40 Microservices Interview Questions

For containerization, Docker is pretty much the go-to tool. It's widely used because it's easy to set up and works seamlessly with most continuous integration and continuous deployment pipelines. For orchestration, Kubernetes is the industry standard. It handles scaling, failover, and deployment in a highly efficient way.

You might also come across Docker Swarm, which is Docker's own orchestrator, but Kubernetes tends to be more feature-rich and robust, especially for complex, large-scale systems. Additionally, tools like Helm can be very useful when deploying to Kubernetes, as it simplifies managing Kubernetes applications.

Microservices offer several benefits, like increased flexibility and scalability because each service can be developed, deployed, and scaled independently. That’s a huge win for teams because it enables faster development cycles and easier inclusion of new technologies and frameworks. Plus, it usually leads to better fault isolation, so if one service fails, it doesn't necessarily bring down the entire system.

However, the challenges are not insignificant. Managing a system composed of many independent services means dealing with the complexity of distributed systems—things like network latency, security, and data consistency become more challenging. Monitoring and debugging can also become more complex since you need to track issues across multiple services. Plus, you often need to implement some sort of orchestration or service discovery mechanism to keep everything working smoothly.

For inter-service communication in a microservices architecture, I mainly use two strategies: synchronous and asynchronous communication.

For synchronous communication, HTTP/REST and gRPC are common choices. REST is straightforward and widely adopted, but gRPC offers better performance and support for contract-based service definitions through Protocol Buffers.

For asynchronous communication, message brokers like RabbitMQ or Kafka are effective. They enable services to communicate without requiring an immediate response, which helps in decoupling services and improving scalability and resilience. This is particularly useful for event-driven architectures where processes can happen independently and concurrently.

An API Gateway acts as a single entry point for all client requests in a microservices architecture. It routes these requests to the appropriate microservice, helping to decouple client interactions from the underlying microservices. This not only simplifies the client-side but also allows for better management of cross-cutting concerns like authentication, logging, rate limiting, and load balancing.

Additionally, an API Gateway can aggregate responses from multiple microservices, reducing the number of round trips a client has to make. This aggregation improves performance and user experience. Essentially, it serves as an abstraction layer that shields clients from the complexities of the microservices system.

Managing versioning of Microservices APIs can be approached in several ways, but one common strategy is to include the version number in the API's URL, like /api/v1/resource. This method is straightforward and makes it clear which version of the API you're dealing with, reducing the risk of breaking changes for consumers of the older versions.

Another approach is to use a versioning scheme in the request header. For instance, you could include a custom header like Accept: application/vnd.myapi.v1+json to inform the service which version of the API to deliver. This keeps the URL cleaner but requires good documentation to ensure that clients know how to specify the API version properly.

It's also important to have backward compatibility in mind. When you introduce new versions, older ones should still work until you can phase them out gracefully, giving clients enough time to transition to the new version without causing disruptions.

Microservices are an architectural style where an application is divided into small, independent services, each of which runs its process and communicates through lightweight mechanisms, often HTTP APIs. Each service is focused on a specific business function and can be developed, deployed, and scaled independently.

In contrast, a monolithic architecture means the entire application is built as a single, cohesive unit. All components and functions are tightly coupled, and changes to one part often require redeploying the whole application. With microservices, you gain flexibility, easier scaling, and resilience since the failure of one service doesn't necessarily bring down the entire application. However, microservices can add complexity in terms of service management and inter-service communication.

Securing a microservices ecosystem involves multiple layers and strategies. Start by implementing API gateways which act as entry points for all your microservices, providing traffic management, routing, and security policies enforcement. Each microservice should authenticate requests, ideally using robust methods like OAuth2, to validate the identity of the user or service making the request.

Next, consider mutual TLS (mTLS) for secure communication between services, ensuring that data transfer is encrypted and that each service can verify the identity of the other service it communicates with. Role-based access control (RBAC) combined with fine-grained authorization checks helps ensure that services and users have the appropriate permissions.

Finally, don't overlook the importance of regular security audits and employing tools for vulnerability scanning and monitoring. Automated deployment pipelines should incorporate security checks to catch issues early. Network segmentation and running services with the least privilege principle further minimize the potential impact of a compromised service.

Handling distributed tracing in microservices usually involves using tools like Jaeger or Zipkin. These tools help you track requests as they flow through different services. Typically, you'd inject unique trace identifiers into the requests. Middleware in each service can automatically log these identifiers along with relevant trace information, like request paths and response times.

