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APIs have become the backbone of modern applications, enabling seamless communication across microservices, mobile apps, and third-party integrations. But as APIs become more exposed, they face challenges such as overload, brute-force attacks, and accidental misuse by clients. To ensure stability, scalability, and security, it’s crucial to implement rate limiting and throttling mechanisms. In this article, we’ll walk through building a rate limiter and throttling layer in Spring Boot with Redis, covering both concepts in depth and providing working code examples. ... How To Build A Rate Limiter And Throttling Layer Using Spring Boot And Redis To Prevent API Overload, Brute-Force Attacks, And Accidental Misuse
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Building a custom system configuration management (SCM) solution allows organizations to precisely control infrastructure and application environments without being constrained by off-the-shelf tools. A well-designed SCM solution typically includes: A Command-Line Interface (CLI) for administrators to interact quickly. An Application Programming Interface (API) for automation and integrations. A Command Execution Layer to perform system-level tasks securely. YAML Configuration Templates for human-friendly, structured configuration. Below, we’ll explore how to design and implement these components step-by-step, with code samples and practical considerations. ... How To Develop Custom System Configuration Management, With Emphasis On CLI And API Design, Command Execution, And YAML Configuration Templates
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In machine learning, building an accurate predictive model is only half the battle — evaluating whether the model generalizes well to unseen data is equally critical. This is where data splitting comes into play. Data splitting involves dividing a dataset into separate subsets for training, validation, and testing to ensure that a model is evaluated fairly and avoids overfitting. Without proper data splitting, a model may appear highly accurate on the data it has seen but fail miserably when faced ... Data Splitting In Machine Learning
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Apache Spark SQL is a powerful engine for large-scale data processing, allowing developers to query data using SQL syntax while leveraging Spark’s distributed computing capabilities. However, one major challenge that teams often face is the cost of running bad queries – whether caused by human error, poorly designed code, or malicious inputs. Even before Spark begins distributed execution, an inefficient or incorrect query can quickly snowball into resource bottlenecks, high compute costs, and system instability. To address this, implementing a ... A Gatekeeper Model for Spark SQL
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Traditional keyword-based search is fast but limited: it only matches exact terms and often fails to understand context, synonyms, or intent. Imagine searching “How do I make spaghetti?” but missing documents titled “Pasta cooking guide” simply because the keyword “spaghetti” wasn’t present. With modern AI techniques — specifically vector embeddings — you can build a search bar that understands meaning, not just words. When combined with OpenAI’s embedding models, this approach lets you perform semantic search, retrieving results based on ... How To Build An AI-Powered Search Bar With Vector Embeddings And OpenAI
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Concurrency has long been a core strength of Java, thanks to its robust threading model and well-established libraries. However, traditional threads have historically been heavyweight and resource-intensive, limiting scalability for modern cloud-native applications. Since Java 8, the language and platform have seen significant improvements to concurrency APIs and mechanisms. By the time we reach Java 21, the introduction of virtual threads has fundamentally redefined how developers build scalable, highly concurrent systems. This article examines the major concurrency advancements from Java ... How Java Concurrency Has Improved From Java 8 Enhancements To Java 21 Virtual Threads, Enabling Lightweight, Scalable, And Efficient Multithreaded Execution
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Feature flags (also known as feature toggles) allow you to control the availability of application features at runtime without deploying new code. Among the different types of feature flags, kill switch feature flags play a crucial role in production stability. They allow you to instantly disable a feature that is causing performance issues, security vulnerabilities, or unexpected errors. In this article, we will walk through how to implement kill switch feature flags in a Spring Boot application using a step-by-step ... How to Implement Kill Switch Feature Flags in a Spring Boot Application
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In modern AI development, enabling language models to interact seamlessly with external tools or APIs is a cornerstone capability. Microsoft’s Semantic Kernel delivers this by serving as an orchestration layer that integrates large language models (LLMs) with external plugins, tools, memory, and planning capabilities. When combined with Azure OpenAI, the Kernel gains powerful LLM endpoints for chat completions and more. The Model Context Protocol (MCP), meanwhile, introduces a standardized interface that unifies how LLMs call tools—bridging the gap between agents ... How To Integrate Semantic Kernel With Azure OpenAI And MCP To Discover Tools, Register Them As Functions, And Enable AI Agents To Invoke Them Dynamically
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Writing network servers that can handle thousands—or even hundreds of thousands—of concurrent connections is a classic challenge, especially under high load. Go makes this remarkably doable with its built-in concurrency model and runtime-integrated I/O multiplexing. Even though developers write seemingly blocking calls like conn.Read() or ln.Accept(), Go handles them efficiently behind the scenes using non-blocking I/O with epoll on Linux and kqueue on BSD-based systems like macOS. This article dives deeply into how this works, with code examples, low-level insight, ... How Go’s Standard Net Package Handles Thousands Of Connections Under High Load Using Non-Blocking I/O Via Epoll On Linux Or Kqueue On BSD/macOS
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Google Cloud Platform (GCP) provides a robust set of tools to build scalable, secure, and reliable cloud infrastructure. This article details how to build a secure GCP stack using a custom Virtual Private Cloud (VPC), a Compute Engine VM with private access, Cloud SQL for PostgreSQL, and BigQuery. We will cover best practices, step-by-step instructions, and code examples to help you implement a secure setup. Designing the Secure GCP Architecture The architecture will include: Custom VPC with segregated subnets to ... How To Build A Secure GCP Stack Using A Custom VPC, VM With Private Access, Managed Cloud SQL PostgreSQL, And BigQuery
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Imagine asking your BigQuery data questions like “Which products sold the most last month?” in plain English—and getting an immediate, accurate answer. This is now possible using the Model Context Protocol (MCP), which lets Claude Desktop securely connect to a BigQuery MCP server and turn natural language into actionable SQL queries. In this article, you’ll learn: What MCP is and why it matters How to install and configure a BigQuery MCP server How to connect Claude Desktop to BigQuery How ... How To Build An MCP Server That Allows Claude’s Desktop To Connect To Your BigQuery Data And Talk To Your Data In Natural Language
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In modern software engineering, data validation is often treated as an implementation detail. It’s something we do at the edges of a system: sanitize user inputs, check constraints, and move on. However, when designing robust systems grounded in solid object-oriented principles, validation deserves deeper consideration. A particularly important lens for this discussion is the Liskov Substitution Principle (LSP), one of the five SOLID principles. The Liskov Substitution Principle states: Objects of a superclass should be replaceable with objects of a ... How Data Validation Relates To The Liskov Substitution Principle — How To Model Validated Data As A Separate Type