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I had a discussion with one of my then-manager-colleagues about ensuring the movement of a planned item even if the only available team member has no expertise to take it on. The simple answer is to get that team member to a starting point with the help of the experienced ones, through a non-heavy pair work format, just enough to give them momentum. It is possible that the task won't be completed on time, but at least they got it moving.
In practice, this is always easier said than done, since we always fall to the mindset of either: "It is going to be faster if the expert will do it" or "I'll just take this non-priority item which I have expertise on rather than mess up that very critical one." This somewhat siloed mentality is especially prevalent in legacy systems, adding to the hurdles of an already challenging DevOps adoption.
With many microservices deployed across multicloud and hybrid infrastructure (cloud, containers, and VMs), the manageability of the network becomes challenging. The transactions among services happen on the public network, so the sensitivity of the matter increases magnitudinally with rising incidents of hacking and cyberattacks.
Istio service mesh is becoming a center of app modernization for large and medium enterprises. Due to Istio’s phenomenal ability to manage and secure the network across cloud and container workloads, the cloud team and DevOps platform teams consider Istio service mesh for the first round of evaluation.
Computing has become increasingly important in our daily lives and society as a whole. While the trend of transistor counts doubling every two years, known as Moore’s Law, may be coming to an end, computing continues to play a vital role. But we aren’t referring to running background applications that hog resources or playing the most visually intensive games.
We are talking about the productive applications of computing for generational discovery. Computation has become an essential tool in many professions, with specialized subfields like computational biology, artificial intelligence and machine learning, and complex computational algorithms for industries like finance, weather, and more. Some of these fields have emerged in the last decade.
Today, AI based on neural networks is at a very interesting stage of its development. It has clearly taken off: we see numerous applications from reading CT scans to picking fruits. But adoption rates vary a lot. Recommendation engines, customer support bots, and other stuff that's been called ”internet AI” are fairly widespread; we see it everywhere in our lives. However, most areas, including software testing, aren’t quite there yet.
What makes AI widely adopted and what’s tripping it up? Let’s take a look at our own field, software testing. Specifically, I want to see how well neural networks can generate automated tests, and what barriers there are to mass adoption of AI in our field. I’ll be looking first and foremost at the problems and difficulties not because I’m trying to be glass-half-empty, but because things that go wrong always tell us more about how a system works than things that go right.
Beta testing is the final phase of testing before releasing a product to the general public. This is usually done by a group of users representing the target audience. It is a crucial step in the development process as it identify and fix any remaining issues with the product before it is released to the global market.
Various methods of beta testing are available, including online platforms where users can test remotely or in-person sessions where users can provide feedback and interact directly with the product.
In the world of microservices architecture, communication between services is of utmost importance. However, with the increasing complexity of microservices, communication between them can become a challenge. That's where the Anti-Corruption Layer (ACL) pattern comes into play. This pattern is designed to improve communication between microservices by establishing a layer between them that acts as a translator, ensuring that services can communicate with each other seamlessly.
The Anti-Corruption Layer pattern is based on the concept of Domain-Driven Design (DDD). It essentially creates a layer between two services that translates one service's language or communication protocol to the other service's language or communication protocol. This layer acts as a mediator, ensuring that the communication between the two services is smooth and efficient.
In the constantly evolving world of software product development, agility isn't just an advantage; it's a necessity. Agile software development, with its dynamic methodologies and iterative approach, has revolutionized how software is developed and delivered.
Agile software development is an approach that promotes iterative development throughout the software lifecycle, encouraging active user involvement, collaboration, and adaptation to changing requirements. It is rooted in the values and principles expressed in the Agile Manifesto, which emphasizes individuals and interactions, working software, customer collaboration, and responding to change.
If you have an aversion to new frameworks, don't even read this. For other kind readers, please note that here I'm going to present a proposal for an API for modeling database queries in a declarative style with strong Kotlin type checking primarily. Only some classes around entities are implemented; the database connection is missing for now. In the project, I tried to evaluate my experience and vision so far. However, not all ideas presented in this paper are completely new. Some of them I drew from the Ujorm framework, and the entity concept was inspired by the Ktorm framework. But the code is new. The prototype results from a mosaic that has been slowly pieced together and has now taken a form that is hopefully worth presenting.
If you are not discouraged by the introduction, I will skip the general talk about ORM and let you get to the code samples. The demonstration examples use two relational database tables. This is an employee/department (unspecified organization) relationship where each employee may (or may not) have a supervisor. Both tables are described by entities from the following class diagram:
If I were going to argue against @scope, the new CSS feature, I might say that CSS already has scope. Like, every selector is “scope”. If you say .el, you’re saying “scope this bit of CSS to elements that have the class name of ‘el'”. But then someone might tell you, ok, but what about “donut scope” 🍩? Donut scope is a way for a scope to stop. Keith Grant shows that off in a recent article:
@scope (.card) to (.slot) {
/* Scoped styles target only inside `.card` but not inside `.slot` */
:scope {
padding: 1rem;
background-color: white;
}
.title {
font-size: 1.2rem;
font-family: Georgia, serif;
}
}Imagine that applied to this HTML:
<div class="card">
<h3 class="title">Moon lander</h3>
<div class="slot">
<!-- scoped styles won’t target anything here! -->
</div>
</div>I’m not sure how I feel about that, honestly. Don’t hate it, but also can’t think of a time when I really really wanted that. That might be because it’s never existed so my brain didn’t try to reach for it. But container queries didn’t exist before, either, and I think we all pined for those anyway. Still, it’s a bit of a +1 for @scope because it’s a thing we can’t do any other way.
