Announcement
Grafana Alloy: Programmable OpenTelemetry Collector Distribution
A Grafana distribution of the OpenTelemetry Collector, designed for flexible and programmable telemetry pipelines using the River configuration language. Collect, process, and export metrics, logs, and traces with advanced control.
Project Introduction
Summary
Grafana Alloy is a specialized distribution of the OpenTelemetry Collector that introduces programmable pipelines powered by the River configuration language. This allows users to build highly flexible and dynamic workflows for collecting, transforming, and exporting telemetry data.
Problem Solved
Standard OpenTelemetry Collector configurations can be rigid for complex scenarios requiring conditional logic, dynamic routing, or advanced data manipulation. Grafana Alloy addresses this by providing a programmable layer for highly customized telemetry processing.
Core Features
Programmable Pipelines (River)
Define complex data processing and routing logic using the expressive River configuration language.
OpenTelemetry Native
Fully compatible with OpenTelemetry data formats and protocols (OTLP).
Unified Telemetry Processing
Process and export all three telemetry signals: metrics, logs, and traces.
Extensible Architecture
Leverages a large ecosystem of OpenTelemetry receivers, processors, and exporters.
Tech Stack
Use Cases
Grafana Alloy's programmable nature makes it suitable for a variety of advanced telemetry processing scenarios:
Conditional Data Routing
Details
Route metrics, logs, or traces to different backend systems (e.g., production data to Prometheus/Loki/Tempo, staging data to a test sink) based on attributes or dynamic conditions.
User Value
Optimize storage costs and improve data organization by directing specific data streams to appropriate destinations.
Advanced Data Transformation & Filtering
Details
Implement custom data transformations, enrich traces with external service metadata, filter noisy logs based on complex patterns, or aggregate metrics before export.
User Value
Improve the signal-to-noise ratio of telemetry data and ensure data sent to backends is relevant and correctly formatted.
Implementing Custom Sampling Logic
Details
Apply intelligent sampling strategies to traces or logs based on attributes like error codes, latency, or user IDs, reducing volume while retaining important signals.
User Value
Manage high telemetry volume effectively, reducing ingest costs without losing critical observability insights.
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