Deciding Whether a Feature for a Social Polling App Should Be a Microservice PDF

Deciding Whether a Feature for a Social Scaling Microservices: Horizontal vs. Vertical [ ]() Polling App Should Be a Microservice: A Comprehensive Guide Preparing for a system d...

Deciding Whether a Feature for a Social Scaling Microservices: Horizontal vs. Vertical [ ]() Polling App Should Be a Microservice: A Comprehensive Guide Preparing for a system design interview as a Principal Software Engineer? You're ** ** ** ** likely grappling with high-level architectural decisions, like whether to implement certain features as microservices or keep them within a monolith. This guide will ** ** ** ** walk you through everything you need to know about making these decisions for a ** Social Polling App, focusing on trade-offs and using engaging analogies to simplify ** ** ** complex concepts. Table of Contents 1. Understanding Monoliths and Microservices [ ]() [ What Are Microservices and Why Do We Love (or Hate) Them? ]() 2. Evaluating Features for Microservices [ ]() [ Real-Time Poll Updates ]() [ User Authentication and Profile Management ]() [ Polling Analytics ]() [ Notifications ]() [ Content Moderation ]() 3. Advanced Considerations [ ]() [ Data Management Across Microservices ]() [ Service Boundaries: Finding the Right Split ]()  [ Scaling Microservices: Horizontal vs. Vertical ]() [ Observability: Monitoring Microservices ]() 4. Eliminating the Monolith: Can You, Should You, and How? [ ]() [ Can You Fully Replace the Monolith? ]() [ The Strangler Fig Pattern: A Gentle Exit ]() [ When to Keep a Minimal Monolith ]() 5. Microsites: Enhancing Your Architecture [ ]() [ What Is a Microsite? ]() [ Why Use a Microsite? ]() [ Should the Microsite Be a Microservice? ]() 6. Recommended Architecture for a Social Polling App [ ]() [ Hybrid Approach: Monolith + Microservices + Microsite ]() [ Technology Recommendations ]() 7. Common Pitfalls and How to Avoid Them [ ]() [ Over-Engineering: The Death by a Thousand Services ]() [ Poor API Design ]() [ Ignoring Observability ]() [ Data Siloing and Consistency Issues ]() 8. Final Takeaways [ ]() 1. Understanding Monoliths and Microservices 1.1 What Are Microservices and Why Do We Love (or Hate) Them? ** Microservices are like the food trucks of the software world: small, self-contained ** ** ** ** units that focus on doing one thing exceptionally well—be it handling user ** authentication, polling analytics, or sending notifications. They operate independently but work together to serve a single platform. ** Why Go Microservice? ** Imagine running a food court instead of a single, massive kitchen (a monolith). Each food truck can operate independently. If the sushi truck runs out of rice, the taco truck keeps slinging burritos uninterrupted. This fault isolation and scalability make ** ** ** ** microservices attractive. ** Why Not Microservice? ** On the flip side, each food truck needs its own generator, water supply, and maintenance crew. Similarly, microservices introduce deployment complexity, ** ** ** monitoring overhead, and inter-service communication challenges. It's like juggling ** ** ** multiple tiny kitchens instead of one big one. ** Trade-Offs: ** ** Pros of Microservices: ** ** Scalability: Each service can scale independently based on demand. ** ** Fault Isolation: If one service fails, others can continue functioning. ** ** Flexibility: Use different technologies or languages best suited for each ** service. ** Team Autonomy: Smaller teams can own and manage individual services. ** ** Cons of Microservices: ** ** Complexity: Managing multiple services requires robust infrastructure and ** coordination. ** Latency: Communication between services can introduce delays. ** ** Operational Overhead: More services mean more things to deploy, ** monitor, and maintain. ** Data Consistency: Ensuring data remains consistent across services can be ** challenging. 2. Evaluating Features for Microservices When designing a Social Polling App, certain features might be better suited as ** ** microservices. Let’s evaluate some key features. 