> Markdown version of [/videos/1618-planet-scale-dashboards](https://www.wearedevelopers.com/videos/1618-planet-scale-dashboards). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Planet-Scale Dashboards

Stop manually copying dashboards for new microservices. Google solved this at a massive scale. Learn how dynamic service discovery enables automatic, context-aware observability.

- **Speakers:** [Robert Lehmann](https://www.wearedevelopers.com/@robert-lehmann)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 27:42
- **URL:** https://www.wearedevelopers.com/videos/1618-planet-scale-dashboards

## Summary

Engineering teams often postpone building observability tools until an incident forces their hand, resulting in divergent, manually copied dashboards across microservices. To solve this at massive scale, Google's internal developer platform team built a highly reusable, automated monitoring system. Instead of configuring specific dashboards for every frontend or backend service, the system provides automatic, out-of-the-box monitoring where developers input a target service and instantly receive relevant, standardized metrics.<br><br>This planetary-scale approach hinges on introducing variables to create a single, deterministic URL schema for each metric class. To prevent overwhelming engineers with thousands of irrelevant views, the system utilizes service discovery to automatically map traits—such as whether a job writes in Java or serves HTTP based on exported metrics—to specific scopes. These scopes, organized by distinct entity types like databases or servers, dynamically filter the dashboard list so teams only see what is contextually relevant to their architecture.<br><br>Handling global drill-downs efficiently requires pre-computation; the system uses graph variants tied to specific dimensional labels to switch queries automatically, preventing backend metric overload. While teams might consider infrastructure as code or dashboards as code to solve dashboard proliferation, those methods lack fine-grained service context, create diverging sources of truth, and fail outside managed provisioning paths. Ultimately, this dynamic discovery system not only scales cleanly but enables advanced incident troubleshooting by visualizing service scopes as an interconnected dependency graph.

**Keywords:** automated dashboard discovery, microservices monitoring, dynamic scoping schemas, service trait discovery, metric dimensions, infrastructure as code limitations, dashboards as code challenges, deterministic monitoring urls, global metric aggregation, incident troubleshooting, telemetry pre-computation, monorepo observability, templated monitoring variables, service dependency graphs, observability platform engineering

## Chapters

1. **Challenges of manual observability setups during service outages** (00:05) — The difficulties of configuring and utilizing manual monitoring tools while attempting to resolve active production incidents.
1. **Building global monitoring solutions within a monorepo architecture** (03:02) — How global data centers and single-repository structures shape the development of internal observability platforms.
1. **Tackling dashboard divergence across independent microservices** (05:43) — The operational impact and troubleshooting difficulties that arise when different service teams configure metrics independently.
1. **Standardizing reusable dashboards with template variables and dimensions** (07:53) — Leveraging dimension parameters to maintain consistency and establish deterministic links across different service instances.
1. **Filtering dashboard noise for context-specific service observability** (10:59) — The challenge of overwhelming metric views when reusing template files across distinct technological stacks.
1. **Automating dashboard relevance through service discovery and traits** (13:20) — Injecting scope contexts and dynamically discovering application settings to surface relevant metric displays automatically.
1. **Managing heterogeneous architecture entities using generic scope types** (16:58) — Organizing distinct infrastructural components like databases and machine learning models into unified monitoring namespaces.
1. **Limitations of infrastructure and dashboards as code approaches** (18:52) — Why declarative configuration files often fall out of sync with actual running application metrics.
1. **Handling massive global scale using pre-aggregation and graph variants** (22:35) — Swapping metric queries dynamically based on current filtering dimensions to maintain fast performance globally.
1. **Leveraging scope topology for systemic incident troubleshooting** (25:45) — Connecting related application tiers to intuitively guide operating teams through dependent services during an outage.

## Related Moments

- [Introduction to metrics and observability challenges in monitoring](https://www.wearedevelopers.com/videos/57-all-your-telemetry-data-from-any-source-in-one-place) (from "All your telemetry data from any source in one place")
- [Understanding observability through a practical dashboard analogy](https://www.wearedevelopers.com/videos/598-why-shifting-left-is-so-important-for-software-developers) (from "Why shifting left is so important for software developers")
- [Transitioning from reactive to proactive platform infrastructure governance](https://www.wearedevelopers.com/videos/100290-how-to-govern-vibe-coding-for-the-enterprise) (from "How to govern Vibe Coding for the Enterprise")
- [Operating critical stack observability and service monitoring](https://www.wearedevelopers.com/videos/548-90daysofdevops-the-devops-learning-journey) (from "#90DaysOfDevOps - The DevOps Learning Journey")
- [Exploring advanced observability stacks and distributed infrastructure challenges](https://www.wearedevelopers.com/videos/544-plan-ci-cd-on-the-enterprise-level) (from "Plan CI/CD on the Enterprise level!")
- [Visualizing test stability using service level agreement dashboards](https://www.wearedevelopers.com/videos/139-proactive-monitoring-and-smoke-testing-in-your-production-environment) (from "Proactive monitoring and smoke testing in your production environment")

## Related Articles

- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Dev Digest 130 - The Diablo is in the details](https://www.wearedevelopers.com/magazine/470-dev-digest-130-the-diablo-is-in-the-details)
- [Dev Digest 129 - Now that's what I call private data!](https://www.wearedevelopers.com/magazine/468-dev-digest-129-now-that-s-what-i-call-private-data)

## Related Jobs

- [Senior Engineer, Infrastructure Platform](https://www.wearedevelopers.com/jobs/ext/328836-senior-engineer-infrastructure-platform) at **Intercom, Inc.**
- [Principal Software Engineer, Database Infrastructure](https://www.wearedevelopers.com/jobs/ext/1465908-principal-software-engineer-database-infrastructure) at **GitHub**
- [Lead Software Engineer - Data Engineering](https://www.wearedevelopers.com/jobs/ext/2000968-lead-software-engineer-data-engineering) at **Dynatrace**
- [Senior Software Engineer, Enterprise Products](https://www.wearedevelopers.com/jobs/ext/1841248-senior-software-engineer-enterprise-products) at **GitHub**
- [Staff Software Engineer, Database Infrastructure](https://www.wearedevelopers.com/jobs/ext/1470125-staff-software-engineer-database-infrastructure) at **GitHub**
- [Cloud Foundations Team](https://www.wearedevelopers.com/jobs/ext/1483289-cloud-foundations-team) at **GitHub**