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Microservices are one of the hottest trends to emerge in the IT world during the last few years. It is relatively easy to identify the most important reasons for their growing popularity. Both their advantages and disadvantages are well known, although what we describe as disadvantages can be easily solved using the right tools. The advantages that they offer include scalability, flexibility, and independent delivery; these are the reasons for its rapidly growing popularity. There are a few earlier IT trends that had some influence over this growth in the popularity of microservices. I'm referring to trends such as the usage of common cloud-based environments and the migration from relational databases to NoSQL.
Before discussing this at length, let's see the topics we will cover in this chapter:
- Cloud-native development with Spring Cloud
- The most important elements in microservices-based architecture
- Models of interservice communication
- Introduction to circuit breakers and fallback patterns
The concept of microservices defines an approach to the architecture of IT systems that divides an application into a collection of loosely coupled services that implement business requirements. In fact, this is a variant of the concept of service-oriented architecture (SOA). One of the most important benefits of a migration to microservices-based architecture is an ability to perform continuous delivery of large and complex applications.
By now, you have probably had an opportunity to read some books or articles about microservices. I think that most of them would have given you a detailed description of their advantages and drawbacks. There are many advantages to using microservices. The first is that microservices are relatively small and easy to understand for a new developer in a project. We usually want to make sure that the change in the code performed in one place would not have an unwanted effect on all the other modules of our application. With microservices, we can have more certainty about this because we implement only a single business area, unlike monolithic applications where sometimes even seemingly unrelated functionalities are put in the same boat. That is not all. I have noticed that, usually, it is easier to maintain expected code quality in small microservices than in a large monolith where many developers have introduced their changes.
The second thing I like about microservices architecture concerns division. Until now, when I had to deal with complex enterprise systems, I always saw that dividing the system into subsystems was done according to other subsystems. For example, telecommunication organizations always have a billing subsystem. Then you create a subsystem that hides the billing complexity and provides an API. Then you find out that you need data that can't be stored in the billing system because it is not easily customizable. So you create another subsystem. This leads in effect to you building a complicated subsystem mesh, which is not easy to understand, especially if you are a new employee in the organization. With microservices, you do not have problems such as this. If they are well-designed, every microservice should be responsible for an entire selected area. In some cases, those areas are similar regardless of the sector in which an organization is active.
Although the concept of microservices has been an important topic for some years, there are still not many stable frameworks that support all the features needed to run full microservices environments. Since the beginning of my adventure with microservices, I have been trying to keep up with the latest frameworks and find out the features developed towards the needs of microservices. There are some other interesting solutions, such as Vert.x or Apache Camel, but none of them is a match for Spring Framework.
Spring Cloud implements all proven patterns that are used in microservice-based architecture, such as service registries, the configuration server, circuit breakers, cloud buses, OAuth2 patterns, and API gateways. It has a strong community, therefore new features are released at a high frequency. It is based on Spring's open programming model used by millions of Java developers worldwide. It is also well-documented. You won't have any problems in finding many available examples of Spring Framework usage online.
Microservices are intrinsically linked to cloud-computing platforms, but the actual concept of microservices is nothing new. This approach has been applied in the IT development world for many years, but now, through the popularity of cloud solutions, it has evolved to a higher level. It is not hard to point out the reasons for this popularity. The use of a cloud offers you scalability, reliability, and low maintenance costs in comparison with on-premises solutions inside the organization. This has led to the rise of cloud-native application development approaches that are intended to give you the benefits from all of the advantages offered by cloud-like elastic scaling, immutable deployments, and disposable instances. It all comes down to one thing—decreasing the time and cost that is needed to meet new requirements. Today, software systems and applications are being improved continuously. If you have a traditional approach to development, based on monoliths, a code base grows and becomes too complex for modifications and maintenance. Introducing new features, frameworks, and technologies becomes hard, which in turn impacts innovations and inhibits new ideas. We can't argue with that.
There is also another side to this coin. Today, practically everyone thinks about migration to the cloud, partly because it's trendy. Does everyone need this? Certainly not. Those who are not absolutely sure about migrating their applications to a remote cloud provider, such as AWS, Azure, or Google, would like to at least have an on-premises private cloud or Docker containers. But will it really bring them the benefits that compensate for expenses incurred? It is worth answering that question before looking at cloud-native development and cloud platforms.
I'm not trying to dissuade you from using Spring Cloud—quite the opposite. We have to thoroughly understand what cloud-native development is. Here is a really fine definition:
"A native cloud application is a program that is specifically designed for a cloud computing environment as opposed to simply being migrated to the cloud."
