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Synchronous microservices communication – In microservices and network messaging, synchronous communication uses blocking and solicited semantics: one party makes a request and waits for the other to respond. These request/response message flows take place over a single connection. This pattern is common for web applications, where the browser connects to a server, requests a web page, and then makes new connections and issues new requests based on the contents of the page, like images, style sheets, and scripts linked in the page.

How do you communicate two microservices each other?

Asynchronous microservice integration enforces microservice’s autonomy – As mentioned, the important point when building a microservices-based application is the way you integrate your microservices. Ideally, you should try to minimize the communication between the internal microservices.

• The fewer communications between microservices, the better.
• But in many cases, you’ll have to somehow integrate the microservices.
• When you need to do that, the critical rule here is that the communication between the microservices should be asynchronous.
• That doesn’t mean that you have to use a specific protocol (for example, asynchronous messaging versus synchronous HTTP).

It just means that the communication between microservices should be done only by propagating data asynchronously, but try not to depend on other internal microservices as part of the initial service’s HTTP request/response operation. If possible, never depend on synchronous communication (request/response) between multiple microservices, not even for queries.

The goal of each microservice is to be autonomous and available to the client consumer, even if the other services that are part of the end-to-end application are down or unhealthy. If you think you need to make a call from one microservice to other microservices (like performing an HTTP request for a data query) to be able to provide a response to a client application, you have an architecture that won’t be resilient when some microservices fail.

Moreover, having HTTP dependencies between microservices, like when creating long request/response cycles with HTTP request chains, as shown in the first part of the Figure 4-15, not only makes your microservices not autonomous but also their performance is impacted as soon as one of the services in that chain isn’t performing well. Figure 4-15, Anti-patterns and patterns in communication between microservices As shown in the above diagram, in synchronous communication a “chain” of requests is created between microservices while serving the client request. This is an anti-pattern.

• In asynchronous communication microservices use asynchronous messages or http polling to communicate with other microservices, but the client request is served right away.
• If your microservice needs to raise an additional action in another microservice, if possible, do not perform that action synchronously and as part of the original microservice request and reply operation.

Instead, do it asynchronously (using asynchronous messaging or integration events, queues, etc.). But, as much as possible, do not invoke the action synchronously as part of the original synchronous request and reply operation. And finally (and this is where most of the issues arise when building microservices), if your initial microservice needs data that’s originally owned by other microservices, do not rely on making synchronous requests for that data.

• Instead, replicate or propagate that data (only the attributes you need) into the initial service’s database by using eventual consistency (typically by using integration events, as explained in upcoming sections).
• As noted earlier in the Identifying domain-model boundaries for each microservice section, duplicating some data across several microservices isn’t an incorrect design—on the contrary, when doing that you can translate the data into the specific language or terms of that additional domain or Bounded Context.

For instance, in the eShopOnContainers application you have a microservice named identity-api that’s in charge of most of the user’s data with an entity named User, However, when you need to store data about the user within the Ordering microservice, you store it as a different entity named Buyer,

The Buyer entity shares the same identity with the original User entity, but it might have only the few attributes needed by the Ordering domain, and not the whole user profile. You might use any protocol to communicate and propagate data asynchronously across microservices in order to have eventual consistency.

As mentioned, you could use integration events using an event bus or message broker or you could even use HTTP by polling the other services instead. It doesn’t matter. The important rule is to not create synchronous dependencies between your microservices.

What are microservices and how do they interact with each other?

Microservices are an architectural approach to creating cloud applications. Each application is built as a set of services, and each service runs in its own processes and communicates through APIs. The evolution that led to cloud microservices architecture began more than 20 years ago.

How microservices communicate with each other synchronously?

Synchronous Communication – Communication is synchronous when one service sends a request to another service and waits for the response before proceeding further. The most common implementation of Sync communication is over HTTP using protocols like REST, GraphQL, and gRPC.

How does 2 microservices communicate with each other in Kubernetes?

Summary – In Kubernetes, pods can communicate with each other a few different ways:

• Containers in the same Pod can connect to each other using localhost, and then the port number exposed by the other container.
• A container in a Pod can connect to another Pod using its IP address. To find out the IP address of a Pod, you can use oc get pods,
• A container can connect to another Pod through a Service. A Service has an IP address, and also usually has a DNS name, like my-service,

If you’re interested in diving deeper, check out the architecture of Kubernetes and see our recommendations for the top Kubernetes courses, Tutorial Works is a website to help you navigate the world of IT, and grow your tech career, with tips, tutorials, guides, and real opinions, Thanks for being here today! 👋

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Can 2 microservices connect to same database?

