Django 5.0: What is New in It?
Django is an open-source Python web framework. It makes the web development process fast and straightforward through its collection of modules. Since its initial release in 2005, the framework has come a long way. With every new update, it is getting more and more robust. Let’s discover what new features and updates it brings in Django 5.0. Significant Updates in Django 5.0 Released on 4 December 2023, Django 5.0 introduces numerous updates to enhance the web development experience. Some of the primary improvements in Django are as per below: Straightforward Rendering of Form Fields One of the notable improvements you can notice in Django 5.0 is that Form Fields are easy to render now. Form fields in Django have numerous elements, such as descriptive labels, help text, error labels, etc. It was always tiresome to lay out all manually. Thankfully in this new version, you don’t need to bother about it. Django 5 features field group templates. These templates simplify the rendering of all form field components, such as widgets, help text, labels, errors, and more. Earlier: <form> … <div> {{ form.name.label_tag }} {% if form.name.help_text %} <div class="helptext" id="{{ form.name.auto_id }}_helptext"> {{ form.name.help_text|safe }} </div> {% endif %} {{ form.name.errors }} {{ form.name }} <div class="row"> <div class="col"> {{ form.email.label_tag }} {% if form.email.help_text %} <div class="helptext" id="{{ form.email.auto_id }}_helptext"> {{ form.email.help_text|safe }} </div> {% endif %} {{ form.email.errors }} {{ form.email }} </div> <div class="col"> {{ form.password.label_tag }} {% if form.password.help_text %} <div class="helptext" id="{{ form.password.auto_id }}_helptext"> {{ form.password.help_text|safe }} </div> {% endif %} {{ form.password.errors }} {{ form.password }} </div> </div> </div> … </form> Now: <form> … <div> {{ form.name.as_field_group }} <div class="row"> <div class="col">{{ form.email.as_field_group }}</div> <div class="col">{{ form.password.as_field_group }}</div> </div> </div> … </form> Database Generated Model Field The database-generated model field is another prominent update you can notice in Django 5.0. The latest GeneratedField in Django lets users create database-generated columns. The good thing is that all database backends support it. It is going to be beneficial for fields computed from other fields. For example: from django.db import models from django.db.models import F class Square(models.Model): side = models.IntegerField() area = models.GeneratedField( expression=F("side") * F("side"), output_field=models.BigIntegerField(), db_persist=True, ) This function can significantly improve the efficiency of the database. Moreover, it maintains the integrity of data. Python Compatibility Django is keeping pace with the ever-evolving Python language. With Django 5.0, users can relish the latest Python features and improvements. This new version supports Python 3.10, 3.11, and 3.12. Not only does it ensure the best performance but also improves security. Now developers can relish the full potential of Django 5.0. Facet Filters in the Admin The Django 5.0 comes with facet counts for applied filters on the admin change list. Developers can toggle this feature using UI (User Interface). It improves the admin interface by presenting facet counts alongside filters. Users can now get a quick insight into the distribution of data. Write Field Choice Easily In the earlier version of Django, it was challenging to list field choices. Users had to make an inconvenient arrangement of 2-tuples or Enumeration subclasses to list the choices available to Field.choices and ChoiceField.choices objects. See the following example: HQ_LOCATIONS = [ ("United States", [("nyc", "New York"), ("la", "Los Angeles")]), ("Japan", [("tokyo", "Tokyo"), ("osaka", "Osaka")]), ("virtual", "Anywhere"), ] Nevertheless, this latest version lets you use concise declarations with the help of dictionary mappings: HQ_LOCATIONS = { "United States": {"nyc": "New York", "la": "Los Angeles"}, "Japan": {"tokyo": "Tokyo", "osaka": "Osaka"}, "virtual": "Anywhere", } It simplifies choices to encode as literals. AsyncClient Django 5.0 features additional asynchronous methods to the Client as well as AsyncClient. It supports asynchronous testing of Django applications. Users can now create tests that replicate the asynchronous behavior of the application. Database-Computed Default Values Django 5.0 lets you define database-computed default values. It means you get more powerful and accurate default settings. The new `Field.db_default` parameter enables users to set database-computed default values for model fields quickly. It is specifically helpful for time stamps or calculated fields. Although it is a minor change, it will have a substantial impact on the integrity of your data. Users can define default values using database functions. Features Deprecated in 5.0 Django 5.0 also has abolished a few old features. Therefore, you must check whether your code relies on any of them. If yes, you will need to update it accordingly. These features were depreciated in previous versions. Some notable ones include: Serialize test setting is no longer available. The undocumented django.utils.baseconv module is abolished. You can’t use undocumented django.utils.datetime_safe module anymore. The USE_TZ setting now has a default value of True. Earlier, it was false. Conclusion Django 5.0 introduces numerous updates and features that take the web development game to the next level. The platform has solidified its position as a powerful and versatile web framework. It has turned into a crucial tool for building websites and web applications. Enhanced flexibility in declaring field choices, improved performance, and numerous security features make it one of the best Python web frameworks. I’ve been working with Django since version 0.96 (2007), so if you need help with it, Contact Now