You'll often need to integrate with libraries or frameworks that support distributed tracing. For instance, Spring Cloud Sleuth is useful in the Spring ecosystem. It automatically adds trace and span IDs to the logs, making it easier to correlate logs between services. Combining this with a central logging solution helps you get a comprehensive view of your application's behavior.

Data consistency in a microservices architecture can be tricky since each service often has its own database. One common approach is to use event-driven architecture with events and messaging queues to keep data synchronized. Services can emit events when data changes, and other services can listen for these events and update their own data accordingly. This way, eventual consistency is achieved.

Another strategy is to use the Saga pattern to manage distributed transactions. This involves breaking down a transaction into a series of smaller steps, each managed by a different service. If one step fails, compensating transactions can be triggered to rollback the changes that have already been made, ensuring that the system remains consistent.

Managing transactions across multiple microservices can be tricky because each service usually operates its own database. One common approach is using the Saga pattern, where a series of compensating transactions are defined to handle failures. Basically, if one step in the transaction fails, you trigger a compensating transaction to undo the previous steps, ensuring the system remains in a consistent state.

Another method is the two-phase commit (2PC), but this is less popular in microservices because it introduces tight coupling and can be a performance bottleneck. Event sourcing can also be useful by capturing a sequence of state-changing events which can then be used to ensure consistency across services. Using eventual consistency models, rather than traditional ACID transactions, is often more appropriate in microservices architectures, leveraging message queues to ensure that data is eventually consistent across services.

Implementing logging and monitoring in a microservices architecture involves a few key components to ensure comprehensive visibility and analysis. For logging, you'd typically centralize your logs using tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Fluentd combined with Elasticsearch and Kibana. Each microservice can push its logs to a central repository, which helps in maintaining consistency and ease of searching. Structured logging is essential so that logs from different services can be easily correlated.

For monitoring, you'd use metrics and distributed tracing. Metrics can be gathered using tools like Prometheus, which scrapes metrics data from your microservices and stores it. You can visualize these metrics using Grafana, which integrates seamlessly with Prometheus. For distributed tracing, tools like Jaeger or Zipkin help trace requests as they flow through various microservices, providing you with a clear picture of latency and bottlenecks within the system.

It’s also important to implement alerting mechanisms. Based on the logs and metrics, you can set up alerts using tools like Alertmanager (part of the Prometheus ecosystem) to notify your team when something goes wrong, ensuring that issues are addressed promptly.

Scaling microservices can primarily be handled both vertically and horizontally. Vertical scaling means adding more resources like CPU or memory to individual services, while horizontal scaling involves adding more instances of the service to handle increased load. Typically, you'd lean towards horizontal scaling for microservices to benefit from better fault isolation and resilience.

A key part of scaling is ensuring statelessness in your services. This means data and session states should be managed externally, like using distributed caches or databases. That way, any instance can handle a request without relying on its previous state.

You should also use orchestration tools like Kubernetes or Docker Swarm to manage the scaling automatically based on load, health checks, etc. These tools can help dynamically adjust the number of instances to meet the current demand and ensure high availability.

Eventual consistency is a consistency model used in distributed systems that guarantees that, given enough time, all updates will propagate through the system and all replicas will converge to the same state. It doesn't guarantee immediate consistency after an update but ensures that the system will be consistent eventually. This model is particularly useful in scenarios where availability and partition tolerance are prioritized over immediate consistency, as per the CAP theorem.

This approach is highly applicable in large-scale distributed systems like social media platforms, e-commerce sites, and content delivery networks. For instance, in a social media platform, when you post a status update, the post might not appear immediately across all your friends' feeds, but it will propagate and become consistent across the system after a short while. This trade-off allows such systems to remain highly available and performant, even during network partitions or high load scenarios.

Sure, to ensure fault tolerance and resilience in a microservices system, you want to implement practices like circuit breakers and retries. Circuit breakers help prevent cascading failures by stopping an application's calls to a failing service after a certain threshold. This not only helps in managing failures but also gives the failing service some breathing room to recover.

Additionally, it's important to use retries with exponential back-off strategies, so when a service fails, the system will wait for progressively longer intervals before retrying. This method reduces the load on the system and avoids overwhelming a recovering service. You'd also want to make sure that your services are stateless or at least capable of recreating their state, so that in the event of a failure, the system can still recover gracefully.

