Reporting Errors to Telemetry

In this lesson, you will learn how to instrument your custom result-handling pipelines to export error context to telemetry systems without sacrificing your application's flow.

The Telemetry-Aware Pipeline

In production systems, simply returning an Err isn't enough; you need observability. The goal is to hook into your error propagation so that every time a failure occurs, the relevant metadata is captured by your logging or tracing infrastructure before the error bubbles up the call stack.

Rather than manual logging at every call site, you can leverage Result extension methods to attach telemetry. By wrapping your error propagation in a trait, you ensure that logging is a side effect of the return rather than a burden on the developer.

Implementation Pattern

Imagine you have a custom error type. You want to ensure that if a process fails, the error variant and its context are sent to a telemetry collector. You can achieve this by extending the Result type with a report method:

trait TelemetryReporter<T, E> {
    fn report(self) -> Result<T, E>;
}

impl<T, E: std::fmt::Display> TelemetryReporter<T, E> for Result<T, E> {
    fn report(self) -> Result<T, E> {
        if let Err(ref e) = self {
            // Integrate with your telemetry crate (e.g., tracing or log)
            tracing::error!(error = %e, "Operation failed");
        }
        self
    }
}

// Usage in your pipeline
fn process_data() -> Result<Data, MyError> {
    fetch_data().report()?
                .validate().report()?
                .save()
}

Why This Matters

By using a pipeline-friendly helper like .report(), you avoid the "log-and-propagate" anti-pattern where every function layer manually calls the logger. This approach keeps your business logic clean—you simply mark the points in the chain where you want visibility. Because this utilizes standard Result propagation, you maintain the performance benefits of zero-cost error handling while ensuring that your telemetry backend receives a structured snapshot of every failure in your system.

Key Takeaway: Integrate telemetry into your error pipelines by defining trait-based decorators that capture error context as a side effect of result propagation.