Stream Processing in Async Pipelines

In this lesson, you will learn how to orchestrate asynchronous data flows using the Stream trait to process sequences of values as they arrive.

The Stream Pattern

While a Future represents a single value arriving at some point in the future, a Stream represents a sequence of values produced asynchronously over time. In async pipelines, streams allow you to treat data processing like a series of transformations, similar to how iterators work in synchronous Rust, but with the added ability to pause execution while waiting for I/O.

In a typical pipeline, you might ingest events from a network socket, map them to a new format, filter out noise, and finally sink them into a database. By using Stream, you avoid loading the entire dataset into memory, processing each item as it becomes available.

Asynchronous Iteration

To work with streams, we use the StreamExt trait, which provides familiar methods like map, filter, and next. Because stream items arrive asynchronously, we use the while let Some(...) = stream.next().await pattern to consume the data.

use futures::stream::{self, StreamExt};

async fn process_pipeline() {
    // Create a stream from a vector
    let mut stream = stream::iter(vec![1, 2, 3, 4]);

    // Process each item as it arrives
    while let Some(value) = stream.next().await {
        let processed = value * 2;
        println!("Processed value: {}", processed);
    }
}

Why Pipelines Matter

The power of this approach lies in composability. You can chain stream combinators to build complex logic pipelines that remain readable and efficient. Since each stage of the pipeline only pulls the next item when it is ready, the system handles backpressure naturally: if your processing logic is slower than the producer, the stream will naturally exert backpressure by not calling poll_next until the consumer is ready for more data.

By chaining these operations, you move from imperative "wait and process" loops to declarative pipelines where data flows through transformations asynchronously, keeping your thread pool busy only when there is actual work to perform.

Key Takeaway: Streams turn asynchronous sequences into composable pipelines, allowing you to process data element-by-element while respecting the non-blocking nature of the async runtime.