Don't forget about monitoring and logging. Implementing robust logging and monitoring allows you to detect issues early, understand the root cause, and address them before they lead to larger system failures. This holistic view aids in maintaining the resilience of the system as a whole.

Sagas are a design pattern used to manage and coordinate distributed transactions in a microservices architecture. Instead of relying on a traditional single transaction that spans multiple services, Sagas break down the process into a series of smaller, local transactions. Each of these local transactions is managed by the respective microservice. If one of the local transactions fails, the Saga pattern ensures that compensating transactions are executed to undo the changes made by the previous transactions.

Sagas can be implemented using two main approaches: the choreography-based approach and the orchestration-based approach. In choreography, each microservice involved in the Saga listens for certain events and reacts accordingly, making the process more decentralized. In orchestration, a central coordinator or orchestrator dictates the sequence of actions across the services, providing more control and oversight.

This pattern is particularly useful in use cases where distributed transactions are necessary, such as order processing in e-commerce systems, where multiple steps like payment, inventory update, and shipping need to be coordinated while ensuring data consistency and reliability.

Managing service configuration in a Microservices environment usually involves centralizing the configuration to maintain consistency and facilitate changes. One common approach is using a configuration server, like Spring Cloud Config, to store configuration files and serve them to your microservices at runtime. This lets you change configs on the fly without needing to redeploy your services.

Another approach is to use environment variables, especially for sensitive information like database credentials. Tools like Docker and Kubernetes make it easier to manage these configurations by allowing you to pass environment variables to your containers or via Kubernetes Secrets and ConfigMaps. This keeps your services lightweight and adaptable while separating the configuration from the codebase.

Rest vs GraphQL is a significant topic in microservices.

REST is widely adopted and well-understood, making it easier to find resources and tools for development. It uses standard HTTP methods, making API design straightforward. However, it can lead to over-fetching or under-fetching of data since each endpoint returns a fixed data structure. This means you might get more data than necessary or have to make multiple requests to gather all needed information.

On the other hand, GraphQL allows you to request exactly the data you need with a single query, reducing the number of network requests and improving performance. It's more flexible and powerful in terms of data retrieval. However, it has a steeper learning curve, and setting up a GraphQL server can be more complex compared to REST. Additionally, the ecosystem around GraphQL is still maturing, so you might encounter fewer tools and community support.

Service discovery in microservices can be handled using two main approaches: client-side discovery and server-side discovery. In client-side discovery, the client is responsible for determining the network locations of available service instances; this is often done via a service registry like Netflix Eureka or Consul, where services register themselves on startup and clients query this registry when making requests.

With server-side discovery, the client makes a request to a load balancer, which then queries the service registry and directs the request to an available service instance. AWS Elastic Load Balancing (ELB) and Kubernetes are common examples of ecosystems that handle server-side discovery. Both approaches have their pros and cons, and the appropriate choice often depends on your application's specific requirements and the infrastructure you are using.

The Circuit Breaker pattern is a design pattern used to detect failures and encapsulate the logic of preventing a failure from constantly recurring. Essentially, it's like an electrical circuit breaker that prevents the overload. If a particular service is failing repeatedly, the circuit breaker trips and stops the service from being called, allowing for fallback methods or other remedial actions to take over.

This pattern is important in microservices because it helps maintain the overall health and resiliency of your application. Microservices often depend on other services, and continuous failures can lead to cascading failures throughout the system. By using the Circuit Breaker pattern, you can prevent a single failing service from causing widespread issues, thereby improving stability and fault tolerance.

Managing the deployment of multiple microservices can be streamlined by using container orchestration tools like Kubernetes or Docker Swarm. With Kubernetes, for instance, you can define the desired state of your applications using YAML files, and the system will ensure that your applications are always running as expected. This includes handling rollouts, rollbacks, and scaling of services.

CI/CD pipelines are also essential. Tools like Jenkins, GitLab CI/CD, or CircleCI automate the integration and deployment process, ensuring that when new code is committed, it's tested and deployed efficiently across your microservices architecture. This helps maintain consistency and reliability across deployments.

Additionally, service meshes like Istio can be employed to manage traffic, security, and monitoring between your microservices, providing another layer of control and observability over your deployments.

