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Microservices and monolithic architecture are two distinct software design paradigms, each with its unique traits.
A monolithic architecture consolidates all software components into a single program, whereas a microservices architecture divides the application into separate, self-contained services.
Microservices offer several advantages but also have their own challenges, requiring careful consideration in the software design process.
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Decomposition: Monolithic applications are not easily separable, housing all functionality in a single codebase. Microservices are modular, each responsible for a specific set of tasks.
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Deployment Unit: The entire monolithic application is packaged and deployed as a single unit. In contrast, microservices are deployed individually.
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Communication: In a monolith, modules communicate through in-process calls. Microservices use standard communication protocols like HTTP/REST or message brokers.
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Data Management: A monolith typically has a single database, whereas microservices may use multiple databases.
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Scaling: Monoliths scale by replicating the entire application. Microservices enable fine-grained scaling, allowing specific parts to scale independently.
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Technology Stack: While a monolithic app often uses a single technology stack, microservices can employ a diverse set of technologies.
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Development Team: Monoliths can be developed by a single team, whereas microservices are often the domain of distributed teams.
Microservices are advantageous for certain types of projects:
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Complex Systems: They are beneficial when developing complex, business-critical applications where modularity is crucial.
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Scalability: If you anticipate varying scaling needs across different functions or services, microservices might be the best pick.
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Technology Diversification: When specific functions are better suited to certain technologies or when you want to use the best tools for unique tasks.
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Autonomous Teams: For bigger organizations with multiple teams that need to work independently.
Microservices is an architectural style that structures an application as a collection of small, loosely coupled services. Each service is self-contained, focused on a specific business goal, and can be developed, deployed, and maintained independently.
Each microservice manages its own codebase and data storage. It uses its own independent infrastructure, ranging from the number of virtual machines to persistence layers, messaging systems, or even data models.
Microservices, instead of resisting failure, respond to it favorably. They self-adapt and become more resilient in the face of breakdowns.
Development teams are responsible for the entire lifecycle of their respective microservices - from development and testing to deployment, updates, and scaling.
Microservices are built to anticipate and handle failures at various levels, ensuring the graceful degradation of the system.
Services are autonomous, making their own decisions without requiring overarching governance. This agility permits independent deployments and ensures that changes in one service do not disrupt others.
Each service is crafted to provide specific and well-defined business capabilities. This focus increases development speed and makes it easier to comprehend and maintain the system.
Services are related through well-defined contracts, mainly acting as providers of specific functionalities. This reduces dependencies and integration challenges.
Each service exposes a well-defined API, sharing only the necessary information. Teams can independently choose the best technology stack, avoiding the need for a one-size-fits-all solution.
Deployments, scaling, and configuration undergo automation, preserving development velocity and freeing teams from manual, error-prone tasks.
Teams are structured around services, aligning with Conway's Law to support the Microservices architecture and promote efficiency.
Services are frequently and iteratively improved, aiming for continual enhancement over major, infrequent overhauls.
Services make their features, capabilities, and interfaces discoverable via well-documented APIs, fostering an environment of interoperability.
The DevOps method for software development merges software development (Dev) with software operation (Ops). It focuses on shortening the system's software development life cycle and providing consistent delivery. The "you build it, you run it" approach, where developers are also responsible for operating their software in production, is often associated with both Microservices and DevOps.
Here is the sample Java code:
@RestController
@RequestMapping("/loan")
public class LoanService {
@Autowired
private CreditCheckService creditCheckService;
@PostMapping("/apply")
public ResponseEntity<String> applyForLoan(@RequestBody Customer customer) {
if(creditCheckService.isEligible(customer))
return ResponseEntity.ok("Congratulations! Your loan is approved.");
else
return ResponseEntity.status(HttpStatus.FORBIDDEN).body("We regret to inform you that your credit rating did not meet our criteria.");
}
}Let's look at the main advantages of using microservices:
Each microservice can be scaled independently, which is particularly valuable in dynamic, going-viral, or resource-intensive scenarios.
