Table of Contents
Glossary of Microservices Terms
Glossary of Microservices Terms
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Microservices Architecture is an architectural style that structures an application as a collection of loosely coupled services. Each service is independent, with its own database and can be developed, deployed, and scaled individually. This approach improves agility and scalability by allowing teams to work on separate services simultaneously.
https://en.wikipedia.org/wiki/Microservices
Service Discovery is a mechanism in microservices architecture that allows services to find and communicate with each other dynamically. It typically uses a service registry, where services register their network locations, and other services query the registry to discover available services. Eureka, Consul, and Zookeeper are popular service discovery tools.
https://en.wikipedia.org/wiki/Service_discovery
API Gateway acts as a reverse proxy that routes client requests to the appropriate backend services in a microservices architecture. It consolidates multiple services into a single entry point, providing functionalities like authentication, rate limiting, caching, and request transformation. API Gateway helps manage client-to-service communications efficiently.
https://en.wikipedia.org/wiki/API_gateway
Circuit Breaker is a design pattern used to prevent service failures from cascading in a microservices architecture. It monitors the interactions between services and trips the circuit when a failure threshold is reached. By doing this, it stops further requests to the failing service, allowing it to recover before being reintegrated into the system. Hystrix and Resilience4j are well-known libraries implementing this pattern.
https://en.wikipedia.org/wiki/Circuit_breaker_design_pattern
Event-Driven Architecture is a common approach in microservices architecture where services communicate asynchronously using events. In this setup, services emit events when they complete operations, and other services listen for and react to those events. This pattern decouples services and makes them more scalable and resilient.
https://en.wikipedia.org/wiki/Event-driven_architecture
Docker is a platform that allows developers to package applications and their dependencies into containers, which can then run consistently across different environments. Microservices often leverage Docker to deploy services as lightweight containers, ensuring portability and scalability across cloud and on-premises environments.
https://en.wikipedia.org/wiki/Docker_(software)
Kubernetes is an open-source platform that automates the deployment, scaling, and management of containerized applications. It is widely used in microservices architectures to manage containers and orchestrate service deployments across clusters of machines. Kubernetes offers features like load balancing, service discovery, and self-healing for deployed services.
https://en.wikipedia.org/wiki/Kubernetes
Service Mesh is a dedicated infrastructure layer in a microservices architecture that manages service-to-service communication. It provides observability, security, and traffic management features without altering the application code. Istio and Linkerd are popular service mesh implementations that help streamline communication between services.
https://en.wikipedia.org/wiki/Service_mesh
Container Orchestration refers to the management of containerized applications in a microservices architecture. It involves automating deployment, scaling, networking, and managing containers. Kubernetes and Docker Swarm are the most commonly used container orchestration tools that provide capabilities for automating complex service deployments.
https://en.wikipedia.org/wiki/Containerization
Continuous Integration and Continuous Deployment (CI/CD) is a practice used in microservices architectures to automate code integration, testing, and deployment. CI/CD pipelines enable developers to frequently update codebases and deploy changes to production quickly, enhancing agility and reducing the risk of errors during deployments.
https://en.wikipedia.org/wiki/CI/CD
Bounded Context is a central concept in Domain-Driven Design (DDD) that helps define the boundaries of a service in a microservices architecture. Each microservice is built around a specific business capability and operates within its own bounded context, ensuring that it has a well-defined domain and is loosely coupled with other services.
https://en.wikipedia.org/wiki/Domain-driven_design
Sidecar Pattern is a design pattern in microservices where auxiliary functions, such as logging, monitoring, or networking, are abstracted into a separate component, called a sidecar, which runs alongside the main service. This pattern isolates operational concerns from the application logic, allowing for better service management without modifying the core service.
https://en.wikipedia.org/wiki/Sidecar_pattern
SAGA Pattern is a distributed transaction pattern in microservices that ensures data consistency across services without using traditional two-phase commits. Each service involved in the transaction performs its work and publishes an event or triggers a compensating action in case of failure, ensuring eventual consistency.
https://en.wikipedia.org/wiki/Saga_pattern
Choreography is an approach in microservices where services communicate through events without a central orchestrator. Each service listens for events and triggers other events based on its logic. This contrasts with orchestration, where a central service controls the workflow. Choreography helps to decouple services and promote flexibility.
https://en.wikipedia.org/wiki/Choreography_(distributed_computing)
Orchestration in microservices refers to the centralized coordination of workflows between services. An orchestrator, such as a workflow engine, directs services on what to do and when, providing control over the order of operations and error handling. This differs from the choreography pattern, where services act independently.
https://en.wikipedia.org/wiki/Service_orchestration
CQRS (Command Query Responsibility Segregation) is a pattern in microservices that separates read and write operations. By dividing the data retrieval logic from data modification logic, CQRS allows for optimized performance and scalability, as read and write processes can be independently scaled and tuned.
https://en.wikipedia.org/wiki/Command_query_responsibility_segregation
Idempotency in the context of microservices ensures that making the same request multiple times has the same effect as making it once. This concept is crucial for ensuring reliability in distributed systems, as services may receive duplicate requests due to retries or failures. Idempotent operations help prevent unintended side effects.
https://en.wikipedia.org/wiki/Idempotence
Polyglot Persistence is the practice of using different database technologies to handle various data storage needs in a microservices architecture. Each service can choose the most appropriate type of database, such as SQL, NoSQL, or in-memory databases, based on its specific requirements, promoting flexibility and performance optimization.
https://en.wikipedia.org/wiki/Polyglot_persistence
Distributed Tracing is a technique used in microservices architecture to track requests as they flow through multiple services. It provides visibility into service interactions, allowing developers to diagnose performance issues, errors, and latency in complex systems. Popular tools like Jaeger and Zipkin implement distributed tracing.
https://en.wikipedia.org/wiki/Distributed_tracing
Blue-Green Deployment is a technique used in microservices to minimize downtime during deployments. Two identical production environments, called blue and green, are maintained. One is active while the other is idle. New versions of services are deployed to the idle environment and, after testing, traffic is switched over, allowing for zero-downtime deployments.
https://en.wikipedia.org/wiki/Blue-green_deployment
Event Sourcing is a pattern used in microservices to store the state of a system as a sequence of events. Instead of updating the current state in place, all changes to the state are captured as events and stored in an event log. This ensures traceability and allows reconstruction of the system state at any point in time.
https://en.wikipedia.org/wiki/Event_store
Reactive Microservices are services built with reactive programming principles to handle asynchronous data streams and provide higher scalability. In microservices architecture, reactive systems use non-blocking calls, enabling services to efficiently handle large volumes of requests without being tied to thread-based concurrency models.
https://en.wikipedia.org/wiki/Reactive_programming
Versioning in microservices is the practice of maintaining multiple versions of APIs or services to ensure backward compatibility. As services evolve, versioning helps manage different client requirements without breaking existing integrations, allowing for smoother transitions when updating services.
https://en.wikipedia.org/wiki/API_versioning
Externalized Configuration is a technique in microservices architecture where configuration settings, such as environment variables, database connections, and feature toggles, are stored outside the service code. This decouples configuration from the application, allowing for easier management across different environments, such as development, testing, and production.