2.1 Real-Time Poll Updates ** Why It Could Be a Microservice ** ** Scalability: Real-time updates can lead to traffic spikes, especially with viral polls ** like “Do you like pineapple on pizza?” ** Tech Stack Fit: Utilize WebSockets or server-sent events with frameworks like ** ** ** ** ** ** Socket.io or services like AWS AppSync. ** ** ** ** Fault Isolation: If real-time updates fail, other parts like user profiles remain ** unaffected. ** Why It Might Stay in the Monolith ** ** Latency Risks: Network communication between services can introduce delays. ** ** Development Overhead: Real-time systems are complex; splitting them might ** add unnecessary complexity. ** Trade-Off Verdict: Opt for a microservice if scalability and fault isolation are ** ** ** paramount. Otherwise, keep it simple within the monolith. ** Anecdote: Imagine your polling app has a viral poll that suddenly explodes with ** votes. If real-time updates are a separate service, it can handle the surge without crashing the entire app. But if it's part of the monolith, that single point of failure could bring everything to a halt. 2.2 User Authentication and Profile Management ** Why It Could Be a Microservice ** ** Security: Authentication requires specialized security measures. Using platforms ** like Okta or Auth0 can be streamlined. ** ** ** ** ** Reusability: A standalone authentication service can serve multiple apps or ** services. ** Why It Might Stay in the Monolith ** ** YAGNI (You Ain’t Gonna Need It): If expansion isn't on the horizon, separating ** adds no immediate value. ** Startup Stage: Early development benefits from integrating directly to save ** time. ** Trade-Off Verdict: For smaller apps, keep it in the monolith. For enterprise-scale ** ** growth, consider breaking it out. ** ** Anecdote: Early-stage startups might find it easier to handle user authentication ** within their main app to iterate quickly, rather than investing time and resources in a separate authentication service. 2.3 Polling Analytics ** Why It Could Be a Microservice ** ** Processing Power: Heavy computations like sentiment analysis can be offloaded ** using Apache Kafka or Apache Flink. ** ** ** ** ** Asynchronous Processing: Analytics can operate independently without **  affecting real-time features. ** Why It Might Stay in the Monolith ** ** Tight Coupling with Data: Deep integration with polling logic can introduce ** latency when split. ** Cost: Separate analytics clusters can be expensive. ** ** Trade-Off Verdict: If analytics are computationally intensive, microservices are ** ** ** beneficial. Otherwise, centralize for simplicity. ** Anecdote: If your app starts offering deep insights like vote trends and user ** sentiment, separating analytics into its own service can keep the main app responsive and focused on core functionalities. 2.4 Notifications ** Why It Could Be a Microservice ** ** Burst Traffic: Notifications can spike during viral polls. ** ** Third-Party Integration: Leverage tools like Twilio, SendGrid, or Firebase ** ** ** ** ** ** Cloud Messaging. ** ** Why It Might Stay in the Monolith ** ** Latency Concerns: Downtime in the notification service affects user ** engagement. ** Added Complexity: Coordinating notifications with other services can introduce ** failure points. ** Trade-Off Verdict: Use a microservice for scalable, reliable notifications. Otherwise, ** ** ** integrate within the monolith. ** Anecdote: Imagine sending thousands of notifications simultaneously when a poll hits ** a milestone. A separate notifications service can handle this surge without overloading the main app. 2.5 Content Moderation ** Why It Could Be a Microservice ** ** Machine Learning Pipelines: Utilize NLP models with services like Hugging ** ** ** ** Face or Google’s AI APIs. ** ** ** ** Isolation: Separates sensitive moderation logic, enhancing security. ** ** Why It Might Stay in the Monolith ** ** Latency Requirements: External AI services can introduce delays. ** ** Simplicity: Manual moderation in early stages doesn’t warrant a separate service. ** ** Trade-Off Verdict: If relying on automated moderation at scale, a microservice is ** ** ** ideal. Otherwise, keep it simple. ** Anecdote: As your app grows, automated content moderation becomes essential to ** maintain quality. Isolating this feature allows for specialized updates and scaling without impacting other services. 