Shared-database-per-service pattern In the shared-database-per-service pattern, the same database is shared by several microservices. You need to carefully assess the application architecture before adopting this pattern, and make sure that you avoid hot tables (single tables that are shared among multiple microservices).

• All your database changes must also be backward-compatible; for example, developers can drop columns or tables only if objects are not referenced by the current and previous versions of all microservices.
• In the following illustration, an insurance database is shared by all the microservices and an IAM policy provides access to the database.

This creates development time coupling; for example, a change in the “Sales” microservice needs to coordinate schema changes with the “Customer” microservice. This pattern does not reduce dependencies between development teams, and introduces runtime coupling because all microservices share the same database.

You don’t want too much refactoring of your existing code base. You enforce data consistency by using transactions that provide atomicity, consistency, isolation, and durability (ACID). You want to maintain and operate only one database. Implementing the database-per-service pattern is difficult because of interdependencies among your existing microservices. You don’t want to completely redesign your existing data layer.

: Shared-database-per-service pattern

How do microservices work together?

In this article, we’re going to learn Microservices Communications, We will learn definitions, communication types and how they can use in microservices architectures on e-commerce domain. By the end of the article, you will learn Microservices Sync and Async Communications with patterns and practices, We will discuss RESTful APIs, gRPC and Message Broker systems, I have just published a new course — Design Microservices Architecture with Patterns & Principles. In this course, we’re going to learn how to Design Microservices Architecture with using Design Patterns, Principles and the Best Practices. We will start with designing Monolithic to Event-Driven Microservices step by step and together using the right architecture design patterns and techniques.

When we are talking about Monolithic applications, we said that the communication in Monolithic applications are inter-process communication, So that means it is working on single process that invoke one to another by using method calls. You just create class and call the method inside of target module.

All running the same process. This communication gives is very simple but at the same time components are highly coupled with each other and hard to separate and scale independently, One of the biggest challenge when moving to microservices – based application is changing the communication mechanism. Because microservices are distributed and microservices communicate with each other by inter – service communication on network level.

Each microservice has its own instance and process, Therefore, services must interact using an inter – service communication protocols like HTTP, gRPC or message brokers AMQP protocol. Since microservices are complex structure into independently developed and deployed services, we should be careful when considering communication types and manage them into design phases.

Before we design our microservices communications, we should understand about communication styles, it is possible to classify them in two axes. The first step is to define communication protocol is synchronous or asynchronous. We are going to learn Microservices Communication types — Synchronous — Asynchronous Communication, Client and services communicate with each other with many different types of communication. Mainly, those types of communications can be classified in two axes. Synchronous and Asynchronous Lets start to talk about Synchronous communication.

How data is share between microservices?

Skip to main content This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. This article describes considerations for managing data in a microservices architecture.

1. Because every microservice manages its own data, data integrity and data consistency are critical challenges.
2. A basic principle of microservices is that each service manages its own data.
3. Two services should not share a data store.
4. Instead, each service is responsible for its own private data store, which other services cannot access directly.

The reason for this rule is to avoid unintentional coupling between services, which can result if services share the same underlying data schemas. If there is a change to the data schema, the change must be coordinated across every service that relies on that database. This approach naturally leads to polyglot persistence — the use of multiple data storage technologies within a single application. One service might require the schema-on-read capabilities of a document database. Another might need the referential integrity provided by an RDBMS.

How do microservices discover each other?

Service discovery implementations – So we’ve now discussed several strategies for service discovery, along with the requirements for clients used to access service discovery. What actual implementations exist? Category 1: DNS With a DNS-based approach to service discovery, standard DNS libraries are used as clients.

In this model, each microservice receives an entry in a DNS zone file, and does a DNS lookup to connect to locate a microservice. Alternatively, microservices can be configured to use a proxy such as NGINX, which can periodically poll DNS for service discovery. This approach works with any language, and requires minimal (or zero) code changes.

There are several limitations to using DNS. First, DNS does not provide a real-time view of the world, and adjusting TTLs is insufficient when different clients have different caching semantics. Second, the operational overhead of managing zone files as new services are added or removed can become expensive.

1. Finally, additional infrastructure for resilience (e.g., local caching when the central DNS server is unavailable) and health checks will need to be added, negating the initial simplicity of DNS.
2. Category 2: Key/Value Store and sidecar With a key/value store and sidecar, a strongly consistent data store such as Consul or Zookeeper is used as the central service discovery mechanism.

To communicate with this mechanism, a sidecar is used. In this model, a microservice is configured to speak to a local proxy. A separate process communicates with service discovery, and uses that information to configure the proxy. With Zookeeper, AirBnb’s SmartStack, built on HAProxy, is a popular choice.