Next.js 13.5: Exploring Features and Improvements
Next.js is an open-source JavaScript framework built using React. It helps web developers make user-friendly web applications and static websites. This renowned React framework has come with its latest version, Next.js 13.5. With each new version, Next.js is getting more powerful. Released on September 19, 2023, this new edition has taken the world by storm. Let us find out what is new about it. This blog explores the exciting Next.js 13.5 features and improvements. Fast Page Loading The latest version of Next.js supports quick page loading. Next.js 13.5 has optimized its core framework. Therefore, web applications load faster without compromising on user experience. The version 13.5 features built-in optimization for fonts, scripts, images, and application scripts. Image Improvements Next.js 13.5 comes with robust image optimization capabilities. In comparison to previous versions, Next.js 13.5 is better in image resizing and compression. As a result, you can deliver your images in a perfect size and format. In addition to this, the framework has added an experimental function unstable_getImgProps(). It supports different use cases without using the <Image> component. Now you can: Work with background-image or image-set. Use <picture> media queries to carry out Art Direction or Light/Dark mode images. Work with canvas context.drawImage() or new Image(). Moreover, now the placeholder prop provides support for arbitrary data: image/ for placeholder images. Improved Startup and Refresh Time Other Next.js 13.5 improvements that are worth mentioning include fast startups and refreshes. You can notice a significant improvement in refresh and startup time. The framework is now more reliable for App router applications. If you compare this new version with the previous Next.js 13.4, it is about 22% faster in local server startup and 29% quicker in HMR (Refresh). Apart from this, it uses about 40% less memory. Next.js 13.5 has optimized expensive file system operations and removed redundant blocking synchronous calls. Caching in Next.js Apps Caching has a crucial role in web application development. It has a direct impact on user experience and performance. Besides this, it minimizes the operation cost of the application by storing rendering work and data requests. Next.js 13.5 allows users to retrieve the stored version of web applications. Since users do not fetch data from scratch every time, they experience fast web loading. Next.js 13.5 supports numerous caching mechanisms. It makes it easier for developers to carry out client-side caching. Some prominent caching mechanisms in Next.js 13.5 include: Data Cache Data Cache is one of the vital Next.js 13.5 updates. It stores the results of different data fetches across server requests and deployments. As a result, once data is fetched and stored, users can access it quickly in subsequent requests. Since the results are not coming from the source, it takes less time. Request Memoization This caching mechanism helps the server to remember the return values of functions. For instance, if the same data is being requested repeatedly in a React component tree, Next.js stores the data instead of fetching it over again. It is beneficial when the same data needs to be accessed. Full Route Cache As the name suggests, this mechanism caches the full HTML. Besides this, it also stores the React Server Component payload of a route on the server. It naturally minimizes the cost of rendering. Router Cache Next.js also stores the cache on the client’s side. It keeps the record of the React Server Component payload for every route segment. The router cache enhances the navigation experience by storing earlier visited routes and prefetching possible future routes. Improved Experience for Developer With the release of every Next.js version, the framework gets better in the developer experience. The developers can notice a noteworthy improvement in TypeScript support, documentation, and error messages. In addition to this, you get access to Next.js CLI. This tool helps you see updates for project setup and management. Metadata API Metadata API is a crucial addition to Next.js 13.5. With this Next.js 13.5 feature, you do not need to struggle with SEO metatags. Earlier, users had to create a file (head.js) to set the metatags for SEO. Next.js version 13.5 features a new way of handling static and dynamic metadata. You can export objects with static information. For dynamic data, you can export functions. This approach is more helpful for managing metadata efficiently. Stable App Router A stable app router is one of the prominent Next.js 13.5 benefits. Now you can use React server components, nested routes & layouts, and simplified data fetching confidently. These updated server components will help you build apps faster. Conclusion It is imperative to stay informed with the evolving world of web development and its latest technologies. Today, Next.js is a keystone of react-based web development. With Next.js 13.5, you enjoy a range of new features and improvements. Whether it is a developer experience, performance, or security, Next.js 13.5 can keep you ahead in the competitive web development world.