Log Aggregation in microservices involves collecting and centralizing logs from different services in a single location for easier monitoring and troubleshooting. Since services run independently, log aggregation tools like ELK Stack (Elasticsearch, Logstash, Kibana) help correlate events across services, providing insights into system behavior and issues.
https://en.wikipedia.org/wiki/Log_aggregation
Rate Limiting is a technique used in microservices to control the number of requests a service can handle within a specific time frame. It prevents overloading of services by throttling excessive requests, ensuring system stability, and protecting against denial-of-service attacks. API gateways often implement rate limiting as part of their functionality.
https://en.wikipedia.org/wiki/Rate_limiting
Telemetry in microservices architecture refers to the collection of metrics, logs, and traces that provide visibility into the performance and behavior of services. Telemetry tools like Prometheus and Grafana are used to monitor system health, track resource usage, and detect potential issues.
https://en.wikipedia.org/wiki/Telemetry
Zero Downtime Deployment is a deployment strategy in microservices where updates to services are performed without interrupting service availability. Techniques like blue-green deployment or canary releases allow new versions to be deployed alongside the old version, ensuring continuous operation while the update is validated.
https://en.wikipedia.org/wiki/Zero-downtime
Immutable Infrastructure is a concept in microservices where infrastructure components, such as servers or containers, are not modified after deployment. Instead, when updates are needed, the entire infrastructure is replaced with a new instance. This approach ensures consistency and reduces the risk of configuration drift.
https://en.wikipedia.org/wiki/Immutable_infrastructure
Canary Deployment is a technique in microservices that involves rolling out new service versions to a small subset of users or traffic before fully deploying to production. This allows for testing in a real-world environment with minimal risk, as issues can be detected early and resolved before broader release.
https://en.wikipedia.org/wiki/Canary_release
API Rate Limiting is a strategy used in microservices to limit the number of API calls a client can make within a defined period. It ensures that no single client or service can overwhelm the system by sending excessive requests. This is crucial for maintaining the performance and availability of services and preventing denial-of-service attacks.
https://en.wikipedia.org/wiki/Rate_limiting
Eventual Consistency is a consistency model in microservices architecture where, in distributed systems, updates to data are not immediately reflected across all services. Instead, after a period of time, all services eventually become consistent. This is often used in systems where availability and partition tolerance are prioritized over immediate consistency.
https://en.wikipedia.org/wiki/Eventual_consistency
Service Oriented Architecture (SOA) is an architectural style that preceded microservices. While similar, SOA is typically more heavyweight, with services communicating through an enterprise service bus (ESB). In contrast, microservices are more decentralized and use lightweight protocols like HTTP or gRPC for communication.
https://en.wikipedia.org/wiki/Service-oriented_architecture
Rollback Strategy in microservices refers to a mechanism used to revert a system to a previous stable state after a deployment or system failure. It is essential for minimizing downtime and ensuring that the system remains operational when issues arise in a new deployment.
https://en.wikipedia.org/wiki/Rollback_(data_management)
Synchronous Communication in microservices is a form of interaction where services communicate in real-time, waiting for a response before proceeding with the next task. While this can simplify interactions between services, it can also introduce latency and tight coupling, making systems less resilient to failures.
https://en.wikipedia.org/wiki/Synchronous_communication
Asynchronous Communication is a communication pattern in microservices where services do not wait for immediate responses. Instead, messages are sent and processed independently, allowing services to continue operating without blocking. This pattern enhances scalability and resilience in distributed systems.
https://en.wikipedia.org/wiki/Asynchronous_communication
Micro Frontends extend the microservices architecture to the frontend by allowing independent teams to develop different parts of a user interface as separate, self-contained units. Each unit can be deployed independently, enabling teams to work on UI components just as they would on backend microservices.
https://martinfowler.com/articles/micro-frontends.html
Distributed Locking is a mechanism in microservices to ensure that only one instance of a service or task can execute a particular operation at a time. This is important in scenarios where multiple services might attempt to perform conflicting actions on shared resources.
https://en.wikipedia.org/wiki/Distributed_lock
Database Sharding in microservices refers to the practice of splitting a large database into smaller, more manageable pieces called shards. Each microservice can manage its own shard, improving performance, scalability, and fault tolerance by distributing data across multiple databases.
https://en.wikipedia.org/wiki/Shard_(database_architecture)
Token-Based Authentication is a security mechanism in microservices that uses tokens (e.g., JWT) to authenticate users and services. After initial authentication, a token is issued, which is then used for subsequent requests, reducing the need for repeated authentication and providing a stateless approach to securing APIs.
https://en.wikipedia.org/wiki/Token-based_authentication
Continuous Monitoring in microservices refers to the process of continuously tracking the performance, availability, and health of services using monitoring tools. It helps detect and address issues in real-time, ensuring that services are operating as expected. Tools like Prometheus and Grafana are commonly used for monitoring in microservices.
https://en.wikipedia.org/wiki/Application_performance_management
Service-Level Agreement (SLA) is a contract between service providers and clients that defines the expected level of service, including uptime, performance metrics, and response times. In microservices architecture, each service can have its own SLA to ensure that it meets the operational standards required by clients or other services.
https://en.wikipedia.org/wiki/Service-level_agreement
Containerization is the process of packaging an application and its dependencies into a container, enabling consistent execution across different environments. In microservices, containers like Docker are widely used to ensure that services are isolated, portable, and easily deployable, regardless of the underlying infrastructure.
https://en.wikipedia.org/wiki/Containerization
Self-Healing Systems in microservices are systems that automatically detect and recover from failures without manual intervention. Using techniques like auto-scaling, circuit breakers, and health checks, a self-healing system can restore normal operations by restarting failed services or rerouting traffic.
https://en.wikipedia.org/wiki/Self-healing_system
Configuration Management in microservices refers to the practice of managing configuration settings across multiple services in a consistent and controlled way. By externalizing configuration, tools like Spring Cloud Config and Consul allow services to be reconfigured without redeployment.
https://en.wikipedia.org/wiki/Configuration_management
Rate Limiting in API gateways controls the number of API calls a service can receive in a specific period. It ensures that services are not overwhelmed by too many requests, preventing denial-of-service attacks and ensuring the fair distribution of resources.
https://en.wikipedia.org/wiki/Rate_limiting
Health Endpoint is an API endpoint exposed by microservices to report their current health status. Monitoring systems use these endpoints to check if a service is operational and ready to handle requests, allowing load balancers to route traffic only to healthy services.
https://en.wikipedia.org/wiki/Health_check
Data Partitioning is the practice of splitting large datasets into smaller, more manageable pieces in a microservices system. Each microservice might own and manage its partition, improving performance and scalability by distributing data across different storage systems or clusters.
https://en.wikipedia.org/wiki/Partition_(database)
Access Control in microservices ensures that only authorized users or services can access certain resources or perform specific actions. OAuth, JWT, and RBAC (Role-Based Access Control) are commonly used to enforce access control in microservices, securing communication between services and with external clients.
https://en.wikipedia.org/wiki/Access_control
Immutable Deployment refers to the practice of deploying microservices in a way that their underlying infrastructure is never modified after deployment. Instead of updating a running instance, new instances are created with the updated code, ensuring consistency and eliminating configuration drift.
https://en.wikipedia.org/wiki/Immutable_infrastructure
Dependency Injection is a design pattern commonly used in microservices to provide service instances and their dependencies at runtime. It allows services to be loosely coupled by injecting their required components, improving flexibility, testability, and maintainability. Spring Framework is widely used for dependency injection in Java-based microservices.
https://en.wikipedia.org/wiki/Dependency_injection
Observability refers to the ability to measure and understand the internal state of microservices based on their outputs, such as logs, metrics, and traces. Observability helps teams gain insights into system performance and detect issues before they impact users, enhancing the system's reliability and performance.
https://en.wikipedia.org/wiki/Observability_(software)
Stateful and Stateless Services differentiate how microservices handle data between requests. Stateful services retain client-specific data between requests, while stateless services do not, allowing for easier scalability. Stateless services are generally preferred in microservices as they simplify scaling and fault tolerance.
https://en.wikipedia.org/wiki/Stateless_protocol
Saga Pattern is a distributed transaction pattern used in microservices to manage long-running business processes and ensure data consistency across multiple services. Each step in a saga is a local transaction that can trigger subsequent transactions or compensating actions in case of failure, providing eventual consistency without global transactions.
https://en.wikipedia.org/wiki/Saga_pattern
Container Networking in microservices refers to the way containers communicate with each other within a distributed system. Tools like Kubernetes provide built-in networking solutions to ensure that services deployed in containers can discover and communicate with each other reliably through dynamic service discovery and routing.