• Consul provides a number of different interfaces including REST and DNS that can be used with a sidecar process.
• For example, Stripe replicates Consul data into DNS and uses HAProxy as its sidecar process.
• The sidecar approach is designed to be completely transparent to the developer writing code.
• With the sidecar, a developer can write code in any programming language and not think about how his or her microservice interacts with other services.

This transparency has two key tradeoffs. First, the sidecar is limited to service discovery of hosts, The proxy is unable to route to more granular resources, e.g., topics (if using a pub/sub system like Kafka) or schemas (e.g., if using a database). A sidecar also adds additional latency, introducing an extra hop for every microservice.

Finally, a sidecar is yet another mission-critical application that needs to be tuned and deployed with each microservice. Category 3: Specialized service discovery and library/sidecar In the final category, a library (and API) is directly exposed to the developer, who uses the library (e.g., Ribbon ) to communicate with a specialized service discovery solution such as Eureka.

This model exposes functionality directly to the end developer, and results in a different set of tradeoffs. Developers need to be aware that they’re coding a microservice, and explicitly call APIs. This approach enables discovery of any resource and is not limited to hosts.

How does two microservices communicate in spring boot?

Using Kafks for asynchronous communication between microservices in Spring Boot: Examples and best practices – To enable communication between microservices using Apache Kafka in a Spring Boot application, you will need to include the spring-kafka dependency in your project and configure a KafkaTemplate bean.

Here is an example of how you can configure Kafka in a Spring Boot application: import org.apache.kafka.clients.consumer.ConsumerConfig;import org.apache.kafka.clients.producer.ProducerConfig;import org.apache.kafka.common.serialization.StringSerializer;import org.springframework.context.annotation.Bean;import org.springframework.context.annotation.Configuration;import org.springframework.kafka.core.DefaultKafkaProducerFactory;import org.springframework.kafka.core.KafkaTemplate;import org.springframework.kafka.core.ProducerFactory;import java.util.HashMap;import java.util.Map;@Configurationpublic class KafkaConfiguration @Bean public KafkaTemplate kafkaTemplate() } Once you have configured the KafkaTemplate bean, you can use it to send and receive messages from Kafka.

Here is an example of how you can use the KafkaTemplate to send a message to a topic: import org.springframework.kafka.core.KafkaTemplate;import org.springframework.beans.factory.annotation.Autowired;@Autowiredprivate KafkaTemplate kafkaTemplate;public void sendMessage(String topic, String message) In the example above, we use the send() method of the KafkaTemplate object to send a message to the specified topic.

• The send() method takes two arguments: the name of the topic and the message to send.
• The message will be serialized using the Serializer configured in the ProducerFactory bean.
• To receive messages from a Kafka topic, you can create a KafkaListener bean and use the @KafkaListener annotation to specify the topic to listen to.

Here is an example of how you can use the @KafkaListener annotation to receive messages from a topic: import org.springframework.kafka.annotation.KafkaListener;import org.springframework.stereotype.Component;@Componentpublic class KafkaConsumer } In the example above, we have annotated the receiveMessage() method with the @KafkaListener annotation and specified the topic to listen to.

1. The receiveMessage() method will be called whenever a message is received on the specified topic, and the message payload will be passed as an argument to the method.
2. The payload will be deserialized using the Deserializer configured in the ConsumerFactory bean.
3. You can use the @KafkaListener annotation on any method that you want to use as a message listener.

You can also specify multiple topics to listen to by separating the topic names with a comma, like this: @KafkaListener(topics = “topic1,topic2”), In conclusion, asynchronous communication is an essential tool for enabling microservices to collaborate effectively.

1. In this article, we explored several different approaches for enabling asynchronous communication in Spring Boot, including making asynchronous REST calls, using asynchronous messaging systems like Apache Kafka or RabbitMQ, and executing methods asynchronously.
2. By understanding the options available and choosing the best approach for your needs, you can ensure that your microservices are able to communicate efficiently and effectively.

I hope you found this article helpful and that it gave you a good understanding of the different options for asynchronous communication in Spring Boot. If you have any questions or would like to learn more, please don’t hesitate to reach out. Thanks for reading! : Asynchronous communication made easy: A guide to inter-microservice communication in Spring Boot

What is an example of microservices communication?

Synchronous or asynchronous protocol – These are the differences between them:

Synchronous protocol, It is characterized for being a system that implies isolating each microservice as much as possible, since sub-processes get locked, HTTP/HTTPS would be a typical example of asynchronous microservice communication protocol, where the client can only continue its operation when it receives a response from the server. Asynchronous protocol, In this case, the sub-processes are not locked and protocols that are compatible with many operating systems and cloud environments are used. One example would be the AMQP protocol, where the client’s or message sender’s code usually does not wait for a reply. What is does is simply send a message to a RabbitMQ queue or any other messaging agent.