Spring is designed to accelerate your cloud-native development. Building an application with Spring Boot is very quick; I'll show you how to do this in detail in the next chapter. Spring Cloud implements microservice architecture patterns and helps us in using the most popular solutions from that field. Applications developed using these frameworks can easily be adapted to be deployed on Pivotal Cloud Foundry or Docker containers, but they might as well be launched in the traditional way as separated processes on one or more machines, and you would have the advantage of a microservices approach. Let's now dive into the microservices architecture.
Let's imagine that a client approaches you, wanting you to design a solution for them. They need some kind of banking application that has to guarantee data consistency within the whole system. Our client had been using an Oracle database until now and has also purchased support from their side. Without thinking too much, we decide to design a monolithic application based on a relational data model. You can see a simplified diagram of the system's design here:
There are four entities that are mapped into the tables in the database:
- The first of them, Customer, stores and retrieves the list of active clients
- Every customer could have one or more accounts, which are operated by the Account entity
- The Transfer entity is responsible for performing all transfers of funds between accounts within the system
- There is also the Product entity that is created to store information such as the deposits and credits assigned to the clients
Without going into further details, the application exposes the API that provides all the necessary operations for realizing actions on the designed database. Of course, the implementation is in compliance with the three-layer model.
Consistency is not the most important requirement anymore; it is not even obligatory. The client expects a solution, but does not want the development to require the redeployment of the whole application. It should be scalable and should easily be able to extend new modules and functionalities. Additionally, the client does not put pressure on the developer to use Oracle or another relational database—not only that, but he would be happy to avoid using it. Are these sufficient reasons to decide on migrating to microservices? Let's just assume that they are. We divide our monolithic application into four independent microservices, each one of them with a dedicated database. In some cases, it can still be a relational database, while in others it can be a NoSQL database. Now, our system consists of many services that are independently built and run in our environment. Along with an increase in the number of microservices, there is a rising level of system complexity. We would like to hide that complexity from the external API client, which should not be aware that it talks to service X but not Y. The gateway is responsible for dynamically routing all requests to different endpoints. For example, the worddynamicallymeans that it should be based on entries in the service discovery, which I'll talk about later in the section Understanding the need for service discovery.
Hiding invocations of specific services or dynamic routing is not the only function of an API gateway. Since it is the entry point to our system, it can be a great place to track important data, collect metrics of requests, and count other statistics. It can enrich requests or response headers in order to include some additional information that is usable by the applications inside the system. It should perform some security actions, such as authentication and authorization, and should be able to detect the requirements for each resource and reject requests that do not satisfy them. Here's a diagram that illustrates the sample system, consisting of four independent microservices, which is hidden from an external client behind an API gateway:
Let's imagine that we have already divided our monolithic application into smaller, independent services. From the outside, our system still looks the same as before, because its complexity is hidden behind the API gateway. Actually, there are not many microservices, but, there may well be many more. Additionally, each of them can interact with the others. That means that every microservice has to keep information about the others' network addresses. Maintaining such a configuration could be very troublesome, especially when it comes down to manually overwriting every configuration. And what if those addresses are changing dynamically after restart? The following diagram shows the calling routes between our example microservices:
Service discovery is the automatic detection of devices and services offered by these devices on a computer network. In the case of microservice-based architecture, this is the necessary mechanism. Every service after startup should register itself in one central place that is accessible by all other services. The registration key should be the name of a service or an identificator, which has to be unique within the whole system in order to enable others to find and call the service using that name. Every single key with the given name has some values assigned to it. In the most common cases, these attributes indicate the network location of the service. To be more accurate, they indicate one of the instances of the microservice because it can be multiplied as independent applications running on different machines or ports. Sometimes it is possible to send some additional information, but it depends on the concrete service discovery provider. However, the important thing here is that under the one key, more than one instance of the same service may be registered. In addition to registration, each service gets a full list of the other services registered on the particular discovery server. Not only that, every microservice must be aware of any changes in the registration list. This may be achieved by periodically renewing the configuration earlier collected from the remote server.