https://en.wikipedia.org/wiki/Container_networking
Loose Coupling is a design principle in microservices that encourages services to be independent of each other, minimizing dependencies. This ensures that changes to one service do not affect others, enabling faster development and more scalable architectures. Achieving loose coupling requires well-defined APIs and minimal shared state.
https://en.wikipedia.org/wiki/Loose_coupling
Load Balancing is a method used in microservices to distribute incoming traffic across multiple instances of a service, ensuring no single instance is overwhelmed. Load balancing helps optimize resource utilization, maximize throughput, and reduce response times, making the system more resilient to high traffic and failures.
https://en.wikipedia.org/wiki/Load_balancing_(computing)
Idempotent Operations are operations that can be safely retried multiple times without changing the result beyond the initial application. In microservices, idempotency is crucial for handling network retries and ensuring data consistency when the same request is processed more than once.
https://en.wikipedia.org/wiki/Idempotence
Rolling Deployment is a deployment strategy in microservices where new versions of services are gradually introduced by replacing instances one at a time. This approach ensures minimal downtime and allows for testing new versions with live traffic before fully replacing the old version.
https://en.wikipedia.org/wiki/Rolling_deployment
Message Broker is a middleware component in microservices used to facilitate communication between services through asynchronous message passing. RabbitMQ, Kafka, and ActiveMQ are common message brokers that decouple services and allow them to communicate without direct dependencies, improving scalability and resilience.
https://en.wikipedia.org/wiki/Message_broker
Pact is a contract testing tool used in microservices to ensure that services can interact correctly. It allows consumers and providers to define contracts for their interactions, ensuring that both sides meet the expectations set by the contract, helping to prevent integration issues between services.
https://en.wikipedia.org/wiki/Contract_testing
Service Choreography refers to a design approach in microservices where each service manages its own behavior in response to events, without relying on a central coordinator. Unlike orchestration, service choreography promotes more decentralized control and is often implemented in event-driven systems, allowing services to react to changes independently.
https://en.wikipedia.org/wiki/Choreography_(distributed_computing)
OAuth is an open standard for authorization commonly used in microservices to enable secure access to resources. It allows third-party services to access a user's resources without exposing their credentials. OAuth is often used to manage authorization between services and external clients in a secure, token-based manner.
https://en.wikipedia.org/wiki/OAuth
Dynamic Scaling in microservices refers to the ability of a system to automatically adjust the number of service instances based on traffic or load. By dynamically scaling services up or down, the system can maintain performance during peak times and save resources during low-demand periods, typically implemented using Kubernetes or Amazon ECS.
https://en.wikipedia.org/wiki/Scalability
Service Versioning is a practice in microservices that involves maintaining multiple versions of a service to support backward compatibility. Service versioning allows new features to be deployed without disrupting existing consumers who rely on older versions, facilitating smoother transitions between service upgrades.
https://en.wikipedia.org/wiki/API_versioning
Eventual Consistency is a data consistency model used in distributed microservices where updates to a system may not be reflected immediately across all services, but will eventually reach consistency over time. This model is widely used in systems where high availability is prioritized over immediate consistency, such as with NoSQL databases.
https://en.wikipedia.org/wiki/Eventual_consistency
Data Replication in microservices refers to the process of duplicating data across multiple instances or services to improve data availability and fault tolerance. Replicating data across services ensures that even if one service instance fails, others can continue providing the necessary data, improving system resilience.
https://en.wikipedia.org/wiki/Data_replication
Federated Identity is a system in which a user’s identity is managed across multiple services or domains, enabling single sign-on (SSO) between services in a microservices ecosystem. This allows users to authenticate once and access multiple services without needing to log in separately for each one, often using protocols like OAuth or SAML.
https://en.wikipedia.org/wiki/Federated_identity
Distributed Logging refers to the practice of collecting and managing logs from multiple services in a microservices architecture. Tools like ELK Stack (Elasticsearch, Logstash, Kibana) and Fluentd are commonly used to centralize logs for better observability and troubleshooting in distributed systems.
https://en.wikipedia.org/wiki/Log_management_and_analysis
Service Isolation is a principle in microservices that ensures each service is independent and runs in isolation from other services. This independence improves fault tolerance and allows services to scale independently. Service isolation is typically implemented using containers or virtual machines to ensure clear boundaries between services.
https://en.wikipedia.org/wiki/Service-oriented_architecture#Microservices
Service Discovery is a mechanism used in microservices to dynamically locate the network addresses of services. This allows services to find and communicate with each other without hard-coding network details. Tools like Eureka, Consul, and Zookeeper are commonly used to implement service discovery.
https://en.wikipedia.org/wiki/Service_discovery
Idempotency Key is a unique identifier used in microservices to ensure that repeated requests, particularly in network retries, do not result in duplicate operations. By checking the idempotency key, services can recognize and ignore duplicate requests, preventing unintended side effects like duplicate payments or actions.
https://en.wikipedia.org/wiki/Idempotence
Circuit Breaker Pattern is a design pattern used in microservices to prevent cascading failures in distributed systems. When a service call fails repeatedly, the circuit breaker “trips” to stop further calls to the failing service. This allows the system to recover gracefully while the failing service is fixed or restarted.
https://en.wikipedia.org/wiki/Circuit_breaker_design_pattern
Remote Procedure Call (RPC) is a communication protocol where one service can invoke methods in another service as if they were local. In microservices, gRPC and Thrift are commonly used RPC frameworks that allow services to interact across different programming languages and platforms.
https://en.wikipedia.org/wiki/Remote_procedure_call
Horizontal Scaling is a method of scaling in microservices where additional instances of services are deployed to handle increased traffic. Unlike vertical scaling, which adds more resources to a single server, horizontal scaling improves system performance by distributing load across many service instances, often using tools like Kubernetes.
https://en.wikipedia.org/wiki/Scalability#Horizontal_and_vertical_scaling
Rate Limiting is a technique used in microservices to control the number of requests a service can handle within a specific time frame. It prevents overloading services, ensuring fair use of resources, and protecting against denial-of-service attacks. It is commonly implemented in API gateways.
https://en.wikipedia.org/wiki/Rate_limiting
Throttling in microservices refers to limiting the rate of requests or processing capacity for a service, preventing it from being overwhelmed by high traffic. Throttling helps maintain service availability by controlling the number of incoming requests, especially during peak traffic or under high load.
https://en.wikipedia.org/wiki/Throttling_process
Container Image in the context of microservices is a lightweight, standalone, and executable package that includes everything needed to run a service—code, runtime, libraries, and system dependencies. Docker and Kubernetes use container images to ensure services run consistently across different environments.
https://en.wikipedia.org/wiki/Container_(virtualization)
Service Contract in microservices is a well-defined agreement between services that specifies how they will interact, typically through APIs. The service contract defines the request and response formats, including data types and expected behavior, ensuring that services can communicate correctly and evolve independently without breaking changes.
https://en.wikipedia.org/wiki/Service-level_agreement
Payload in microservices refers to the data transmitted in a request or response between services. It is typically in formats like JSON or XML, and payload size can affect performance, making it critical to optimize data sent across services to ensure efficient communication and processing.
https://en.wikipedia.org/wiki/Payload
Distributed Database in microservices refers to a database architecture where data is stored across multiple servers or regions. Each microservice may manage its own database, or databases can be split across geographical regions to improve performance and fault tolerance. Cassandra and MongoDB are examples of databases used in distributed systems.
https://en.wikipedia.org/wiki/Distributed_database
Bulkhead Pattern is a design pattern in microservices where system components are isolated from each other to prevent failure in one service from affecting the rest of the system. It creates separate areas, or “bulkheads,” to contain faults, ensuring that critical services remain functional even when others fail.