Decoupling services means one service's issues or updates generally won't affect others, promoting agility.
Different services can be built using varied languages or frameworks. While this adds some complexity, it allows for best-tool-for-the-job selection.
If a microservice fails, it ideally doesn't bring down the entire system, making the system more resilient.
Microservices mesh well with Agile, enabling teams to iterate independently, ship updates faster, and adapt to changing requirements more swiftly.
No more unwieldy, monolithic codebases to navigate. With microservices, teams can focus on smaller, specific codebases, thereby enabling more targeted maintenance.
Security policies and mechanisms can be tailored to individual services, potentially reducing the overall attack surface.
Thanks to reduced codebase ownership and the interoperability of services, smaller, focused teams can thrive and communicate more efficiently.
When designing a microservices architecture, you are likely to encounter the following challenges:
-
Database Per Microservice: Ensuring that each microservice has its own database can be logistically complex. Data relationships and consistency might be hard to maintain.
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Eventual Consistency: Different microservices could be using data that might not be instantly synchronized. Dealing with eventual consistency can raise complications in some scenarios.
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Service Synchronization: Maintaining a synchronous communication between numerous services can result in a more tightly coupled and less scalable architecture.
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Service Discovery: As the number of services grows, discovering and properly routing requests to the appropriate service becomes more challenging.
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Decentralized Security: Implementing consistent security measures, such as access control and authentication, across all microservices can be intricate.
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Externalized Authorization: When security-related decisions are taken outside the service, coherent and efficient integration is crucial.
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Server Deployment: Managing numerous server deployments entails additional overhead and might increase the risk of discrepancies among them.
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Monitoring Complexity: With each microservice operating independently, gauging the collective functionality of the system necessitates more extensive monitoring capabilities.
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Domain and Data Coupling: Microservices, especially those representing different business domains, may find it challenging to process complex business transactions that require data and logic from several services.
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Cross-Cutting Concerns Duplication: Ensuring a uniform application of cross-cutting concerns like logging or caching across microservices is non-trivial.
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Fine-Grained Scalability: While microservices allow selective scale-up, guaranteeing uniform performance across varying scales might be troublesome.
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Service Bottlenecks: Certain services might be hit more frequently, potentially becoming bottlenecks.
- Integration Testing: Interactions between numerous microservices in real-world scenarios might be challenging to replicate in testing environments.
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System-Wide Transactions: Ensuring atomic operations across multiple microservices is complex and might conflict with certain microservice principles.
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Data Integrity: Without a centralized database, governing data integrity could be more intricate, especially for related sets of data that multiple microservices handle.
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Deployment Orchestration: Coordinated updates or rollbacks, particularly in hybrid environments, can present difficulties.
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Version Compatibility: Assuring that multiple, potentially differently-versioned microservices can still work together smoothly.
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Siloed Teams: Without a unified architectural vision or seamless communication, different teams developing diverse microservices might make decisions that are not entirely compatible with the overall system.
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Documentation and Onboarding: With an extensive number of microservices, their functionalities, interfaces, and usage need to be well-documented for efficient onboarding and upkeep.
Microservices often work together, and they need efficient communication mechanisms...
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Synchronous: Web services and RESTful APIs synchronize requests and responses. They are simpler to implement but can lead to tighter coupling between services. For dynamic traffic or workflow-specific requests, this is a suitable choice.
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Asynchronous: Even with service unavailability or high loads, queues lead to the delivery of messages. The services do not communicate or interact beyond their immediate responsibilities and workloads. For unpredictable or lengthy processes, use asynchronous communication.
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Data Streaming: For continuous data needs or applications that work with high-frequency data, such as stock prices or real-time analytics, this method is highly effective. Kafka or AWS Kinesis are examples of this pattern.
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RESTful APIs: Simple and clean, they utilize HTTP's request-response mechanism. Ideal for stateless, cacheable, and stateless resource interactions.
