Microservices are a Commodity

I'm a big fan of Simon Wardley. I'm not able to follow everything he writes about, but even the old writings are very interesting and somehow it all makes sense in retrospective. If you haven't read anything from him, this video is an excellent start (or go back a few years to the longer talk). In this article I'll try to make sense about what is happening in Microservices world using Wardly's theory (and diagrams).

How things evolve?

Any idea, product, system, etc., starts with its genesis, and if it is successful, it evolves, others copy it and create new custom solutions from it. If it is still successful, it diffuses further and others create new products which get improved, extended, and becomes widespread and available, “ubiquitous”, well understood and more of a commodity. This lifecycle can be observed in many successful products when looked over the years such as computers, mobiles, virtualization/cloud, etc.

If we think about the Microservices, the architectural style, the projects that support it, platforms that were born from it, containers, DevOps practice, etc... each of them is at a certain stage in the diagram above. But as a whole the Microservices movement now is a pretty widespread, well understood concept, and turning into commodity already. And there are many indications confirming that, starting from number of publications, conferences, books, confirmed success stories on production, etc. No doubt about it, not any longer.

How did we get here?

The Microservices genesis started 5-6 years ago with Fred George and James Lewis from ThoughtWorks sharing their ideas. In the next few months Thoughtworks did lot of thinking, writing, and talking about it, while Netflix did a lot of hacking and created the first generation of Microservices libraries. Most of those libraries were still not very popular, and usable by the wider developer community, and only the pioneers and startup minded companies would try them at times. Then SpringSource joined the bandwagon, they wrapped and packaged the Netflix libraries into products and made all the custom build solutions accessible and easy to consume for Java developers. In the meantime, all this interest in Microservices drove further innovation and containers were born. That brought in another wave of innovation, more funding, shuffling, new set of tools, which made the DevOps theory a practise.

Containers being the primary means for deploying Microservices, soon created the need for container orchestration i.e. Cloud Native platforms. And today, the Cloud Native landscape is in transition, taking its next shape. If you look around there are multiple Cloud Native platforms, each of which started its journey from different point in time and a unique value proposition , but slowly getting into a common feature set, similar concepts and even standards. For example the feature parity of platforms such as AWS ECS, Kubernetes, Apache Mesos, Cloud Foundry, are getting close, each being feature rich, used in production, and comparable primitives. As you can see from the diagram above, now what becomes important as technology strategy is to bet on platforms with open standards, open source, large community and high chance of long term success. That means for example choosing a container runtime that is OCI compliant, choosing a tracing tool that is based on Open Tracing standard rather than custom implementation, supporting the industry standard logging and monitoring solutions, supported by companies that are good in commodity products.

Organization Types

According to Wardly, there are three types of people/teams/organizations and each is good at certain stages of the evolutions:

• Pioneers are good in exploring uncharted territories and undiscovered concepts. They turn into life the crazy ideas.
• Settlers are good in turning the half baked prototype into something useful for a larger audience. They build trust, understanding and refine the concept. They turn the prototype into a product, make it manufacturable and turn it profitable.
• Town Planners are good in taking something and industrialise it taking advantage of economies of scale. They build the trusted platforms of the future which requires immense skill. They find ways to make things faster, better, smaller, more efficient, more economic and good enough.

With this definition and the above table showing the characteristics of each type of organisation, we can make the following hypothetical classification:

• Netflix are definitely the Pioneers. The creative, path finder people they have, the way the company is set around experimentation, uncertainty, their culture around freedom, responsibility, everything they have brought into the microservice services world makes them pioneers.
• SpringSource for me are more the settler type. They already had a popular Java stack, and they managed to spot the trend in Microservices, and created a good consumable product in the form of Spring Boot and Spring Cloud.
• Amazon, Google, Microsoft are the town planners. They may come late, but they come well prepared, with the long term strategy defined, with web scale solutions and unbeatable pricing. Platforms such as Kubernetes, ECS (not entirely sure about the latter as it is pretty closed) are build on over 10 years of experience, and indented to last long, and become the undustry standard.