3. Advanced Considerations 3.1 Data Management Across Microservices Splitting a monolithic database into separate databases for each microservice can be tricky. Think of it like roommates sharing a single fridge—convenient but often leads to disputes and confusion. ** Trade-Offs: ** ** Service-Specific Databases: ** ** Pros: ** ** Independence: Each microservice can scale and evolve its data ** schema independently. ** Reduced Cascade Failures: If one service’s database goes down, ** others remain unaffected. ** Polyglot Persistence: Use the best database for each service (e.g., ** ** PostgreSQL for users, Redis for caching). ** ** ** ** Cons: ** ** Data Duplication: Multiple services may store the same data, ** increasing storage costs and complexity. ** Eventual Consistency: Ensuring data is consistent across services can ** introduce delays and errors. ** Complex Transactions: Managing distributed transactions (using ** protocols like 2PC or sagas) is challenging. ** ** ** ** ** Shared Databases: ** ** Pros: ** ** Simpler Implementation: Easier to query and manage data without ** synchronization issues. ** Consistent Data Access: No need to handle data duplication or ** consistency across services. ** Cons: ** ** Tight Coupling: Services become dependent on a shared resource, ** making independent scaling difficult. ** Performance Bottlenecks: One service’s heavy usage can impact **  others sharing the same database. ** Anecdote: A team tried to split their monolithic database for a polling app but ended ** up with duplicate user data across services, leading to synchronization nightmares and increased maintenance overhead. 3.2 Handling Data Communication: Synchronous vs. Asynchronous Deciding between synchronous calls (like REST or gRPC) and asynchronous ** ** ** messaging (like RabbitMQ, Kafka, or AWS SQS) impacts how services interact. ** ** ** ** ** ** ** ** Synchronous Communication: ** ** Pros: ** ** Simplicity: Easier to implement when requests and responses need to ** happen in real-time. ** Easier Debugging: Clear request-response paths make tracing issues ** simpler. ** Cons: ** ** Tight Coupling: If one service is down, dependent services might also fail. ** ** Latency Sensitivity: A slow service can become a bottleneck for the entire ** system. ** Asynchronous Communication: ** ** Pros: ** ** Loose Coupling: Services operate independently, enhancing resilience. ** ** Scalability: Message queues can handle massive loads without breaking a ** sweat. ** Cons: ** ** Eventual Consistency: Data updates aren’t immediate, requiring ** mechanisms to handle delayed data.  ** Harder Debugging: Tracing issues is more complex as messages flow ** through multiple systems. ** Anecdote: Using REST for every inter-service communication in a polling app led to ** increased latency during peak times. Switching to Kafka for asynchronous messaging helped decouple services and improved overall responsiveness. 3.3 Service Boundaries: Finding the Right Split Defining clear service boundaries is crucial. Use Domain-Driven Design (DDD) to ** ** ** ** align services with business capabilities. ** Principles: ** ** Single Responsibility Principle (SRP): Each microservice should have one clear, ** focused purpose. ** Domain-Driven Design (DDD): Align services with business domains, like poll ** ** management, user engagement, or analytics. ** ** ** ** ** ** Avoid Chatty Services: Minimize the number of inter-service calls to reduce ** latency and complexity. ** Trade-Offs: ** ** Pros: ** ** Clear Ownership: Each service has a distinct responsibility, reducing ** accidental coupling. ** Reduced Coupling: Services are more independent, enhancing flexibility ** and scalability. ** Cons: ** ** Upfront Effort: Defining and maintaining clear boundaries requires careful ** planning. ** Potential Over-Splitting: Too many services can lead to management and**  operational challenges. ** Anecdote: A team initially split their polling app into too many microservices, leading ** to excessive inter-service communication. By redefining service boundaries based on business domains, they streamlined interactions and improved performance. 