How microservices communicate with each other using API Gateway?

Main features in the API Gateway pattern – An API Gateway can offer multiple features. Depending on the product it might offer richer or simpler features, however, the most important and foundational features for any API Gateway are the following design patterns: Reverse proxy or gateway routing.

The API Gateway offers a reverse proxy to redirect or route requests (layer 7 routing, usually HTTP requests) to the endpoints of the internal microservices. The gateway provides a single endpoint or URL for the client apps and then internally maps the requests to a group of internal microservices. This routing feature helps to decouple the client apps from the microservices but it’s also convenient when modernizing a monolithic API by sitting the API Gateway in between the monolithic API and the client apps, then you can add new APIs as new microservices while still using the legacy monolithic API until it’s split into many microservices in the future.

Because of the API Gateway, the client apps won’t notice if the APIs being used are implemented as internal microservices or a monolithic API and more importantly, when evolving and refactoring the monolithic API into microservices, thanks to the API Gateway routing, client apps won’t be impacted with any URI change.

• For more information, see Gateway routing pattern,
• Requests aggregation.
• As part of the gateway pattern you can aggregate multiple client requests (usually HTTP requests) targeting multiple internal microservices into a single client request.
• This pattern is especially convenient when a client page/screen needs information from several microservices.

With this approach, the client app sends a single request to the API Gateway that dispatches several requests to the internal microservices and then aggregates the results and sends everything back to the client app. The main benefit and goal of this design pattern is to reduce chattiness between the client apps and the backend API, which is especially important for remote apps out of the datacenter where the microservices live, like mobile apps or requests coming from SPA apps that come from JavaScript in client remote browsers.

1. For regular web apps performing the requests in the server environment (like an ASP.NET Core MVC web app), this pattern is not so important as the latency is very much smaller than for remote client apps.
2. Depending on the API Gateway product you use, it might be able to perform this aggregation.
3. However, in many cases it’s more flexible to create aggregation microservices under the scope of the API Gateway, so you define the aggregation in code (that is, C# code): For more information, see Gateway aggregation pattern,

Cross-cutting concerns or gateway offloading. Depending on the features offered by each API Gateway product, you can offload functionality from individual microservices to the gateway, which simplifies the implementation of each microservice by consolidating cross-cutting concerns into one tier.

Authentication and authorization Service discovery integration Response caching Retry policies, circuit breaker, and QoS Rate limiting and throttling Load balancing Logging, tracing, correlation Headers, query strings, and claims transformation IP allowlisting

For more information, see Gateway offloading pattern,

How do Kafka and microservices work together?

Architecture – This reference architecture uses Apache Kafka on Heroku to coordinate asynchronous communication between microservices. Here, services publish events to Kafka while downstream services react to those events instead of being called directly.

1. In this fashion, event-producing services are decoupled from event-consuming services.
2. The result is an architecture with services that are scalable, independent of each other, and fungible.
3. Using Kafka for asynchronous communication between microservices can help you avoid bottlenecks that monolithic architectures with relational databases would likely run into.

Because Kafka is highly available, outages are less of a concern and failures are handled gracefully with minimal service interruption. As Kafka retains data for a configured amount of time you have the option to rewind and replay events as required. Apache Kafka for scalable, decoupled coordination of a set of microservices

How microservices communicate with each other asynchronous?

Asynchronous event-driven communication – When using asynchronous event-driven communication, a microservice publishes an integration event when something happens within its domain and another microservice needs to be aware of it, like a price change in a product catalog microservice.

Additional microservices subscribe to the events so they can receive them asynchronously. When that happens, the receivers might update their own domain entities, which can cause more integration events to be published. This publish/subscribe system is performed by using an implementation of an event bus.

The event bus can be designed as an abstraction or interface, with the API that’s needed to subscribe or unsubscribe to events and to publish events. The event bus can also have one or more implementations based on any inter-process and messaging broker, like a messaging queue or service bus that supports asynchronous communication and a publish/subscribe model.

1. If a system uses eventual consistency driven by integration events, it’s recommended that this approach is made clear to the end user.
2. The system shouldn’t use an approach that mimics integration events, like SignalR or polling systems from the client.
3. The end user and the business owner have to explicitly embrace eventual consistency in the system and realize that in many cases the business doesn’t have any problem with this approach, as long as it’s explicit.

This approach is important because users might expect to see some results immediately and this aspect might not happen with eventual consistency. As noted earlier in the Challenges and solutions for distributed data management section, you can use integration events to implement business tasks that span multiple microservices.

Thus, you’ll have eventual consistency between those services. An eventually consistent transaction is made up of a collection of distributed actions. At each action, the related microservice updates a domain entity and publishes another integration event that raises the next action within the same end-to-end business task.

