Once upon a time, lots of people got very excited about microservices. The promise of speed, scalability and agility prompted many to abandon monoliths and spin up microservices ecosystems to achieve their business goals faster and more successfully. The trouble was, for plenty of businesses, that’s where microservices complexity got in the way.
Tyk’s CEO, Martin, didn’t hold back when he shared his thoughts on microservices being stupid, pointing out that complex dependencies between services managed by different teams are far from an ideal scenario. And this is the crux of the issue – managing microservices can be a pain. You have all these great little services designed to accomplish precise tasks swiftly and efficiently. Then you have to integrate and connect them, where microservices complexity starts becoming a headache.
Understanding microservices complexity
Microservices complexity can spring up in a range of forms, none of them massively enjoyable to manage. Yes, some solutions can help – and we’ll talk about containerisation and the role of API management in microservices below – but before we look at the solutions, it’s important to understand the root of the problems.
Common sources of complexity in microservices
It’s easy to see why the promise of microservices holds so much sway. Being able to scale each service independently and quickly roll it back or forward as required has plenty of potential, thanks to the idea that the services are decoupled. But as soon as you tie the services together to create a system, they become coupled, however loosely. Suddenly, your distributed microservices architecture is home to greater system complexity than the average monolith. Hmm.
Implementation and integration can be a source of system complexity when it comes to distributed microservices. You have to put the right connections and access patterns in place to ensure everything works seamlessly and joins up while ensuring robust security across every service.
Then, there’s the fact that you may have different teams responsible for different microservices. This is usually how managing microservices moves from being a headache to a full-blown migraine.
Illustrative examples of complexity in microservices
Uber provides an interesting example of the complexity of microservices. The company built a microservices architecture that grew to encompass around 2,200 critical microservices. As the ecosystem grew, the benefits of flexibility, independent deployments, clear ownership, improved system stability and better separation of concerns began to be weighed down by complexity.
Uber went from this increasingly stultifying complexity to a self-proclaimed exemplar of microservice architecture. Its microservices interact with one another via remote procedure calls (RPC), with a critical path analysis tool reducing latency by optimising services along the crucial path.
The impact of complexity on microservices performance and scalability
Increasing complexity, in broad terms, can impact both performance and scalability – two things that microservices should be able to improve. Before implementing a solution, Uber observed that its microservices complexity grew to the point that it was “sometimes making even trivial features difficult to build.” It was hardly a good foundation for delivering optimal performance or scaling.
Assessing microservices complexity
If your business is becoming bogged down by the complexity of microservices, it’s time to take action. The first step towards managing microservices better is assessing the causes of complexity in your architecture. Mapping inter-service communication, examining data and dependency management, and looking at issues relating to scaling and orchestration should help shed some light on what you need to address.
Key metrics for measuring complexity
There are various ways that you can quantify your system’s complexity. Doing so can be a helpful way of providing insights into where your problems lie and measuring improvements as you implement your chosen solution.
Measuring latency and response times is a good place to start. You can examine the impact of microservices communication on these and establish benchmarks to define what is and isn’t acceptable.
In addition to runtime behaviour, you can delve into codebase metrics to evaluate microservices individually and collectively. You could look at lines of code, although there is plenty of nuance to the correlation between codebase size and complexity. Consider measuring the number of independent paths through the code using cyclomatic complexity. Another option is to examine code coupling to measure the degree of interdependence between your microservices.
Tools and techniques for complexity assessment
Numerous tools and techniques help you analyse and visualise your microservice dependencies and complexity. A service dependency graph analysis tool could be a positive starting point. Distributed tracing can also be very useful in understanding and optimising microservices communication.
Container orchestration platforms can also be a powerful tool to reduce complexity. There’s plenty that Kubernetes can do to simplify deployment and manage microservices more easily. It can deliver isolation, portability and resource efficiency, improving scalability and simplifying dependency management. All are most helpful in terms of cutting through complexity.
Mitigating microservices complexity
The way you design your microservices architecture can do much to mitigate complexity. So can the way you manage them. Let’s break this down a bit.
Design principles to reduce complexity
A self-service, fully automated infrastructure is key to reducing friction. Without fully automating the provisioning and deployment pipeline, you could end up with several huge siloes.
