Reactive services
Understanding Microservices
Before we talk about reactive services, we need to understand how traditional services work. More specifically, how microservices operate. What exactly is a microservice architecture, and how does it compare to traditional approaches like monolithic architectures? Let’s break it down in simple terms.
The Traditional Monolithic Architecture
Before diving into microservices, it’s essential to understand what a typical monolithic architecture looks like. In a monolithic setup, you have a client interacting with a single, large server. This server handles everything—processing requests, interacting with the database, and executing all the business logic. It’s a one-stop-shop for your application’s backend.
While this approach works well initially, as your application grows, so does the complexity. All your code is bundled together, making it harder to manage and scale. Eventually, this can lead to slower development cycles, as teams have to navigate through large, intertwined codebases. This is usually when companies start thinking about microservices.
Transitioning to Microservices
Microservices come into play when a monolithic system becomes too complex to manage. Instead of one large application, microservices break it down into smaller, independent services. Each service handles a specific piece of functionality and can be developed, deployed, and scaled independently.
For example, in a monolithic architecture, you might have one giant application handling user authentication, product catalogs, and payment processing. In a microservice architecture, these would be separate services, each with its own database and API.
This separation not only makes it easier for teams to work independently but also allows the system to scale more efficiently.
Horizontal vs. Vertical Scaling
When you split a monolith into microservices, there are two main ways to approach it: horizontal scaling and vertical scaling.
Horizontal Scaling
This is the easier method, where you take different APIs or endpoints from your monolith and turn them into separate services. For example, you might break up a single large API into smaller, focused services, each responsible for a specific part of your application.
While horizontal scaling simplifies development, it can make discoverability a challenge. As the number of services grows, it becomes harder to find the right one. This requires good documentation and organizational discipline to manage.
Vertical Scaling
This approach involves consolidating shared logic into a separate service. For instance, if several services need to calculate a user’s score for rating movies, you might create a dedicated service for this task.
Unfortunately, vertical scaling introduces latency, as services now have to wait for responses from other services. Techniques like asynchronous programming and caching can help mitigate some of this latency, but they introduce their own complexities and trade-offs.
Dealing with Latency and Dependencies
Latency is one of the biggest challenges in a microservices architecture. When services depend on each other, you end up with a chain of dependencies where each service must wait for the previous one to respond. This can slow down your application.
To combat this, developers often use caching, where data is temporarily stored to avoid repeated calls to a service. However, caching comes with its own trade-offs, such as stale data and memory overhead.
Another significant issue is cascading failures. In a microservice architecture, if one service fails, it can cause a ripple effect, leading to multiple services timing out or crashing, potentially bringing down the entire system.
Debugging Microservices
Debugging in a microservice architecture is notoriously difficult. Unlike a monolithic system where everything runs on a single machine, microservices are distributed across multiple machines, making it hard to get a complete picture of what’s happening. You can’t just take a snapshot of the whole system’s state. Instead, you often have to rely on extensive logging and spend hours piecing together logs from different services to understand what went wrong.
The Complexity of Changing Microservices
One of the selling points of microservices is agility—the ability to make changes quickly. However, this is only true if you don’t need to change the system’s overall structure. If you need to modify how services interact, it can become a complex and time-consuming task. Adding a new service or changing dependencies between services often requires significant effort and can introduce new points of failure and latency.
The Frontend Developer’s Dilemma
Frontend developers often face the most challenges with microservices. They need data from multiple services to build new features or views, which can mean interacting with numerous teams and APIs. This can be slow and frustrating, especially if those services weren’t designed with the frontend’s needs in mind. This has led to the concept of backend-for-frontend (BFF), where a dedicated backend service aggregates the necessary data for the frontend.
However, this BFF approach adds more complexity to the system, creating yet another service that needs to be maintained and scaled.
Enters reactive services
Microservices can greatly improve scalability and flexibility in your system, but they come with significant challenges, including increased latency, debugging complexity, and potential for cascading failures. They work best when the overall architecture remains stable and services don’t need to change frequently.
Now, let’s dive into a related concept—reactive services—and how they can help address some of these issues while enabling real-time, efficient systems.
What Are Reactive Services?