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Messaging: Deploys a message broker whereby services use HTTP or a messaging protocol (like AMQP or MQTT). This approach offers decoupling, and the broker ensures message delivery. Common tools include RabbitMQ, Apache Kafka, or AWS SQS.
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Service Mesh and Sidecars: A sidecar proxy, typically running in a container, works alongside each service. They assist in monitoring, load balancing, and authorization.
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Remote Procedure Call (RPC): It involves a client and server where the client sends requests to the server with a defined set of parameters. They're efficient but not perfectly decoupled.
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Event-Based Communication: Here, services interact by producing and consuming events. A service can publish events into a shared event bus, and other services can subscribe to these events and act accordingly. This pattern supports decoupling and scalability. Common tools include Apache Kafka, AWS SNS, and GCP Pub/Sub.
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Database per Service: It involves each microservice owning and managing its database. If a service A needs data from service B, it uses B's API to retrieve or manipulate data.
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API Gateway: Acts as a single entry point for services and consumers. Netscaler, HAProxy, and Kong are popular API Gateway tools.
Here is the Python code:
import requests
# Make a GET request to receive a list of users.
response = requests.get('https://my-api/users')
users = response.json()Here is the Python code:
# Import the generated server and client classes.
import users_pb2
import users_pb2_grpc
# Create a gRPC channel and a stub.
channel = grpc.insecure_channel('localhost:50051')
stub = users_pb2_grpc.UserStub(channel)
# Call the remote procedure.
response = stub.GetUsers(users_pb2.UserRequest())-
Ease of Development: If you need to onboard a large number of developers or have strict timelines, RESTful APIs are often easier to work with.
-
Performance: gRPC and other RPC approaches are superior to RESTful APIs in terms of speed, making them ideal when performance is paramount.
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Type Safety: gRPC, due to its use of Protocol Buffers, ensures better type safety at the cost of being slightly less human-readable when compared to RESTful JSON payloads.
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Portability: RESTful APIs, being HTTP-based, are more portable across platforms and languages. On the other hand, gRPC is tailored more towards microservices built with Protobufs.
Domain-Driven Design (DDD) provides a model for designing and structuring microservices around specific business domains. It helps teams reduce complexity and align better with domain experts.
In DDD, a Bounded Context establishes clear boundaries for a domain model, focusing on a specific domain of knowledge. These boundaries help microservice teams to operate autonomously, evolving their services within a set context.
Ubiquitous Language is a shared vocabulary that unites developers and domain experts. Microservices within a Bounded Context are built around this common language, facilitating clear communication and a deeper domain understanding.
Within a Bounded Context, microservices share a consistent data model, often dealing with strong consistency and using relational databases. This cohesion simplifies integrity checks and data relationships.
-
PaymentServiceMicroservice:@Entity public class Payment { @Id private String paymentId; private String orderId; // ... other fields and methods }
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OrderServiceMicroservice:@Entity public class Order { @Id private String orderId; // ... other fields and methods } public void updateOrderWithPayment(String orderId, String paymentId) { // Update the order }
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OrderDetailsServiceMicroservice:@Entity public class OrderDetail { @EmbeddedId private OrderDetailId orderDetailId; private String orderId; private String itemId; private int quantity; // ... other fields and methods }
Decomposing a monolithic application into microservices involves breaking down a larger piece of software into smaller, interconnected services. This process allows for greater development agility, flexibility, and often better scalability.
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Domain-Driven Design (DDD): Microservices should be independently deployable and manageable pieces of the application, typically built around distinct business areas or domains.
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Database Strategy: Each microservice should have its own data storage, but for ease of data access and management, it's beneficial for microservices to share a database when practical.
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Communication: The microservices should interact with each other in a well-coordinated manner. Two common models are Direct communication via HTTP APIs or using events for asynchronous communication.
- Identify Domains: Break down the application into major business areas or domains.