An important point is that you might want to communicate to multiple microservices that are subscribed to the same event. To do so, you can use publish/subscribe messaging based on event-driven communication, as shown in Figure 4-19. This publish/subscribe mechanism isn’t exclusive to the microservice architecture. Figure 4-19, Asynchronous event-driven message communication In asynchronous event-driven communication, one microservice publishes events to an event bus and many microservices can subscribe to it, to get notified and act on it. Your implementation will determine what protocol to use for event-driven, message-based communications.

AMQP can help achieve reliable queued communication. When you use an event bus, you might want to use an abstraction level (like an event bus interface) based on a related implementation in classes with code using the API from a message broker like RabbitMQ or a service bus like Azure Service Bus with Topics,

Alternatively, you might want to use a higher-level service bus like NServiceBus, MassTransit, or Brighter to articulate your event bus and publish/subscribe system.

How microservices communicate with each other in Docker?

How do microservices communicate with each other in Docker? – Docker uses built-in networking features to connect containers running different microservices to the same network, this allows them to communicate with each other using their hostnames or container names as the address. Therefore discover each other dynamically and communicate using the internal IP address of the container.

How do I connect two microservices?

With the main pros and cons of each – Photo by Aaron Barnaby on Unsplash When an application consists of a single back-end service (commonly referred to as a monolithic application), classes or modules of that app are in the same process and usually invoke each other through method calls.

1. However, an application can be composed of several services: in a monolithic application, some parts of the logic can be separated into separate services for different purposes (scalability, fault tolerance, etc.).
2. Another case of multiple services is the microservice architecture.
3. When an application consists of more than one service, sooner or later one microservice will have to request or pass some data to another microservice, or notify one or more other microservices that something has happened.

There are few integration patterns available, and developers will have to weigh the pros and cons of each in their particular case before choosing one. One microservice can easily expose a REST endpoint for other microservices to call it. Integration via RESTful endpoints is typically implemented when a microservice needs to call another and receive an immediate ( synchronous ) response. This is probably the first integration pattern that comes to mind for developers when they start thinking about integrating microservices due to the ease of implementation. In the simplest scenario, the Microservice 1 can just use the HttpClient class to call the GET endpoint of Microservice 2.

If the REST API endpoint approach is chosen as the primary integration pattern among the many microservices in a project, developers might consider building a web client using NSwag tools and storing that client as a shared NuGet package in a custom NuGet repository. One of the benefits of this style of integration is the low latency between calls that are routed from one microservice to another directly, without any additional components such as message brokers.

However, the following RESTful Web API endpoint integration style characteristics can significantly increase development time and system complexity when used extensively in a project:

Tight coupling. The client microservice knows about the server microservice at compile time. What if today microservice A calls only microservice B, but tomorrow it will have to call microservices B and C? Developers will have to change the code of microservice A. API versioning. Changing the interface of an endpoint used by other microservices can result in breaking changes that can be avoided using API versioning techniques. However, having too many versions of the same API can lead to a lot of redundant code in the microservice codebase. Client side error handling. Every client microservice that calls APIs of other microservices should implement retry logic for the failed requests. Also, client microservices must store information about requests to some storage that could not be processed due to failures or unavailability of the server microservice.

gRPC is a framework that allows developers to implement a remote procedure call (RPC) integration pattern for communication between microservices. From an architectural point of view, integrating microservices through gRPC is no different from integrating them through RESTful Web API endpoints. gRPC framework, like RESTful Web API endpoints, creates a tight coupling between microservices. Also, breaking changes can be easily made to the system by changing the contract defined in the,proto file and not updating the client in a timely manner.

Similar to the RESTful Web API endpoint approach, gRPC is well suited for cases where the client initiating the request must wait for a response, However, gRPC generally provides better latency and high throughput than a RESTful Web API endpoints approach due to the Protobuf protocol used. Unlike RESTful Web API endpoints or gRPC integration, the messaging pattern allows microservices to communicate with each other indirectly.

Communication passes through a message bus component that acts as an intermediary. Messaging integration pattern is well suited for asynchronous (fire-and-forget) communication, where the microservice needs to send a message and not wait for a response from the receivers. Microservice 1 will only wait for a response from service bus that the message was successfully submitted to the queue, but will not wait for microservice 2 to finish processing the message. Messaging integration is really different from RESTful Web API endpoints or gRPC in terms of architecture.

1. Microservices are decoupled from each other.
2. There is no need to change and recompile the client microservice code if its message tomorrow needs to be delivered to more consumer microservices than today.
3. In addition, the use of service bus provides a better separation of concerns,
4. Message brokers such as Azure Service Bus provide a wide range of out-of-the-box functionality such as message redelivery, dead-letter queue storage, monitoring features, sessions, etc.

