Top Microservices Interview Questions and Answers

Monolithic is one big app. Monolithic applications can have internal modularity, and the key difference is in deployment and runtime independence. Monolithic is simpler but less flexible.

For example, think of an online store where the login, products, cart, orders, and payments are all built together in one big block. If you want to change one part, like the cart, you still have to update and deploy the whole app.

Microservices split an app into small parts that work on their own. Microservices are easier to change and grow.

For example, an Order Service in an online store. It does just one job: handles orders, like placing, updating, or canceling them. It talks to other services like:

• User Service (for customer info)
• Inventory Service (to check stock)
• Payment Service (to process payments)

Microservices are small, independent, and focused on one task. They communicate through APIs, can be developed and deployed separately, and are easy to scale.

The advantages are,

• Independent deployment and scaling
• Technology diversity (different services can use different tech stacks)
• Fault isolation (failure in one service doesn’t affect others)
• Team autonomy (different teams can own different services)

The challenges are,

• Distributed system complexity (e.g., network failures, latency)
• Data consistency across services
• Increased operational overhead (e.g., monitoring, logging)

Microservices communicate over a network using APIs (e.g., HTTP/REST, gRPC) for synchronous communication or messaging systems

API Gateways help us to handle and direct requests between users and microservices. Hence, communication becomes easier and more secure.

To find and communicate with each other automatically, microservices follow service discovery. Services usually change their locations, like IP addresses or ports, when they start, stop, or scale in a microservices system. At this time, service discovery keeps track of these changes to allow services to connect without hardcoding addresses.

Service discovery is important because it helps services find each other easily. It supports dynamic scaling and updates, avoids manual configuration, and reduces errors.

Synchronous communication means one service waits and talks directly to another service, getting an immediate reply. 

Synchronous = wait for reply now

Asynchronous communication means a service sends a message and keeps going without waiting for a response. The other service will respond later.

Asynchronous = send message, reply comes later

Containers are like lightweight boxes that hold a microservice and everything it needs to run (code, tools, settings). Containers make it easy to run microservices anywhere. They keep each microservice separate and isolated. They help start, stop, and manage services quickly and consistently.

Microservices architecture supports scalability by breaking an app into small, independent services. We can scale only the services that need more power, not the whole app. Each service can run on its server or container. It’s easier to handle more users or data by adding more copies of busy services. Ultimately, Microservices let you grow parts of your app as needed, saving time and resources. By running several service instances, Microservices support horizontal scaling. Orchestration tools like Kubernetes manage this by distributing traffic through load balancers.

To handle inter-service communication failures in microservices, we can use smart ways to keep the system running even if one service fails. Use safety tools to keep services working even when others fail. Common ways are,

• Retries: Try again if a service doesn’t respond.
• Timeouts: Don’t wait too long for a reply.
• Circuit breakers: Stop trying if a service keeps failing, and try again later.
• Fallbacks: Use backup plans or default responses.

This is known as fault tolerance.

• API Gateway: One entry point for all services, like a front desk.
• Service Discovery: Helps services find each other.
• Circuit Breaker: Stops calls to a failing service to avoid crashes.
• Database per Service: Each service has its database.
• Saga Pattern: Manages data changes across services in steps.

These patterns solve common problems and keep microservices running smoothly.

Data consistency in microservices is tricky because each service has its database. To handle it as follows.

• Event-driven updates: Services send events when data changes, so others can update too.
• Saga pattern: Breaks big updates into small steps across services, with rollback if needed.
• Async communication: Helps keep services in sync without waiting.

Microservices stay consistent by sharing updates through events and smart steps.

The database-per-service pattern means each microservice has its database. It is recommended because:

• Services stay independent and don’t affect each other’s data.
• It avoids tight coupling between services.
• Each team can choose the best database for their service.

One service, one database keeps things clean, independent, and flexible.

To monitor and log microservices effectively, you collect and track data from all services in one place. To do it, use the right tools to see logs, watch performance, and trace problems across all services.

• Centralized logging: Send logs from all services to one system (like ELK or Splunk).
• Monitoring tools: Use tools like Prometheus or Grafana to track health and performance.
• Tracing: Use tools like Jaeger or Zipkin to see how requests move across services.

Orchestration is when one central service controls how others work together.

Orchestration = one boss tells the services what to do.

Choreography is when each service reacts to events without a central controller.

Choreography = services listen and act on their own.

