Lens is an open-source integrated development environment (IDE) that allows users to connect and manage multiple Kubernetes clusters on Mac, Windows, and Linux platforms. It provides a feature-rich, intuitive graphical interface that allows users to deploy and manage clusters directly from the console.
Kubernetes Lens provides built-in dashboards that provide key metrics and insights into resources running on a Kubernetes cluster, including deployment, configuration, networking, storage, access control, and custom resources.
Brute force attacks are an attempt by a user to gain access to an account or system by constantly entering credentials, either manually or automatically.
The objective of the breach is to discover passwords to enter accounts or find hidden links and get hold of confidential data.
Alternator is an open-source project that allows teams to build "run-anywhere" DynamoDB-compatible applications with ScyllaDB. This means you can deploy wherever you want: on-premises or on any public cloud.
In this tutorial, we'll start by introducing the project. Afterward, we'll see a hands-on example of creating a one-node ScyllaDB cluster and performing some basic operations on it.
To understand how graph databases work, let's define it first. A graph database is a type of NoSQL databases which stores nodes and relationships instead of tables or documents. The data is stored without restricting it to a pre-defined model giving flexibility. Abovementioned NoSQL databases are often perceived as "no SQL" at all but it's actually "not only SQL." In SQL databases, the data is only stored in tables. Using NoSQL, you can store four types of databases: document, key-value, wide-column, and graph.
SQL database examples: MySQL, PostgreSQL, OrientDB, MariaDB, CouchDB, and more.
NoSQL database examples: MongoDB, Apache Cassandra, Neo4j, Redis, DynamoDB, HyperTable, and more.
In fact, there are so many databases now that it's difficult to choose. However, there are lists of best or top databases that have descriptions of functionality and use cases.
I recently gave a talk about debugging for the London Java Community. During the Q&A part of the talk, someone asked me about my approach to Test Driven Development. In the past, I looked at that practice in a more positive light. Writing lots of tests — how can that be bad?
But as time moved on, I now see it in a different light. I see it as a very limited tool that has very specific use cases. It doesn't fit into the type of projects I build and often hinders the fluid processes it's supposed to promote. But let's backtrack for a second. I really liked this post that separates the types and problems in TDD. But let's simplify it a bit, let's clarify that every PR should have good coverage. This isn't TDD. It's just good programming.
Cloud-native development has revolutionized application development in ways that are both positive and challenging. The adoption of microservices architectures on container-based infrastructures enables faster software development lifecycles. At the same time, problems can strike when changes are made to apps, such as adding new features. Moreover, app updates can happen multiple times a day. So how do teams track down problems when error messages pop up, or when it suddenly takes longer to load an application?
Unlike the monolithic approach to application development, where a straightforward application call makes it easy to find where a problem exists, cloud-native applications and the container-based infrastructure they run on are ephemeral. This means problems are elusive. The need for distributed tracing, which tells you exactly where a problem is happening, becomes acutely important for teams needing to quickly fix their applications.
I make a lot of silly projects, and one thing I've wanted for a long time is a way to automate the deployment process using Git. So in this blog post, we are going to be tackling just that.
First things first, let's settle on what the target is. What I want is a system with the following:
Shifting left has brought massive benefits for testing, security and DevOps, but not for tech debt and issue tracking. Yet, technical debt is one of the main reasons development teams struggle or fail to deliver.
Let's face it – developers don't want to work through an endless backlog of tech debt. It just sucks. Tech debt hampers productivity, which impacts morale. There's nothing worse than slowly grinding to a halt as a team.
OTT (Over-the-top) content platforms have become a part of entertainment today.
Due to the pandemic and lockdown, OTT platforms have become popular among consumers seeking home entertainment. Most of the major media companies have become a part of the OTT platform.
This is a mathematical term that originated in the early 20th century in number theory and came almost immediately to computer science as questions arose with resource optimization. Wiki-defined Big O notation is a mathematical notation that describes the limiting behaviour of a function when the argument tends towards a particular value or infinity. Let's rephrase it, and make this definition a little simpler and closer to software development. Any task is solved according to one or another approach, one or another algorithm of action. To compare the effectiveness of heterogeneous solutions written in different programming languages using different approaches, you can start analyzing its execution through the notation. Next, let's look at the most common classes of frequently encountered time complexities.
Caching is a strategy that can help you conserve resources and improve performance. When you have an oft-run expensive query with a result that seldom changes, caching is an ideal solution. By caching the result of that query, you can return the cached result when necessary. The result is the same, but you save the need to run the expensive query. Everybody wins.
In this article, we will walk through the use of Salesforce Functions to cache expensive queries. For instance, we want to query for some value across a large number of records, and the page requiring this query is often loaded. However, the result will not change from one query execution to the next.
I haven't been writing much lately. It's not that I wouldn't like to do it, but it's hard to get time off from a regular job. And being in a position to write some new code is even less realistic. It's no surprise that I felt like Alice in Wonderland when I hit the keyboard last weekend.
I've been thinking for a while now about providing a dependency injection framework for Go that wouldn't be based on reflection. Everything I tried before March of this year was a dead end.
Why March? Because in March, Google released a new version of Go, which allowed us to use Generics. So, this summer, I used my time at the beach, as any good software engineer should, to think about new possibilities for dependency injection.
Motivated by some recent M&A news and general productivity pressures in time of tight budgets, we present some anti-patterns to the use of engineering metrics and give an overview of how to use metrics for productivity insights instead. First, let us first start with what to avoid:
Anti-Pattern to Engineering Metrics
Lines of code written: While lines of code can be a proxy metric for how much code you have to maintain (and therefore costs to incur), it has been proven to be a terrible metric for productivity. This shouldn't need much explanation, but: Output does not necessarily equate to the outcome. Different programming languages have different verbosity, and e.g., just including some open-source packages or pasting in some code from Stack Overflow does not make you more productive. And, of course, solving hard problems often does not require a lot of code but a lot of thinking, exploration, and collaboration.
WordPress is the world's most popular content management system, used by over 40% of all websites. This wide adoption makes it a top target for threat actors and security researchers that get paid for reporting security issues through their public bug bounty program.
Vulnerability brokers are also very interested in acquiring unpatched vulnerabilities enabling them to take over WordPress instances, sometimes offering up to $300,000 for critical ones. As such, WordPress has a heavily reviewed code base in which researchers are not expected to find low-hanging fruits anymore. My previous research on this target required extensive expertise and effort to uncover security issues.
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