Types of NoSQL Databases: Everything You Need to Know About Them
NoSQL or Not Only SQL is a renowned database management system (DBMS) that manages a large volume of unstructured or semi-structured data. Since it eliminates various limitations of conventional relational databases, the NoSQL database has become popular. Google, Facebook, Amazon, and Netflix are some reputable companies that use NoSQL. This blog makes you aware of different types of NoSQL databases. In addition, you will learn their features. Before we move further, let’s find out how NoSQL is diverse from SQL. SQL vs. NoSQL Databases: Quick Comparison Type SQL databases are Relational Databases, while NoSQL databases are known as non-relational databases. Language of Query SQL databases use a Structured Query Language to do jobs like Delete, Select, Update, and Insert. On the other hand, NoSQL has its query language for manipulating data. NoSQL works on a framework or API, depending on the type of database. Expandability Traditional SQL databases are vertically scalable. You can enhance their performance by upgrading hardware. On the contrary, NoSQL databases are horizontally scalable from the ground up. Consequently, they are better at handling large amounts of data and traffic. Property Followed SQL follows ACID (Atomicity, Consistency, Isolation, and Durability) transactions when it comes to managing data integrity. NoSQL databases use the CAP theorem (Consistency, Availability, and Partition Tolerance). Types of NoSQL Databases We can categorize NoSQL databases into the following 4 types. Each has its pros and limitations. You can choose them based on your requirements. Let us learn about them in detail. Key Value Pair Database Key-Value Pair Database is one of the simplest types of NoSQL Databases. It is a non-relational database storing data elements in key-value pairs. Key-Value Pair Database can handle heavy loads of data. It stores data as a hash map and has two columns, i.e., the Key and the Value. Each database key is different, while the value can be String, Binary Large Objects, or JavaScript Object Notation. The three major features of the Key Value Pair Database are speed, straightforwardness, and scalability. Generally, this type of database is used for creating dictionaries, user profiles, user preferences, etc. Graph-Based Database The graph-based database helps users store entities and relations between those entities. Commonly, this database is used to store data on social networking websites, fraud detection systems, healthcare networks, and more. The graph-based database stores the data as a node. The connections between nodes are known as edges. Every edge and node has a different identifier. The database allows users to find the relationship between the data with the help of links. Unlike relational databases, graph-based databases are multi-relational. A few well-known graph-based databases are Flock DB, Neo4J, Infinite Graph, etc. All-in-all, we can say that a graph-based database stores, manages, and queries data as a graph structure. Column Oriented Database Column Oriented Database is a non-relational database. The database lets you store data in rows and read it row by row. It is like a collection of columns like we see in a table. Each column stores one type of information. The database reads and retrieves the data at high speed. You can run analytics on a limited number of columns to read those columns without consuming memory on unwanted data. Column Oriented Database performs queries like Count, SUM, AVG, and MIN quite quickly. Therefore, the database is used for analytics and reporting, data warehousing, and library card catalogs. Document-Oriented Database A document-oriented database is one of the prominent types of NoSQL databases. It stores and manages data like we organize documents in the real world. Although the data is stored and retrieved as a key-value pair, the value is stored as a document. The database uses the JSON, XML, or BSON documents to store the data. Users can store and retrieve documents from their networks in a form that is closer to the data objects. Therefore, negligible translation is needed to access and use data in an application. Document-Oriented Database supports flexible schema, scalability, and quick retrieval. MongoDB and Couchbase are two fine examples of these databases. This database is used in CMS (Content Management Systems), E-commerce websites, gaming applications, collaboration tools, etc. So these are four types of NoSQL databases. Let’s find out why this database system is getting popular. Features of NoSQL NoSQL has several advancements over traditional databases. We have listed a few significant ones. Compatible with Multiple Data Models Like relational databases, NoSQL is not strict. It can handle multiple data models. Additionally, the database can manage structured, semi-structured, and unstructured data with the same speed. Schema Flexibility Unlike conventional database systems, Not SQL databases do not require a fixed schema. It supports relaxed schemas. NoSQL is capable of managing different data formats and structures. As it does not have a strict predefined schema, it permits changes in data models. Scalable As mentioned above, the NoSQL database is scalable. Users can scale it horizontally by adding more modes and servers. Consequently, it is suitable for websites and web applications with continuously growing data. Excellent Uptime NoSQL databases have excellent uptime. They support serverless architecture and create multiple copies of data on various nodes. Consequently, businesses manage their database smoothly with minimal downtime. If one note breaks down, another takes its place and gives access to the data copy. Examples of NoSQL Now you know the different types of NoSQL databases and their uses. Below are some examples of them. Document Database MongoDB is a well-known document-oriented database. It stores data in JSON-like documents. MongoDB is popular for its scalability and flexibility. Column Database Apache Cassandra is a well-known column-based database system that handles large amounts of data across different commodity servers. Graph Database Amazon Neptune is a managed graph database service by AWS. It can work with both RDF graph and property graph models. Key-Value Database Amazon DynamoDB is a database service that provides high uptime and low-latency key-value storage. This service from Amazon Web Service is the epitome of a Key-Value type database. Conclusion Various types of NoSQL databases are a crucial
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.
