ensuring consistency and reducing human errors. Infrastructure as Code allows network infrastructure to be defined and managed through machine-readable configuration files, promoting version control and reproducibility. Continuous Integration/Continuous Deployment Continuous Integration/Continuous Deployment (CI/CD) practices are integral to NetDevOps, enabling organizations to rapidly and reliably deploy network changes. CI/CD pipelines automate the process of integrating code changes, testing them, and deploying them to
