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Observability

Grafana Tempo

An open-source distributed tracing backend for Grafana, designed to store and query large-scale distributed traces efficiently

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Summary

A scalable, easy-to-use distributed tracing backend, part of the Grafana observability platform. It enables users to store and query large volumes of distributed traces efficiently, providing insights into application performance and dependencies. With its high scalability and integration with other Grafana components, Tempo simplifies distributed tracing for monitoring and troubleshooting complex microservices architectures.

Key Features

1. Tempo is designed to handle large volumes of distributed traces, making it suitable for high-traffic applications and microservices architectures.
2. It provides efficient storage and retrieval mechanisms for distributed traces, optimizing resource utilization and query performance.
3. Tempo seamlessly integrates with other Grafana observability components, allowing users to correlate traces with metrics and logs for comprehensive monitoring and troubleshooting.
4. With its user-friendly interface and intuitive APIs, Tempo makes it easy for developers and operators to adopt distributed tracing in their environments.

How does it work?

Real world example

Pros

1. Tempo is designed to handle large-scale distributed tracing workloads, making it suitable for high-traffic applications with extensive tracing requirements.
2. It seamlessly integrates with other Grafana observability tools, such as Grafana and Prometheus, providing a unified platform for monitoring and troubleshooting.
3. Being open source, Tempo can be more cost-effective compared to proprietary tracing solutions, especially for organizations with limited budgets.
4. Tempo allows for customization and extensibility, enabling users to tailor it to their specific tracing needs and environments.

Cons

1. Running Tempo at scale may require significant resources in terms of storage, compute, and networking, which can add operational overhead and costs.
2. Like any distributed system, Tempo requires ongoing maintenance, updates, and monitoring to ensure optimal performance and reliability, which can require dedicated resources and attention.
3. While Tempo integrates well with Grafana tools, its ecosystem may have limited integration options with other observability platforms and tools, potentially limiting interoperability and extensibility.
4. Setting up and configuring Tempo, especially for large-scale deployments, may require a deep understanding of distributed tracing concepts and infrastructure.

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Capillary reduced ops tickets by 95%

“Our releases are fast. And with less developer time needed our teams can focus on building exciting features. We’ve saved countless hours and costs.”

Piyush K,
Chief Architect, Capillary Technologies,

Treebo reduced production issues by 70%

"With Facets, our staging environments look identical to production environments. So in case of production issues, we can be sure there are no infra drifts."

Kadam Jeet Jain,
Co-Founder & CTO, Treebo Hotels and Hotel Superhero.

GGX switched from AWS to GCP in 2 weeks

"Facets has radically changed our DevOps for the better. They did all the heavy lifting and saved us precious time and resources in our when we switched from AWS to GCP."

Kaustubh Bhoyar,
Head of engineering, GGX

Trusted by companies to run production at scale

Capillary | FacetsMPL | FacetsTreebo | FacetsPurplle | Facets

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