Some solutions combine the usage of service discovery with the server configuration feature. When it comes right down to it, both approaches are pretty similar. The configuration of the server lets you centralize the management of all configuration files in your system. Usually, such a configuration is then a server as a REST web service. Before startup, every microservice tries to connect to the server and get the parameters prepared especially for it. One of the approaches stores such a configuration in the version control system—for example, Git. Then the configuration server updates its Git working copy and serves all properties as a JSON. In another approach, we can use solutions that store key-value pairs and fulfill the role of providers during the service discovery procedure. The most popular tools for this are Consul and Zookeeper. The following diagram illustrates an architecture of a system that consists of some microservices with a database backend that are registered in one central service known as a discovery service:
In order to guarantee the system's reliability, we cannot allow a situation where each service would have only one instance running. We usually aim to have a minimum of two running instances in case one of them experiences a failure. Of course, there could be more, but we'll keep it low for performance reasons. Anyway, multiple instances of the same service make it necessary to use load balancing for incoming requests. Firstly, the load balancer is usually built into an API gateway. This load balancer should get the list of registered instances from the discovery server. If there is no reason not to, then we usually use a round-robin rule that balances incoming traffic 50/50 between all running instances. The same rule also applies to load balancers on the microservices side.
The following diagram illustrates the most important components that are involved in interservice communication between multiple instances of two sample microservices:
Most people, when they hear about microservices, consider it to consist of RESTful web services with JSON notation, but that's just one of the possibilities. We can use some other interaction styles, which, of course, apply not only to microservices-based architecture. The first categorization that should be performed is one-to-one or one-to-many communication. In one-to-one interaction, every incoming request is processed by exactly one service instance while, in one-to-many, it is processed by multiple service instances. But the most popular division criterion is whether the call is synchronous or asynchronous. Additionally, asynchronous communication can be divided into notifications. When a client sends a request to a service, but a reply is not expected, it can just perform a simple asynchronous call, which does not block a thread and replies asynchronously.
Furthermore, it is worth mentioning reactive microservices. Now, from version 5, Spring also supports this type of programming. There are also libraries with Reactive support for interaction with NoSQL databases, such as MongoDB or Cassandra. The last well-known communication type is publish-subscribe. This is a one-to-many interaction type where a client publishes a message that is then consumed by all listening services. Typically, this model is realized using message brokers, such as Apache Kafka, RabbitMQ, and ActiveMQ.
We have discussed most of the important concepts related to the microservices architecture. Such mechanisms, such as service discovery, API gateways, and configuration servers, are useful elements that help us to create a reliable and efficient system. Even if you have considered many aspects of these while designing your system's architecture, you should always be prepared for failures. In many cases, the reasons for failures are totally beyond the control of the holder, such as network or database problems. Such errors can be particularly severe for microservice-based systems, where one input request is processed in many subsequent calls. The first good practice is to always use network timeouts when waiting for a response. If a single service has a performance problem, we should try to minimize the impact on the rest. It is better to send an error response than to wait on a reply for a long time, blocking other threads.
An interesting solution for the network timeout problems might be the circuit breaker pattern. It is a concept closely related to the microservice approach. A circuit breaker is responsible for counting successful and failed requests. If the error rate exceeds an assumed threshold, it trips and causes all further attempts to fail immediately. After a specific period of time, the API client should get back to sending requests, and if they succeed, it closes the circuit breaker. If there are many instances of each service available and one of them works slower than others, the result is that it is overlooked during the load balancing process. The second often-used mechanism for dealing with partial network failures is fallback. This is a logic that has to be performed when a request fails. For example, a service can return cached data, a default value, or an empty list of results. Personally, I'm not a big fan of this solution. I would prefer to propagate error code to other systems than return cached data or default values.
The big advantage of Spring Cloud is that it supports all the patterns and mechanisms we have looked at. These are also stable implementations, unlike some other frameworks. I'll describe in detail which of the patterns are supported by which Spring Cloud project inChapter 3,Spring Cloud Overview.
In this chapter, we have discussed the most important concepts related to microservices architecture, such as cloud-native development, service discovery, distributed configuration, API gateways, and the circuit breaker pattern. I have attempted to present my point of view about the advantages and drawbacks of this approach in the development of enterprise applications. Then, I described the main patterns and solutions related to microservices. Some of these are well-known patterns that have been around for years and are treated as something new in the IT world. In this summary, I would like to turn your attention to some things. Microservices are cloud-native by their nature. Frameworks such as Spring Boot and Spring Cloud help you to accelerate your cloud-native development. The main motivation of migrating to cloud-native development is the ability to implement and deliver applications faster while maintaining high quality. In many cases, microservices help us to achieve this, but sometimes the monolithic approach is not a bad choice.
Although microservices are small and independent units, they are managed centrally. Information such as network location, configuration, logging files, and metrics should be stored in one central place. There are various types of tools and solutions that provide all these features. We will talk about them in detail in almost all of the chapters in this book. The Spring Cloud project is designed to help us in integrating with all that stuff. I hope to efficiently guide you through the most important integrations it offers.
