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Microservices Scalability Challenges and How to Overcome Them

As you scale microservices-based applications, you may face several challenges. This article provides a discussion of the challenges of scaling microservices and how you can overcome them.

Understand the scalability problem of microservices

Typically, an application based on microservices often consists of several loosely coupled services that can be run on different platforms. Since a solution based on microservices is structured differently than a monolithic application, we also have to think about its scalability. You also need to develop strategies to protect your microservices-based application from an unexpected failure and to make your application fault-tolerant and resilient.

Scaling a monolithic application is relatively easy – just add more resources as the volume of transactions grows over time. To scale a monolithic application, you can use a load balancer and allocate more resources. A monolithic application is usually deployed as a unit behind a load balancer on one or more identical servers.

However, unlike a microservice application, where each service can be scaled independently, you may have to scale the entire application even if you changed only one component of the application while working with a monolith. If the application gets more stressful, you can even rotate new instances of the application so that the load is evenly distributed. However, scaling a microservices-based application is not that easy – it requires a completely different approach than its monolithic counterpart.

A microservices-based application usually contains several services and components. Therefore, if you want to scale a microservices-based application, you must either scale the entire application at once, or identify the components and services that need to be scaled up and then scale them up individually. The traditional way of scaling an application by replicating copies of it is analogous to the x-axis of the scale cube. Note that the Scale Cube is a model created by Martin L. Abbott and Michael T. Fisher that describes scalability using a cube. Martin L. Abbott and Michael T. Fisher have
created a scale cube – a model that describes the scalability of an application using the X, Y, and Z axes. The scalability of microservices-based applications falls along the Y-axis.

Read: An introduction to microservices

How do microservices scale?

In order to build a scalable microservice architecture, it is essential to understand how a microservice scales. To determine the growth scale of a microservice, you should consider two aspects. These include the qualitative growth scale and the quantitative growth scale. While the former relates to where a service fits into the microservice ecosystem, the latter is a quantitative measure of the traffic that a microservice can process.

An understanding of how a microservice can be scaled is a prerequisite for building a scalable microservices architecture. In this regard, there are two metrics that you should be aware of – the qualitative growth scale and the quantitative growth scale. These growth scales give you an insight into how scalable a microservice is. While the qualitative growth scale relates to where a service fits into the microservice ecosystem, the quantitative growth scale is a measurable estimate of how much traffic your microservice can handle. The qualitative growth scale makes it possible to link the scalability of a microservice with a higher-level business indicator.

For example, a microservices-based application can grow with the number of users who use it. The qualitative scaling scale combines the scalability of a microservice with higher-level business indicators. For example, suppose you have a microservices-based application that grows in proportion to the number of users. The quantitative growth metric uses a quantifiable number to evaluate the microservice’s performance in terms of requests per minute and transactions per minute.

Incidentally, requests per second and transactions per second are two key parameters that are used to determine the scalability of a microservice. The scalability of a microservice is determined by its quantitative growth scale, which takes into account the number of transactions per second, the number of requests per second and the latency of transactions.

Read: Scaling the microservices architecture using caching

Monitoring and optimizing microservices performance

Scalability and quality are intertwined – in a unique way. While the former deals with how we can efficiently conquer the processing of tasks and increase the throughput of the application, the latter indicates how efficiently these tasks can be processed by a microservice-based application.

The performance of your microservices-based application is of paramount importance from the end-user perspective. Despite using the recommended technologies and tools, it would be unacceptable to the customer if the application was slow and not intuitive. For the end user, i.e. the customer, the overall experience of the application is of the utmost importance and is important for the user alone.

There are certain factors you need to consider in order to scale a microservices-based application. Of these, application performance and the end user perspective are two of the most important factors that you should consider when solving scalability problems.

Read: Observability and monitoring of microservices

Use Application Delivery Controller (ADC)

You can use Application Delivery Controllers (ADC) to identify performance issues in a microservices-based application. However, traditional legacy ADCs may not be of much help in this regard, as these ADCs are designed to handle applications that run on a single platform in a single location.

You should only use modern ADC systems. Traditional ADCs can handle applications that can run on one platform in one location. Modern ADC systems are well versed in handling microservice-based applications. You can use modern ADCs to monitor and optimize the performance of your microservices-based application.

To effectively scale a microservices-based application, it is important to understand its performance and efficiency goals. This should be supported by an effective monitoring system and strategy to help you keep your microservices-based application performing at its best.

Logging and tracing of microservices

Logging is a cross-cutting issue and it is imperative that all software applications use logging. However, tracing in a microservices-based application is difficult because you have to deal with multiple components. Your microservices-based application can consist of multiple services and service instances that are distributed across multiple systems. Each of these service instances can write data – errors, warnings and other information. Hence, you would need to aggregate the logs and then analyze and search the logs. You might even want to use a correlation ID to understand the initiating event in a flow.

Read: Logging Microservices: Challenges and Solutions

Allocate microservice resources appropriately

Resource allocation is important in determining the scalability of a microservice application. However, it comes with several challenges – you need to choose the first tier (the hardware tier) according to your microservice ecosystem. When scaling your microservices-based application, you should consider resources like CPU, RAM, disk space, etc. You should be able to prioritize resource allocation accordingly. For example, business-critical microservices should be given higher priority in resource allocation.

Summary of the challenges in the scalability of microservices

Microservices are developed with stateless back-end architectures and are easily containerized by design. However, this does not mean that microservices-based applications can be easily and automatically scaled in an elegant manner. You need to know your goals and then take appropriate action to meet the scalability challenges and scale your microservices-based application.

Read more tutorials on microservices and software architecture.

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