One important takeway from this section is that, not everything invented by pioneers is meant for general consumption. Pioneers move fast, and unless your organisation has similar characteristics, it might be difficult to follow all the time. On the other hand, town planners create products and services which are interoperable and based on open standards. That in the longer term becomes an important axes of freedom. 

Conclusion

In the Microservices world things are moving from uncharted to industrialised direction. Most of the activities are not that chaotic, uncertain and unpredictable. It is almost turning into a boring and dull activity to plan, design and implement Microservices. And since it is an industrialised job with a low margin, the choice of tool, and ability to swap those platforms plays a significant role.

Last but not least, a nice side effect from this evolution is that we should hear less about Conway's Law, the two pizzas, and circuit breaker during conferences, and should hear more about managing Microservices at scale, automation, business value, serverless and new uncharted ideas from the pioneers in our industry.

Spring Cloud for Microservices Compared to Kubernetes

(This post was originally published on Red Hat Developers, the community to learn, code, and share faster. To read the original post, click here.)

Spring Cloud and Kubernetes both claim to be the best environment for developing and running Microservices, but they are both very different in nature and address different concerns. In this article we will look at how each platform is helping in delivering Microservice based architectures (MSA), which areas they are good at, and how to use the best of both worlds in order to succeed in the Microservices journey.

Background Story

Recently I read a great article about building Microservice Architectures With Spring Cloud and Docker by A. Lukyanchikov. If you haven't read it, you should, as it gives a comprehensive overview of what it takes to create a simple Microservices based system using Spring Cloud. In order to build a scalable and resilient Microservices system that could grow to tens or hundreds of services, it must be centrally managed and governed with the help of a tool set that has extensive build time and runtime capabilities. With Spring Cloud, that involves implementing both functional services (such as statistics service, account service and notification service) and supporting infrastructure services (such as log analysis, configuration server, service discovery, auth service). A diagram describing such a MSA using Spring Cloud is below:

MSA with Spring Cloud (by A. Lukyanchikov)

This diagram covers the runtime aspects of the system, but doesn't touch on the packaging, continuous integration, scaling, high availability, and self-healing which are also very important in the MSA world. Assuming that the majority of Java developers are familiar with Spring Cloud, in this article we will draw a parallel and see how Kubernetes relates to Spring Cloud by addressing these additional concerns.

Microservices Concerns

Rather than doing a feature by feature comparison, let's take a look at wider Microservices concerns and see how Spring Cloud and Kubernetes approach those. The good thing about MSA today is that it is an architectural style with well understood benefits and trade-offs. Microservices enable strong module boundaries, with independent deployment and technology diversity. But they come at the cost of developing distributed systems and significant operational overhead. A key success factor is to focus on being surrounded by tools that will help you address as many MSA concerns as possible. Making the starting process quick and easy is important, but the journey to production is a long one, and you need to be this tall to get there.

Microservices Concerns

In the diagram above, we can see a list with the most common technical concerns (we are not covering the non-technical concerns such as organisation structure, culture and so on) that have to be addressed in a MSA.

Technology Mapping

The two platforms, Spring Cloud and Kubernetes, are very different and there is no direct feature parity between them. If we map each MSA concern to the technology/project used to address it in both platforms, we come up with the following table.

Spring Cloud and Kubernetes Technologies

The main takeaways from the above table are:

• Spring Cloud has a rich set of well integrated Java libraries to address all runtime concerns as part of the application stack. As a result, the Microservices themselves have libraries and runtime agents to do client side service discovery, load balancing, configuration update, metrics tracking, etc. Patterns such as singleton clustered services and batch jobs are managed in the JVM too.
• Kubernetes is polyglot, doesn't target only the Java platform, and addresses the distributed computing challenges in a generic way for all languages. It provides services for configuration management, service discovery, load balancing, tracing, metrics, singletons, scheduled jobs on the platform level and outside of the application stack. The application doesn't need any library or agents for client side logic and it can be written in any language.
• In some areas both platforms rely on similar third party tools. For example the ELK and EFK stacks, tracing libraries, etc. Some libraries such as Hystrix, Spring Boot are useful equally well on both environments. There are areas where both platforms are complementary and can be combined together to create a more powerful solution (KubeFlix and Spring Cloud Kubernetes are such examples).