One common anti-pattern is the "Distributed Monolith." This happens when services are not properly decoupled, leading to tight coupling and shared databases, and it ends up defeating the purpose of microservices by making deployments and updates just as complex as a monolithic application. Another anti-pattern is "Improper Service Boundaries," where services are either too granular, leading to excessive inter-service communication, or too broad, resembling mini-monoliths.

Additionally, "Overly Chatty Services" occur when services require too many calls to each other to complete a single transaction, leading to latency and performance issues. Finally, there's "Inconsistent Data Management," where different microservices handle data differently or share the same database schema, causing data integrity and consistency problems. Ensuring proper design can help avoid these common pitfalls.

Handling schema changes in a Microservices architecture requires a strategy that maintains backward compatibility to ensure that services don't break during updates. Start with versioning your APIs and database schemas, so new changes don't disrupt existing services. You can use the "expand and contract" pattern, where you first expand by adding new fields that old services can ignore, then contract by removing old fields after all services have been updated to use the new schema.

Another effective method is using database migration tools like Flyway or Liquibase to manage changes in a controlled and reversible manner. This allows you to roll out schema changes in small increments, monitoring their effects before fully committing. Coordination and communication between teams are crucial to ensure everyone is aware of the timeline and details of the schema changes.

One major consideration for database design in microservices is ensuring that each service has its own dedicated database. This helps to maintain loose coupling between services and ensures that they can evolve independently. It's essential to avoid creating a distributed monolith by sharing a single database schema across multiple services, as this introduces tight coupling and can lead to coordination headaches.

Another critical factor is data consistency. With microservices, achieving strong consistency becomes challenging, so you'll often need to rely on eventual consistency and design your system accordingly. Techniques like Saga patterns or compensating transactions can help manage complex, multi-step processes across services.

You also have to think about performance and scalability. For instance, some services might benefit from using NoSQL databases due to their schema flexibility and ease of scaling, while others might be better off with traditional relational databases for transactional support. The choice of database technology should align closely with the specific requirements and constraints of each service.

To structure a Microservices-based project, I'd start by identifying the different bounded contexts within the domain to define clear service boundaries. Each microservice would be responsible for a single piece of business functionality and would communicate with other services through well-defined APIs, typically using REST or gRPC.

Each service would have its own database to ensure loose coupling and avoid shared state issues. I'd implement a centralized logging and monitoring system to keep track of the operations across various services. For deployment, I'd use containerization with Docker and orchestrate it with Kubernetes or another orchestration platform to manage scaling and reliability. Ensuring automated CI/CD pipelines will allow each microservice to be independently deployed and updated without affecting the others.

An idempotent operation is one that can be performed multiple times without changing the result beyond the initial application. In other words, no matter how many times you apply the operation, the outcome will always be the same as if it was done once.

In the context of microservices, idempotency is crucial for ensuring reliability and consistency, especially in distributed systems where network issues might cause retries. For example, if a service receives a request to create a resource multiple times due to a network timeout, an idempotent operation ensures that only one resource is created. This helps in avoiding duplicate entries and maintaining the integrity of transactions across the system.

The role of DevOps in a Microservices setup is crucial for managing the complexity that comes with breaking down applications into smaller, independent services. DevOps practices, such as continuous integration and continuous deployment (CI/CD), enable teams to automate and streamline the deployment process, which is essential for the frequent updates and releases typical in a microservices architecture.

Moreover, DevOps helps in monitoring and maintaining the health of microservices through robust logging, tracking, and alerting systems. This proactive management ensures that issues are quickly identified and resolved, ensuring the reliability and scalability of each microservice. Additionally, through practices like infrastructure as code, DevOps simplifies the creation and replication of complex environments, making it easier to manage and deploy microservices efficiently.

Blue-Green Deployment is a strategy for releasing software updates with minimal downtime. It involves maintaining two identical environments: Blue (the live environment) and Green (the updated environment). Once the Green environment is fully tested and ready, the router or load balancer is switched to direct traffic from Blue to Green, essentially making Green the live environment.

One of the major benefits is the rollback capability. If something goes wrong after the switch, you can easily revert to the Blue environment, which is still intact and running the previous version. It also minimizes downtime to virtually zero since the switchover is instantaneous, providing a smooth user experience. Additionally, it allows for better testing in a production-like environment before going live.