https://en.wikipedia.org/wiki/Bulkhead_pattern
gRPC is a high-performance RPC (Remote Procedure Call) framework commonly used in microservices to facilitate communication between services. gRPC supports multiple programming languages and enables efficient binary serialization using Protocol Buffers, making it ideal for low-latency, high-throughput systems.
https://en.wikipedia.org/wiki/GRPC
JWT (JSON Web Token) is a compact and secure token format used for authorization in microservices. JWT tokens contain user information and are signed to ensure their authenticity. They are often used in stateless authentication systems, allowing services to verify users without storing session information.
https://en.wikipedia.org/wiki/JSON_Web_Token
Fallback Mechanism in microservices refers to an alternative execution path when a service is unavailable or fails. It provides resilience by ensuring the system can continue functioning in a degraded mode, offering default responses or cached data until the service recovers. Hystrix is a popular library that implements fallback mechanisms.
https://en.wikipedia.org/wiki/Fallback_(software_engineering)
Sidecar Pattern in microservices involves deploying a secondary service alongside the main service in the same runtime environment (usually a container). The sidecar manages auxiliary tasks such as logging, monitoring, or security, allowing the main service to focus solely on business logic. Istio is an example of using the sidecar pattern for service mesh management.
https://en.wikipedia.org/wiki/Sidecar_pattern
Service Mesh is an infrastructure layer in microservices that controls service-to-service communication, typically in a containerized environment. It manages aspects like traffic management, load balancing, and security without requiring changes to application code. Istio and Linkerd are popular service mesh implementations.
https://en.wikipedia.org/wiki/Service_mesh
Latency in microservices refers to the time delay between a request being made and the response being received. In a distributed system, latency can be caused by network delays, processing time, or load balancing. Monitoring and reducing latency is crucial for maintaining a responsive microservices architecture.
https://en.wikipedia.org/wiki/Latency_(engineering)
Event-Driven Architecture in microservices is a design pattern where services communicate asynchronously by emitting and responding to events. This decouples services, allowing them to act independently. Events can trigger workflows or state changes in other services, providing scalability and resilience.
https://en.wikipedia.org/wiki/Event-driven_architecture
CQRS (Command Query Responsibility Segregation) is a pattern used in microservices to separate read and write operations. By dividing the responsibilities, CQRS allows for more efficient data handling and scaling, with the ability to optimize read and write paths independently. This pattern is especially useful in event-sourced systems.
https://en.wikipedia.org/wiki/Command_query_responsibility_segregation
API Gateway is a critical component in microservices that serves as a single entry point for client requests, routing them to the appropriate backend services. It handles tasks such as request routing, composition, rate limiting, and security. An API Gateway also helps abstract the internal architecture of microservices from the client.
https://en.wikipedia.org/wiki/API_gateway
Transactional Outbox Pattern is a pattern used to ensure consistency in microservices that rely on both a database and message broker for communication. It guarantees that both the database update and the message publication happen within the same transaction, preventing data inconsistencies due to failures during event emission.
https://microservices.io/patterns/data/transactional-outbox.html
Idempotent Producer in microservices refers to a messaging pattern where the same message can be sent multiple times without changing the state of the receiving service. This ensures that duplicate messages do not lead to inconsistent or erroneous states, which is especially important when dealing with unreliable network communications.
https://en.wikipedia.org/wiki/Idempotence
Sharded Database is a technique in microservices where a single logical database is split into smaller, horizontal partitions known as shards. Each shard contains a portion of the data and can be hosted on separate servers, improving performance and scalability by distributing the load across multiple instances.
https://en.wikipedia.org/wiki/Shard_(database_architecture)
Message Queue in microservices is used to decouple services by storing and forwarding messages between producers and consumers. This enables asynchronous communication, ensuring that the receiving service can process messages at its own pace. Popular message queue systems include RabbitMQ and Kafka.
https://en.wikipedia.org/wiki/Message_queue
Service Registry is a dynamic database used to store information about available services in a microservices environment. Services register themselves with the service registry, and other services use it to discover available endpoints. Tools like Eureka, Consul, and Zookeeper are often used for service registration and discovery.
https://en.wikipedia.org/wiki/Service_registry
Resilience in microservices refers to the ability of the system to remain functional in the face of failures or adverse conditions. Techniques like circuit breakers, bulkheads, and retries are commonly used to build resilient services that can recover or fail gracefully without affecting the entire system.
https://en.wikipedia.org/wiki/Resilience_(engineering_and_construction)
Eventual Consistency is a consistency model used in distributed microservices where updates to data are not immediately reflected across all services but will eventually propagate to achieve consistency. This model is often used in systems that prioritize availability over strict consistency, such as in NoSQL databases.
https://en.wikipedia.org/wiki/Eventual_consistency
Cross-Cutting Concerns in microservices are aspects like logging, security, monitoring, and error handling that affect multiple services across an application. These concerns are typically handled using frameworks, middleware, or service mesh layers, allowing each service to focus on its core business logic without managing these shared aspects.
https://en.wikipedia.org/wiki/Cross-cutting_concern
Canary Release is a deployment strategy in microservices where a new version of a service is gradually rolled out to a small subset of users before a full deployment. This allows for real-time testing in production and minimizes the risk of system-wide failures by detecting issues early in the release process.
https://en.wikipedia.org/wiki/Canary_release
Distributed Tracing is a technique used in microservices to track the flow of requests as they traverse through multiple services. By collecting and correlating trace data, developers can pinpoint bottlenecks and identify issues in a complex, distributed system. Tools like Jaeger and Zipkin are commonly used for distributed tracing.
https://en.wikipedia.org/wiki/Distributed_tracing
Service-Level Objective (SLO) is a measurable target for service performance, typically derived from a Service-Level Agreement (SLA). In microservices, each service can have its own SLO that defines the acceptable level of performance, such as response time or availability, helping to ensure service reliability and customer satisfaction.
https://en.wikipedia.org/wiki/Service-level_objective
Service Versioning allows multiple versions of the same service to coexist in a microservices architecture. This ensures backward compatibility for older clients while allowing new features to be introduced in newer versions. Versioning is essential for maintaining flexibility and reducing disruption during service updates.
https://en.wikipedia.org/wiki/API_versioning
Polyglot Persistence in microservices refers to the use of different types of databases depending on the requirements of each service. Each microservice can choose the best-suited database technology, such as SQL, NoSQL, or graph databases, based on its specific data and query requirements.
https://en.wikipedia.org/wiki/Polyglot_persistence
Graceful Degradation is a strategy in microservices that ensures the system continues to operate at a reduced level of functionality when certain services or components fail. Instead of crashing, the system can degrade its service level, allowing non-critical functionality to be temporarily disabled while maintaining core operations.
https://en.wikipedia.org/wiki/Graceful_degradation
Outbox Pattern is a design pattern used in microservices to ensure that changes to a database and the emission of events happen in a transactional and consistent manner. By storing outgoing messages in a dedicated outbox table in the database, events are reliably sent after the transaction is completed, ensuring eventual consistency.
https://microservices.io/patterns/data/transactional-outbox.html
Contract Testing in microservices is a testing approach where interactions between services are validated based on predefined contracts. These contracts specify how services communicate, ensuring that both providers and consumers of a service maintain compatibility, especially as services evolve.
https://en.wikipedia.org/wiki/Contract_testing
Sticky Sessions in microservices refer to the practice of routing a user's requests to the same service instance during a session. This can improve performance by maintaining session state in-memory for the same user, but it can complicate scaling and failover since requests are tied to specific instances.
https://en.wikipedia.org/wiki/Sticky_session
Clustered Service is a group of service instances in a microservices architecture that work together to provide high availability and scalability. By running multiple instances of the same service across a cluster, the system can balance the load and recover from failures, ensuring continuous availability.
https://en.wikipedia.org/wiki/Computer_cluster
Message Broker is a middleware component in microservices that enables asynchronous communication by receiving, holding, and forwarding messages between services. This decouples services, allowing them to operate independently without needing to wait for each other. Popular message brokers include Kafka and RabbitMQ.
