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The best way to protect your Java code from avoidable bugs is to use static code analysis tools that can help you find and fix problematic code before it reaches production. Let’s look at some popular static code analysis tools that can be used to test code from a number of different angles.

DeepSource

DeepSource delivers what is probably the best static code analysis you can find for Java. The DeepSource Java analyzer detects 190+ code quality issues, including performance bugs, security risks, bug risks, and anti-patterns. Currently, It supports Gradle Java projects, and in the future, DeepSource will add support for Maven and Android too. DeepSource is also working on bringing Autofix support to the Java analyzer, which will let developers fix issues without writing a single code line.

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What is the Azure Service Bus Queue?

Queues are a very common piece of infrastructure. It offers First In, First Out (FIFO) message delivery to one or more competing consumers. As the queue grows the last item added to the queue typically must wait longer to be processed. Just like someone waiting in line to order food, the first one in line gets to order first. Microsoft Azure Service Bus (ASB) is Microsoft’s premier enterprise-level messaging technology that uses this principle of FIFO.

Types of Queues:

  • Primary queue called main queue or active queue.
  • Secondary sub-queue called a dead-letter queue (DLQ). 

Main Queue

ASB queues always have two parties involved-a producer and a consumer. The producer pushes the messages into the queue, while the consumer periodically polls for messages and consumes them. The main queue holds the messages until it is consumed or moved to the dead-letter queue.

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Load average is an age-old metric, which is in existence since the 1970s to indicate whether the system is under heavy/average/low load. It is useful to indicate whether the system’s load is on an increasing trend or a decreasing trend. In this article, let’s learn more about ‘Load Average.’

How to Understand ‘Load Average?’

In most cases, ‘Load Average’ is reported as an average of 1 minute, 5 minutes, and 15 minutes. Please refer to the below screenshot:

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The CMO at Twitter, Leslie Berland, has announced a substantial rebrand intended to reflect the experience of using the site. The move was announced on the platform first and later confirmed on its blog.

Twitter hopes the visual identity will “fully reflect the complexity, fluidity, and power of the conversations today.”

The instantly recognizable bird logomark stays, as does the inoffensive tech-blue. But everything else has been grunged up.

rather than build the system up from each component part or build around a specific element, we embarked upon building a creative design system that’s intentionally imperfect

— Donna Lamar, Global ECD, Twitter

There’s a brand new custom typeface named “Chirp” designed in collaboration with Grilli Type. It mixes features of gothics and grotesques with hipster-friendly quirks and replaces the decidedly corporate Helvetica.

The most visually arresting elements are the print-inspired collages, faux-print effects, and distress marks. It’s a move away from the safe, minimal style that has dominated the design industry for more than a decade.

There are layers of bill posters torn off in pieces revealing text beneath, macroscopic views of people, and an all-pervading effortless cool. Think Paris, on a Sunday morning, circa 1997.

It’s exciting to see a major brand strike out in a new direction, particularly one that isn’t Google-derived. There’s plenty of energy in the new artwork, but it doesn’t escape notice that this is a surface level restyle; the core design remains.

Is this a glimpse of design over the next few years: a braver, irreverent, and decidedly less-corporate style of corporate design? Or, a misstep we’ll all forget as soon as Material Design 3.0 is released?

Source

The post Is Twitter Rebrand a Glimpse at the Future of Design? first appeared on Webdesigner Depot.


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I am an « automation fundamentalist ». What I mean by that, is that I will go through large amounts of pain to avoid having to manually type code if I am able to have my computer do it automatically for me. The reasons are fairly self evident, because all bugs originates from a human being having written code at some point in time. Hence, if I can completely avoid manually writing code, the argument is that I can create 100% perfect software systems, that won’t even in theory be allowed to contain bugs of any kind. This idea extends to writing Unit Tests too.

Therefor, I created the ability to automatically generate Unit Tests in Magic. To understand how, watch the following video, where I demonstrate how I invoke an HTTP REST endpoint, for then to persist the invocation, allowing me to « replay » it later. The idea of course, is that being able to replay an HTTP invocation, can help me sanity check my system further down the road, as I modify it for whatever reasons I have to modify it.

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Now, we have everything prepared and ready to go to a Kubernetes Cluster in a cloud provider. It is a fact that creating a cluster in any cloud provider manually is a difficult task. Moreover, if we want to automate this deployment, we need something that helps us in this tedious task. In this article, we will see how to create a Kubernetes Cluster and all of its required objects, deploying our Alexa Skill with Terraform using Google Kubernetes Engine.

Pre-Requisites

Here, you have the technologies used in this project:

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If you are building an application using Node.js, it can get a little overwhelming since there are a variety of databases to choose from and different ways to build APIs. One way to reduce development time and focus on the problem you are trying to solve is to use a Database as a service to store the data. The advantage of this approach is to use a cloud database system without purchasing hardware which can be cost and time-effective.

One such database service is HarperDB Cloud. To build REST APIs rapidly this service allows us to perform all database operations using a single endpoint. It supports a variety of programming languages such as JavaScript, Java, Python, and so on. Some of the features of HarperDB are the following:

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