3.4 Scaling Microservices: Horizontal vs. Vertical ** Vertical Scaling: ** ** What It Is: Upgrading a single instance’s resources (e.g., adding more CPU or ** memory). ** Pros: ** ** Simplicity: No need to change your architecture. ** ** Low Initial Cost: Cheaper for smaller-scale apps. ** ** Cons: ** ** Hardware Limits: Eventually, you hit a ceiling. ** ** Downtime Risk: If the machine goes down, everything goes down. ** ** Horizontal Scaling: ** ** What It Is: Adding more instances of a service to handle increased load. ** ** Pros: ** ** Unlimited Scalability: Add more instances as needed. ** ** Fault Tolerance: If one instance fails, others can take over. ** ** Cons: ** ** Complexity: Requires load balancers, orchestrators (like Kubernetes), and ** ** ** service discovery tools (like Consul or Eureka). ** ** ** ** ** Cost: Cloud bills can skyrocket if scaling isn’t optimized. ** ** Anecdote: During a sudden surge in poll participation, a horizontally scaled ** notification service effortlessly handled the load, whereas their vertically scaled real- time updates service struggled, causing delays and user frustration. 3.5 Observability: Monitoring Microservices Effective monitoring is non-negotiable in a distributed system. Tools like Prometheus, ** ** ** Grafana, and Jaeger are essential. ** ** ** ** Key Tools: ** ** Prometheus: Collects metrics from services. ** ** Grafana: Visualizes metrics through dashboards. ** ** Jaeger or Zipkin: Provides distributed tracing to track requests across services. ** ** ELK Stack (ElasticSearch, Logstash, Kibana): Centralizes and visualizes logs. ** ** Trade-Offs: ** ** Pros: ** ** Faster Debugging: Identify and resolve issues quickly across services. ** ** Performance Optimization: Pinpoint bottlenecks and optimize resource ** usage. ** Cons: ** ** Increased Costs: Collecting and storing observability data can be ** expensive. ** Learning Curve: Teams must learn and maintain these tools effectively. ** ** Anecdote: Implementing Jaeger for distributed tracing in their polling app helped a ** team quickly locate a hidden latency issue between the analytics and notification services, saving hours of debugging time. 4. Eliminating the Monolith: Can You, Should You, and How? 4.1 Can You Fully Replace the Monolith? ** Yes, it's possible to fully replace a monolith with microservices. Companies like Netflix ** and Amazon have done it, proving its feasibility. However, it’s rarely necessary or practical from the start. ** Example Analogy: ** Imagine transforming a single-family home into a neighborhood of tiny apartments. It’s doable, but managing utilities, maintenance, and tenant relations becomes exponentially more complex. ** Trade-Offs: ** ** Pros: ** Complete independence and scalability for each feature. ** ** ** ** ** Flexibility to adopt new technologies for different services. ** ** Cons: ** ** High Complexity: Managing numerous services requires robust ** infrastructure. ** Operational Overhead: More services mean more deployments, ** monitoring, and maintenance. ** Data Consistency Issues: Ensuring data integrity across services is ** challenging. 4.2 Why Is It So Hard to Kill the Monolith? ** Data Coordination: Shared data within a monolith is easier to manage. Splitting it ** introduces synchronization and consistency challenges. ** Cross-Cutting Concerns: Features like logging and security are simpler when ** centralized. ** Legacy Code: Existing monoliths often contain tightly coupled, legacy code that's ** hard to separate. ** Example Anecdote: ** A social media company tried splitting its messaging system into microservices, resulting in latency spikes during message delivery. They reverted to a hybrid model, keeping a monolith for core messaging coordination. ** Trade-Offs: ** ** Pros of Keeping a Monolith: ** ** Simpler Data Management: Single database with straightforward queries. ** ** Easier Cross-Cutting Concerns: Centralized logging, security, and ** monitoring. ** Lower Initial Costs: Less infrastructure and operational overhead. ** ** Cons of Keeping a Monolith: ** ** Limited Scalability: Scaling the entire application for one high-demand ** feature is inefficient. ** Tight Coupling: Changes in one part can inadvertently affect others, ** slowing down development. 