Design a Cloud-Native, Multi-Tenant Metrics Monitoring System
Problem Statement
Design a cloud-native, multi-tenant system for monitoring internal services. This platform must support various metric types (counters, gauges, and histograms) enriched with labels/tags. It needs to handle metric ingestion via both pull (scraping endpoints) and push mechanisms, provide a powerful query language, and support robust alerting and dashboarding capabilities. The system must exhibit strong operational characteristics, including high availability (HA), horizontal scalability, multi-tenant quotas, and isolation.
Illustrative Scale & SLOs
- Aggregate Ingest: Up to 10 million samples per second across all tenants.
- Data Retention: 7 days for hot storage, 12 months for cold storage.
- Query SLO: P99 query latency less than 2 seconds for 6-hour range queries.
- Availability Target: 99.9%.
Key Requirements
1. Ingestion
- Collect numeric metrics (counter, gauge, histogram) with associated labels/tags.
- Support both pull-based (scraping endpoints) and push-based ingestion.
- Handle high throughput efficiently and provide effective backpressure mechanisms.
2. Storage
- Implement efficient time-series storage with robust compression.
- Support tiered retention: hot storage for recent data and cold storage for long-term archives.
- Incorporate downsampling capabilities for older data.
3. Query
- Provide a comprehensive query language for aggregations, label filtering, and downsampling.
- Support federated queries that seamlessly span across both hot and cold storage tiers.
4. Alerting
- Enable threshold and SLO-based alerts.
- Include features for alert silencing, deduplication, and flexible routing.
5. Operations
- Ensure high availability and horizontal scalability.
- Provide robust multi-tenant isolation.
- Implement mechanisms to control cardinality growth, enforce quotas, and apply rate limits.
- Expose data through dashboards and APIs.
6. Architecture Deep Dives
- Discuss the chosen data model, sharding strategy, and indexing approach.
- Detail the write and read paths, including failure handling and consistency choices.
Deliverables
- An end-to-end architecture proposal, outlining all components and data flow.
- Rationale and trade-offs for all key design choices.
- Guardrails and strategies for managing cardinality, quotas, and backpressure.
- Detailed failure scenarios and corresponding recovery strategies.
- Definition of the API surface and a comprehensive operability plan (including dashboards and SLOs for the monitoring system itself).
AI interview prep
Core dilemma
Balancing extreme write throughput (10M samples/sec) and low-latency query SLOs (p99 < 2s) for time-series data, while ensuring multi-tenant isolation, cost-effective long-term retention, and robust operational characteristics (HA, scalability, backpressure) in a cloud-native environment.
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 Cloud-Native, Multi-Tenant Metrics Monitoring System
Problem Statement
Design a cloud-native, multi-tenant system for monitoring internal services. This platform must support various metric types (counters, gauges, and histograms) enriched with labels/tags. It needs to handle metric ingestion via both pull (scraping endpoints) and push mechanisms, provide a powerful query language, and support robust alerting and dashboarding capabilities. The system must exhibit strong operational characteristics, including high availability (HA), horizontal scalability, multi-tenant quotas, and isolation.
Illustrative Scale & SLOs
- Aggregate Ingest: Up to 10 million samples per second across all tenants.
- Data Retention: 7 days for hot storage, 12 months for cold storage.
- Query SLO: P99 query latency less than 2 seconds for 6-hour range queries.
- Availability Target: 99.9%.
Key Requirements
1. Ingestion
- Collect numeric metrics (counter, gauge, histogram) with associated labels/tags.
- Support both pull-based (scraping endpoints) and push-based ingestion.
- Handle high throughput efficiently and provide effective backpressure mechanisms.
2. Storage
- Implement efficient time-series storage with robust compression.
- Support tiered retention: hot storage for recent data and cold storage for long-term archives.
- Incorporate downsampling capabilities for older data.
3. Query
- Provide a comprehensive query language for aggregations, label filtering, and downsampling.
- Support federated queries that seamlessly span across both hot and cold storage tiers.
4. Alerting
- Enable threshold and SLO-based alerts.
- Include features for alert silencing, deduplication, and flexible routing.
5. Operations
- Ensure high availability and horizontal scalability.
- Provide robust multi-tenant isolation.
- Implement mechanisms to control cardinality growth, enforce quotas, and apply rate limits.
- Expose data through dashboards and APIs.
6. Architecture Deep Dives
- Discuss the chosen data model, sharding strategy, and indexing approach.
- Detail the write and read paths, including failure handling and consistency choices.
Deliverables
- An end-to-end architecture proposal, outlining all components and data flow.
- Rationale and trade-offs for all key design choices.
- Guardrails and strategies for managing cardinality, quotas, and backpressure.
- Detailed failure scenarios and corresponding recovery strategies.
- Definition of the API surface and a comprehensive operability plan (including dashboards and SLOs for the monitoring system itself).