- Data Segregation: Determine the entities and relationships within each microservice. Use techniques like database-per-service or shared-database.
- Service Boundary: Define the boundaries of each microservice - what data and functionality does it control?
- Define Contracts: Intelligently design the APIs or events used for communication between microservices.
- Decouple Services: The services should be loosely coupled, to the maximum extent possible. This is especially important in scenarios where you have independent development teams working on the various microservices.
Here is the Java code:
@Entity
@Table(name = "product")
public class Product {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
private String name;
private double price;
//...
}
@Entity
@Table(name = "order_item")
public class OrderItem {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
private Long productId;
private Integer quantity;
private double price;
//...
}
public interface OrderService {
Order createOrder(String customerId, List<OrderItem> items);
List<Order> getOrdersForCustomer(String customerId);
//...
}
@RestController
@RequestMapping("/orders")
public class OrderController {
//...
@PostMapping("/")
public ResponseEntity<?> createOrder(@RequestBody Map<String, Object> order) {
//...
}
//...
}In this example, a Product microservice could manage products and expose its services through RESTful endpoints, and an Order microservice could manage orders. The two microservices would communicate indirectly through APIs, following DDD principles. Each would have its own database schema and set of tables.
Managing transactions across multiple microservices presents certain challenges, primarily due to the principles of independence and isolation that microservices are designed around. However, there are both traditional and modern strategies to handle multi-service transactions, each with its own benefits and trade-offs.
Two-Phase Commit is a transaction management protocol in which a global coordinator communicates with all participating services to ensure that the transaction can either be committed globally or rolled back across all involved services.
While it offers transactional integrity, 2PC has seen reduced popularity due to its potential for blocking scenarios, performance overhead, and the difficulties associated with its management in distributed ecosystems.
A direct evolution of the 2PC model, 3PC provides a more robust alternative. By incorporating an extra phase, it tries to overcome some of the drawbacks of 2PC, such as the potential for indefinite blocking.
While 3PC is an improvement over 2PC in this regard, it's not without its complexities and can still introduce performance penalties and maintenance overhead.
The Transactional Outbox pattern involves using messaging systems as a mechanism to coordinate transactions across multiple microservices. In this approach:
- The primary DB records changes in the outbox.
- An event message is added to a message broker.
- Subscribers read the message and execute the corresponding local transaction.
Transactional outbox offers high decoupling but does not provide the same level of strong consistency as the previous pattern.
Derived from the Greek word for a "long, epic poem," a saga is a sequence of local transactions, each initiated within a microservice. In a distributed setting, a saga is a coordination mechanism between these local transactions, aiming for eventual consistency.
With SAGA, you trade immediate consistency for long-term consistency. If something goes wrong during the saga, you need to define a strategy for compensation actions to bring the overall system back to a consistent state, hence the "epic journey" metaphor.
The philosophy here is one of embracing a partially reliable set of distributed systems. Instead of trying to guarantee strong consistency across services, the focus is on managing and mitigating inconsistencies and failures through robust service designs and effective monitoring.
When microservices are designed using Domain-Driven Design (DDD), each microservice focuses on a specific business domain, or "Bounded Context." By doing so, services tend to be more independent, leading to fewer cross-service transactions in the first place.
This approach promotes a strong focus on clear service boundaries and effective communication and collaboration between the stakeholders who understand those boundaries and the associated service behavior.
The Command Query Responsibility Segregation (CQRS) pattern involves separating read and write operations. This clear separation of concerns reduces the need for cross-service transactions.
With Event Sourcing, each state change is represented as an event, providing a reliable mechanism to propagate changes to multiple services in an asynchronous and non-blocking manner.
What is crucial here is that the proliferation of these patterns and concepts in modern software and system design is a direct result of the unique needs and opportunities presented by new paradigms such as microservices. Instead of retrofitting old ways of thinking into a new environment, the focus is on adapting notions of consistency and reliability to the realities of modern, decentralized, and highly dynamic systems.