It’s also important to design your architecture so that each microservice owns its own data. If you don’t, you can end up with shared data, turning new releases into highly coordinated and complex marathons.
Of course, you’ll need to bring all that individually owned data together for reporting purposes. Data streaming can be helpful with this, helping you to manage your analytics in a way that makes sense within a microservices environment.
Best practices for developing simple microservices
If you’re committed to the microservices approach but want to dodge the perils of too much complexity, there are a few best practices to keep in mind. Release cycles are a good example to consider. Coordinating releases for multiple services simultaneously can result in a tight coupling that leans more towards a distributed monolith than a distributed microservices architecture. Aim to release microservices when they are ready; you should find things easier to manage over the longer term.
Remember that microservices are about more than technology, too. If your team isn’t up to speed with concepts such as distributed tracing, observability, eventual consistency, fault tolerance, and distributed sagas, managing microservices and reducing complexity will be more challenging.
Microservices refactoring: strategies for simplification
There are various strategies you can use to end up with a more manageable and scalable system of microservices. Through refactoring microservices, you can make internal structural changes that don’t impact external behaviour, enhancing the codebase and architecture to bring it closer to your desired state.
Modularisation can help achieve this by breaking down larger, tightly coupled microservices into smaller, focused units. You can also use refactoring to implement more efficient communication patterns to help reduce dependencies and overall complexity.
Refactoring has other uses that feed into the simplification of your microservices. Optimising database queries can address performance bottlenecks and improve efficiency. Cleaning up codebases to eliminate redundant code and improve code quality can enhance maintainability. These changes can quickly add up to provide a microservices architecture far closer to the initial flexible, efficient setup you envisaged.
Managing microservices complexity with API gateways
We mentioned the value of API management in reducing microservices complexity above. Given the great reputation of our open source API gateway, did you really expect us not to?! If reducing microservices complexity is your goal, read on to see how an API gateway can help.
Role of API gateways in microservices architecture
An API gateway provides a centralised entry point for managing your microservices. It’s also easy to secure communication between clients and your various services. You can position the gateway between your client applications and your microservices to prevent the exposure of your internal concerns. It’s a win on the security front and for managing microservices.
An API gateway can also help relieve cross-cutting concerns. You can use the gateway to implement security, logging, monitoring, tracing, caching and more, instead of doing so for every individual microservice.
If you need to, you can use a per-service API gateway pattern rather than the edge service pattern mentioned above. In a per-service pattern, each microservice has its own dedicated API gateway. Your particular organisational needs will dictate whether or not this is the right approach for your business.
How API gateways can help manage complexity
An API gateway for microservices can handle various tasks that help manage and reduce complexity. Let’s run through a few of these and look at how they can turn your microservices tangle into something slick and manageable.
For starters, an API gateway can aggregate multiple microservices into one API endpoint. Clients that need to interact with various microservices can, therefore, make one single request to the gateway rather than make a separate request to each service.
An API gateway also enables dynamic request routing, providing plenty of flexibility without exposing your microservices’ internal structure to external clients. Meanwhile, load balancing adds resilience by distributing requests across multiple microservices instances, meaning you can provide more performant, highly available services. You can cache responses at the gateway level to further support overall performance.
An API gateway handles authentication and authorisation on the security front, reducing the burden on individual microservices and delivering centralised consistency. You can supplement this with rate limiting and throttling to prevent abuse or overuse of your resources.
Another super handy function of an API gateway for microservices is its ability to prevent cascading failures. Sometimes, microservices go wrong. With an API gateway in place, you can use circuit breaking to prevent one failing microservice from triggering a domino effect that brings your system crashing down.
An API gateway also provides response compression and transformation to reduce bandwidth and monitoring and analytics to provide insights into your system’s performance. This means you can identify and reduce performance bottlenecks and spot and troubleshoot issues more effectively. All of which underpins happier, more efficient and more straightforward microservices management.
Choosing the right API gateway for your needs
Choosing the right API gateway for your microservices architecture is an important decision. Opt for a gateway with a microservices-first approach to gain maximum benefit.
If it’s time to reduce the complexity of your microservices, it’s time to talk to Tyk. There are many benefits of Tyk, in addition to easier microservices management. Discover them today.
