The Evolution of Distributed Messaging: From Point-to-Point to ML and AI Integration
As the number of messages and computers that communicate in real-time grew exponentially, the systems needed to become more reliable, scalable, and persistent. The result – distributed messaging evolution is among the fastest ones in the world of technology.
Ever since the idea of reliable message queuing was born, the systems that enable it kept improving. However, the concept remained the same. The data is still being exchanged and processed between multiple computers.
You might wonder where we started and where we are now. To answer your question, we will take you through the entire evolution of distributed messaging.
Computers able to run multiple tasks at the same time enabled the evolution of distributed messaging computers, the first system was a point-to-point one. The concepts you can find in a point-to-point system are the same as those you can see in modern ones.
There are message producers, and they are called senders. There are consumers, as well, and they are called receivers. Senders and consumers exchange messages via a queue. Once a message is produced, the sender will ship it to a queue where the receivers come to consume the messages. But, in a point-to-point system, a message can be consumed by only one receiver at a time.
The veteran in the point-to-point domain is RabbitMQ which initially started as a point-to-point messaging system. Today RabbitMQ is modernized as they built a pub-sub on top of their point-to-point system. However, it’s rather complex to set up and doesn’t shine in the scalability department, especially when compared to cutting-edge platforms such as Apache Pulsar.
Distributed system developers continued to look for opportunities to improve the platforms. Finally, the opportunity presented itself. The hardware improvements and innovations enabled developers to connect computers and make them a part of a single messaging system.
Now they could build software able to provide services to the other components by application components on a single network. Moreover, services could communicate with each other either through passing data or several activities managing an activity. Thus, Service Oriented Architecture (SOA) was born.
Thanks to SOA, distributed messaging system developers could build a platform and integrate several services while it all runs on a single computer or network of computers. They used WSDL for SOAP to define service interfaces.
Meanwhile, one concept remained the same. Distributed messaging platforms built on SOA were still point-to-point systems. A single message was still restricted to only one receiver at a time. From our point of view, this was simply unsustainable. At that time, though, SOA was a true breakthrough. So let’s see what led to its demise: spoiler alert, Enterprise Service Bus.
Enterprise Service Bus
SOA buzz dominated the industry for quite a while. Prices of computing power and storage plummeted, which made SOA-based systems even more attractive for companies. SOA was not only used for distributed messaging but also enterprise IT systems.
Remember the scalability and sustainability issues we brought up in the previous section? They started to emerge once the number of services and point-to-point started to grow exponentially. It was inevitable to see drops in performance. The only way to scale up is to invest in more storage and more computing power. The problem demanded a new solution.
But what could solve the complex puzzle of having server-side and client-side implementations that use entirely different communication protocols?
The solution came in the form of an Enterprise Service Bus. It was able to leverage hub-type architecture to connect all different distributed messaging systems. It acted as a sort of translator enabling systems to communicate despite the messaging protocol or messaging format they were using.
While the ESB worked perfectly and still offers more functionality than API when connecting enterprise software, the technology kept up going forward. It was only a question of time before a new solution replaced ESB and pushed distributed messaging forward. Developers started experimenting with communication over the REST model, and they developed Application Programming Interface. However, there were still some concerns before the API could become the mainstream in distributed messaging. Most of the worries came from the REST model because it is too simple. It didn’t come with some basic features such as monitoring, cashing, throttling, authentication, and authorization.
Once they solved it by having a common component apply all these features on top of the API, API management platforms bloomed. As a result, all popular distributed messaging platforms have API support today.
VM and Containers
There was nothing wrong with the API approach; it’s still a paradigm in app-to-app communication. New tech is not always the main culprit behind a disruption in the industry. The next step of the distributed messaging evolution came as the result of a disruption caused by IT giants such as Google, Facebook, and Amazon, among others.
They wanted to expand worldwide, and to do it, they needed these vast distributed messaging systems that run across multiple data centers all over the globe. So engineers came up with a solution to leverage the existing hardware to create multiple computers out of one computer.
The virtual machine was created. VMs could run simultaneously on one computer enabling distributed messaging systems to work across networks with minimal latency in different geo-locations. The only problem was the resources. Every VM runs on an operating system, and having multiple OSs run on one machine is resource-heavy. Engineers managed to find a great work-around – the containers.
Containers use the computer operating system, more specifically the Linux kernel. Containers run on a separate runtime, and they can contain multiple apps along with required dependencies. So simply put, a container is like a VM, but it doesn’t run an operating system that makes resource allocation easier.
While it enables better resource utilization, container-based deployment is very complex to manage. Orchestration across multiple containers and platform maintenance emerged as the most complicated things to do. Monolithic distributed messaging systems had to be divided into smaller parts, leading to the next step of their evolution.
The next architecture that pushed distributed messaging forward was microservices. Microservices architecture enables developers to break down the functionality of a monolithic solution into different elements. One element stands for one functionality, and it is a service on its own.
So, instead of scaling up by replicating the monolith distributed messaging on several servers, microservices enables scaling by distributing services across servers and replicating them only when needed. In addition, the container orchestration system streamlines management and maintenance.
Meanwhile, many different container orchestration frameworks were released, including Google’s Kubernetes. So for a brief period, it seemed as if distributed messaging was not going anywhere. And then cloud tech came and made distributed messaging take the next step in its evolution.
Why was the cloud so well received in the distributed messaging niche? Well, dockers, container orchestration frameworks, and effortless scalability make things easier. Don’t forget the servers. Someone has to manage them too. With distributed messaging, we are not talking about “servers.” These are actual data centers that need to be consistently and properly managed.
With serverless architecture, you no longer need to manage the servers. Instead, it’s something a cloud service provider does. Your team can remain solely focused on programming the distributed messaging system while the cloud service provider takes care of cloud infrastructure maintenance, security, and updates.
It seems that distributed systems are unable to lay dormant for long periods. They keep on evolving. At the pinnacle of distributed messaging evolution, we have fully managed cloud-based distributed systems built with AI and ML capabilities.
AI and ML-enabled Distributed Messaging Platforms
Many companies see the value in having and using distributed messaging platforms. However, they don’t have the funds to have a team of experts on payroll to be in charge of all things related to a distributed system.
Cutting-edge distributed messaging platforms such as Apache Pulsar offer many benefits such as geo-replication, multi-tenacity, and zero data loss. Still, they need to be appropriately configured and maintained. This is where fully managed cloud-based distributed messaging solutions such as Pandio come into play. It is the latest evolutionary leap distributed messaging took.
A managed distributed messaging service wouldn’t be considered an evolutionary step, though, if it didn’t encompass big data, machine learning, and artificial intelligence. For instance, companies can use PandioML with distributed messaging to train and deploy ML models using the data from the pipeline in real-time.
Over the last two decades, one can say that distributed messaging took a quantum leap towards the future. However, given how things went, it is safe to assume that distributed messaging evolution is far from over. Meanwhile, feel free to explore what the cutting edge of this evolution has to offer and how Pandio can help you streamline your data management, reduce operational costs, and never again worry about downscaling or upscaling your operation.