In the context of microservices architecture, the principle of "Bounded Context" emphasizes the need to segment a complex business domain into distinct and manageable sections.
It suggests a partitioning based on business context and clearly defined responsibilities to enable individual teams to develop and manage independent microservices.
- Each microservice and its bounded context must have a clearly defined "domain language" that is comprehensible to all the members of the team and aligns with the business context.
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A bounded context delineates the scope within which a particular model or concept is operating, ensuring that the model is consistent and meaningful within that context.
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It establishes clear boundaries, acting as a bridge between domain models, so that inside the context a specific language or model is used.
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For instance: in the context of a customer, it might use a notion of "sales leads" to represent potential customers, while in the context of sales, it would define leads as initial contact or interest in a product.
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The data consistency and integrity is local to the bounded context. Each context's data is safeguarded using transactions, and data is only propagated carefully to other contexts to which it has a relationship.
-
It ensures that the understanding of data by each service or bounded context is relevant and up-to-date.
Example: In an e-commerce system, the product catalog context is responsible for maintaining product data consistency.
Each bounded context is maintained and evolved by a specific team responsible for understanding the business logic, making it self-governing and allowing teams to work independently without needing to understand the logic of other contexts.
- The concept of a bounded context is implemented and realized within the source code using Domain-Driven Design (DDD) principles. Each bounded context typically has its own codebase.
Here is the Tic Tac Toe game Model:
// Very specific to the context of the game
public enum PlayerSymbol {
NOUGHT, CROSS
}
// Specific to the game context
public class TicTacToeBoard {
private PlayerSymbol[][] board;
// Methods to manipulate board
}
// This event is purely for the game context to indicate the game has a winner.
public class GameWonEvent {
private PlayerSymbol winner;
// getter for winner
}In a microservices architecture, multiple smaller components, or microservices, work together to deliver an application. Consequently, a failure in one of the services can have downstream effects, potentially leading to system-wide failure. To address this, several best practices and resilience mechanisms are implemented.
-
Circuit Breaker Pattern: Implement a circuit breaker that isolates the failing service from the rest of the system. This way, the failure doesn't propagate and affect other services.
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Bulkhead Pattern: Use resource pools and set limits on the resources each service can consume. This limits the impact of failure, ensuring that it doesn't exhaust the whole system's resources.
-
Retry Strategy: Implement a retry mechanism that enables services to recover from transient errors. However, it's important to determine the maximum limit and backoff policies during retries to prevent overload.
-
Failsafe Services: Set up backup systems so that essential functionalities are not lost. For example, while one service is down, you can temporarily switch to a reduced-functionality mode or data backup to avoid complete system failure.
- Resource Reallocation: Implement auto-scaling to dynamically adjust resources based on load and performance metrics, ensuring the system is capable of handling the current demand.
-
Eventual Consistency: In asynchronous communication between services, strive for eventual consistency of data to keep services decoupled. This ensures data integrity is maintained even when a service is temporarily unavailable.
-
Transaction Management: Use a two-phase commit mechanism to ensure atomicity of transactions across multiple microservices. However, this approach can introduce performance bottlenecks.
-
Data Redundancy: Introduce redundancy (data duplication) in services that need access to the same data, ensuring data availability if one of the services fails.
-
Caching: Implement data caching to reduce the frequency of data requests, thereby lessening the impact of failure in the data source.
-
Data Sharding: Distribute data across multiple databases or data stores in a partitioned manner. This reduces the risk of data loss due to a single point of failure, such as a database server outage.
-
Versioning: Maintain backward compatibility using API versioning. This ensures that services can communicate with older versions if the newer one is experiencing issues.
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Message Queues: Decouple services using a message queuing system, which can help with load leveling and buffering of traffic to handle temporary fluctuations in demand.
-
Health Checks: Regularly monitor the health of microservices to identify and isolate services that are malfunctioning or underperforming.
