Easier, Faster, Better: Real-Time Microservices
Kubernetes and microservices. They usually go hand in hand, although one does not depend on the other. You can develop a microservice without Kubernetes orchestration and use Kubernetes to build applications for different architectures. However, when used together, these technologies are a powerful and widely used combination for software development and deploying a persistent data layer for any application.
When architects start working on an application, there are several considerations for the architectural model, which include security, costs, operational efficiency and integration with other services and applications. One crucial aspect to consider is the scalability and reliability of the data layer, simply because the data layer consumes vast amounts of resources and affects the entire application’s performance more than other components.
The Rise of Real-Time Data
A long time ago, real-time systems were the domain of science-fiction novels. In recent years, companies started developing expensive and purpose-built systems that could provide real-time support for specialized work. However, with the emergence of real-time data platforms, software architects can design systems that incorporate the capability to move data in and out of their databases in a few milliseconds using general-purpose components, which means that any application, on-premises or in the cloud, can provide real-time query responses without the need for specialized and expensive hardware.
This new capability allows you to incorporate new customer experiences into your services that become a differentiator or competitive advantage over other applications.
Some common use cases include:
- Personalization. Applications that provide individualized recommendations and custom-tailored offers are considered the norm in the post-pandemic era and were even before that in the online retail industry or any system that makes adjustments based on user behavior.
- Decision-making. Systems that use real-time data to make decisions are extremely valuable in the financial, industrial and healthcare markets to execute trades, minimize risk and improve patient outcomes.
- Monitoring systems. These could be industrial control or cyber threat systems that allow administrators to respond to changing signals to reduce downtime or improve safety, identifying fraud or anomalous patterns that otherwise would take too much time.
- Real-time collaboration. People need quick response times in remote collaborative environments. The pandemic emphasized the need to deliver real-time conferencing, document access and editing for companies of any size.
- AI/ML applications. Vector databases and online feature stores provide real-time data for machine learning models, including large language models (LLMs) powering chatbots, virtual assistants and document search.
Any of these implementations of real-time data can provide a competitive advantage through insights that used to take days to realize and analytics that can help businesses make better decisions and improve customer outcomes. Every industry and every segment will benefit from a real-time data layer.
Real-time platforms offer incredible opportunities, but they also present a new set of challenges related to the operationalization and efficiency of the data layer.
What is the meaning of efficiency?
Uptime? Throughput? Scalability? Cost? There are many answers. As you can see, this is a multidimensional problem with two main components: performance and operationalization.
Performance refers to how quickly the data service responds. The second dimension, operationalization, is how flexible, responsive and administrator-friendly your system is. Together they form the foundation of an efficient real-time data layer.
In today’s fast-paced and interconnected systems, users expect immediate responses from interactions with digital systems. Even slight delays cause disruptions and harm the customer experience, resulting in a loss of business and a negative reputation. These delays caused by latency became critical for gaming, videoconferencing, online transactions and many more applications.
Users abandon applications and websites that they perceive as slow. Delays even of a few seconds significantly affect user experience.
You may be thinking, “What is the impact of a traditional database versus a real-time platform? And how does that relate to microservices?”
Glad you asked.
In a microservices architecture, you have a group of small and independent services, often loosely coupled, designed to perform a minimum set of functions, thus their name, “microservices.” These microservices are connected through lightweight APIs to exchange messages, receive and send requests or replies. The payload of those messages is the data you need to move between them, your data layer.
What are the points to consider when designing a microservices architecture with a real-time data platform?
The simple answer is to plan for those two main aspects:
Imagine that your application’s “authentication service” is slow because the database can’t handle a login storm or users connecting from remote locations. The slow response hurts the rest of your application, and messages will queue up waiting for replies.
Some microservices will trigger a “retry” process causing more traffic. When users cannot log in to your service, you lose customers and you lose revenue.
You should design your microservices architecture considering all the things that can affect the performance of your data layer, things such as,
- Data models. Does your application require multiple data representations? Strings, lists or JSON?
- Data replication. Do you want multiple copies of your data to improve availability?
- Active-active database. If you have remote users, do you require geo-distributed data?
- Scalability. Is your application and data layer ready to react to unexpected or seasonal demand?
By understanding these requirements and designing your architecture around them, you can optimize your real-time data layer and build a scalable and performant application.
The next point to consider is how you will operate this complex architecture.
Distributed applications, including those in cloud environments, have an inherited complexity that exposes administrators to inconsistencies in the management of the different components of the solution. In this case, Kubernetes provides the orchestration layer, the management tool that, combined with a microservices architecture, breaks down large and complex systems into smaller, more manageable components with predictable scalability and consistent administration.
Consider setting up and managing the entire solution while monitoring, scaling and troubleshooting every microservice. Usually, data platform providers offer tools for that purpose called operators for Kubernetes.
Once you select a real-time data platform that fits your data models and provides the level of replication you need, you can use Kubernetes to provide high availability and active-active geo-replication for those distributed apps.
Research the capabilities of the data platform. If it offers an operator for Kubernetes, you’ll thank yourself for that.