Microservices Requirements

In order to demonstrate the scope of each project, here is a table with (almost) end-to-end MSA requirements starting from the hardware on the bottom, up to the DevOps and self service experience at the top, and how it relates to Spring Cloud and Kubernetes platforms.

Microservices Requirements

In some cases both projects address the same requirements using different approaches and in some areas one project may be stronger than the other. But there is also a sweet spot where both platforms are complementary to each other and can be combined for a superior Microservices experience. For example Spring Boot provides Maven plugins for building single jar application packages. That combined with Docker and Kubernetes declarative Deployments and Scheduling capabilities makes running Microservice a breeze. Similarly, Spring Cloud has in-application libraries for creating resilient, fault tolerant Microservices using Hystrix (with bulkhead and circuit breaker patterns) and Ribbon (for load balancing). But that alone is not enough, and when it is combined with Kubernetes health checks, process restarts and auto-scaling capabilities turns Microservices into a true antifragile system.

Strengths and Weaknesses

Since both platforms are not directly comparable feature by feature, and rather than digging into each item, here are the (summarized) advantages and disadvantages of each platform.

Spring Cloud

Spring Cloud provides tools for developers to quickly build some of the common patterns in distributed systems such as configuration management, service discovery, circuit breakers, routing, etc. It is build on top of Netflix OSS libraries, written in Java, for Java developers.

Strengths

• The unified programing model offered by the Spring Platform itself, and rapid application creation abilities of Spring Boot, give developers a great Microservice development experience. For example, with few annotations you can create a Config Server, and few more annotation you can get the client libraries to configure your services.
• There are a rich selection of libraries covering the majority of runtime concerns. Since all libraries are written in Java, it offers multiple features, greater control and fine tuning options.
• The different Spring Cloud libraries are well integrated with one another. For example a Feign client will also use Hystrix for Circuit Breaking, and Ribbon for load balancing requests. Everything is annotation driven, making it easy to develop for Java developers.

Weaknesses

• One of the major advantages of the Spring Cloud is also its drawback - it is limited to Java only. A strong motivation for the MSA is the ability to interchange technology stacks, libraries and even languages when required. That is not possible with Spring Cloud. If you want to consume Spring Cloud/Netflix OSS infrastructure services such as configuration management, service discovery, load balancing, the solution is not elegant. The Netflix Prana project implements the sidecar pattern to exposes Java-based client libraries over HTTP to make it possible for applications written in Non-JVM languages that exist in the NetflixOSS eco-system, but it is not very elegant.
• There is too much responsibility for Java developers to care about and the Java applications to handle. Each Microservice needs to run various clients for configuration retrieval, service discovery and load balancing. It is easy to set those up, but that doesn't hide the buildtime and runtime dependencies to the environment. For example, developers can create a Config Server with @EnableConfigServer annotation easily, but that is only the happy path. Every time developers want to run a single Microservice, they need to have the Config Server up and running. For a controlled environment, developers have to think about making the Config Server highly available and since it can be backed by Git or Svn, they need shared file system for it. Similarly for service discovery, developers need to start Eureka Server first. For a controlled environment, they need to cluster it with multiple instances on each AZ, etc. It feels like as a Java developers have to build and manage a non-trivial Microservices platform in addition to implementing all the functional services.
• Spring Cloud alone has a shorter scope in the Microservices journey, and developers will also need to consider automated deployments, scheduling, resource management, process isolation, self healing, build pipelines, etc. for a complete Micorservices experience. For this point, I think it is not fair to compare Spring Cloud alone to Kubernetes, and a more fair comparison would be between Spring Cloud + Cloud Foundry (or Docker Swarm) and Kubernetes. But that also means that for a complete end-to-end Microservices experience, Spring Cloud must be supplemented with an application platform like Kubernetes itself.

