There are several challenges that you might have to face when scaling microservices-based applications. This article presents a discussion on the challenges faced in scaling microservices and how you can overcome them.
Understanding the Microservices Scalability Problem
Typically, a microservices-based application is often composed of several loosely coupled services that can execute on different platforms. Since a microservices-based solution is architected differently than a monolithic application, we need to think about its scalability differently as well. You also need to come up with strategies to protect your microservices-based application from an unexpected failure, make your application fault tolerant, and resilient.
Scaling a monolithic application is relatively straightforward – all that is required is the addition of more resources, as the transaction volume grows over time. To scale a monolithic application, you can take advantage of a load balancer and allocate more resources. A monolithic application is usually deployed as one unit behind a load balancer on one or more identical servers.
However, unlike a microservices application in which each service can be scaled independently, you might need to scale the entire application even if you have changed just one component of the application when working with a monolith. If there is more load on the application, you can even spin new instances of the application so that the load can be distributed evenly. However, scaling a microservices based application is not that easy – it needs a completely different approach than its monolithic counterpart.
A microservices based application typically contains several services and components. Hence, if you are to scale a microservices-based application, you must either scale the entire application at the same time or identify the components and services that need to be upscaled and then scale them one at a time. The traditional method 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 scalability of an application using the X, Y and Z axis. Scalability of Microservices-based applications falls along the Y-axis.
How Do Microservices Scale?
To build a scalable microservices architecture, it is imperative that you are aware of 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 handle.
An understanding on how a microservice can scale is a prerequisite for you to build a scalable microservices architecture. In this regard, there are two metrics you should be aware of – the qualitative growth scale and the quantitative growth scale. These growth scales provide you an insight on 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 allows a microservice’s scalability to be linked to a high-level business indicator.
A microservices-based application might, for example, grow according to how many users are using it. The qualitative scaling scale connects the scalability of a microservice to high-level business indicators. For example, suppose you have a microservices-based application that grows in proportion to the number of users. The quantitative growth score uses a quantifiable number to assess 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 quantities used to determine the scalability of a microservice. The scalability of a microservice is determined using the quantitative growth scale, which considers the number of transactions per second, the number of requests per second, and the latency of transactions.
Monitoring and Optimizing Microservices Performance
Scalability and quality are interconnected – in a unique way. While the former deals with how we efficiently conquer the processing of tasks and increase the application’s throughput, the latter points out how efficiently these tasks can be processed by a microservice-based application.
Performance of your microservices-based application is of utmost importance from the end user’s perspective. Despite having used the recommended technologies and tools, it would not be acceptable to the client if the application is slow and not intuitive. For the end user, i.e., the customer, it’s the overall experience of the application that is of utmost importance and it’s all that matters to the user.
You must consider certain factors to scale a microservices-based application. Of these, application performance and the end user’s perspective are two of the most important factors that you should consider when addressing scalability issues.
Use Application Delivery Controller (ADC)
You can leverage Application Delivery Controller (ADC) to determine performance issues in a microservices-based application. However traditional old ADCs might not be much help in this regard since these ADCs are designed to handle applications that run on a single platform in a single location.
You should use modern ADC systems only. Traditional ADCs are capable of handling applications that can run on one platform in one location. Modern ADCs systems are adept at handling microservices-based applications. You can leverage modern ADCs to monitor and optimize the performance of your microservices-based application.
To be able to scale a microservices-based application effectively, it is imperative that you are aware of the performance and efficiency goals. This should be backed up by an effective monitoring system in place and a strategy that would help you maintain optimal performance of your microservices-based application.
Logging and Tracing Microservices
Logging is a cross cutting concern, and it is imperative that every software application leverages logging. However, tracing in a microservices-based application is difficult since you must deal with several components. Your microservices-based application might consist of several services and service instances spread across multiple systems. Each of such service instances might 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 take advantage of a correlation Id to be able to understand the initiating event in a flow.
Allocate Microservice Resources Appropriately
Resource allocation is important in determining scalability of a microservices application. However, it presents several challenges – you must choose the first layer (the hardware layer) appropriately for your microservice ecosystem. When scaling your microservices-based application, you should consider the resources such as, the CPU, RAM, the disk storage, etc. You should be able to prioritize resource allocation appropriately. As an example, the Business critical microservices should be given higher priority as far as resource allocation is concerned.
Summary of Microservice Scalability Challenges
Microservices are developed with stateless back-end architectures and are easily containerizable by design. However, this doesn’t mean that microservices-based applications can scale automatically and gracefully without any problems. You must know your goals and then apply appropriate measures to address the scalability challenges and scale your microservices-based application.