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Self-Healing Components: Develop microservices capable of self-diagnosing and recovering from transient faults, decreasing reliance on external mechanisms for recovery.
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Graceful Degradation: When a service fails or becomes overloaded, gracefully degrade the quality of service provided to users.
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Continuous Monitoring: Regularly monitor all microservices and alert teams in real-time when there is a deviation from the expected behavior.
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Failure Isolation: Localize and contain the impact of failures, and provide backup operations and data whenever possible to provide ongoing service.
Several design patterns lend themselves well to microservices architectures, offering best practices in their design and implementation.
-
API Gateway: A single entry point for clients, responsible for routing requests to the appropriate microservice.
-
Circuit Breaker: A fault-tolerance pattern that automatically switches from a failing service to a fallback to prevent service cascading failures.
-
Service Registry: Microservices register their network location, making it possible to discover and interact with them dynamically. This is essential in a dynamic environment where services frequently start and stop or migrate to new hosts.
-
Service Discovery: The ability for a microservice to locate and invoke another through its endpoint, typically facilitated by a service registry or through an intermediary like a load balancer.
-
Bulkhead: The concept of isolating different parts of a system from each other to prevent the failure of one from affecting the others.
-
Event Sourcing: Instead of persisting the current state of an entity, the system persists a sequence of events that describe changes to that entity, allowing users to reconstruct any state of the system.
-
Database per Service: Each microservice has a dedicated database, ensuring autonomy and loose coupling.
-
Saga Pattern: Orchestrates multiple microservices to execute a series of transactions in a way that maintains data consistency across the services.
-
Strangler Fig: A deployment pattern that gradually replaces
$monolithic\ or ( conventional$ systems with a modern architecture, such as microservices. -
Blue-Green Deployment: This strategy reduces downtime and risk by running two identical production environments. Only one of them serves live traffic at any point. Once the new version is tested and ready, it switches.
-
A/B Testing: A/B testing refers to the practice of making two different versions of something and then seeing which version performs better.
-
Cache-Aside: A pattern where an application is responsible for loading data into the cache from the storage system.
-
Chained Transactions: Instead of each service managing its transactions, the orchestration service controls the transactions between multiple microservices.
Here is the Java code:
@CircuitBreaker(name = "backendA", fallbackMethod = "fallback")
public String doSomething() {
// Call the service
}
public String fallback(Throwable t) {
// Fallback logic
}The term "Circuit Breaker" is from Martin Fowler's original description. It's a well-known hardware pattern used in electrical engineering. When the current is too high, the circuit "breaks" or stops working until it is manually reset. The software equivalent, in a microservices architecture, is designed to stop sending requests to a failing service, giving it time to recover.
The API Gateway acts as a single entry point for a client to access various capabilities of microservices.
- Request Aggregation: Merges multiple service requests into a unified call to optimize client-server interaction.
- Response Aggregation: Collects and combines responses before returning them, benefiting clients by reducing network traffic.
- Caching: Stores frequently accessed data to speed up query responses.
- Authentication and Authorization: Enforces security policies, often using JWT or OAuth 2.0.
- Rate Limiting: Controls the quantity of requests to safeguard services from being overwhelmed.
- Load Balancing: Distributes incoming requests evenly across backend servers to ensure performance and high availability.
- Service Discovery: Provides a mechanism to identify the location and status of available services.
- Reduced Latency: By optimizing network traffic, it minimizes latency for both requests and responses.
- Improved Fault-Tolerance: Service failures are isolated, preventing cascading issues. It also helps in providing fallback functionality.
- Enhanced Security: Offers a centralized layer for various security measures, such as end-to-end encryption.
- Simplified Client Interface: Clients interact with just one gateway, irrespective of the underlying complicated network of services.
- Protocol Normalization: Allows backend services to use different protocols (like REST and SOAP) while offering a consistent interface to clients.
- Data Shape Management: Can transform and normalize data to match what clients expect, hiding backend variations.
- Operational Insights: Monitors and logs activities across services, aiding in debugging and analytics.