Django vs Flask — Which Python Framework is Perfect for Your Web Development Process?
When it comes to web development in Python, two prominent frameworks stand out: Django and Flask. These frameworks offer developers a robust foundation to build powerful web applications efficiently. Based on Model-View-Controller (MVC) architectural pattern, Django is favored for large-scale, complex projects. On the other hand, Flask is a microframework offering a lightweight and flexible approach, empowering developers to have greater control over the application structure. Both platforms have exclusive capabilities and drawbacks complicating the decision-making. In this article, we’ll delve into the technical aspects and industrial attributes of Django and Flask to help you make an informed decision for your web development endeavors. So, let’s get started! Django — Self-Sufficient Web Framework From the house of the Django Software Foundation, Django is a robust and scalable web framework known for its “batteries-included” philosophy. With built-in features and packages, Django promotes rapid development by minimizing the need for external dependencies. Its core components include an Object-Relational Mapping (ORM) layer, a template engine, form handling, authentication, and authorization. Django’s ORM simplifies database interactions, allowing seamless integration with various database systems. The framework follows the Model-View-Controller (MVC) architectural pattern, providing a clear separation of concerns. Additionally, the admin interface offers an out-of-the-box solution for managing application data, making it a popular choice for content-heavy websites. Flask — Minimalistic Microframework Flask is a lightweight and flexible micro-framework designed for simplicity and minimalism. Developed by Armin Ronacher, it provides a solid foundation for web development, offering developers greater control over the application structure. It follows a “micro” philosophy, providing essential tools and leaving the choice of additional libraries to the developers. Furthermore, the framework leverages the Werkzeug toolkit for handling routing and the Jinja2 template engine for rendering dynamic content. Its flexibility and scalability make Flask an excellent choice for small to medium-sized projects, RESTful APIs, and microservices. In addition to its features, the active community and extensive documentation ensure continuous support and updates, contributing to its widespread adoption. Comparison of Django and Flask Based on Industrial Attributes Development Capabilities Django’s batteries-included approach provides a wide array of built-in features, making development faster and more efficient. Its robust ORM simplifies database interactions, while the template engine streamlines UI development. Besides, Flask offers greater flexibility allowing developers to choose and integrate only the necessary components. This makes Flask ideal for lightweight and highly customizable applications. So, Django’s extensive feature set makes it better suited for complex projects that require rapid development and adherence to best practices. If you’re working on smaller projects that require fine-grained control over the application structure, Flask can be a great choice. Scalability Django’s scalability is what makes it a perfect choice for large-scale applications. With its ability to handle heavy workloads, Django’s robust architecture and efficient request handling ensure optimal performance. On the other hand, Flask is inherently scalable, allowing developers to add or remove components as needed. It features a modular design and customizable nature that enables developers to optimize performance for specific use cases. Architecture As discussed above, Django follows the Model-View-Controller (MVC) architectural pattern. This promotes code organization and maintainability, making it easier for multiple developers to collaborate on a project. By default, Flask works with the MVT pattern and offers a similar structure but with a more flexible design. Developers have more freedom to choose how to structure their projects and interact with components. Components and Reutilization Django is famous for its comprehensive set of built-in components, such as the ORM, template engine, and authentication system. As it reduces external dependencies, this promotes code reusability and reduces development time. While Flask provides greater flexibility, it still requires developers to rely on external packages for specific functionality. Flask’s modular design facilitates component reusability, enabling developers to build custom solutions tailored to their project requirements. Community and Support