In a microservices architecture, load balancing is crucial for distributing incoming traffic across multiple servers or instances of your service. Typically, you would use a combination of hardware and software solutions for this. Software load balancers like NGINX or HAProxy are popular choices to manage this at a high level. They can route requests to different service instances based on various algorithms like round-robin, least connections, or IP hash.

Additionally, cloud-native solutions like Azure Application Gateway, AWS Elastic Load Balancing, or Google Cloud Load Balancing can be very efficient. They offer more integration with other cloud services and dynamic scaling capabilities. For internal service-to-service communication, you could leverage service mesh technologies like Istio or Linkerd, which can automatically balance loads across service instances, handle retries, and monitor system health.

It's also vital to ensure that your services are stateless or can handle state efficiently; otherwise, session affinity needs to be configured to maintain state consistency. This holistic approach helps in achieving high availability and reliability.

Testing a microservices application involves multiple levels of testing. You need to start with unit tests for individual services to ensure each module behaves as expected. After unit tests, integration tests become crucial to verify that services work correctly together. These tests simulate interactions between microservices, databases, and other components.

API testing is another key aspect, which involves testing the endpoints of each service for their expected inputs and outputs. You'd use tools like Postman or automated API test frameworks for this. Finally, end-to-end testing checks the complete flow of the application from start to finish, making sure everything integrates seamlessly to deliver the expected user experience. This might involve automated tests that mimic user interactions or manual exploratory testing.

A Data Mesh is a decentralized approach to managing and delivering data in complex and scalable environments. Instead of treating data as a monolithic entity managed by a central team, a Data Mesh distributes responsibility across different domains, allowing teams closest to the data to manage it as a product. Each domain owns its data pipelines and services, promoting better data ownership, quality, and agility.

In terms of how it relates to microservices, think of it this way: microservices break down application logic into smaller, self-contained services that teams can develop, deploy, and scale independently. Similarly, a Data Mesh breaks down data ownership and operations into smaller, self-sufficient domains. Both approaches advocate for decentralization, promoting autonomy and reducing bottlenecks. Essentially, while microservices apply these principles to application development, a Data Mesh applies them to data management and analytics.

Service decomposition is the process of breaking down a monolithic application into smaller, independent microservices, each responsible for a specific business function. This makes the system more modular, scalable, and easier to manage.

When approaching service decomposition, I start by identifying the different business capabilities within the application. From there, I look for natural boundaries within the functionality, such as different domains or sub-domains. It’s helpful to map out the workflows and data flows to see how they interact, ensuring that each microservice has a single responsibility and minimal dependencies on other services. Lastly, I consider how data storage will be handled for each service to maintain consistency and avoid unnecessary coupling.

Dealing with data redundancy in a microservices architecture involves embracing some level of redundancy because each microservice typically manages its own database. This approach enhances autonomy and decouples services but introduces the need for maintaining data consistency across services. Implementing event-driven communication and using events to propagate state changes can help keep different data stores in sync. Techniques such as using change data capture (CDC) or a centralized event bus, like Kafka, can also be beneficial.

Additionally, adopting eventual consistency rather than strong consistency can be a pragmatic approach. This means accepting that data can be temporarily out of sync but will converge to a consistent state over time. This approach aligns well with the distributed nature of microservices and helps to maintain performance and scalability.

CI/CD is crucial in a microservices architecture because it helps manage the complexity and frequent updates that come with having multiple, independently deployable components. Continuous Integration ensures that code changes are automatically tested and integrated into the main branch frequently, catching bugs and conflicts early. This is essential in microservices, where changes in one service can affect others.

Continuous Deployment takes it a step further by automatically deploying these changes to production after passing the necessary tests. This means you can deliver updates faster and more reliably. Since microservices allow teams to work on different services independently, CI/CD pipelines help synchronize their efforts, ensuring that all parts of the system work together seamlessly without manual intervention.

To ensure backward compatibility when updating a Microservices API, you should follow a few key practices. First, instead of removing or drastically changing existing endpoints, you add new versions of them. This way, consumers who rely on the existing API continue to function without needing immediate changes. For example, you could version your API like /api/v1/resource and introduce new features in /api/v2/resource.

Another practice is to use feature toggles or flags to gradually roll out new functionalities, which allows you to test new changes in production without breaking the current system. Additionally, you can maintain backward compatibility by adding parameters that are optional rather than mandatory, which ensures current clients don't break when new parameters are introduced.