Without the use of service bus, microservices might need to implement all of these features themselves. However, if the service bus component fails, the application will stop working. Service bus must be highly available so as not to be a single point of failure, In addition, a streaming broker acts the same as a messaging broker in terms of message delivery : one microservice sends messages to the streaming broker, and one or more other services can subscribe to consume and process them. However, the first key difference between streaming and messaging integration is that messages are not removed from the streaming broker after they have been processed by consumer microservices.

When using message brokers, newly created microservices cannot receive old messages that have already been processed by another microservice in order to process them again. But this can be done naturally with streaming brokers that can get messages from any point in time in the past. Unlike messaging brokers, which only act as a message transport, stream brokers additionally act as a data store,

RESTful web API endpoints and RPC integration styles are well suited for synchronous communication between microservices, where a client microservice needs to initiate a request and wait for a response. However, both approaches lead to a tight coupling between microservices and versioning issues.

Can a microservice have more than one API?

Microservices vs. API – Before comparing these concepts, let’s quickly review:

An API is a part of a web application that communicates with other applications. A software’s API defines a set of acceptable requests to be made to the API and responses to these requests. A microservice is an approach to building an application that breaks down an application’s functions into modular, self-contained programs. Microservices make it easier to create and maintain software.

While different things, microservices and APIs are frequently paired together because services within a microservice use APIs to communicate with each other, Similar to how an application uses a public API to integrate with a different application, one component of a microservice uses a private API to access a different component of the same microservice. Notice how just one module interacts with third-party developers. In our example, this particular service handles integrations with other applications. So, that particular API is public-facing, while all other APIs in this microservice are private. It’s important to note that no two microservices are alike, and all utilize APIs differently.

Some might assign multiple APIs to one service, or use a single API for accessing multiple services. The visualization above is to help you grasp the overall concept of microservices and APIs, but not every application follows a one-to-one API-to-service pairing. Finally, remember that APIs have uses beyond microservices.

As we discussed, web APIs enable data-sharing between systems, which is necessary for many web applications. Also, APIs can be used internally but without a microservice implementation.

What happens if one microservice is down?

Fault Tolerance – Consider a scenario in which six microservices are communicating with each other. The microservice-5 becomes down at some point, and all the other microservices are directly or indirectly depend on it, so all other services also go down. Fault tolerance can be achieved with the help of a circuit breaker, It is a pattern that wraps requests to external services and detects when they fail. If a failure is detected, the circuit breaker opens. All the subsequent requests immediately return an error instead of making requests to the unhealthy service.

Does each microservice has its own DB?

In this article – Tip This content is an excerpt from the eBook,,NET Microservices Architecture for Containerized,NET Applications, available on,NET Docs or as a free downloadable PDF that can be read offline. An important rule for microservices architecture is that each microservice must own its domain data and logic. Just as a full application owns its logic and data, so must each microservice own its logic and data under an autonomous lifecycle, with independent deployment per microservice.

1. This means that the conceptual model of the domain will differ between subsystems or microservices.
2. Consider enterprise applications, where customer relationship management (CRM) applications, transactional purchase subsystems, and customer support subsystems each call on unique customer entity attributes and data, and where each employs a different Bounded Context (BC).

This principle is similar in Domain-driven design (DDD), where each Bounded Context or autonomous subsystem or service must own its domain model (data plus logic and behavior). Each DDD Bounded Context correlates to one business microservice (one or several services). Figure 4-7, Data sovereignty comparison: monolithic database versus microservices In the traditional approach, there’s a single database shared across all services, typically in a tiered architecture. In the microservices approach, each microservice owns its model/data.

The centralized database approach initially looks simpler and seems to enable reuse of entities in different subsystems to make everything consistent. But the reality is you end up with huge tables that serve many different subsystems, and that include attributes and columns that aren’t needed in most cases.

It’s like trying to use the same physical map for hiking a short trail, taking a day-long car trip, and learning geography. A monolithic application with typically a single relational database has two important benefits: ACID transactions and the SQL language, both working across all the tables and data related to your application.

This approach provides a way to easily write a query that combines data from multiple tables. However, data access becomes much more complicated when you move to a microservices architecture. Even when using ACID transactions within a microservice or Bounded Context, it is crucial to consider that the data owned by each microservice is private to that microservice and should only be accessed either synchronously through its API endpoints(REST, gRPC, SOAP, etc) or asynchronously via messaging(AMQP or similar).

Encapsulating the data ensures that the microservices are loosely coupled and can evolve independently of one another. If multiple services were accessing the same data, schema updates would require coordinated updates to all the services. This would break the microservice lifecycle autonomy.