4.3 The Strangler Fig Pattern: A Gentle Exit Instead of a “big bang” migration, use the strangler fig pattern to gradually replace ** ** the monolith. ** Steps: **  1. Identify Independent Features: Start by extracting features that can function ** ** autonomously, such as notifications or analytics. 2. Create Microservices: Develop these features as separate microservices. ** ** 3. Route Traffic: Use an API Gateway (e.g., Kong, AWS API Gateway) to direct ** ** ** ** ** ** ** ** traffic to either the monolith or the new microservices. 4. Gradually Shrink the Monolith: Continue extracting features until the monolith ** ** either becomes a minimal core or is entirely replaced. ** Trade-Offs: ** ** Pros: ** ** Lower Risk: Gradual transition minimizes disruption. ** ** Continuous Delivery: Allows for ongoing updates and improvements ** without halting the system. ** Cons: ** ** Extended Migration Period: The process can take considerable time and ** resources. ** Maintaining Dual Systems: Operating both the monolith and microservices ** simultaneously adds complexity. ** Anecdote: ** Netflix started with a monolithic architecture and gradually strangled it with microservices, allowing them to scale and innovate without disrupting their existing services. 4.4 When to Keep a Minimal Monolith Even with microservices, retaining a small monolith can make sense for: ** Authentication and Authorization: Centralizing user management simplifies ** security.  ** Orchestration: Managing workflows involving multiple services. ** ** Example: ** Your Social Polling App could keep the user profile management in a small monolith ** ** while other services like poll voting, notifications, and analytics are split into microservices. ** Trade-Offs: ** ** Pros: ** ** Simplifies Cross-Cutting Concerns: Centralizes security and logging. ** ** Reduces Latency: Avoids excessive service-to-service calls for shared ** concerns. ** Eases Migration Efforts: Provides a stable fallback during the transition. ** ** Cons: ** ** Maintains Some Coupling: Shared services can still become bottlenecks. ** ** Added Complexity: Managing both a monolith and microservices requires ** careful coordination. ** Anecdote: ** Twitter retained a core monolith for authentication even after moving other features to microservices, ensuring consistent user management without redundant implementations. 5. Microsites: Enhancing Your Architecture 5.1 What Is a Microsite? A microsite is a standalone front-end application that serves a specific purpose, ** ** ** ** such as a home/landing page. It can be static or semi-dynamic, relying on APIs for ** ** backend data. ** Example Analogy: ** Think of a microsite as a kiosk outside a store. It provides specific information or ** ** services without needing access to the entire store layout. 5.2 Why Use a Microsite? ** Performance: Static microsites load faster, especially when backed by a CDN (e.g., ** ** ** ** Cloudflare, AWS CloudFront). ** ** ** ** SEO: Static content is easier for search engines to index, boosting your app’s visibility. ** ** Decoupled Development: Front-end and back-end teams can work independently, ** speeding up development. ** Trade-Offs: ** ** Pros: ** ** Offloads Static Assets: Reduces load on backend services. ** ** Independent Deployment Cycles: Front-end teams can deploy changes ** without affecting the backend. ** Enhanced SEO and Performance: Faster load times improve user ** experience and search rankings. ** Cons: ** ** Dynamic Features Require APIs: Real-time data needs well-designed APIs, ** introducing potential latency and dependency issues. ** Potential Latency: Fetching dynamic content from backend services can ** slow down the microsite. ** Additional Development Overhead: Maintaining a separate codebase for ** the microsite requires more resources. ** Anecdote: ** A polling app introduced a microsite for its landing page, drastically improving load times and SEO rankings. However, integrating real-time poll data required robust API design to ensure seamless user experience. 