If I was still arguing against @scope, then I’d say, well, it turns out actually you can replicate donut scope, thanks to another modern CSS feature, :has(), as Bramus blogged:
.from ~ :has(~ .to) {
outline: 1px solid red;
}And actually, you don’t even need that.
So I’m afraid I have to rescind the point I gave @scope. Are there any other tricks up its sleeve? It does have one! It’s called proximity. This is just kind of a bummer situation in CSS:
<div class="green">
<p>I’m green</p>
<div class="blue">
<p>I’m blue</p>
</div>
</div>If you wrote the CSS like this:
.blue p {
color: blue;
}
.green p {
color: green;
}Then both paragraphs are green. Just because the green selector came second. That’s just how CSS selectors work. Both have the same specificity so the latter one wins. We can fix this with scopes:
@scope (.green) {
p {
color: green;
}
}
@scope (.blue) {
p {
color: blue;
}
}Now that second paragraph is properly blue because the closer scope wins (and it doesn’t matter what order they are in). This is nice for color theming when you use classes to change the theme “higher up” in the DOM. But it doesn’t save you from increased specificity of other selectors, which are probably a more common “bug”. Still, I’ll say this is worth a +1!
Still, I’m not salivating for @scope. It doesn’t solve any big pain points that I’ve personally had. But maybe it does for you, the web is a big place, so all good.
I’ve said it before, but I think the super old-school idea of wanking a <style scoped> into the DOM anywhere and having those styles scoped to the parent is still a cool idea and actually does solve problems. Coupled with :scope { } it means I don’t even have to think of a name of how to select an element, yet still get the ability to apply pseudo-elements and the like. So inline styles with more abilities. And without having to name anything, there is no possible way for the styles to leak anywhere else. Sure, it’s neat to stop style leaking with a @scope too, but because I have to apply it to a class, I need to make a super obscurely named class like .card_987adf87d (a build tool concern) for it to actually worry-lessly “scope”.
The post Chris’ Corner: Scoping appeared first on CodePen Blog.
This is the third and final post about my OCP-17 preparation. In part one, I explained how playing a human virtual machine and refreshing your mastery of arcane constructs is not pointless, even if the OCP doesn’t — and doesn’t claim to — make you a competent developer. In the second part, I showed you how intrinsic motivation keeps itself going without carrots or sticks, provided you can find ways to make your practice fun and memorable. It's time to share some of these examples and tips.
But first some advice about logistics and time management. As with physical exercise, short and frequent trumps long and sporadic. It’s more effective and more likely to become a habit, like brushing your teeth. Choose the time of day when you are most energetic and productive. The early morning works best for me because I’m a morning person. And there is a satisfaction in getting the daily dose crossed off your to-do list, even when it doesn’t feel like a chore.
Dependency Injection (DI) is widely used to manage class dependencies and avoid issues that can arise from implicit dependency usage. Most modern frameworks have native support for the DI feature or can use third-party libraries for it. In this article, we will describe the implementation of DI in the Symfony framework.
Symfony uses the PSR-11 compatible service container to store and obtain services. The service container is aware of all registered services and their dependencies and can provide an already initialized and properly created instance of the required service.
In modern architecture, most of the system has some component of Asynchronous computing. Any application which you see has one versus other Asynchronous communications. Popular Examples of such applications are Video Streaming Services, where you stream the packet of data and then render it on client applications, Popular messaging applications, or any of the collaboration tools. These are just a handful of examples, but you can think of asynchronous communication anywhere where you are not transiting the data in the same request/response.
Event-driven architecture can be visualized as a bunch of the applications module triggered by some kind of event, and then it processes and puts these events back into some place to be consumed by the other application.
Ant Group, formerly known as Ant Financial, is an affiliate company of the Chinese conglomerate Alibaba Group. The group owns the world’s largest mobile payment platform Alipay, which serves over 1.3 billion users and 80 million merchants.
Our team works on the AI platform. With the exponential growth of data, we create and optimize infrastructure that enables large-scale model training and overcomes the performance bottleneck while reducing the cost of data storage and computation.
Sometimes you need to control access to the data in your databases in a very granular way - much more granular than most databases allow.
For instance, you might want some database users to be able to read only the last few digits of some credit card number, or you may need certain columns of certain rows to be readable by certain users only. Or maybe you need to hide some rows from some users under specific circumstances.
Streamlining DevOps workflows with Go service templates can be an intimidating task, especially when dealing with YAML files that contain Go-inspired string interpolation, as seen in tools like Prometheus, Loki, and Promtail. However, when these files are managed with Ansible, a versatile IT automation tool, and Jinja2, a powerful Python templating engine, these complex tasks can be made easier and more efficient for DevOps professionals.
A common challenge when using Ansible is distinguishing between Ansible variables and Go template expressions within YAML files. It's critical to generate a YAML file that includes literal {{ mustaches }} that Ansible should not interpolate. This is where Jinja2's {% raw %}{% endraw %} tags come into play, telling Ansible not to treat the enclosed text as Jinja2 code, thus avoiding variable interpolation.