Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
If you’re still paying a lot every time you pick up the phone to call grandma in the next state or your best friend in another country, you’re doing it wrong. Subscribing to an expensive long-distance calling plan or paying a high per minute rate is a thing of the […]
The post Long Distance Calling Isn’t Worth It – Here’s Why appeared first on .
If you’ve implemented a cache, message broker, or any other data use case that prioritizes speed, chances are you’ve used Redis. Redis has been the most popular in-memory data store for the past decade and for good reason; it’s built to handle these types of use cases. However, if you are operating a Redis instance, you should be aware of the most common points of failure, most of which are a result of its single-threaded design.
If your Redis instance completely fails, or just becomes temporarily unavailable, data loss is likely to occur, as new data can’t be written during these periods. If you're using Redis as a cache, the result will be poor user performance and potentially a temporary outage. However, if you’re using Redis as a primary datastore, then you could suffer partial data loss. Even worse, you could end up losing your entire dataset if the Redis issue affects its ability to take proper snapshots, or if the snapshots get corrupted.
The concept of a liquid workforce (see Forbes, Banco Santander, etc.) is mostly about this: A part of the workforce is not permanent and can be adapted to dynamic market conditions. In short, in a liquid workforce, a proportion of the staff is made of freelancers, contractors, and other non-permanent employees. Today, it is reported that about 20% of an IT workforce, including software developers, is liquid in a significant part of the Fortune 500 companies.
Actually, working as a freelancer has been a common practice in the media and entertainment industry for a long time. Many other industries are catching up to this model today. From the gig economy to the increasing sentiment stemming from Gen-Y and Gen-Z’ers that employment should be flexible, multiple catalysts are contributing to the idea that the liquid approach is likely to continue eroding the classic workforce.
In software development, requirements, code, and tests may form the backbone of our activities. Requirements, specifications, user stories, and the like are essentially a way to depict what we want to develop. The implemented code represents what we’ve actually developed. Tests are a measure of how confident we are that we’ve built the right features in the right way. These elements, intertwined yet distinct, represent the essential building blocks that drive the creation of robust and reliable software systems. However, navigating the relationships between requirements, code implementation, and testing can often prove challenging, with complexities arising from varying perspectives, evolving priorities, and resource constraints.
In this article, we delve into the symbiotic relationship between requirements, code, and tests, exploring how Venn diagrams serve as a powerful visual aid to showcase their interconnectedness. From missed requirements to untested code, we uncover many scenarios that can arise throughout the SDLC. We also highlight questions that may arise and how Venn diagrams offer clarity and insight into these dynamics.
Native Web Components are still enjoying something of a moment lately. Lots of chatter, and a good amount of it positive. Other sentiment may be critical, but hopeful. Even more important, we’re seeing people actually use Web Components more and more. Like make them and share them proudly. Here are some recently:
<storage-form> component. Here’s an example that happens to me regularly enough that I really notice it. Have you ever been on GitHub, typing up a PR description or something, but then accidentally navigated away or closed the tab? Then you go back, and everything you typed was still there. Phew! They are using the localStorage API to help there. They save the data you type in the form behind the scenes, and put it back if they need to. <wobbly-box>, which draws a border around itself that is every so slightly kittywampus. It uses border-image which is nigh unlearnable so I’d be happy to outsource that. Also interesting that the original implementation was a Houdini paint worklet thing, but since that’ll never be cross-browser compatible, this was the improvement.<target-toggler>, which wraps a <button> and helps target some other element (anywhere in DOM) and hides/shows it with the hidden attribute. Plus toggles the aria-expanded attribute properly on the button. Simple, handy, probably catches a few more details that you would crafting it up quick, and is only like 1KB. <cruk-textarea> that implements Stephen’s trick on auto-growing text areas automatically. Probably isn’t needed for too much longer, but we’ll see. <roving-tabindex> component such that you can put whatever DOM stuff inside to create a focus trap on those things (as it required for things like modal implementations). I think you get this behavior “for free” with <dialog> but that assumes you want to and can use that element. I also thought inert was supposed to make this easier (like inert the entire body and un-inert the part you want a focus trap on), but it doesn’t look like that’s as easily possible as I thought. Just makes this idea all the more valuable. Part of the success story, as it were.Interesting point here: every single one of these encourages, nay requires, useful HTML inside of them to do what they do. Web Components in that vein have come to be called HTML Web Components. Scott Jehl took a moment to codify it:
They are custom elements that
- are not empty, and instead contain functional HTML from the start,
- receive some amount of progressive enhancement using the Web Components JavaScript lifecycle, and
- do not rely on that JavaScript to run for their basic content or functionality
He was just expanding on Jeremy Keith’s original coining and the excitement that followed.
Speaking of excitement, Austin Crim has a theory that there are two types of Web Components fants:
I don’t know if I’m quite feeling that distinction. They feel pretty similar to me, really. At least, I’m a fan for both reasons. We could brainstorm some more fan types maybe! There’s This is the Best Way to Make a Design System group. There’s the This is Progressive Enhancement at its Finest group. There’s the If Chrome Says it’s Good, Then I Say it’s Good group. There’s the Ooooo Something New To Play With group. Your turn.
