Intelligently Scaling Microservices

Microservices offer a lot of technical advantages, but getting the balance requires some experience. You need to dynamically adjust resources based on where they are needed, just as you do when optimizing application performance. Here we delve into the decision-making process behind when and how to scale microservices effectively.

Imagine a bustling city on a typical weekday. The streamlined transportation infrastructure accommodates the flow of commuters efficiently. Traffic lights are timed based on the number of the cars on the road, the buses and trains are scheduled to match commute times, and technology is employed to create alternate routes during sports matches and weather events. The result – in our idealized city – is that the traffic system is scaled appropriately to ensure smooth travel, minimize traffic congestion, and create a happy citizenry that has one less item to complain about.

In other words: scaling matters. But it needs to be done thoughtfully.

Microservices architecture brought about a radical departure from the monolithic status quo. Instead of treating applications as a single, monolithic entity, developers embraced a modular approach that makes it easier to achieve flexibility, scalability, and resilience.  Developers have the freedom to develop, test, and deploy services independently, responding rapidly to business needs and leading to faster time-to-market.

The benefits? Adaptability in the face of changing requirements, less-fragile codebases (because you can update one service without affecting other services), and you can scale individual services independently based on demand. 

Scaling microservices vs monolithic architecture

In monolithic architecture, scaling is rather an all-or-nothing affair. When the demand for a particular function increases, the entire monolithic application must be scaled, whether or not other components experience high traffic. 

Imagine a scenario where only a single feature within an application experiences a surge in usage, while the rest of the system remains relatively idle. In a monolithic setup, additional resources are allocated to handle the increased load, even though most components do not actually need the added capacity. The lack of scaling granularity leads to resource allocation inefficiencies.

Going back to our traffic scenario, a city might have one highway that regularly gets backed up during rush hour.  The monolithic solution would be akin to adding additional lanes to both the overloaded highway (sensible) and to all other roads in the city (not so much). The end result is a reduction in traffic congestion, but the time, cost, and materials involved are overkill.

Microservices architecture offers a more precise and targeted scaling approach. Each microservice operates as an independent entity with its own boundaries and responsibilities. This autonomy allows individual services to scale independently. When a particular microservice faces increased demand, only that service needs to be scaled up, leaving the rest of the system untouched.

Each microservice can have its own dedicated database. By contrast, monoliths often rely on a single shared database that serves all system components. Sure, you could use a relational database for microservices. But using other types of databases, such as NoSQL databases or specialized databases, allows each microservice to have its own data storage that best suits its requirements. This is in line with the domain-driven design principle of microservices. 

Designing relational databases for scalability requires careful consideration. NoSQL databases were designed to scale. However, balancing data consistency, transactional integrity, and performance is crucial when choosing database strategies.

How to scale microservices

Simply scaling every microservice leads to inefficient resource allocation and unnecessary costs – rather like paying to add more roads in a city when those roads aren’t necessary. You need to understand your application’s behavior and to identify the services that require scaling. 

Here are some key considerations to determine if scaling is needed:

  • Monitoring tools identify usage patterns (in network latency, applications, and database) and the performance of your microservices and underlying data layer. This data identifies bottlenecks and determines which services are under strain. 
  • Performance metrics measure microservices responsiveness and efficiency. Track metrics such as response time, throughput, and error rates to identify services that may require additional resources.
  • User feedback helps you understand their experience. What frustrates them? This qualitative input can provide valuable insights into areas that require optimization and scaling.

Once you decide that your microservices need to scale, there are several approaches to consider, including horizontal scaling, vertical scaling, scaling data stores and databases. You can also look at leveraging caching and content delivery networks (CDNs) to enhance performance. 

Horizontal scaling

Horizontal scaling involves adding more instances of a service to distribute the load and to increase capacity. Load balancing and service discovery mechanisms play a crucial role in distributing traffic across multiple instances. Container orchestration platforms like Kubernetes provide convenient ways to manage and scale microservices horizontally.

Think of a city growing its transportation infrastructure by adding more roads and highway lanes to accommodate growing traffic.

When to use: This approach is beneficial when a service experiences increased traffic or high demand. 

Vertical scaling

Vertical scaling focuses on increasing the resource capacity of a single instance. This may involve upgrading hardware or adjusting resource allocation such as CPU and memory. Vertical scaling is suitable for services with specific resource requirements or limited options for horizontal scaling. 

Vertical scaling, in our transportation scenario, is like upgrading the public transport system. The city turns its low capacity buses into much larger buses that can carry more people, that use less energy, or that run more often.

When to use: This approach is suitable when a service has specific resource requirements or limited options for horizontal scaling.

Caching and CDN

Implementing caching mechanisms helps improve performance and scalability. Caching frequently accessed data or computed results reduces the need for repeated computations or database queries. Distributed caching and CDNs can further enhance performance by bringing data closer to the end-users, reducing latency and network traffic. 

Compare caching and CDNs to implementing an express lane on the highway to improve traffic flow and efficiency.

When to use: These approaches are particularly useful when dealing with querying data (reads) from traditional databases that can’t be scaled easily and static content or large media files, resulting in faster delivery and improved user experience.

Scaling microservice deployments with Kubernetes

Once you’re contemplating the importance of scaling microservices intelligently, consider whether Kubernetes, a powerful container orchestration platform, can help you achieve this goal. Kubernetes provides robust capabilities that address many of the challenges inherent in deploying and scaling containerized microservices and underlying data layer. 

Autoscaling and dynamic resource allocation

Kubernetes’s horizontal pod autoscaling feature automatically adjusts the number of instances based on resource utilization and predefined rules. By defining resource limits and requests, Kubernetes optimizes resource allocation and ensures efficient scaling. You can implement custom scaling rules and metrics to adapt to specific application requirements.

Service discovery and load balancing

Kubernetes simplifies service discovery and load balancing for microservices. Services can be registered and discovered dynamically using Kubernetes service discovery mechanisms. Load balancing strategies, such as round-robin and session affinity, ensure even distribution of traffic across instances. Ingress controllers provide external traffic management capabilities.

Redis Enterprise helps you achieve scalability and performance in microservices

Scaling microservices requires the right tools and technologies, and Redis Enterprise proves to be an invaluable asset in achieving high performance and scalability. By harnessing the power of distributed caching, Pub/Sub messaging, advanced data structures, and Redis Enterprise Operator for Kubernetes, Redis Enterprise enhances microservices architectures, enabling seamless scaling and efficient event-driven communication.

Redis Enterprise empowers organizations to build resilient, scalable, and high-performing microservices applications. Embrace Redis Enterprise as a key component of your microservices infrastructure and unlock the full potential of your scalable and distributed applications.
Accelerate your microservices now! Read the Cache and Message Broker for Microservices solution brief  to harness the power of Redis Enterprise for fast, simplified, and resilient microservices applications.