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Design a metrics platform without alerting
Design a Metrics Platform
Design a metrics platform, similar to a simplified Prometheus or Datadog, capable of collecting, storing, querying, and visualizing time-series metrics.
Scope
- Ingest time-series metrics from numerous hosts and services.
- Store metrics along with tags or labels (dimensions).
- Support queries for dashboards, including both instant and time-range queries.
- An alerting or notification system is not required.
Clarifications to Cover
- Data model (e.g., counters, gauges, histograms; labels/tags).
- Ingestion model (push vs. pull).
- Storage considerations (hot vs. cold data, retention policies, downsampling).
- Query API and common query patterns.
- Scale assumptions and potential bottlenecks (e.g., write QPS, cardinality).
- Multi-tenancy, authentication/authorization, and isolation.
- Reliability and cost controls.
AI interview prep
Core dilemma
Balancing high-volume, high-cardinality time-series data ingestion and storage with efficient, low-latency querying for diverse dashboard needs, while managing operational costs and reliability.
Mid-level signals3
Senior signals3
Red flags3
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a metrics platform without alerting
Design a Metrics Platform
Design a metrics platform, similar to a simplified Prometheus or Datadog, capable of collecting, storing, querying, and visualizing time-series metrics.
Scope
- Ingest time-series metrics from numerous hosts and services.
- Store metrics along with tags or labels (dimensions).
- Support queries for dashboards, including both instant and time-range queries.
- An alerting or notification system is not required.
Clarifications to Cover
- Data model (e.g., counters, gauges, histograms; labels/tags).
- Ingestion model (push vs. pull).
- Storage considerations (hot vs. cold data, retention policies, downsampling).
- Query API and common query patterns.
- Scale assumptions and potential bottlenecks (e.g., write QPS, cardinality).
- Multi-tenancy, authentication/authorization, and isolation.
- Reliability and cost controls.