• But distributed data structures mean that you can’t make a single ACID transaction across microservices.
• This in turn means you must use eventual consistency when a business process spans multiple microservices.
• This is much harder to implement than simple SQL joins, because you can’t create integrity constraints or use distributed transactions between separate databases, as we’ll explain later on.

Similarly, many other relational database features aren’t available across multiple microservices. Going even further, different microservices often use different kinds of databases. Modern applications store and process diverse kinds of data, and a relational database isn’t always the best choice.

• For some use cases, a NoSQL database such as Azure CosmosDB or MongoDB might have a more convenient data model and offer better performance and scalability than a SQL database like SQL Server or Azure SQL Database.
• In other cases, a relational database is still the best approach.
• Therefore, microservices-based applications often use a mixture of SQL and NoSQL databases, which is sometimes called the polyglot persistence approach.

A partitioned, polyglot-persistent architecture for data storage has many benefits. These include loosely coupled services and better performance, scalability, costs, and manageability. However, it can introduce some distributed data management challenges, as explained in ” Identifying domain-model boundaries ” later in this chapter.

Can we have multiple API gateway in microservices?

Central API account – Using a central API account is similar to the separate account architecture, except the API Gateway APIs are in a central account. This architecture is the best approach for most users. It offers a balance of the benefits of microservice separation with the unification of particular services for a better end-user experience. Each microservice has an AWS account, which isolates it from the other services and reduces the risk of AWS service limit contention or accidents due to sharing the account with other engineering teams.

Because each microservice lives in a separate account, that account’s bill captures all the costs for that microservice. You can track the API costs, which are in the shared API account, using tags on API Gateway resources, While the microservices are isolated in separate AWS accounts, the API Gateway throttling, metering, authentication, and authorization features are centralized for a consistent experience for customers.

You can use subdomains or API Gateway base path mappings to route traffic to different API Gateway APIs. Also, the TLS certificates for your domains are centrally managed and available to all API Gateway APIs. You can now split CloudWatch metrics, X-Ray traces, and application logs across accounts for a given microservice and its fronting API Gateway API.

How do you deploy multiple microservices?

Multiple Service Instances per Host Pattern – One way to deploy your microservices is to use the Multiple Service Instances per Host pattern. When using this pattern, you provision one or more physical or virtual hosts and run multiple service instances on each one. There are a couple of variants of this pattern. One variant is for each service instance to be a process or a process group. For example, you might deploy a Java service instance as a web application on an Apache Tomcat server. A Node.js service instance might consist of a parent process and one or more child processes.

The other variant of this pattern is to run multiple service instances in the same process or process group. For example, you could deploy multiple Java web applications on the same Apache Tomcat server or run multiple OSGI bundles in the same OSGI container. The Multiple Service Instances per Host pattern has both benefits and drawbacks.

One major benefit is its resource usage is relatively efficient. Multiple service instances share the server and its operating system. It is even more efficient if a process or process group runs multiple service instances, for example, multiple web applications sharing the same Apache Tomcat server and JVM.

1. Another benefit of this pattern is that deploying a service instance is relatively fast.
2. You simply copy the service to a host and start it.
3. If the service is written in Java, you copy a JAR or WAR file.
4. For other languages, such as Node.js or Ruby, you copy the source code.
5. In either case, the number of bytes copied over the network is relatively small.

Also, because of the lack of overhead, starting a service is usually very fast. If the service is its own process, you simply start it. Otherwise, if the service is one of several instances running in the same container process or process group, you either dynamically deploy it into the container or restart the container.

1. Despite its appeal, the Multiple Service Instances per Host pattern has some significant drawbacks.
2. One major drawback is that there is little or no isolation of the service instances, unless each service instance is a separate process.
3. While you can accurately monitor each service instance’s resource utilization, you cannot limit the resources each instance uses.

It’s possible for a misbehaving service instance to consume all of the memory or CPU of the host. There is no isolation at all if multiple service instances run in the same process. All instances might, for example, share the same JVM heap. A misbehaving service instance could easily break the other services running in the same process.

Moreover, you have no way to monitor the resources used by each service instance. Another significant problem with this approach is that the operations team that deploys a service has to know the specific details of how to do it. Services can be written in a variety of languages and frameworks, so there are lots of details that the development team must share with operations.

This complexity increases the risk of errors during deployment. As you can see, despite its familiarity, the Multiple Service Instances per Host pattern has some significant drawbacks. Let’s now look at other ways of deploying microservices that avoid these problems.

How to communicate between 2 microservices in Spring Boot?

Using Kafks for asynchronous communication between microservices in Spring Boot: Examples and best practices – To enable communication between microservices using Apache Kafka in a Spring Boot application, you will need to include the spring-kafka dependency in your project and configure a KafkaTemplate bean.