https://en.wikipedia.org/wiki/Message_broker
Data Partitioning in microservices is the practice of dividing large datasets into smaller, more manageable pieces, typically based on some key or attribute. Each partition is handled by a separate instance or database, improving performance and scalability. This helps distribute the load across multiple nodes and allows services to handle large volumes of data more efficiently.
https://en.wikipedia.org/wiki/Partition_(database)
Service Granularity refers to the size and scope of functionality provided by a microservice. In microservices architecture, determining the right level of granularity is important for balancing maintainability, performance, and scalability. Smaller services may be easier to manage but can lead to more communication overhead, while larger services can become monolithic.
https://microservices.io/patterns/decomposition/service-granularity.html
Retry Pattern is a common design pattern in microservices that automatically retries failed requests after a short delay. This helps services recover from temporary issues such as network interruptions or transient errors, improving resilience. Libraries like Resilience4j provide built-in support for retry mechanisms.
https://en.wikipedia.org/wiki/Resilience_pattern
Event Sourcing is a pattern used in microservices where changes to the state of a service are captured as a sequence of events. Instead of storing just the current state, each state change is recorded as an immutable event, allowing the system to replay events to reconstruct the current state or any past state.
https://en.wikipedia.org/wiki/Event_sourcing
Saga Pattern is a distributed transaction pattern used in microservices to manage long-running processes and ensure data consistency across multiple services. Each service involved in a saga executes a local transaction, and in the event of failure, compensating transactions are triggered to undo previous actions, ensuring eventual consistency.
https://en.wikipedia.org/wiki/Saga_pattern
Sidecar Proxy is a component in a service mesh that runs alongside each service instance, handling communication between services. The sidecar proxy manages network traffic, security, and observability features without modifying the application code, decoupling these concerns from the core business logic.
https://en.wikipedia.org/wiki/Sidecar_pattern
Immutable Infrastructure in microservices refers to the practice of deploying services in a way where infrastructure components, such as servers or containers, are never modified after deployment. Instead, new instances are deployed with updated configurations or code, ensuring consistency and reducing configuration drift.
https://en.wikipedia.org/wiki/Immutable_infrastructure
Consumer-Driven Contracts (CDC) is a testing strategy in microservices where the consumers of a service define the contract for how they expect the service to behave. This ensures that services maintain backward compatibility with their consumers even as they evolve, preventing breaking changes in the interaction.
https://en.wikipedia.org/wiki/Contract_testing
Health Check in microservices refers to an endpoint exposed by services that reports on their health and readiness. Monitoring systems use health checks to determine if a service is functioning correctly and can accept traffic, allowing for better load balancing and automatic failover of unhealthy instances.
https://en.wikipedia.org/wiki/Health_check
API Throttling is a technique used in microservices to limit the number of API requests a service can process in a given period. By enforcing rate limits, throttling ensures that no single user or service can overwhelm the system, protecting against abuse and ensuring fair use of resources.
https://en.wikipedia.org/wiki/Rate_limiting
Correlation ID is a unique identifier attached to each request in a microservices architecture, allowing developers to track and trace requests across multiple services. This helps correlate logs and trace messages to a single request, providing better visibility into distributed systems and simplifying debugging.
https://en.wikipedia.org/wiki/Correlation_identifier
Circuit Breaker Pattern is used in microservices to prevent a service from making requests to another service that is likely to fail. When failures exceed a certain threshold, the circuit breaker trips, and the service will not attempt further requests until the other service becomes healthy again. This improves system resilience and reduces the risk of cascading failures.
https://en.wikipedia.org/wiki/Circuit_breaker_design_pattern
Backpressure in microservices refers to the ability of a system to regulate the flow of data between services to prevent overloading. When one service produces data faster than another can consume it, backpressure mechanisms help control the data flow to avoid overwhelming downstream services, ensuring system stability.
https://en.wikipedia.org/wiki/Backpressure_(software)
Chaos Engineering is the discipline of testing microservices by intentionally introducing faults or failures to understand how the system behaves under adverse conditions. The goal is to identify weaknesses and improve the system's resilience. Tools like Chaos Monkey are used to randomly shut down services to see how the system handles failures.
https://en.wikipedia.org/wiki/Chaos_engineering
Query Side in CQRS (Command Query Responsibility Segregation) refers to the part of the system responsible for handling read operations. In a microservices context, the query side is optimized for reading data, often using denormalized data structures or caching to improve performance and reduce query complexity.
https://en.wikipedia.org/wiki/Command_query_responsibility_segregation
Service Contract in microservices defines the formal agreement between services about how they will communicate. The contract specifies the data formats, request/response structure, and expected behavior. Service contracts ensure that changes to one service do not break its interactions with others, promoting loose coupling and service independence.
https://en.wikipedia.org/wiki/Service_level_agreement
Pessimistic Locking is a concurrency control mechanism used in some microservices to prevent conflicts by locking the data when a service starts modifying it. While pessimistic locking ensures data consistency, it can reduce system throughput by holding locks for long periods, potentially leading to deadlocks or reduced scalability.
https://en.wikipedia.org/wiki/Pessimistic_concurrency_control
Distributed Cache in microservices is used to improve performance by storing frequently accessed data across multiple cache nodes. By distributing the cache, data retrieval is faster, and the load on the database is reduced, enhancing overall system efficiency. Tools like Redis and Memcached are commonly used for distributed caching.
https://en.wikipedia.org/wiki/Distributed_cache
Telemetry in microservices refers to the collection of metrics, logs, and traces that provide insights into the health and performance of services. It allows developers to monitor and understand system behavior in real-time, identifying potential issues and ensuring that services are meeting performance goals.
https://en.wikipedia.org/wiki/Telemetry
Dead Letter Queue (DLQ) is a specialized message queue in microservices where undeliverable messages are placed after several failed delivery attempts. It helps capture problematic messages for further investigation, preventing them from disrupting normal message processing while ensuring no data is lost.
https://en.wikipedia.org/wiki/Dead_letter
Horizontal Scaling in microservices refers to the practice of adding more instances of a service to handle increased load. This contrasts with vertical scaling, where more resources (CPU, memory) are added to a single instance. Horizontal scaling allows systems to be more resilient and handle more traffic by distributing the load across multiple instances.
https://en.wikipedia.org/wiki/Scalability#Horizontal_and_vertical_scaling
Service Discovery is a process in microservices where services automatically register their availability and discover other services dynamically. This allows services to communicate without hardcoding endpoints, making the system more flexible and scalable. Tools like Consul, Eureka, and Zookeeper are commonly used for service discovery.
https://en.wikipedia.org/wiki/Service_discovery
Request Throttling in microservices is a mechanism used to limit the number of requests that a service can handle within a specific time period. This helps prevent overwhelming services, ensures fair resource usage, and can be a protective measure against denial-of-service attacks. API gateways often implement throttling features.
https://en.wikipedia.org/wiki/Rate_limiting
Content Delivery Network (CDN) is a distributed network of servers that deliver content to users based on their geographic location. In microservices, a CDN is often used to cache static assets like images or CSS files, reducing load on backend services and improving load times for users.
https://en.wikipedia.org/wiki/Content_delivery_network
Dependency Management in microservices involves tracking and managing the dependencies between services and external libraries. Proper dependency management ensures that services are compatible with each other and reduces the risk of version conflicts. Tools like Maven and Gradle are often used to manage dependencies in Java-based microservices.
https://en.wikipedia.org/wiki/Dependency_management
Service Mesh is an infrastructure layer for controlling service-to-service communication within a microservices architecture. It provides features like load balancing, service discovery, and secure communication. Istio and Linkerd are popular service mesh tools that manage the complexity of microservice interactions.
