Netflix's Ontology-Driven Observability: Building an E2E Knowledge Graph for AIOps

The Challenge of Observability at Netflix Scale

Netflix operates at an immense scale, handling 65 million concurrent streams during peak events, over 2 billion requests daily, and processing more than 38 million real-time logging events per second across thousands of microservices and client platforms. This scale makes traditional, reactive monitoring approaches unsustainable. Incidents often involve numerous siloed data sources (metrics, events, logs, traces) and lead to disconnected alerting, complex manual triaging across many teams, and significant delays in root cause analysis, ultimately impacting user experience.

Vision: From Reactive Monitoring to Proactive AIOps

Netflix's vision for end-to-end observability is to create a system that can automatically detect, prioritize, and triage issues across the entire stack within minutes. Furthermore, the goal is to predict potential issues before they impact users and even suggest or take corrective actions. This requires seamless visibility from the user's device through networks, gateway services, and deep into backend dependencies, all connected in real-time to ensure an uninterrupted user experience.

The 'Connectedness' Solution

The core of Netflix's solution is connectedness. This involves creating an integrated observability layer that unifies data, alerting, debugging workflows, and root cause analysis. The aim is to move away from multiple siloed data sources and tools towards a single, comprehensive abstraction that provides a consistent language for dashboards, alerts, debugging, and incident response. This reduces duplicated efforts, streamlines the breadcrumb trail from user impact to root cause, and improves diagnostic accuracy.

Building the E2E Knowledge Graph with MELT Unification

The foundational step is to unify all telemetry data – Metrics, Events, Logs, and Traces (MELT) – into a single, accurate, and actionable layer. This unified MELT data is then used to construct an ontology-driven knowledge graph. This graph represents the intricate relationships between users, devices, applications, services, and infrastructure, allowing for complex queries and automated reasoning. By applying AI/ML, this knowledge graph powers agentic workflows (potentially using models like Claude) to enable:

• Automated Issue Detection: Identifying anomalies and regressions across the entire stack.
• Smart Prioritization: Ranking issues based on actual user impact.
• Automated Triage: Routing issues to the correct teams without manual intervention.
• Root Cause Analysis: Pinpointing the exact offending service or change.
• Proactive Prediction & Self-Healing: Identifying potential problems before they manifest and suggesting/applying fixes.