Design a Logging and Metrics Service

To design a logging service for a platform like Netflix, I would first establish that we need to handle terabytes of data daily with sub-second latency for anomaly detection. The solution starts with lightweight sidecar agents, such as Fluent Bit, deployed alongside every microservice to minimize overhead. These agents buffer logs locally before sending them to an Apache Kafka cluster. Kafka is essential here because it decouples collection from processing, allowing us to absorb massive traffic spikes without losing data, which aligns with Netflix's focus on reliability. Next, consumers read from Kafka topics and forward structured JSON logs to an Elasticsearch cluster. To manage costs and performance, I would implement time-based index rollovers and tiered storage, keeping recent logs in hot nodes while moving older data to cold storage. For visualization, Kibana dashboards would provide real-time error tracking and user experience metrics. Finally, I'd ensure schema validation at the ingestion point to prevent query failures downstream and set up automated alerting rules based on specific error codes to enable rapid incident response.