Enhance Your Learning with Microservices Architecture Flash Cards for quick learning
A software architectural style that structures an application as a collection of small, loosely coupled services, each running in its own process and communicating with lightweight mechanisms.
A traditional software architecture where an application is built as a single, indivisible unit, making it harder to scale, maintain, and deploy independently.
The process of locating and identifying available services in a microservices architecture, enabling dynamic service registration and discovery.
A server that acts as an entry point for all client requests, providing a single point of entry and handling tasks such as authentication, rate limiting, and request routing.
The process of encapsulating an application and its dependencies into a lightweight, isolated container, enabling consistent deployment and scalability.
A software architecture pattern where components communicate through events, enabling loose coupling, scalability, and flexibility.
A programming paradigm that focuses on asynchronous and event-driven programming, enabling responsiveness, resilience, and elasticity.
An approach to software development that emphasizes understanding the domain and designing software based on domain concepts, enabling better alignment with business requirements.
A design pattern that prevents cascading failures in a distributed system by providing fallback options when a service is unavailable or experiencing high latency.
A design pattern for managing long-lived transactions in a distributed system, ensuring consistency and reliability across multiple services.
A design pattern that isolates failures in one part of a system from affecting other parts, improving overall system resilience and fault tolerance.
A decentralized approach to communication in a microservices architecture, where services collaborate by publishing and subscribing to events.
A centralized approach to communication in a microservices architecture, where a central component coordinates and controls the interactions between services.
The ability of a system to recover from failures and continue functioning, often achieved through techniques like fault tolerance, redundancy, and graceful degradation.
The ability of a system to handle increasing workloads by adding resources, such as servers or instances, without sacrificing performance or availability.
The ability of a system to continue operating properly in the presence of faults or failures, often achieved through redundancy and error handling mechanisms.
The process of distributing incoming network traffic across multiple servers or instances, improving performance, availability, and reliability.
A centralized directory of available services in a microservices architecture, enabling service discovery and dynamic routing.
A transaction that involves multiple services in a distributed system, requiring coordination and consistency across all participating services.
Systems that are responsive, resilient, elastic, and message-driven, capable of handling and adapting to dynamic and unpredictable environments.
The ability to understand and measure the internal state of a system based on its external outputs, often achieved through monitoring, logging, and tracing.
The practice of collecting and aggregating log data from multiple services in a distributed system, enabling centralized analysis and troubleshooting.
The practice of tracking and monitoring requests as they flow through a distributed system, providing insights into performance, latency, and dependencies.
Periodic checks performed by services to assess their own health and availability, often used by load balancers to make routing decisions.
The practice of intentionally injecting failures and disruptions into a system to test its resilience and identify potential weaknesses.
A deployment strategy where a new version of an application is deployed alongside the existing version, allowing for seamless switching between the two.
A deployment technique where a new version of an application is gradually rolled out to a subset of users or servers, allowing for testing and monitoring before full release.
A technique where two or more versions of a feature or application are tested simultaneously to determine which one performs better or meets the desired goals.
A technique where the availability of a feature or functionality is controlled by a configuration setting, allowing for easy toggling and gradual rollout.
Strategies and techniques for reverting to a previous version of an application or service in case of issues or failures during deployment or operation.
The practice of assigning unique identifiers or labels to different versions of an application or service, enabling compatibility and managing changes over time.
The integration of microservices architecture with machine learning techniques, enabling intelligent and data-driven applications.
The combination of microservices architecture with IoT devices and platforms, enabling scalable and interconnected smart systems.
The utilization of microservices architecture in blockchain-based applications, enabling decentralized and secure transactions and interactions.
The incorporation of microservices architecture in AI systems, facilitating modular and scalable development of intelligent applications.
The integration of microservices architecture with big data technologies and frameworks, enabling efficient processing and analysis of large datasets.
The combination of microservices architecture with serverless computing platforms, enabling event-driven and scalable application development.
The utilization of microservices architecture with GraphQL, a query language for APIs, enabling efficient and flexible data retrieval and manipulation.
The adoption of microservices architecture in frontend development, enabling independent and composable user interface components.
The utilization of microservices architecture in low-code development platforms, enabling rapid and modular application development.
The integration of microservices architecture with serverless databases, enabling on-demand and scalable data storage and retrieval.
The practice of continuously delivering software updates and improvements to production environments, enabled by microservices architecture.
The utilization of microservices architecture in M2M communication systems, enabling seamless and efficient interaction between devices and services.
The integration of microservices architecture with real-time analytics platforms, enabling instant and actionable insights from streaming data.
The incorporation of microservices architecture in cognitive computing systems, facilitating intelligent and adaptive decision-making processes.
The combination of microservices architecture with quantum computing technologies, enabling advanced and parallel processing of complex problems.
The utilization of microservices architecture in AR applications, enabling interactive and immersive user experiences.
The integration of microservices architecture with VR technologies, enabling realistic and immersive virtual environments.
The incorporation of microservices architecture in NLP systems, facilitating the analysis and understanding of human language.
The combination of microservices architecture with RPA technologies, enabling the automation of repetitive and rule-based tasks.
The utilization of microservices architecture in quantum machine learning systems, enabling the development of advanced and intelligent algorithms.
The integration of microservices architecture with cybersecurity practices and technologies, enabling secure and resilient software systems.
The incorporation of microservices architecture in data privacy frameworks and regulations, facilitating the protection and control of personal data.
The combination of microservices architecture with explainable AI techniques, enabling transparent and interpretable machine learning models.
The utilization of microservices architecture in quantum cryptography systems, enabling secure and unbreakable communication channels.
The integration of microservices architecture with edge computing infrastructure, enabling low-latency and distributed processing at the network edge.
The adoption of microservices architecture in cloud-native application development, leveraging the scalability and flexibility of cloud platforms.
The utilization of microservices architecture with low-latency messaging systems, enabling real-time and event-driven communication between services.
The combination of microservices architecture with quantum networking technologies, enabling secure and high-speed communication between distributed systems.
The incorporation of microservices architecture in robotics systems, facilitating the transparency and interpretability of robotic behaviors.
The utilization of microservices architecture in digital twin systems, enabling virtual representations and simulations of physical entities.