Finally, thorough integration testing is crucial. By running regression tests and end-to-end tests that cover both the old and new versions of your API, you discover any backward compatibility issues early. These tests should mimic real-world usage scenarios to effectively catch potential problems before they affect your users.

Sidecars are auxiliary containers that run alongside the main application container within the same pod, especially in Kubernetes environments. They help manage, support, and enhance the primary service by handling concerns like logging, monitoring, configuration, networking, or even security, without intruding on the main application's codebase.

They're used because they enable separation of concerns. By offloading responsibilities such as service discovery, load balancing, or secrets management to a sidecar, the primary application can remain focused on its domain logic. This modularity allows for easier maintenance, development, and scaling, and ultimately leads to more resilient and manageable microservice architectures.

Asynchronous communication between microservices is often handled using message brokers like RabbitMQ, Apache Kafka, or AWS SQS. These systems facilitate the decoupling of services by allowing one service to publish messages to a queue or a topic, which can then be consumed by another service at its own pace. This setup is useful for tasks that do not require an immediate response and helps in distributing the workload more evenly.

Another method is using event-driven architectures, where services react to events published to an event stream. This allows services to subscribe to specific types of events and process them asynchronously. This pattern not only improves scalability but also enhances modularity since services are loosely coupled and interact through well-defined events.

Additionally, using asynchronous communication helps improve resilience, as the message broker can act as a buffer, and if a service is down or busy, messages can be retried or held until the service is ready to process them. This leads to more robust and flexible systems better suited for handling modern cloud environments and microservices architectures.

API composition is a design pattern used in microservices architecture where a composite service gathers data from multiple underlying microservices and presents it to the client in a unified response. This pattern is particularly useful when you need to collect and aggregate data from various services to fulfill a single client request, reducing the need for the client to make multiple round-trips.

You would use API composition when creating a complex view in an application that requires information from several microservices. For example, in an e-commerce application, displaying a product page might require details from the product information service, pricing service, inventory service, and reviews service. Instead of the front end calling each service separately, an API composition layer can aggregate all that data and send a single response to the client.

Health checks are crucial in a microservices architecture because they let you monitor the availability and performance of your services in real time. These checks help ensure that each microservice is functioning correctly and can communicate with other services. If a service fails its health check, it can be flagged for investigation, and traffic can be rerouted to other, healthy instances to maintain system stability.

Implementing health checks typically involves creating health endpoints within your services that return the status of critical components, like databases, external APIs, and essential internal processes. These endpoints are periodically queried by monitoring tools or load balancers to get a quick status report. If all checks pass, the service is considered healthy; otherwise, proactive measures can be taken to address issues, such as restarting the service or notifying the development team.

An example implementation could involve a REST endpoint, like /health, that returns a simple JSON object indicating the status of various dependencies. Tools such as Spring Boot in Java have built-in support for creating these endpoints easily, but the concept is applicable across different languages and frameworks. This makes your services self-monitoring and resilient, which is essential for the overall reliability of your system.

Error handling in a microservices ecosystem is all about creating resilient and fault-tolerant systems. First off, each microservice should have its own localized error handling to catch and manage exceptions within its own boundary. This might involve retry logic, circuit breakers, and fallback methods. Tools like Hystrix or the Resilience4j library can be super helpful for implementing these patterns.

On a broader scale, you'll want centralized logging and monitoring to track errors across the entire system. Using tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Prometheus and Grafana can provide insights into where and why errors are occurring. This helps in quickly diagnosing and resolving issues.

Lastly, APIs should be designed to provide consistent error responses for better client-side handling. Implementing standardized HTTP status codes and detailed error messages makes it easier for clients to understand what went wrong and potentially recover gracefully.

I see API security in a microservices architecture as a multi-layered defense strategy. It starts with authentication, making sure only legitimate users or services can access the API. Usually, I'll use OAuth2 with JWT tokens for this since they are widely supported and can handle token expiration.

Beyond authentication, authorization is key. Each microservice should have clear boundaries and access policies to ensure that users or services can only do what they're allowed to do. This often involves a combination of role-based access control (RBAC) or attribute-based access control (ABAC), depending on the complexity of the system.

Finally, I pay close attention to data encryption both at rest and in transit, using TLS for communication between services. API gateways can also help manage and enforce security policies by providing a single entry point for all requests, where additional security measures like rate limiting and IP whitelisting can be applied.