The gateway pattern is particularly useful:
- In systems built on SOA, where it is used to adapt to modern web-friendly protocols.
- For modern applications built with microservices, especially when multiple services need to be accessed for a single user action.
- When integrating with third-party services, helping in managing and securing the integration.
Here is the Python code:
from flask import Flask, request
import requests
app = Flask(__name__)
@app.route('/')
def api_gateway():
# Example: Aggregating and forwarding requests
response1 = requests.get('http://service1.com')
response2 = requests.get('http://service2.com')
# Further processing of responses
return 'Aggregated response'The Circuit Breaker pattern is a key mechanism in microservices architecture that aims to enhance fault tolerance and resilience.
- State Management: The circuit breaker can be in one of three states: Closed (normal operation), Open (indicating a failure to communicate with the service), and Half-Open (an intermittent state to test if the service is again available).
- State Transition: The circuit breaker can transition between states based on predefined triggers like the number of consecutive failures or timeouts.
- Failure Isolation: Preventing cascading failures ensures that malfunctioning services do not drag down the entire application.
- Latency Control: The pattern can quickly detect slow responses, preventing unnecessary resource consumption and improving overall system performance.
- Graceful Degradation: It promotes a better user experience by continuing to operate, though possibly with reduced functionality, even when services are partially or completely unavailable.
- Fast Recovery: After the system or service recovers from a failure, the circuit breaker transitions to its closed or half-open state, restoring normal operations promptly.
In a microservices environment, the circuit breaker pattern is often employed with libraries like Netflix's Hystrix or Resilience4J and languages like Java or .NET.
Here is the Java code:
HystrixCommand<?> command = new HystrixCommand<>(HystrixCommand.Setter
.withGroupKey(HystrixCommandGroupKey.Factory.asKey("ExampleGroup"))
.andCommandPropertiesDefaults(HystrixCommandProperties.Setter()
.withCircuitBreakerErrorThresholdPercentage(50)));Here is the Java code:
CircuitBreakerConfig config = CircuitBreakerConfig.custom()
.failureRateThreshold(20)
.ringBufferSizeInClosedState(5)
.build();
CircuitBreaker circuitBreaker = CircuitBreakerRegistry.of(config).circuitBreaker("example");Here is the C# code:
var circuitBreakerPolicy = Policy
.Handle<SomeExceptionType>()
.CircuitBreaker(3, TimeSpan.FromSeconds(60));For asynchronous activities, such as making API calls in a microservices context, the strategy can adapt to handle these as well. Libraries like Polly and Resilience4j are designed to cater to asynchronous workflows.
A Service Mesh is a dedicated infrastructure layer that simplifies network requirements for microservices, making communication more reliable, secure, and efficient. It is designed to reduce the operational burden of communication between microservices.
-
Zero Trust: Service Meshes ensure secure communication, without relying on individual services to implement security measures consistently.
-
Service Health Monitoring: Service Meshes automate health checks, reducing the risk of misconfigurations.
-
Traffic Management: They provide tools for controlling traffic, such as load balancing, as well as for A/B testing and canary deployments.
-
Adaptive Routing: In response to dynamic changes in service availability and performance, Service Meshes can redirect traffic to healthier services.
The Service Mesh architecture comprises two primary components:
-
Data Plane: Controls the actual service-to-service traffic. It's made up of proxy servers, such as Envoy or Linkerd, which sit alongside running services to manage traffic.
-
Control Plane: Manages the configuration and policies that the Data Plane enforces. It includes systems like Istio and Consul.
-
Load Balancing: Service Meshes provide intelligent load balancing, distributing requests based on various criteria, like latency or round-robin.
-
Security Features: They offer a suite of security capabilities, including encryption, authentication, and authorization.
-
Traffic Control: Service Meshes enable fine-grained traffic control, allowing you to manage traffic routing, failover, and versioning.
