Design a Distributed Logging & Monitoring System (Datadog / Prometheus)
Ingest and query trillions of metrics/logs: Agent collector (Vector/FluentBit), Kafka ingestion buffer, Time-Series DB (M3DB/Prometheus), and inverted index log storage.
01.1. Functional & Non-Functional Requirements
A Distributed Observability Platform (Datadog, Prometheus, Grafana Loki, New Relic) ingests, indexes, queries, and alerts on trillions of telemetry signals (metrics, logs, traces) emitted across thousands of microservice containers in real time.
Functional Requirements
- High-Frequency Metrics Ingestion: Collect infrastructure and custom application metrics (counters, gauges, histograms) at
10sscrape intervals. - Structured Log Aggregation: Ingest, parse, and search structured JSON log streams across services with full-text and label filtering.
- Sub-Second Metric Querying & Dashboards: Power real-time dashboards displaying graphs, CPU/memory stats, and latency percentiles (p50, p95, p99).
- Real-Time Alerting Engine: Continuously evaluate threshold rules (e.g., "Alert oncall if error rate
> 1\%over 5 minutes") and dispatch alerts to PagerDuty/Slack within 5 seconds.
Non-Functional Requirements
- Massive Ingestion Scale: Ingest
10 million metric points/secand500,000 log lines/sec. - Storage & Cost Efficiency: Store historical telemetry for 13 months cost-effectively using extreme compression.
- Fault-Tolerant Buffering: An outage in the storage tier must never impact production application containers (asynchronous decoupled ingestion).
Distributed Observability, Metrics & Logging Ingestion Pipeline 📈
Distributed Observability, Metrics & Logging Ingestion Pipeline 📈
Dual pipeline separating high-frequency Gorilla-compressed time-series metrics (TSDB) from structured indexed logs (OpenSearch/Loki), backed by Kafka and real-time alert engines.
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