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. In the world of microservices, SerDe, or Serialization/Deserialization, plays a similar critical role. 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 I was troubleshooting performance issues in our microservices architecture, I discovered the often-overlooked impact of SerDe (Serialization/Deserialization). If you are interested, 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 the world of 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. This is where Serialization and Deserialization, in short, known as “SerDe”, come into play a significant role.

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/file in standard formats such as JSON, XML, or binary. Deserialization is simply 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 latency for a request is spent on serialization/deserialization. This is especially true for microservices, where each of the inner-service communications going to have additional serialization/deserialization needs. Which also happens to be often on the same data/information. Having in-efficient SerDe going to affect the collective performance.

Scalability: Having several services communicating with each other also means all these services will be exchanging 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 adding up inefficiencies among all these network hops.

Language/Platform Independence: In a world of polyglot microservice architecture, there may be different services in different technology stacks that require access/sharing to 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 of the key preventative ways you can keep SerDe performance overhead in check is by having some kind of monitoring for SerDe performance. Capturing relevant metrics like SerDe latency for top percentile requests could help tremendously in the long run.

Have limit on response payload size: For external facing APIs(JSON/XML), it often could be useful to have a certain limit of response payload. 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 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 going into details about 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 actual 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 of course. If you can adopt binary serialization/deserialization in that layer, SerDe efficiency can be improved significantly overall.

Conclusion:

I find SerDe to be an indispensable component of microservices architecture, but yet often overlooked. We should understand its role and 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 what you think and whether you agree or not on this viewpoint.