Principle:Fede1024 Rust rdkafka Message Stream Processing
| Knowledge Sources | |
|---|---|
| Domains | Messaging, Async_Programming, Stream_Processing |
| Last Updated | 2026-02-07 19:00 GMT |
Overview
A mechanism for consuming Kafka messages as an asynchronous Rust Stream, enabling composable stream operators for processing pipelines.
Description
Message Stream Processing bridges Kafka consumption with Rust's futures::Stream trait. Instead of receiving messages one at a time via recv(), the consumer exposes a stream() method that returns a MessageStream implementing the Stream trait. This enables use of stream combinators like for_each, map, filter, buffer_unordered, and others from the futures and tokio-stream ecosystems.
The stream is waker-based: when the internal message queue is empty, the MessageStream registers a waker that is triggered when a new message arrives from librdkafka. Multiple streams from the same consumer are legal but share state; a message goes to only one stream.
Usage
Use this principle when building processing pipelines that benefit from stream combinators or when you need to integrate Kafka consumption with other async stream sources. It is especially powerful for fan-out, buffered concurrency, and composition with other async operations.
Theoretical Basis
Stream processing maps naturally to the Iterator pattern extended to async:
Pseudo-code logic:
// Abstract algorithm
stream = consumer.stream()
stream
.for_each(|message| async {
let owned = message.detach()
let result = process(owned).await
produce(result).await
})
.await
The key advantage over recv() is composability with the rich ecosystem of stream combinators.