Let’s end here with two things related to the technology of Web Components you might want to know about.
One of the reasons people reach for JavaScript frameworks is essentially data binding. Like you have some variable that has some string in it (think: a username, for example) and that needs to make it’s way into HTML somewhere. That kind of thing has been done a million times, we tend to think about putting that data in braces, like {username}. But the web platform doesn’t have anything like that yet. Like Rob Eisenberg says:
One of the longest running requests of the Web Platform is the ability to have native templating and data binding features directly in HTML. For at least two decades innovative developers have been building libraries and frameworks to make up for this platform limitation.
The Future of Native HTML Templating and Data Binding
DOM Parts is maybe the closest proposal so far, but read Rob’s article for more in-depth background.
Another thing I’m interested in, forever, is the styling of Web Components. I think it’s obnoxious we can’t reach into the Shadow DOM with outside CSS, even if we know fully what we’re doing. The options for styling within Web Components all suck if you ask me. Who knows if we’ll ever get anything proper (the old /deep/ stuff that had a brief appearance in CSS was removed apparently for good reason). But fortunately Brian Kardell has a very small and cool library that looks entirely usable.
Let’s say you are totally fine with making a request for a stylesheet from within a Web Component though, how does that work? Well there is a such thing as a Constructable StyleSheet, and if you have one of those on hand, you can attach it to a Shadow Root via adoptedStyleSheets. How do you get one of those from requesting a CSS file? The trick there is likely to be import assertions for CSS, which look like:
import sheet from './styles.css' assert {type: 'css'};Now sheet is a Constructable StyleSheet and usable. I like that. But let’s say you’re bundling your CSS, which is generally a smart thing to do. Does that mean you need to start breaking it apart again, making individual component styles individually importable? Maybe not! There is a proposal that looks solid for declaring individually importable chunks of CSS within a @sheet block. Then, just like non-default exports in JavaScript, you can pluck them off by name.
@sheet sheet1 {
:host {
display: block;
background: red;
}
}
@sheet sheet2 {
p {
color: blue;
}
}import {sheet1, sheet2} from './styles1and2.css' assert {type: 'css'};Pretty solid solution I think. I’d be surprised if it didn’t make it into the platform. If it doesn’t, I promise I’ll go awww sheet.
In this tutorial, we will learn how to build and deploy a conversational chatbot using Google Cloud Run and Dialogflow. This chatbot will provide responses to user queries on a specific topic, such as weather information, customer support, or any other domain you choose. We will cover the steps from creating the Dialogflow agent to deploying the webhook service on Google Cloud Run.
The webhook service processes requests from Dialogflow and returns dynamic responses. We'll use Flask, a lightweight WSGI web application framework in Python, to create this service.
WordPress (WP) is an industry-leading web publishing platform with a content management system (CMS) that’s easy to use, SEO-friendly, and completely free. One of the main advantages of using WP over other popular alternatives is its high degree of customizability due to its vast library of themes. In WordPress, themes […]
The post Remove WordPress Themes – How to Uninstall in Under a Minute appeared first on .
Imagine the challenge of rapidly aggregating and processing large volumes of data from multiple point-of-sale (POS) systems for real-time analysis. In such scenarios, where speed is critical, the combination of Kafka and ClickHouse emerges as a formidable solution. Kafka excels in handling high-throughput data streams, while ClickHouse distinguishes itself with its lightning-fast data processing capabilities. Together, they form a powerful duo, enabling the construction of top-level analytical dashboards that provide timely and comprehensive insights. This article explores how Kafka and ClickHouse can be integrated to transform vast data streams into valuable, real-time analytics.
This diagram depicts the initial, straightforward approach: data flows directly from POS systems to ClickHouse for storage and analysis. While seemingly effective, this somewhat naive solution may not scale well or handle the complexities of real-time processing demands, setting the stage for a more robust solution involving Kafka.
Dynamic Programming (DP) is a technique used in computer science and mathematics to solve problems by breaking them down into smaller overlapping subproblems. It stores the solutions to these subproblems in a table or cache, avoiding redundant computations and significantly improving the efficiency of algorithms. Dynamic Programming follows the principle of optimality and is particularly useful for optimization problems where the goal is to find the best or optimal solution among a set of feasible solutions.
You may ask, I have been relying on recursion for such scenarios. What’s different about Dynamic Programming?
The focal point of this article centers on harnessing the capabilities of Artificial Intelligence (AI) and Machine Learning (ML) to enhance the relevance and value of software applications. The key focus of this article is to illuminate the critical aspect of ensuring the sustained relevance and value of AI/ML capabilities integrated into software solutions. These capabilities constitute the core of applications, imbuing them with intelligent and self-decisioning functionalities that notably elevate the overall performance and utility of the software.