https://en.wikipedia.org/wiki/Service_mesh
Data Sharding is a database architecture pattern in microservices where data is divided across multiple servers to improve performance and scalability. Each shard contains a subset of the data, allowing services to manage large datasets by distributing storage and computational loads across the system.
https://en.wikipedia.org/wiki/Shard_(database_architecture)
Dependency Injection (DI) is a design pattern in microservices where objects receive their dependencies from an external source, rather than creating them internally. This promotes loose coupling and easier testing. Spring Framework is a popular choice for implementing dependency injection in Java microservices.
https://en.wikipedia.org/wiki/Dependency_injection
Load Balancer in microservices is responsible for distributing incoming traffic across multiple instances of a service to ensure even load distribution and high availability. Load balancing helps avoid overloading any single instance, improving the performance and fault tolerance of the system.
https://en.wikipedia.org/wiki/Load_balancing_(computing)
Bearer Token Authentication is a security mechanism used in microservices where the client provides a bearer token as proof of identity when accessing services. Tokens are typically issued after successful authentication and are used to authorize subsequent requests without re-authenticating.
https://en.wikipedia.org/wiki/Bearer_token
Log Aggregation in microservices refers to the practice of centralizing logs from multiple services into a single system. This allows for easier searching, monitoring, and debugging by correlating events across different services. Tools like ELK Stack (Elasticsearch, Logstash, Kibana) and Fluentd are commonly used for log aggregation.
https://en.wikipedia.org/wiki/Log_management_and_analysis
Graceful Shutdown in microservices ensures that when a service is terminated, it completes all ongoing requests before shutting down. This prevents data loss and ensures a smooth transition, especially during deployments or scaling operations. Graceful shutdown allows services to stop accepting new requests while finishing the existing ones.
https://en.wikipedia.org/wiki/Graceful_shutdown
Inversion of Control (IoC) is a design principle used in microservices where the control of service dependencies is transferred from the application to a container or framework. This allows services to be loosely coupled and more modular, with the IoC container handling the creation and injection of dependencies. Spring Framework is widely known for implementing this principle.
https://en.wikipedia.org/wiki/Inversion_of_control
Telemetry Data in microservices involves collecting real-time data about system performance, resource usage, and service health. This data helps in monitoring and understanding how different parts of the system behave under various loads, enabling proactive troubleshooting and optimization.
https://en.wikipedia.org/wiki/Telemetry
Eventual Consistency is a data consistency model often used in microservices where updates to data are propagated asynchronously across services. Over time, the system becomes consistent, but immediate consistency is not guaranteed. This model is typically used in distributed systems to ensure high availability and partition tolerance.
https://en.wikipedia.org/wiki/Eventual_consistency
Rate Limiting in microservices is a technique used to restrict the number of API requests a service can handle within a specific time frame. It helps prevent overloading the service, ensuring stability and fair usage among multiple clients. Rate limiting can be enforced using API gateways.
https://en.wikipedia.org/wiki/Rate_limiting
Service Isolation refers to the practice of keeping microservices independent from each other to ensure that a failure in one service does not affect others. Service isolation is achieved through practices like dedicated resources, independent deployment pipelines, and separate databases for each service.
https://en.wikipedia.org/wiki/Service-oriented_architecture
Asynchronous Messaging in microservices allows services to communicate without waiting for an immediate response. By decoupling communication through message queues or event streams, services can handle requests and process data more efficiently, improving scalability and resilience. Technologies like Kafka and RabbitMQ enable asynchronous messaging.
https://en.wikipedia.org/wiki/Message-oriented_middleware
Health Check Endpoint is a feature in microservices that exposes an API endpoint to report the service's health status. Monitoring systems can periodically check this endpoint to ensure that the service is operational and ready to handle requests, allowing automated failover or load balancing if a service becomes unhealthy.
https://en.wikipedia.org/wiki/Health_check
Cold Start in microservices refers to the latency experienced when a service instance is started from an inactive state. This can occur when services are auto-scaled, particularly in serverless environments, where new instances take time to initialize. Cold starts can lead to temporary slowdowns until the instance is fully operational.
https://en.wikipedia.org/wiki/Cold_start_(computing)
Distributed Locking is a mechanism used in microservices to coordinate access to shared resources across multiple instances or nodes. It ensures that only one instance can modify a resource at a time, preventing race conditions. Tools like Zookeeper and Redis are often used to implement distributed locks in microservices.
https://en.wikipedia.org/wiki/Distributed_lock
API Composition in microservices is a design pattern where data from multiple services is aggregated by a composite service or API gateway to provide a unified response. This approach is often used when a client request requires data from several services, helping reduce the number of client-server interactions.
https://microservices.io/patterns/data/api-composition.html
Event Streaming refers to the real-time processing of events as they are produced in a microservices system. By continuously processing streams of data, services can react to events as they occur. Apache Kafka is a commonly used platform for building event streaming systems in microservices.
https://en.wikipedia.org/wiki/Event_stream_processing
Actor Model is a concurrency model used in some microservices architectures where individual “actors” represent the basic units of computation. Actors communicate via messages and maintain their own state, which makes them highly suited for distributed systems. The Akka framework is an example of the actor model implementation.
https://en.wikipedia.org/wiki/Actor_model
Service-to-Service Encryption is a security practice in microservices that ensures communication between services is encrypted to prevent unauthorized access. This is often implemented using TLS (Transport Layer Security) and is a critical component in securing internal microservices communications.
https://en.wikipedia.org/wiki/Transport_Layer_Security
Asynchronous Task Execution in microservices allows tasks to be executed in the background, without blocking the main service flow. By using message queues or task schedulers, services can offload long-running processes, improving responsiveness. This pattern is commonly implemented with RabbitMQ or Amazon SQS.
https://en.wikipedia.org/wiki/Asynchronous_method_invocation
Failover in microservices refers to the automatic switching to a backup service instance or node when the primary one fails. This ensures high availability and minimal downtime. Kubernetes and other orchestration platforms manage failover by monitoring service health and redirecting traffic as needed.
https://en.wikipedia.org/wiki/Failover
Retry Pattern is a resilience pattern in microservices where failed requests are automatically retried after a brief delay. This is particularly useful for transient failures, such as network glitches or temporary service unavailability, and can help improve system stability without user intervention.
https://docs.microsoft.com/en-us/azure/architecture/patterns/retry
Command Query Separation (CQS) is a design principle in microservices where commands (which modify state) are separated from queries (which read state). This helps optimize both read and write operations, allowing them to be handled independently, improving system efficiency and scalability.
https://en.wikipedia.org/wiki/Command-query_separation
Load Shedding in microservices is a technique used to prevent system overload by discarding or deprioritizing requests when a service is under heavy load. This helps maintain service availability by only processing essential or higher-priority requests during peak traffic times, avoiding total service failure.
https://en.wikipedia.org/wiki/Load_shedding
Service Endpoint in microservices refers to the URL or network location where a service can be accessed by clients or other services. Service discovery mechanisms dynamically register and locate these endpoints, allowing services to communicate without hard-coded addresses.
https://en.wikipedia.org/wiki/Endpoint_(computing)
Immutable Infrastructure is a practice in microservices where once a service or environment is deployed, it is never modified. Instead, updates are made by deploying new instances. This ensures consistency and eliminates issues related to configuration drift, making systems easier to manage and more reliable.
https://en.wikipedia.org/wiki/Immutable_infrastructure
Microservices Observability is the ability to understand the internal state of a microservices system based on the outputs such as logs, metrics, and traces. It enables engineers to monitor and debug issues in distributed systems, ensuring the overall health of the architecture. Observability tools include Prometheus and Grafana.
https://en.wikipedia.org/wiki/Observability_(software)
API Gateway Pattern in microservices is where a single point of entry handles all requests to multiple underlying services. The API gateway acts as a reverse proxy, routing requests to the appropriate service and often performing additional functions such as authentication, logging, and rate limiting.