5.3 Should the Microsite Be a Microservice? ** Recommended Approach: ** ** Use a Static Site Generator: Tools like Next.js or Gatsby are ideal for building ** ** ** ** ** fast, SEO-optimized microsites. ** Host on a CDN: Platforms like Vercel, Netlify, or AWS CloudFront ensure high ** ** ** ** ** ** ** availability and performance. ** Connect to Backend Services via API Gateway: Use an API Gateway (e.g., ** ** ** ** Kong, AWS API Gateway) to manage and route API requests. ** ** ** ** Trade-Offs: ** ** Pros: ** ** Decouples Front-End and Back-End: Allows independent scaling and ** development. ** Improves Performance and SEO: Static hosting ensures quick load times ** and better search engine rankings. ** Cost-Effective: Static sites hosted on CDNs are typically cheaper and ** require less maintenance. ** Cons: ** ** Requires Robust API Design: Dynamic interactions depend on well- ** designed and maintained APIs. ** Introduces Latency Risks: Fetching data from backend services can slow ** down the microsite if not optimized. ** Adds Development Overhead: Managing a separate front-end codebase **  increases complexity. ** Example Stack for Microsites: ** ** Static Site Generator: Next.js, Gatsby. ** ** Hosting/CDN: Vercel, Netlify, AWS CloudFront. ** ** APIs: Backend services exposed through an API Gateway (e.g., Kong, AWS API ** ** ** ** Gateway). ** 6. Recommended Architecture for a Social Polling App 6.1 Hybrid Approach: Monolith + Microservices + Microsite Combine the strengths of monoliths, microservices, and microsites for a balanced architecture. ** Step-by-Step: ** 1. Start with a Modular Monolith: ** ** Use frameworks like Spring Boot, Django, or Ruby on Rails. ** ** ** ** ** ** Organize features into clear modules (e.g., user management, poll creation). 2. Extract High-Demand Features into Microservices: ** ** ** Notifications Service: Handles email and push notifications. ** ** Analytics Service: Processes and analyzes poll data. ** ** Real-Time Updates: Manages live poll results via WebSockets or SSE. ** ** ** ** ** 3. Deploy a Microsite for the Landing Page: ** **  Build with Next.js or Gatsby. ** ** ** ** Host on Vercel or Netlify. ** ** ** ** Fetch dynamic data through APIs. 4. Gradually Shrink the Monolith: ** ** Retain a core monolith for shared concerns like authentication. Continue extracting services as the app scales. ** Example Architecture: ** ** Microsite: Static landing page hosted on Vercel, fetching data from backend ** ** ** APIs. ** Microservices: ** ** Polling Service (stateless) ** ** Analytics Service (stateful) ** ** Notification Service (event-driven) ** ** Monolith: Core services for authentication and user profiles. ** 6.2 Technology Recommendations ** Frontend (Microsite): ** ** Static Site Generators: Next.js, Gatsby. ** ** Hosting/CDN: Vercel, Netlify, AWS CloudFront. ** ** Backend: ** ** Frameworks for Microservices: Spring Boot (Java), Express.js (Node.js), FastAPI ** (Python). ** API Gateways: Kong, NGINX, AWS API Gateway. ** ** Communication: **  ** Synchronous APIs: REST, gRPC. ** ** Asynchronous Messaging: Kafka, RabbitMQ. ** ** Databases: ** ** Relational: PostgreSQL, MySQL. ** ** NoSQL: MongoDB, DynamoDB. ** ** Caching: Redis, Memcached. ** ** Observability: ** ** Prometheus: Metrics collection. ** ** Grafana: Dashboards. ** ** Jaeger: Distributed tracing. ** ** ELK Stack: Centralized logging. ** ** Example Stack: ** ** Frontend: Next.js hosted on Vercel. ** ** Backend: Spring Boot for microservices, managed through Kubernetes. ** ** API Gateway: Kong for routing and security. ** ** Databases: PostgreSQL for user profiles, MongoDB for poll data, Redis for ** caching. ** Messaging: Kafka for event-driven communication. ** ** Monitoring: Prometheus and Grafana for metrics, Jaeger for tracing. ** 7. Common Pitfalls and How to Avoid Them 7.1 Over-Engineering: The Death by a Thousand Services ** Problem: Splitting every small feature into microservices leads to dependency hell ** ** ** and skyrocketing operational costs. ** ** ** Solution: Start with a monolith and extract services organically based on clear needs ** ** ** ** ** like scalability or team autonomy. ** Anecdote: A team eagerly split their polling app into numerous microservices, each ** handling minute functionalities. They soon faced issues with service dependencies and increased deployment times, forcing them to consolidate several services back into a manageable number. 