SerDe: The Unsung Hero of Microservices Architecture

Imagine a bustling city where thousands of cars rely on a well-functioning traffic management system to navigate smoothly. If even a minor glitch occurs in this system, the entire city can grind to a halt. SerDe, or Serialization/Deserialization, plays a similar critical role in microservices. Think of it as the traffic management system that ensures data flows efficiently between services, which in turn impacts overall application performance and scalability.

When troubleshooting performance issues in our microservices architecture, I discovered the often-overlooked impact of SerDe (Serialization/Deserialization). You can read more about this story in this AWS Blog article. Thus, in this blog post, I will delve into the importance of SerDe and provide practical tips for optimizing its performance.

What is SerDe?

In microservices, applications are built as a collection of loosely coupled small services instead of one big service/application. Efficient data exchange between these services is vital in such distributed systems architecture. Serialization and deserialization, in short, known as “SerDe,” play a significant role here.

Serialization is the process of converting an object into a format that can be easily transported over the network or stored in persistent storage like a database or file in standard formats such as JSON, XML, or binary. Deserialization is the reverse process, where the serialized data is converted back into an object.

Why is SerDe Important in Microservices?

Data Transportation/Exchange: In a microservice architecture, several services may be engaged during the lifecycle of a single external API request. SerDe facilitates these inter-service communications to help transport object data/information between services.

Performance: Often, a significant chunk of a request’s latency is spent on serialization/deserialization. This is especially true for microservices, where each of the inner-service communications will have additional serialization/deserialization needs, which also happen to be often on the same data/information. Having inefficient SerDe will affect the collective performance.

Scalability: Several services communicating with each other also means all these services will exchange data between themselves over the transport layer, which will require serialization/deserialization of data each time. Thus, even slight inefficiency can become a bigger bottleneck when added up among all these network hops.

Language/Platform Independence: In a world of polyglot microservice architecture, different services in different technology stacks may require access to/sharing of the same data. SerDe occurs whenever we need data to be represented in a standard format shared by all those technology stacks.

Important considerations regarding SerDe:

SerDe Performance Monitoring: One key preventative way to keep SerDe performance overhead in check is to monitor SerDe performance. Capturing relevant metrics like SerDe latency for top percentile requests could help tremendously in the long run.

Have a limit on response payload size: For external-facing APIs(JSON/XML), it often could be useful to have a certain limit of response payload size. For example, instead of a large response in a single payload, break it down into smaller chunks with the help of pagination, etc. If not a hard limit, at least a soft limit with some kind of observability that would help you make sure we are not being completely unreasonable for our response payload size.

Use binary SerDe instead of JSON/XML for inner-service communication: I am not discussing benchmarking the performance of binary vs JSON/XML serialization/deserialization here. There are plenty out there on the internet that you will find useful(e.g. this one). In microservices, the number of inter-service API calls is often significantly higher than the number of incoming external requests. For example, I have seen 10/12 inter-service API calls per single external request in our microservices. Of course, it depends on the application size/scale. If you can adopt binary serialization/deserialization in that layer, SerDe efficiency can improve overall.

Conclusion:

I find SerDe to be an indispensable component of microservices architecture, yet it is often overlooked. We should understand its role, carefully select the appropriate SerDe format, and add observability for SerDe performance metrics. In that way, as developers, we can significantly enhance system performance, scalability, and maintainability. As microservices continue to gain popularity, the importance of SerDe will only grow, IMO. I will be happy to hear your thoughts and whether you agree with this viewpoint.