
2025/02/15
Building a trait service with Rust and Kafka
In the world of microservices and distributed systems, managing user activity events can be a complex task. This article delves into the development of a trait-based service specifically designed to streamline this process in Rust.
Central hub for user Interactions
This service acts as a central hub, functioning as a single point of entry for user activity data originating from various sources within your application. Based on the collected user activity data, the service constructs a simplified user profile.
Responsibilities
Since this service will be directly accessed by clients (like your application's frontend), it comes with a set of key responsibilities to ensure smooth operation and reliable data handling:
Clear API: A well-defined API with clear documentation guides clients on data format and submission methods.
Robust Validation: Strong validation checks ensure only valid data reaches Kafka, safeguarding data integrity.
Scalability & Performance: The service must handle increasing data volume efficiently as your user base grows.
Security: Robust authentication, authorization, and data encryption protect sensitive user activity information.
Error Handling & Monitoring: Graceful error handling and proactive monitoring guarantee smooth data delivery and facilitate troubleshooting.
Project setup
Let's move forward with defining the API and building the endpoint to receive those user event messages! Here's a breakdown of the project setup using the Rocket framework and the rdkafka library for interacting with Kafka:
Cargo.toml
This file defines the project's dependencies, including Rocket for building the web API server, rdkafka for interacting with Kafka, and serde for data serialization and deserialization.
src/main.rs
We'll establish the connection between user requests and the logic that handles them by linking routes to the controller. https://rocket.rs/guide/v0.5/
src/event/user/controllers/user_controller.rs
We are getting the parameters from the user POST request. Here we are using the request guards. The request guards check incoming requests against your rules (like valid login or required data) before letting them reach the endpoint's code.
Additionally, we can integrate data encoding options, including popular formats like Avro. This allows for compact and schema-based encoding, further enhancing data clarity and interoperability within the system.
Let’s see what the track request expects.
src/event/user/models/event.rs
src/kafka.rs
And here is the Kafka client. It sets up the Kafka producer using the environment variables and constants. We can make this method more generic so you can replace the underlying Kafka backend but I will skip for this project.
src/tests/event/user/controllers/user_controller.rs
The controller tests are a great start! To truly ensure the service functions flawlessly, we can expand testing to include the Kafka client, data validation, and model behavior.
To streamline development, we'll leverage Docker and Docker Compose. These tools simplify setting up the entire tech stack (including this service, Kafka, and other dependencies) on your local machine. We'll also delve into the fascinating realm of Kafka Streams and ksqlDB, revealing how they work together to seamlessly process and analyze the user activity data we collect.