-
Metrics and Tracing: They collect and provide key operational telemetry, making it easier to monitor the health and performance of your microservices.
Here is the YAML configuration:
For the Control Plane:
apiVersion: v1
kind: Pod
metadata:
name: control-plane-pod
labels:
component: control-plane
spec:
containers:
- name: controller
image: control-plane-image
ports:
- containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
name: control-plane-service
spec:
selector:
component: control-plane
ports:
- protocol: TCP
port: 80
targetPort: 8080For the Data Plane:
apiVersion: v1
kind: Pod
metadata:
name: service-1-pod
labels:
app: service-1
spec:
containers:
- name: service-1-container
image: service-1-image
ports:
- containerPort: 8080
- name: proxy
image: envoyproxy/envoy-alpine
containers:
- name: service-2-container
image: service-2-image
ports:
- containerPort: 8080
- name: proxy
image: envoyproxy/envoy-alpineIn this example, Envoy serves as the sidecar proxy (Data Plane) injected alongside service-1 and service-2, and the control-plane-pod and control-plane-service represent the control plane.
Data consistency in a microservices architecture is crucial for ensuring that business-critical operations are executed accurately.
- Synchronous Communication: Via REST or gRPC, which ensures immediate consistency but can lead to performance issues and tight coupling.
- Asynchronous Communication: Using message queues which ensure eventual consistency but are more resilient and performant.
- Eventual Consistency with Compensating Actions: Involves completing a series of potentially inconsistent operations within a microservice and compensating for any errors, often orchestrated through a dedicated event handler.
Here is the Python code:
# Synchronous Communication with RESTful APIs
import requests
def place_order(order):
response = requests.post('http://order-service/api/v1/orders', json=order)
if response.status_code == 201:
return "Order placed successfully"
else:
return "Order placement failed"
# Potential downside: If the order service is down, the basket service cannot commit the transaction.Here is the RabbitMQ code:
Producer:
import pika
def send_order(order):
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.queue_declare(queue='order_queue')
channel.basic_publish(exchange='', routing_key='order_queue', body=order)
connection.close()
# No blocking operation or transactional context ensures high performance.Consumer:
# Consumes the 'order_queue'
# Processes the order asynchronouslyThe CAP theorem states that it's impossible for a distributed data store to simultaneously provide more than two of the following three guarantees: Consistency, Availability, and Partition Tolerance.
BASE is an acronym that describes the properties of many NoSQL databases. It includes:
- Basically Available: The system remains operational most of the time.
- Soft state: The state of the system may change over time, even without input.
- Eventually Consistent: The system will become consistent over time, given that the applications do not input any new data.
This pattern, used in conjunction with an event store or message broker, ensures atomic operations across services. The microservice first writes an event to an "outbox" table within its own database before committing the transaction. A specialized, outbox-reading process then dispatches these events to the message broker.
- Ensures atomicity, preventing events from being disclosed due to a partially committed transaction.
- Mitigates the risk of message loss that might occur if an external event publishing action happens after the transaction is committed.
Here is the Java code:
// Using Java's Spring Data JPA and RabbitMQ
import org.springframework.data.jpa.repository.JpaRepository;
import org.springframework.data.jpa.repository.Modifying;
import org.springframework.data.jpa.repository.Query;
import org.springframework.data.repository.query.Param;
public interface OutboxEventRepository extends JpaRepository<OutboxEvent, Long> {
@Modifying
@Query(value = "INSERT INTO outbox_event (id, eventType, payload) VALUES (:id, :eventType, :payload)", nativeQuery = true)
void create(@Param("id") long id, @Param("eventType") String eventType, @Param("payload") String payload);
}
public class OrderService {
private final OutboxEventRepository outboxEventRepository;
public void placeOrder(Order order) {
// ... Perform order placement
outboxEventRepository.create(order.getId(), "OrderPlacedEvent", toJson(order));
// ... Commit transaction
}
}Explore all 60 answers here 👉 Devinterview.io - Microservices