Django boasts a large and active community, with numerous contributors and a wealth of resources available. The community-driven nature of Django ensures continuous development, frequent updates, and comprehensive documentation. This support system provides assistance, encourages best practices, and addresses issues promptly. Flask also enjoys an active community, although smaller in comparison to Django. However, Flask’s community thrives on its simplicity and flexibility, offering extensive documentation and a range of community-contributed extensions. While Django’s larger community offers broader support, Flask’s community provides a close-knit environment for developers seeking minimalistic solutions. Establishment and Updates With its long history, Django has established itself as a mature and stable framework, trusted by many large-scale projects and enterprises. Its consistent updates, bug fixes, and security patches ensure reliability and compatibility with the latest technologies. Despite being a younger framework, Flask has also gained substantial popularity and has seen regular updates, although at a relatively smaller scale. Flask’s updates focus on maintaining stability and introducing new features based on community feedback. Testing Django provides a robust testing framework as part of its core, enabling developers to write comprehensive tests for their applications. Its testing utilities simplify unit testing, integration testing, and user interaction testing. Flask, being a microframework, does not include a built-in testing framework. However, Flask integrates seamlessly with popular Python testing libraries such as pytest and unittest, offering flexibility in choosing the desired testing approach. Both frameworks promote test-driven development and provide the necessary tools and extensions for efficient and thorough testing. End of the Line In conclusion, the choice between Django and Flask ultimately depends on the specific requirements and goals of your web development project. Django’s batteries-included approach, mature ecosystem, and adherence to MVC architecture make it an excellent choice for large-scale, complex applications On the other hand, Flask’s lightweight and flexible nature, coupled with its simplicity and customizability, make it ideal for smaller projects, RESTful APIs, and microservices. It empowers developers to have fine-grained control over the application structure and offers the freedom to choose and integrate only the necessary components. Consider your project’s scale, complexity, customization needs, and community support when making your decision, ensuring the best fit for your web development process.
Kubernetes Persistent Volumes — 5 Detailed Steps to Create PVs
If you want to persist data in Kubernetes, you may utilize the readable and writable disk space available in Pods as a convenient option. But one thing you must know is that the disk space depends on the lifecycle of Pod. Unsurprisingly, your application development process features independent storage available for every node and can handle cluster crashes. Kubernetes Persistent Volumes got your back with their independent lifecycle and great compatibility for stateful applications. This article will lead you to 5 extensive steps to create and implement persistent volumes in your cluster. Before that, let’s dig down to know what exactly persistent volumes in Kubernetes are along with some important terms! Persistent Volumes in Kubernetes A Kubernetes Persistent Volume is a provisioned storage in a cluster and works as a cluster resource. It’s a volume plugin for Kubernetes with an independent lifecycle and no dependency on the existence of a particular pod. Unlike containers, you can read, write and manage your databases without worrying about disk crashes because of restart or termination of the pod. As a shared unit, all the containers in a pod can access the PV and can restore the database even if an individual container crashes. Here are some important terms you must know! Access Modes The accessModes represent the nodes and pods that can access the volume. The field ReadWriteOnce defines every pod having access to read and write the data in a single mode. If you’re using Kubernetes v1.22, you can read or write access on a single node using ReadWriteOncePod. Volume Mode The volumeMode field is mounting functionality of volume into the pods based on a pre-set directory. It defines the behaviour of volume in each Filesystem of a pod. Alternatively, you can use a volume as a raw block storage without any configuration with a Block field. Storage Classes As the name describes, storage classes are the different storage types you can use according to