In microservices, authentication and authorization are handled in a secure, shared way. A central system gives tokens, and services use them to allow or block access. To do it, follow these:

• Central auth service: One service checks who you are (authentication) and what you can do (authorization).
• Tokens (like JWT): After login, users get a token to access other services.
• API Gateway: Checks tokens before requests reach the services.

A service mesh helps microservices talk to each other safely and smoothly. It manages traffic between services, adds security (like encryption), tracks and monitors requests, and handles retries and failures. In short, a service mesh makes service-to-service communication smarter, safer, and easier.

Versioning helps manage changes in microservices without breaking things or updating services without messing up others. It can be done by,

• API versioning: Use version numbers in URLs (like /v1/users).
• Separate deployments: Run old and new versions side by side.
• Backward compatibility: Make sure new versions don’t break old clients.

Other strategies include header-based versioning (e.g., using custom headers) and consumer-driven contracts to ensure compatibility.

Use multiple tests to make sure each service and the whole system work right. For instance, Chaos testing (e.g., using Chaos Monkey) validates resilience, and canary testing deploys updates to a small subset of users first. We can use the following common strategies:

• Unit tests: Test small pieces of code inside a service.
• Integration tests: Test how services work together.
• Contract tests: Make sure services agree on data formats.
• End-to-end tests: Test the whole system from start to finish.
• Load tests: Check how services handle lots of users.

Eureka Server is used for service discovery in microservices. It keeps a list of all running services and helps services find and talk to each other without hardcoding addresses. It updates the list as services start or stop.

To be clear, Eureka is like a phonebook for microservices that helps them find and connect with each other.

We can scale individual microservices by running more copies of just the service that needs it. Use containers or servers to add more instances. Use a load balancer to spread traffic between them. Scale only the busy services, not the whole app.

To secure microservices, you make sure only the right users and services can access them. Common ways are,

• OAuth2: Let users log in safely and get permission to use services.
• JWT (JSON Web Tokens): Tokens that prove who you are and what you can do.
• Mutual TLS: Both services check each other’s identity before talking.

Based on the CAP theorem, a system can only have two of these three at the same time:

• Consistency: All users see the same data at once.
• Availability: The system always responds to requests.
• Partition Tolerance: The system keeps working even if parts can’t talk to each other.

Similarly, Microservices usually run on many machines, so they must handle network problems (partition tolerance). That means, sometimes microservices choose to be always available even if the data isn’t perfectly the same everywhere. Otherwise, they choose to have the same data, but it might be slower or unavailable sometimes.

Distributed tracing helps us follow a user’s request as it moves through many microservices. To implement distributed tracing, do the following.

• Add unique IDs (trace IDs) to each request.
• Services pass these IDs along when they call each other.
• Use tools like Jaeger or Zipkin to collect and show the trace.

Track requests across services to find where problems happen.

Docker helps by packaging each microservice and everything it needs into a small, portable container that can run anywhere.

Kubernetes helps by managing these containers. Managing means starting, stopping, and scaling them automatically.

To migrate a monolithic app to microservices, follow these steps.

1. Break it into smaller parts based on features or functions.
2. Create separate microservices for each part.
3. Move data and logic from the big app to these services.
4. Make services talk to each other through APIs.
5. Test and deploy each service one by one.

CI/CD pipelines help automate building, testing, and deploying microservices. They work as follows.

• CI (Continuous Integration): When code changes, the pipeline builds and tests each microservice automatically.
• CD (Continuous Delivery/Deployment): After tests pass, the pipeline deploys the updated microservice to production or staging.
• Pipelines run separately for each microservice, so updates are faster and safer.

Managing separate data in many services makes syncing and consistency more difficult.

• Each service has its database, so keeping data synced is hard.
• Handling data consistency across services can be tricky.
• Managing transactions that involve multiple services is complex.
• Backups and data recovery are harder because the data is spread out.

• Use immutable infrastructure to ensure consistent deployments and zero-downtime deployments to avoid service interruptions.
• Use containers (like Docker) for consistent environments.
• Automate deployments with CI/CD pipelines.
• Monitor services for health and performance.
• Use load balancers to manage traffic.
• Implement security with authentication and encryption.
• Plan for failures with retries and circuit breakers.

Cohesion means how well the parts inside one service or module work together. High cohesion is good because everything fits well.

High cohesion = parts inside a service work closely together.

Coupling means how much one service or module depends on another. Low coupling is good because parts can work independently.

Low coupling = services don’t depend too much on each other.