https://en.wikipedia.org/wiki/API_gateway
Consistency Model in microservices defines the rules for ensuring data consistency across distributed services. Strong consistency ensures that all users see the same data at the same time, whereas eventual consistency allows temporary inconsistencies, which are resolved over time. Choosing the right consistency model is crucial for balancing performance and reliability.
https://en.wikipedia.org/wiki/Consistency_model
Resilience Testing is a method used in microservices to evaluate the system's ability to recover from failures. By intentionally introducing faults, like killing service instances or simulating network issues, developers can observe how well the system responds, helping to build more reliable, fault-tolerant services.
https://en.wikipedia.org/wiki/Chaos_engineering
Tenant Isolation in multi-tenant microservices ensures that data and resources for one tenant (customer) are isolated from others. Each tenant's data is managed separately, either through database partitioning or by providing dedicated resources for each tenant, ensuring data privacy and performance isolation.
https://en.wikipedia.org/wiki/Multitenancy
Service Monitoring in microservices involves continuously tracking the health and performance of services using metrics like response time, CPU usage, memory consumption, and error rates. Tools like Prometheus, Grafana, and Datadog are widely used for monitoring and providing insights into service performance.
https://en.wikipedia.org/wiki/Application_performance_management
Command Bus is a mechanism in microservices where commands (requests to change system state) are handled asynchronously. Each command is dispatched to a handler responsible for processing it, ensuring decoupling between services and allowing for better scalability by distributing command processing.
https://en.wikipedia.org/wiki/Message_broker
Circuit Breaker Timeout is a strategy within the circuit breaker pattern where service calls that exceed a certain response time are considered failures. By setting a timeout, the circuit breaker ensures that long-running requests do not overload the system and that failing services are quickly identified.
https://en.wikipedia.org/wiki/Circuit_breaker_design_pattern
Service Affinity in microservices refers to the practice of directing a user’s requests to the same instance of a service for the duration of a session. This is commonly used to maintain session state and reduce the overhead of reinitializing data for each request. It is also known as sticky sessions.
https://en.wikipedia.org/wiki/Sticky_session
Data Federation is an approach in microservices where a single query can retrieve data from multiple underlying data sources without the need for data replication. Data federation provides a unified view of data across services, allowing for more flexible queries and reducing data duplication across services.
https://en.wikipedia.org/wiki/Data_federation
Canary Deployment is a deployment strategy in microservices where a new version of a service is gradually rolled out to a small percentage of users or traffic before being fully deployed. This allows developers to monitor the impact of the new version and catch issues early before a full release.
https://en.wikipedia.org/wiki/Canary_release
Eventual Consistency in microservices refers to the consistency model where data updates are not immediately propagated across all services but will eventually reach a consistent state. This is commonly used in distributed systems to achieve high availability while accepting temporary inconsistency in exchange for faster response times.
https://en.wikipedia.org/wiki/Eventual_consistency
Shadow Deployment is a method used in microservices where a new version of a service runs in parallel with the existing version, but does not serve live traffic. Instead, it processes the same requests in the background. This allows developers to monitor and test the new version without affecting users.
https://microservices.io/patterns/deployment/shadow-deployment.html
Dynamic Scaling in microservices refers to the system’s ability to automatically increase or decrease the number of running service instances based on current demand. Kubernetes and cloud services like AWS Auto Scaling manage dynamic scaling, ensuring optimal resource usage and handling traffic spikes.
https://en.wikipedia.org/wiki/Scalability
Consumer-Driven Contract Testing is a testing approach in microservices where service contracts between consumers and providers are defined by the consumers. This ensures that providers adhere to the agreed contract, making integration testing more reliable as the system evolves.
https://en.wikipedia.org/wiki/Contract_testing
Health Check Mechanism in microservices refers to the built-in ability of services to expose their current health status via an endpoint. Monitoring systems use health checks to detect failures or issues, allowing load balancers to route traffic only to healthy instances.
https://en.wikipedia.org/wiki/Health_check
Domain Event is a concept in microservices where an event represents a significant change in state within a bounded context. Domain events are published by services and consumed by others, enabling decoupled communication and asynchronous workflows in an event-driven architecture.
https://en.wikipedia.org/wiki/Domain_event
Shared Nothing Architecture is an architecture used in microservices where each service operates independently without relying on shared memory or storage. This improves scalability and fault tolerance by eliminating single points of failure and making each service fully autonomous.
https://en.wikipedia.org/wiki/Shared-nothing_architecture
Service Registry in microservices is a central database where services register themselves upon startup and deregister upon shutdown. Other services can discover available endpoints through the registry. This enables dynamic service discovery, allowing services to find each other without hard-coded locations. Eureka and Consul are commonly used service registries.
https://en.wikipedia.org/wiki/Service_registry
Command and Control (CnC) in microservices refers to centralized coordination where one service directs the operations of other services. This contrasts with choreography, where services act independently and respond to events. Command and control is typically implemented in workflows where a single service orchestrates tasks.
https://en.wikipedia.org/wiki/Service_orchestration
Message Routing is a method in microservices to direct messages between different services based on certain criteria, such as message content, headers, or service load. This allows for more intelligent distribution of requests or data, ensuring that each service receives only the messages relevant to its operations.
https://en.wikipedia.org/wiki/Message-oriented_middleware
Container Orchestration in microservices refers to the automated management, deployment, scaling, and networking of containers. Tools like Kubernetes are used for container orchestration, ensuring that containerized microservices run reliably, are scaled appropriately, and can recover from failures.
https://en.wikipedia.org/wiki/Container_orchestration
Event Broker in microservices acts as an intermediary to facilitate the publishing and subscribing of events between services. It decouples event producers from consumers, ensuring asynchronous communication. Popular event brokers include Apache Kafka and RabbitMQ, which are used to manage and distribute events at scale.
https://en.wikipedia.org/wiki/Message_broker
Fault Injection is a testing method used in microservices to deliberately introduce failures into the system to test its resilience. By simulating network failures, high latency, or service crashes, developers can observe how services handle disruptions, helping to improve system robustness. Tools like Chaos Monkey are commonly used for fault injection.
https://en.wikipedia.org/wiki/Fault_injection
Saga Orchestration is a pattern in microservices that coordinates a long-running transaction across multiple services. Instead of allowing each service to handle its own saga steps, an orchestrator service manages the flow and determines when to execute compensating actions in case of failures, ensuring consistent transaction results.
https://en.wikipedia.org/wiki/Saga_pattern
Horizontal Partitioning in microservices is a database design technique where data is distributed across multiple databases based on specific criteria, such as customer ID or region. This approach, also known as sharding, allows systems to scale by distributing data across many nodes, improving performance for large-scale applications.
https://en.wikipedia.org/wiki/Shard_(database_architecture)
Telemetry Pipeline refers to the flow of collected metrics, logs, and traces from microservices into monitoring systems. The telemetry pipeline ensures that performance data from services is captured in real-time, enabling proactive monitoring and alerting of potential issues before they impact users.
https://en.wikipedia.org/wiki/Telemetry
API Rate Limiting in microservices ensures that no single client or user can overwhelm a service by sending too many requests. Rate limiting policies prevent system overload by limiting the number of API requests allowed in a specific time frame, often managed by an API gateway.
https://en.wikipedia.org/wiki/Rate_limiting
Blue-Green Deployment in microservices is a strategy that uses two identical production environments—blue and green. At any given time, only one environment (e.g., blue) is live. When a new version is ready, it's deployed to the idle environment (e.g., green) and tested before switching traffic over, ensuring a seamless deployment with zero downtime.
https://en.wikipedia.org/wiki/Blue-green_deployment
Service Throttling in microservices involves controlling the rate at which a service processes requests to prevent overloading. When traffic exceeds a specified threshold, incoming requests may be delayed or rejected, ensuring that the service remains available without degrading performance during spikes in demand.