Kubernetes

Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It is polyglot and provides primitives for provisioning, running, scaling and managing distributed systems.

Strengths

• Kubernetes is a polyglot and language agnostic container management platform that is capable of running both cloud native and traditional containerized applications. The services it provides such as configuration management, service discovery, load balancing, metrics collection, log aggregation are consumable by a variety of languages. This allows having one platform in the organisation that can be used by multiple teams (including Java developers using Spring framework) and serve multiple purposes: application development, testing environments, build environments (to run source control system, build server, artifact repositories), etc.
• When compared to Spring Cloud, Kubernetes addresses a wider set of MSA concerns. In addition to providing runtime services, Kubernetes also lets you provision environments, set resource constraints, RBAC, manage application lifecycle, enable autoscaling and self healing (behaving almost like an antifragile platform).
• Kubernetes technology is based on Google's 15 years of R&D and experience of managing containers. In addition, with close to 1000 committers, it is one of the most active Open Source communities on Github.

Weaknesses

• Kubernetes is polyglot and as such its services and primitives are generic and not optimised for different platforms such as Spring Cloud for JVM. For example configurations are passed to applications as environment variables or a mounted file system. It doesn't have the fancy configuration updating capabilities offered by Spring Cloud Config.
• Kubernetes is not a developer focused platform. It is intended to be used by DevOps minded IT personnel. As such, Java developers need to learn some new concepts and be open for learning new ways of solving problems. Despite it being super easy to start a developer instance of Kubernetes using MiniKube, there is a significant operation overhead to install a highly available Kubernetes cluster manually.
• Kubernetes is still a relatively new platform (2 years old) and it is still actively developed and growing. Therefore there are many new features added with every release which may be difficult to keep up with. The good news is that, this has been envisaged, and the API is extensible and backward compatible.

Best of Both Worlds

As you have seen both platforms have strengths in certain areas, and things to improve upon in other areas. Spring Cloud is a quick to start with, developer friendly platform, whereas Kubernetes is DevOps friendly, with a steeper learning curve, but covers a wider range of Microservices concerns. Here is a summary of those points.

Strengths and Weaknesses

Both frameworks address a different range of MSA concerns, and they do it in a fundamentally different way. The Spring Cloud approach is trying to solve every MSA challenge inside the JVM, whereas the Kubernetes approach is trying to make the problem disappear for the developers by solving it at platform level. Spring Cloud is very powerful inside the JVM, and Kubernetes is powerful in managing those JVMs. As such, it feels like a natural progression to combine them and benefit from best parts of both projects.

Spring Cloud backed by Kubernetes

With such a combination, Spring provides the application packaging, and Docker and Kubernetes provides the deployment and Scheduling. Spring provides in-application bulkheading through Hystrix thread pools, and Kubernetes provides bulkheading through resource, process and namespace isolation. Spring provides health endpoint for every microservice, and Kubernetes performs the healthchecks and traffic routing to healthy services. Spring externalizes and updates configurations, and Kubernetes distributes the configurations to every Microservice. And this list goes on and on.

My Favourite Microservices Stack

What about my favourite Microservices platform? I like them both. I like the developer experience offered by the Spring framework. It is all annotation driven, and there are libraries covering all kind of functional requirements. I also like Apache Camel (rather that Spring Integration) for anything to do with integration, connectors, messaging, routing, resilience and fault tolerance at the application level. Then for anything to do with clustering and managing multiple application instances, I prefer the magical Kubernetes powers. And whenever there is an overlap of functionality, such as for service discovery, load balancing, configuration management, I try to use the polyglot primitives offered by Kubernetes.