Reactive Systems

In a nutshell reactive systems refers to an architectural style that enables applications composed of multiple microservices working together as a single unit. The aim of this architecture style is to enable applications to better react to their surroundings and one another, which manifests in greater elasticity when dealing with ever-changing workload demands and resiliency when components fail.

Applications are composed of systems of microservices. How an application’s microservices behave internally, as well as how they interact with one another, determines the application’s ability to scale dynamically, exhibit resiliency in the presence of failures, and maintain responsiveness as workload increases. In short, these behaviors establish the fundamentals of building a reactive system.

Networking applications (e.g. applications built on top of SDN controllers or K8s APIs) are no exception. So, how do we define exactly what a standard reactive system is across the industry? Enter the reactive manifesto.

The Reactive Manifesto

The following is copied from the reactive manifesto.

Organisations working in disparate domains are independently discovering patterns for building software that look the same. These systems are more robust, more resilient, more flexible and better positioned to meet modern demands.

These changes are happening because application requirements have changed dramatically in recent years. Only a few years ago a large application had tens of servers, seconds of response time, hours of offline maintenance and gigabytes of data. Today applications are deployed on everything from mobile devices to cloud-based clusters running thousands of multi-core processors. Users expect millisecond response times and 100% uptime. Data is measured in petabytes. Today’s demands are simply not met by yesterday’s software architectures.

We believe that a coherent approach to systems architecture is needed, and we believe that all necessary aspects are already recognised individually: we want systems that are Responsive, Resilient, Elastic and Message Driven. We call these Reactive Systems.

Systems built as Reactive Systems are more flexible, loosely-coupled and scalable. This makes them easier to develop and amenable to change. They are significantly more tolerant of failure and when failure does occur they meet it with elegance rather than disaster. Reactive Systems are highly responsive, giving users effective interactive feedback.

Reactive Systems are:

• Responsive: The system responds in a timely manner if at all possible. Responsiveness is the cornerstone of usability and utility, but more than that, responsiveness means that problems may be detected quickly and dealt with effectively. Responsive systems focus on providing rapid and consistent response times, establishing reliable upper bounds so they deliver a consistent quality of service. This consistent behaviour in turn simplifies error handling, builds end user confidence, and encourages further interaction.
• Resilient: The system stays responsive in the face of failure. This applies not only to highly available, mission-critical systems — any system that is not resilient will be unresponsive after a failure. Resilience is achieved by replication, containment, isolation and delegation. Failures are contained within each component, isolating components from each other and thereby ensuring that parts of the system can fail and recover without compromising the system as a whole. Recovery of each component is delegated to another (external) component and high availability is ensured by replication where necessary. The client of a component is not burdened with handling its failures.
• Elastic: The system stays responsive under varying workload. Reactive Systems can react to changes in the input rate by increasing or decreasing the resources allocated to service these inputs. This implies designs that have no contention points or central bottlenecks, resulting in the ability to shard or replicate components and distribute inputs among them. Reactive Systems support predictive, as well as Reactive, scaling algorithms by providing relevant live performance measures. They achieve elasticity in a cost-effective way on commodity hardware and software platforms.
• Message Driven: Reactive Systems rely on asynchronous message-passing to establish a boundary between components that ensures loose coupling, isolation, and location transparency. This boundary also provides the means to delegate failures as messages. Employing explicit message-passing enables load management, elasticity, and flow control by shaping and monitoring the message queues in the system and applying back-pressure when necessary. Location-transparent messaging as a means of communication makes it possible for the management of failure to work with the same constructs and semantics across a cluster or within a single host. Non-blocking communication allows recipients to only consume resources while active, leading to less system overhead.

Large systems are composed of smaller ones and therefore depend on the Reactive properties of their constituents. This means that Reactive Systems apply design principles so these properties apply at all levels of scale, making them composable. The largest systems in the world rely upon architectures based on these properties and serve the needs of billions of people daily. It is time to apply these design principles consciously from the start instead of rediscovering them each time.

Event Loop

Reactor Pattern

In the typical implementation of the pattern, asynchronously received requests are demultiplexed (in a sense, serialized) for processing. The event loop, running on one thread, cycles through the incoming events and handles them. Callback functions are registered for requests that will result in a long-running task or blocking operation. The handle for the event gets added to a queue. The event loop iterates through the queue and will eventually observe the completion of the long running task, trigger a callback, and return the result to the application.

During the lecture we will meet two dominant subsystems that can support reactive architectures: RapidMQ and Kafka. Such message passing mechanisms can be used to “wire up” microservices that act as stages in data and machine learning pipelines.

Reactive Application with Serving Model