https://en.wikipedia.org/wiki/Throttling_process
Dead Letter Queue (DLQ) is a queue in microservices used to store messages that could not be processed successfully after a defined number of attempts. A DLQ enables the system to handle failed messages gracefully by isolating them for further investigation without disrupting the normal message flow.
https://en.wikipedia.org/wiki/Dead_letter
Idempotent Operations in microservices refer to operations that can be safely repeated without changing the system's state after the initial application. This property is crucial for handling retries in distributed systems where network failures or timeouts may lead to duplicate requests.
https://en.wikipedia.org/wiki/Idempotence
Distributed Consensus is a protocol used in microservices to ensure multiple services agree on a single source of truth, despite failures or inconsistencies in communication. Algorithms like Paxos and Raft are widely used to achieve consensus in distributed systems, ensuring consistency across nodes.
https://en.wikipedia.org/wiki/Consensus_(computer_science)
Circuit Breaker is a pattern in microservices that monitors the communication between services and stops further requests to a service if it is failing or unresponsive. By “tripping” the breaker, the system prevents repeated failures from cascading across services, allowing the system to recover more gracefully.
https://en.wikipedia.org/wiki/Circuit_breaker_design_pattern
Time-to-Live (TTL) in microservices refers to the expiration time set on resources, such as cache entries or messages, to ensure they are automatically removed after a certain period. This prevents stale data from being used and helps control memory and storage usage in distributed systems.
https://en.wikipedia.org/wiki/Time_to_live
Event-Driven Architecture (EDA) in microservices is a model where services communicate by emitting and reacting to events. Events are published when a service's state changes, and other services that are interested in those changes can react, making the system more decoupled and scalable.
https://en.wikipedia.org/wiki/Event-driven_architecture
Graceful Degradation is a design principle in microservices where a system continues to provide limited functionality during partial failures, instead of completely shutting down. This ensures a better user experience by keeping core services operational even if non-critical services fail.
https://en.wikipedia.org/wiki/Graceful_degradation
Command Query Responsibility Segregation (CQRS) in microservices is a pattern that separates reading and writing operations into different models. The command model handles updates, while the query model handles reads, optimizing both operations for performance and scalability, especially in systems with complex data requirements.
https://en.wikipedia.org/wiki/Command_query_responsibility_segregation
Vertical Scaling in microservices refers to increasing the resources (CPU, memory) of a single service instance to handle more load. While horizontal scaling involves adding more instances, vertical scaling enhances the capacity of an existing instance. This method has limits but can be useful for improving performance in specific cases.
https://en.wikipedia.org/wiki/Scalability#Horizontal_and_vertical_scaling
Eventual Consistency is a consistency model in distributed microservices where updates to data are propagated asynchronously. Services may see temporary inconsistencies, but over time the system reaches a consistent state. Eventual consistency is used to balance high availability with acceptable delays in data synchronization.
https://en.wikipedia.org/wiki/Eventual_consistency
Distributed Transactions in microservices refer to transactions that span multiple services. To ensure consistency across these services, patterns like sagas or two-phase commit are used. Distributed transactions are complex due to the inherent difficulties of coordinating state changes across networked services.
https://en.wikipedia.org/wiki/Distributed_transaction
API Security in microservices involves securing service-to-service communication and external access to APIs. Techniques include using OAuth, JWT, and mutual TLS to authenticate and authorize requests. Ensuring proper API security prevents unauthorized access and ensures data integrity across services.
https://en.wikipedia.org/wiki/API_security
Service Decoupling in microservices refers to the practice of designing services to be independent from each other, minimizing the impact of changes in one service on others. Decoupled services are easier to maintain, scale, and modify, as they rely on well-defined interfaces and do not share internal state.
https://en.wikipedia.org/wiki/Loose_coupling
Stateful Microservices maintain internal state across requests, unlike stateless services that treat each request independently. Stateful microservices are useful for scenarios where session data, user information, or transactional data needs to persist between interactions. Managing state requires careful handling of consistency and availability.
https://en.wikipedia.org/wiki/State_(computer_science)
Microservice Decomposition refers to breaking down a monolithic application into smaller, independently deployable services. This decomposition is often driven by domain boundaries or functionality, enabling teams to work in parallel and scale individual services based on demand, improving flexibility and maintainability.
https://en.wikipedia.org/wiki/Service_decomposition
Leader Election in microservices is a process where multiple service instances coordinate to elect a leader that will perform certain tasks, such as managing shared resources or orchestrating workflows. Leader election ensures that only one instance takes responsibility, preventing conflicts in distributed systems. Tools like Zookeeper and Consul implement leader election.
https://en.wikipedia.org/wiki/Leader_election
Data Replication in microservices involves copying data across multiple services or locations to improve availability, fault tolerance, and performance. Replication ensures that if one data source fails, others are available to continue operations, making the system more resilient to failures.
https://en.wikipedia.org/wiki/Data_replication
Microservice Observability refers to the ability to monitor, trace, and log microservices effectively to understand their behavior and performance in production. Observability ensures that system issues can be detected and diagnosed quickly by collecting logs, metrics, and traces from services.
https://en.wikipedia.org/wiki/Observability_(software)
Service Mesh in microservices is an infrastructure layer that handles service-to-service communication, security, and observability without modifying application code. A service mesh abstracts network concerns, providing features like load balancing, service discovery, and traffic management. Istio and Linkerd are popular service mesh implementations.
https://en.wikipedia.org/wiki/Service_mesh
Transactional Outbox is a pattern in microservices used to ensure reliable message delivery in systems with distributed transactions. By writing events to an outbox table within the same transaction as the database changes, the events can be reliably published after the transaction commits, ensuring consistency.
https://microservices.io/patterns/data/transactional-outbox.html
Rate Limiting is a technique used in microservices to control the number of requests a service can handle within a given time period. It helps prevent services from being overwhelmed by traffic surges and ensures fair usage of resources. This is often implemented at the API Gateway level to protect backend services.
https://en.wikipedia.org/wiki/Rate_limiting
CQRS (Command Query Responsibility Segregation) is a pattern in microservices that separates read and write operations into distinct models. The command model handles updates to the system, while the query model is optimized for reading data. This pattern improves scalability by allowing the read and write sides to evolve independently.
https://en.wikipedia.org/wiki/Command_query_responsibility_segregation
Service Proxy in microservices is a component that sits between a client and a service, forwarding requests and responses. A service proxy can provide additional functionality such as caching, logging, security, and load balancing without changing the service itself. Envoy is a common service proxy used in microservice environments.
https://en.wikipedia.org/wiki/Proxy_server
Bulkhead Pattern is a design pattern used in microservices to isolate different parts of the system into independent partitions. This prevents failure in one part from cascading to other parts, ensuring that critical services remain operational. The term is borrowed from ship design, where bulkheads prevent flooding from spreading throughout the vessel.
https://en.wikipedia.org/wiki/Bulkhead_pattern
Message Broker in microservices facilitates communication between services by receiving, storing, and forwarding messages. It decouples producers from consumers, allowing asynchronous interactions between services. Popular message brokers include Apache Kafka and RabbitMQ.
https://en.wikipedia.org/wiki/Message_broker
Health Check Endpoint is an API exposed by a microservice to report its operational status. Monitoring systems regularly check the health of services through this endpoint, ensuring they are functioning correctly. Load balancers and service discovery systems use these health checks to route traffic only to healthy instances.
https://en.wikipedia.org/wiki/Health_check
API Gateway in microservices is a single entry point for external clients to interact with multiple services. It routes requests to the appropriate backend services and can handle tasks such as authentication, rate limiting, and response aggregation, simplifying client-service communication.
https://en.wikipedia.org/wiki/API_gateway
Event Sourcing is a pattern in microservices where the state of a service is persisted as a sequence of immutable events, rather than just storing the current state. This allows the system to reconstruct the state by replaying events, providing a complete history of state changes and enabling better auditability and recovery.
https://en.wikipedia.org/wiki/Event_sourcing
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