7.2 Poor API Design ** Problem: Inconsistent APIs can break service interactions and introduce cascading ** failures. ** Solution: Establish clear API contracts, use tools like Swagger for documentation, ** ** ** ** ** and enforce versioning to manage changes. ** ** ** Anecdote: Without proper API versioning, updates to the polling service’s API caused ** the microsite to malfunction, leading to frustrated users and a scramble to implement quick fixes. 7.3 Ignoring Observability ** Problem: Distributed systems are harder to debug without proper monitoring. ** ** Solution: Invest in observability tools like Prometheus, Grafana, and Jaeger to ** ** ** ** ** ** ** ** ** monitor logs, metrics, and traces effectively. ** Anecdote: A team neglected to implement distributed tracing, making it nearly ** impossible to identify why the real-time updates were lagging, resulting in prolonged downtimes and user dissatisfaction. 7.4 Data Siloing and Consistency Issues ** Problem: Separate databases can lead to data duplication and synchronization ** ** ** ** issues. ** ** Solution: Use an event-driven architecture with message queues (e.g., Kafka) to ** ** ** ** ** manage data consistency across services. ** Anecdote: After splitting their user and poll data into separate databases, the team ** struggled with outdated user preferences in the polling service, leading to irrelevant poll suggestions and decreased user engagement. 8. Final Takeaways When deciding whether a feature in a Social Polling App should be a microservice, ** ** ** ** focus on the following trade-offs: ** ** 1. Scalability vs. Complexity: Microservices offer better scalability but introduce ** ** deployment and communication complexity. 2. Fault Isolation vs. Latency: Isolating faults is easier with microservices, but ** ** inter-service communication can add latency. 3. Development Speed vs. Operational Overhead: Starting with a monolith allows ** ** faster development. Microservices require robust tooling and monitoring. 4. Cost vs. Flexibility: Microservices can increase operational costs but provide ** **  flexibility for scaling and independent deployments. ** Recommended Approach: ** ** Start with a Modular Monolith: Develop features with clear internal boundaries. ** ** Gradually Extract Microservices: Focus on high-demand, scalable features. ** ** Deploy a Microsite: Use it for the landing page to enhance performance and ** SEO. ** Maintain a Core Monolith: Retain essential shared services to simplify security ** and authentication. ** Key Technologies to Consider: ** ** Frontend: Next.js, Gatsby, hosted on Vercel or Netlify. ** ** Backend: Spring Boot, Express.js, FastAPI. ** ** Communication: REST, gRPC, Kafka, RabbitMQ.** ** Databases: PostgreSQL, MongoDB, Redis. ** ** API Gateways: Kong, NGINX, AWS API Gateway. ** ** Observability: Prometheus, Grafana, Jaeger. ** ** Final Recommendations: ** 1. Can You Fully Get Rid of the Monolith? ** ** ** Yes, but it’s often more practical to keep a small core monolith for shared ** ** ** concerns. 2. Should You Use a Microsite? ** ** Absolutely. Microsites are great for landing pages and public-facing content. 3. What’s the Best Approach for a Social Polling App? ** ** Start with a modular monolith. ** ** Gradually extract high-traffic features into microservices. ** **  Deploy a microsite for public-facing content. ** ** By understanding and articulating these trade-offs and real-world considerations, ** ** ** ** you'll showcase your ability to design scalable, maintainable, and efficient systems— exactly what a Principal Software Engineer role demands. ** ** ** Remember: Microservices are powerful tools, but they're not a one-size-fits-all ** solution. Use them judiciously, balancing technical needs with business goals to ** ** ** ** create an architecture that stands the test of time.