the hosting environment of your cluster. For instance, you can choose azurefile-csi for Microsoft Azure Kubernetes (AKS) clusters while do-block-storage is great for DigitalOcean Managed Kubernetes. Creating a Persistent Volume Step 1: YAML file The process of creating Kubernetes persistent volumes starts with creating a YAML file. The storage configuration represents a simple persistent volume of 1 Gi capacity. Here’s how you can create a YAML file for your PV in Kubernetes: apiVersion: v1 kind: PersistentVolume metadata: name: example-pv spec: accessModes: ReadWriteOnce capacity: storage: 1Gi storageClassName: standard volumeMode: Filesystem Step 2: Adding Volume to the Cluster Once you have created the Persistent Volume, you can add your new persistent volume to your cluster. We recommend using Kubectl for this to make it easier. To add new persistent volume, run: $ kubectl apply -f pv.yaml If you see the following error message while running the command, The PersistentVolume "example-pv" is invalid: spec: Required value: must specify a volume type Try using dynamic volume creation which will automatically create a persistent volume whenever it’s used. That’s because the cloud providers usually restrict allocating inactive storage in the cluster and dynamic volume can be your good-to-go option. Step 3: Linking Volumes to Pods Linking PVs with the pods requires the request to read/write files in a volume. Here the Persistent Volume Claim (PVC) can get you access to the example-pv volume. Let’s see how an example volume claim looks like! apiVersion: v1 kind: PersistentVolumeClaim metadata: name: example-pvc spec: storageClassName: "" volumeName: example-pv As discussed above, you may need dynamic volume creation in some scenarios. You can request a claim for that in the way mentioned below. apiVersion: v1 kind: PersistentVolumeClaim metadata: name: example-pvc spec: accessModes: – ReadWriteOnce resources: requests: storage: 1Gi storageClassName: standard Now, you have unlocked accessModes and storageClassName fields after the claim. All you need to do is to apply the claim to your cluster using Kubectl. Run the following command to quickly apply the claim to your cluster. $ kubectl apply -f pvc.yaml persistentvolumeclaim/example-pvc created In the last, use the volumes and volumeMount fields to link the claim to your pods. This will add pv to your containers section of the manifest and make the files overlive the container instances. To link the claim, run: apiVersion: v1 kind: Pod metadata: name: pod-with-pvc spec: containers: name: pvc-container image: nginx:latest volumeMounts: – mountPath: /pv-mount name: pv volumes: – name: pv persistentVolumeClaim: claimName: example-pvc Step 4: Demonstrating Persistence In the demonstration, you can verify the behaviour of PV in different scenarios. Let’s take a quick example for better understanding. Get a shell to the pod: $ kubectl exec –stdin –tty pod-with-pvc — sh Write a file to the /pv-mount directory mounted to: $ echo "This file is persisted" > /pv-mount/demo Detach the file from the container: $ exit Delete the pod using kubectl: $ kubectl delete pods/pod-with-pvc pod "pod-with-pvc" deleted Recreate the pod: $ kubectl apply -f pvc-pod.yaml pod/pod-with-pvc created Get a shell to the container and read the file: $ kubectl exec –stdin –tty pod-with-pvc — sh $ cat /pv-mount/demo This file is persisted Step 5: Managing Persistent Volumes Kubectl allows you to manage your Kubernetes Persistent Volumes whether you want to retrieve a list or remove a volume. To retrieve a list of PVs, run: $ kubectl get pv NAME CAPACITY ACCESS MODES RECLAIM POLICY STATUS CLAIM STORAGECLASS REASON AGE pvc-f90a46bd-fac0-4cb5-b020-18b3e74dd3b6 1Gi RWO Delete Bound pv-demo/example-pvc do-block-storage 7m52s Review persistent volume claims: $ kubectl get pvc NAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLASS AGE example-pvc Bound pvc-f90a46bd-fac0-4cb5-b020-18b3e74dd3b6 1Gi RWO do-block-storage 9m Sometimes, a volume or PV claim may show a Pending status as the storage class is yet to provision storage. But you can check what’s slowing down the claim process in object’s event history with describe command. $ kubectl describe pvc example-pvc … Events: Type Reason Age From Message —- —— —- —- ——- Normal Provisioning 9m30s dobs.csi.digitalocean.com_master_68ea6d30-36fe-4f9f-9161-0db299cb0a9c External provisioner is provisioning volume for claim "pv-demo/example-pvc" Normal ProvisioningSucceeded 9m24s dobs.csi.digitalocean.com_master_68ea6d30-36fe-4f9f-9161-0db299cb0a9c Successfully provisioned volume pvc-f90a46bd-fac0-4cb5-b020-18b3e74dd3b6 Conclusion: By combining Kubernetes and Persistent Volumes, you can effectively and easily