Here is an example of how you can configure Kafka in a Spring Boot application: import org.apache.kafka.clients.consumer.ConsumerConfig;import org.apache.kafka.clients.producer.ProducerConfig;import org.apache.kafka.common.serialization.StringSerializer;import org.springframework.context.annotation.Bean;import org.springframework.context.annotation.Configuration;import org.springframework.kafka.core.DefaultKafkaProducerFactory;import org.springframework.kafka.core.KafkaTemplate;import org.springframework.kafka.core.ProducerFactory;import java.util.HashMap;import java.util.Map;@Configurationpublic class KafkaConfiguration @Bean public KafkaTemplate kafkaTemplate() } Once you have configured the KafkaTemplate bean, you can use it to send and receive messages from Kafka.

Here is an example of how you can use the KafkaTemplate to send a message to a topic: import org.springframework.kafka.core.KafkaTemplate;import org.springframework.beans.factory.annotation.Autowired;@Autowiredprivate KafkaTemplate kafkaTemplate;public void sendMessage(String topic, String message) In the example above, we use the send() method of the KafkaTemplate object to send a message to the specified topic.

The send() method takes two arguments: the name of the topic and the message to send. The message will be serialized using the Serializer configured in the ProducerFactory bean. To receive messages from a Kafka topic, you can create a KafkaListener bean and use the @KafkaListener annotation to specify the topic to listen to.

Here is an example of how you can use the @KafkaListener annotation to receive messages from a topic: import org.springframework.kafka.annotation.KafkaListener;import org.springframework.stereotype.Component;@Componentpublic class KafkaConsumer } In the example above, we have annotated the receiveMessage() method with the @KafkaListener annotation and specified the topic to listen to.

The receiveMessage() method will be called whenever a message is received on the specified topic, and the message payload will be passed as an argument to the method. The payload will be deserialized using the Deserializer configured in the ConsumerFactory bean. You can use the @KafkaListener annotation on any method that you want to use as a message listener.

You can also specify multiple topics to listen to by separating the topic names with a comma, like this: @KafkaListener(topics = “topic1,topic2”), In conclusion, asynchronous communication is an essential tool for enabling microservices to collaborate effectively.

• In this article, we explored several different approaches for enabling asynchronous communication in Spring Boot, including making asynchronous REST calls, using asynchronous messaging systems like Apache Kafka or RabbitMQ, and executing methods asynchronously.
• By understanding the options available and choosing the best approach for your needs, you can ensure that your microservices are able to communicate efficiently and effectively.

I hope you found this article helpful and that it gave you a good understanding of the different options for asynchronous communication in Spring Boot. If you have any questions or would like to learn more, please don’t hesitate to reach out. Thanks for reading! : Asynchronous communication made easy: A guide to inter-microservice communication in Spring Boot

How do you authenticate between two microservices?

External Entity Identity Propagation – This strategy can make authorization decisions while taking into account user context. For example, it can change the authorization decision based on user ID, user roles or groups, user location, time, or other parameters.

1. To perform authentication based on entity context, you must receive information about the end-user and propagate it to downstream microservices.
2. A simple way to achieve this is to take an Access Token received at the edge and transfer it to individual microservices.
3. This strategy provides the most granular control over microservice authentication.

However, it has two main drawbacks:

Not secure—the content of the token is shared with all microservices, and as a result, attackers can compromise it. A possible solution is to sign tokens via a trusted issuer. It requires internal microservices to support multiple authentication techniques, such as JWT, OIDC, or cookies.

How to interact two microservices in Spring Boot?

In this tutorial, we will learn how to create multiple Spring boot microservices and how to use RestTemplate class to make Synchronous communication between multiple microservices. There are two styles of Microservices Communications:

Synchronous CommunicationAsynchronous Communication

In the case of Synchronous Communication, the client sends a request and waits for a response from the service. The important point here is that the protocol (HTTP/HTTPS) is synchronous and the client code can only continue its task when it receives the HTTP server response.

For example, Microservice1 acts as a client that sends a request and waits for a response from Microservice2. We can use RestTemplate or WebClient or Spring Cloud Open Feign library to make a Synchronous Communication multiple microservices. In the case of Asynchronous Communication, The client sends a request and does not wait for a response from the service.

The client will continue executing its task – It doesn’t wait for the response from the service. For example, Microservice1 acts as a client that sends a request and doesn’t wait for a response from Microservice2. We can use Message brokers such as RabbitMQ and Apache Kafka to make Asynchronous Communication between multiple microservices. We will create a separate MySQL database for each microservice. We will create and set up two Spring boot projects as two microservices in IntelliJ IDEA. Let’s first create and setup the department-service Spring boot project in IntelliJ IDEA