The application of AI and ML capabilities has the potential to yield components endowed with predictive intelligence, thereby enhancing user experiences for end-users. Additionally, it can contribute to the development of more automated and highly optimized applications, leading to reduced maintenance and operational costs.
In the dynamic realm of software development, developers often encounter the age-old dilemma of whether to build upon an existing, unmaintainable codebase or embark on the journey of creating a new module from scratch. This decision, akin to choosing between untangling a complex web and starting anew on a blank canvas, carries significant implications for the project's success. In this exploration, we delve into the nuances of these approaches, weighing the advantages, challenges, and strategic considerations that shape this pivotal decision-making process.
Time and Cost Efficiency
In this article, we will look at Vite core features, basic setup, styling with Vite, Vite working with TypeScript and frameworks, working with static assets and images, building libraries, and server integration.
Vite is a build tool and development server that is designed to make web development, particularly for modern JavaScript applications, faster and more efficient. It was created with the goal of improving the developer experience by leveraging native ES modules (ESM) in modern browsers and adopting a new, innovative approach to development and bundling. Here are the core features of Vite:
In the realm of Java development, mastering concurrent programming is a quintessential skill for experienced software engineers. At the heart of Java's concurrency framework lies the ExecutorService, a sophisticated tool designed to streamline the management and execution of asynchronous tasks. This tutorial delves into the ExecutorService, offering insights and practical examples to harness its capabilities effectively.
ExecutorServiceAt its core, ExecutorService is an interface that abstracts the complexities of thread management, providing a versatile mechanism for executing concurrent tasks in Java applications. It represents a significant evolution from traditional thread management methods, enabling developers to focus on task execution logic rather than the intricacies of thread lifecycle and resource management. This abstraction facilitates a more scalable and maintainable approach to handling concurrent programming challenges.
In the fast-paced digital world, where every millisecond counts, understanding the nuances of network latency becomes paramount for developers and system architects. Latency, the delay before a transfer of data begins following an instruction for its transfer, can significantly impact user experience and system performance. This post dives into the critical metrics of latency: P90, P99, and mean response times, offering insights into their importance and how they can guide in optimizing services.
Before diving into the specific metrics, it is crucial to understand why they matter. In the realm of web services, not all requests are treated equally, and their response times can vary greatly. Analyzing these variations through latency metrics provides a clearer picture of a system's performance, especially under load.
Data lineage is the tracking and visualization of the flow and transformation of data as it moves through various stages of a data pipeline or system. In simpler terms, it provides a detailed record of the origins, movements, transformations, and destinations of data within an organization's data infrastructure. This information helps to create a clear and transparent map of how data is sourced, processed, and utilized across different components of a data ecosystem.
Data lineage allows developers to comprehend the journey of data from its source to its final destination. This understanding is crucial for designing, optimizing, and troubleshooting data pipelines. When issues arise in a data pipeline, having a detailed data lineage enables developers to quickly identify the root cause of problems. It facilitates efficient debugging and troubleshooting by providing insights into the sequence of transformations and actions performed on the data. Data lineage helps maintain data quality by enabling developers to trace any anomalies or discrepancies back to their source. It ensures that data transformations are executed correctly and that any inconsistencies can be easily traced and rectified.
In today's cloud computing world, all types of logging data are extremely valuable. Logs can include a wide variety of data, including system events, transaction data, user activities, web browser logs, errors, and performance metrics. Managing logs efficiently is extremely important for organizations, but dealing with large volumes of data makes it challenging to detect anomalies and unusual patterns or predict potential issues before they become critical. Efficient log management strategies, such as implementing structured logging, using log aggregation tools, and applying machine learning for log analysis, are crucial for handling this data effectively.
One of the latest advancements in effectively analyzing a large amount of logging data is Machine Learning (ML) powered analytics provided by Amazon CloudWatch. It is a brand new capability of CloudWatch. This innovative service is transforming the way organizations handle their log data. It offers a faster, more insightful, and automated log data analysis. This article specifically explores utilizing the machine learning-powered analytics of CloudWatch to overcome the challenges of effectively identifying hidden issues within the log data.
There are people in the development world who dismiss low-code and no-code platforms as simplistic tools not meant for serious developers.
But the truth is that these platforms are becoming increasingly popular among a wide range of professionals, including seasoned developers.
Client-server communication is a fundamental part of modern software architecture. Clients (on various platforms — web, mobile, desktop, and even IoT devices) request functionality (data and views) that servers compute, generate, and serve. There have been several paradigms facilitating this: REST/Http, SOAP, XML-RPC, and others.
gRPC is a modern, open source, and highly performant remote procedure call (RPC) framework developed by Google enabling efficient communication in distributed systems. gRPC also uses an interface definition language (IDL) — protobuf — to define services, define methods, and messages as well as serializing structure data between servers and clients. Protobuf as a data serialization format is powerful and efficient — especially compared to text-based formats (like JSON). This makes a great choice for applications that require high performance and scalability.