At its core, a reactive service differs from a traditional service in how it communicates with clients. In a typical service, the client sends a request, waits for a response, and then closes the connection. Reactive services flip this model by keeping the connection open after the initial response, allowing the service to push updates to the client whenever there’s a change. This approach is particularly useful in scenarios where you need real-time updates or want to reduce latency in a complex system.
Why Use Reactive Services?
Reactive services are valuable for the following reasons:
Real-Time Experiences
In applications like real-time collaboration tools, gaming, or live data feeds, you need the client to be immediately aware of any changes. For instance, in a chess game, when one player makes a move, the other player should see that move instantly without refreshing the page or constantly polling the server for updates. Reactive services enable this seamless experience by pushing updates to the client as soon as they occur.
Improving Latency in Microservices
As we discussed earlier, one of the main pain points in microservices is the accumulation of latency across multiple layers. By converting a service into a reactive one, you can maintain a reactive cache of needed data within your service. This means that instead of fetching data with each request and paying the associated network latency, your service can instantly access the data it needs from a local, up-to-date cache.
Taming complexity
Reactive services can help tame the complexity of a microservice architecture by allowing you to create reactive services that act like “views” over the data of other services. These reactive views are maintained automatically, staying up-to-date with the latest changes from the underlying services. This approach simplifies the architecture by reducing the need for complex data orchestration between services, and because the views are reactive, they stay current without introducing additional latency.
How Reactive Views Works
Consider a service that provides movie recommendations by calculating a score for each user-movie pair. In a traditional setup, each time the service needs to display these scores, it must fetch the data from a remote service, introducing latency.
With a reactive service, however, you can define a subset of the data—such as all movie scores for a specific user—and store it in a reactive view. This cache is constantly updated by the reactive service whenever any of the scores change. As a result, when your service needs to display a score, it can retrieve it directly from the cache, effectively reducing the latency to zero.
This approach works well in cases where you can clearly define the subset of data you need. However, it’s not always applicable. For example, in a search function where the data required is unpredictable, maintaining a complete cache of all possible results would be inefficient.
Building Reactive Services with Streaming
One of the traditional ways to build reactive services is by using streaming. In a streaming model, changes in data are represented as streams of events. Each time an event occurs, it gets pushed into a stream, which can then be consumed by the client.
For instance, in our recommendation engine example, instead of returning a static score, the service could return a stream of scores. Each time the score changes, the new value is pushed to the stream, and the client updates accordingly.
However, streaming can be challenging because it forces you to think in terms of events, which isn’t always intuitive. Managing state and keeping caches up-to-date manually can be error-prone and complex, which is why streaming solutions are often limited to stateless scenarios.
A New Approach: SkipLabs’ Reactive Framework
At SkipLabs, we’ve been developing a new approach to building reactive services that abstracts away the complexities of streams and state management. Our framework allows developers to write reactive services without worrying about streams or updates. Instead, you organize your data into collections and use simple mapping functions to define how your service operates.
Here’s how it works:
- Collections: You split your data into collections, each with keys and values.
- Mapping Functions: You define how data in these collections should be transformed or combined using mapping functions. The framework tracks dependencies between collections, so when data in one collection changes, the affected collections are automatically updated.
- Dynamic Dependencies: The system can handle complex, dynamic dependencies between data, ensuring that only the necessary parts of your service are recomputed when changes occur.
This approach is inspired by the simplicity of frameworks like React.js, where you build UIs as if they never change, and the framework handles the updates behind the scenes. Similarly, in our framework, you write your service as if it’s initializing with static data, and we take care of keeping everything up-to-date reactively.
The Advantages of SkipLabs’ Approach
- Simplicity: You don’t need to manage streams or worry about state. Our framework abstracts these complexities, allowing you to focus on building your service logic.
- Efficiency: By minimizing unnecessary dependencies and recomputations, our framework ensures that your service runs as efficiently as possible.
- Scalability: Whether you’re working with a simple service or a complex microservice architecture, our approach scales to meet your needs, reducing latency and improving performance.
Conclusion
Reactive services offer a powerful way to build real-time, low-latency systems, especially within a microservice architecture. By leveraging reactive caching and modern frameworks like the one we’re developing at SkipLabs, you can simplify the development process, reduce latency, and create more responsive applications. Whether you’re dealing with real-time updates or optimizing complex service interactions, embracing reactive services can significantly enhance the performance and usability of your systems.