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A microservices-style "hello world" for putting distributed systems patterns into practice.
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Hello Microservices!


Distributed systems is a highly interesting and exciting topic. There're so many papers, books and blogs out there covering different aspects of resilient, highly available as well as fault tolerant systems or applications. If you're like me, you really enjoy plunging into all this. However, all theory is gray. Therefore, I've decided to get my hands dirty while exploring distributed systems in a practical manner. What you see here is the result of my work over the past few weeks.

So, what is this project about?

During the previous winter semester, together with two fellow students I worked on a project which was about getting a Docker Swarm up and running on a cluster of several RaspberryPis. After we had finished, I thought that giving up our freshly established cluster would be a pity. Though, I decided on building on top of that platform and using it as a foundation for developing and deploying a microservices-style demo application. My plan was to get in touch with some distributed systems principles, patterns as well as their practical implementations in the first place. The upshot of these efforts is a small application which I simply call "Weather Service". It consists of three individual components:

  1. Weather Data Producer:
    This first component is a RESTful web service which takes a zip code as well as an optional country code as input and queries the OpenWeatherMap API for the latest weather data according to the provided parameters.

  2. Weather Data Consumer:
    The second service offers a simple GUI which accepts a zip code and a country code as user input, which is then transferred to the Weather Data Producer by means of an HTTP GET request. Afterwards, the data received as the payload of the HTTP response gets displayed on the GUI.

  3. Service Discovery Application:
    In order to enable the consumer service to "discover" the producer instances within our local network without actually knowing their location, we need a service discovery. The purpose and the advantage of such a component is discussed in the next section.

On top of that, another goal should be to establish a highly automated CI/CD infrastructure. In a nutshell, the resulting system was planned to do the following:

  • Check out the latest code base from VCS (Github in this case).
  • Compile and package the application as a single JAR.
  • Build a Docker Image by means of a module-dependent Dockerfile.
  • As soon as the image has been built, push it to a local Docker Registry.

Patterns and Frameworks

In order to keep the project's complexity manageable, I chose three distributed system patterns which I wanted to focus on and which I'd like to shortly introduce below. I also made the decision to not unnecessarily implement any tools on my own, since there're already many well-proven tools out there which have been developed by people who know what they're doing. In particular, I made use of Netflix' open source solutions, which I will also describe in a minute. So let's straightly get into the patterns I applied for my Weather Service:

  1. Service Discovery:
    In order to end up with a scalable and reliable application, we need our services to be loosely coupled. What that means is that we don't want our consumer service to be bound to a hard coded IP address and port for the producer service. Instead, every producer service instance available will register with our service discovery application, where it can be looked up and therefore accessed by the consumer app. As a consequence, we can arbitrarily add and remove producer service instances running on different IPs and ports without breaking clients.

  2. Circuit Breaker and Failover:
    With distributed systems, having servers and applications crashing and going down is rather standard than an exception. When relying on a blocking communication protocol like HTTP requires an application to protect against starvation. What that means is that if multiple services depend on each other, a single server being slow or entirely crashing will bind all resources (e.g. threads), causing the whole system to come to a sudden halt. In order to avoid such a scenario, each dependency an application declares on a remote service has to be backed by what can be called a lock as well as a thread pool. Having a server thread passing control to a dedicated request thread from a fix-sized pool when a remote service gets called comes with many advantages:

    • Imagine a client wants to contact a server which is temporarily or permanently down. With the client re-trying over and over again, it doesn't take long until a mass of threads idles, waiting for the server to respond. In the worst case, the client runs out of memory and gets killed by the operating system. By means of a thread pool per service we can define a maximum number of threads which are allowed to be in use a the same time. Remote calls will simply be rejected if there's no thread available in the pool.

    • In case a remote service stays down permanently, keep on sending requests is not the best way in order to give the crashed server a chance to recover. A so-called "circuit breaker" blocks every call on a service in case the error rate exceeds a certain threshold. At the same time, the remote server gets pinged in constant intervals. Assuming that the crashed server finally comes back to live and responds to these messages over a certain amount of time, the circuit breaker can decide to re-open the gate, letting requests pass again.

    • Another advantage of this approach is that an unreachable remote service must not necessarily in a client request simply being rejected. Instead, there's also the possibility to execute a "fallback" procedure if either a service is unavailable or the circuit breaker is open. For example, a reasonable operation within a fallback could be to fetch old data from a cache and send it to the client. In general, this approach is called "failover". The concrete failover behavior that makes sense for an application mainly depends on its use case.

    A very popular tool which exactly tackles these requirements is Hystrix by Netflix. It provides a a rich and comprehensible API for easily wrapping remote calls in HystrixCommands and letting the framework take care of all the issues described above. Consider the docs of the Weather Producer Service as well as the Weather Consumer to learn more on how Hystix has been applied in the context of this project.

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