OpenTelemetry vs Thanos: What are the differences?

Introduction

In this article, we will compare the key differences between OpenTelemetry and Thanos. Both OpenTelemetry and Thanos are popular open-source projects in the field of observability, but they serve different purposes and have distinct features. We will explore six key differences between the two.

1. Purpose: OpenTelemetry is an observability framework that provides a set of APIs, libraries, and instrumentation to collect, process, and export telemetry data from applications. It enables developers to automatically instrument their code and capture metrics, traces, and logs. On the other hand, Thanos is a scalable observability platform designed for storing, querying, and visualizing time series data from multiple Prometheus instances.

2. Scope: OpenTelemetry focuses on instrumenting applications and capturing telemetry data at the source. It provides a unified approach to collect metrics, traces, and logs across different programming languages and frameworks. Thanos, on the other hand, primarily extends the capabilities of Prometheus, a popular monitoring and alerting toolkit. It adds features like global view, long-term storage, and downsampling to Prometheus, making it suitable for large-scale deployments.

3. Data Model: OpenTelemetry follows a distributed tracing data model that captures the causal relationship between different components of a distributed system. It provides context propagation and correlation between spans to visualize end-to-end traces across multiple services. Thanos, on the other hand, focuses on time series data model, which represents the evolution of a metric over time. It stores and aggregates time series data from multiple Prometheus instances, enabling efficient querying and visualization of metrics.

4. Scalability: OpenTelemetry provides scalability through an agent-based architecture, where telemetry data is collected at the source and sent to a collector for processing and exporting. This allows for distributed and scalable deployments of OpenTelemetry. Thanos, on the other hand, provides scalability by federating multiple Prometheus instances and storing data in a highly scalable and distributed object storage system like GCS or S3. It allows for horizontal scaling and efficient query federation across multiple Prometheus instances.

5. Querying and Visualization: OpenTelemetry focuses on exporting telemetry data to observability platforms like Jaeger or Zipkin for visualization and analysis. It provides standardized protocols and interfaces for integrating with various telemetry backends. Thanos, on the other hand, provides its own query language (PromQL) and a dedicated query component for querying time series data from multiple Prometheus instances. It also offers built-in support for data visualization using Grafana.

6. Interoperability: OpenTelemetry aims to provide a vendor-neutral standard for telemetry data collection and export. It provides SDKs and instrumentation libraries for popular programming languages, allowing developers to easily integrate OpenTelemetry with their applications. Thanos, on the other hand, is tightly integrated with Prometheus and built as an extension to it. It leverages Prometheus' ecosystem and tools to store, query, and visualize time series data.

In summary, OpenTelemetry is an observability framework that focuses on instrumenting applications and capturing telemetry data, while Thanos is an observability platform designed for storing, querying, and visualizing time series data from multiple Prometheus instances. OpenTelemetry follows a distributed tracing data model and provides scalability through an agent-based architecture, while Thanos extends Prometheus and provides scalability by federating multiple instances and storing data in a distributed object storage system. They also differ in their data models, querying and visualization approaches, and interoperability.