The Future of Observability: Netflix Pioneers the “Knowledge Graph” Approach
Netflix is pushing the boundaries of observability, moving beyond traditional monitoring to a system built on interconnected knowledge. Engineers Prasanna Vijayanathan and Renzo Sanchez-Silva recently unveiled their function at QCon London 2026, detailing how a knowledge graph is transforming how the streaming giant understands and responds to issues across its vast infrastructure.
From Siloed Data to a Unified View: The Challenge of E2E Observability
Traditional observability often struggles with fragmented data. Metrics, events, logs and traces exist in silos, making it difficult to correlate information and pinpoint root causes. Here’s the core challenge of End-to-End (E2E) Observability – the ability to monitor a complex system from the user interface to the underlying infrastructure. Netflix’s approach directly addresses these issues.
The MELT Layer: A Foundation for Unified Observability
Central to Netflix’s strategy is the MELT Layer (Metrics, Events, Logs, Traces). This unified layer aims to improve incident resolution time by consolidating observability data. It’s a crucial step towards breaking down silos and providing a more holistic view of system health.
Ontology: Encoding Knowledge for Machine Understanding
But simply collecting data isn’t enough. Netflix leverages the power of Ontology – a formal specification of types, properties, and relationships – to encode knowledge about its systems. This isn’t just about the data itself, but about understanding the connections between data points. The fundamental unit of this knowledge is the Triple: (Subject | Predicate | Object), representing a single fact within the knowledge graph.
For example, a triple might state: “api-gateway | rdf:type | ops:Application,” defining the api-gateway as an application. Another could be: “INC-5377 | ops:affects | api-gateway,” indicating that incident INC-5377 impacts the api-gateway.
12 Operational Namespaces: Connecting the Netflix Universe
To manage the complexity of its infrastructure, Netflix utilizes 12 Operational Namespaces – including Slack, Alerts, Metrics, Logs, and Incidents – to categorize and connect all elements. The ontology captures, structures, and preserves this information in a machine-readable format, transforming operational chaos into a structured understanding.
The Knowledge Flywheel: Continuous Learning and Adaptation
Netflix’s system isn’t static. The Knowledge Flywheel embodies a continuous learning loop. It operates through three states – Observer, Enrich, and Infer – constantly adapting and improving its understanding of the system. This flywheel is integrated with a development process utilizing Claude, where the AI proposes code changes (pull requests) that are then reviewed and merged by human engineers.
This integration of AI and human expertise is a key element, allowing for automated improvements while maintaining control and oversight.
Future Trends: Automation and Self-Healing Infrastructure
Netflix’s vision extends beyond simply understanding incidents. They aim to automate root cause analysis, provide auto-remediation, and ultimately create a self-healing infrastructure. This represents a significant leap forward in operational efficiency and reliability.
The Rise of AI-Powered Observability
The integration of AI, as demonstrated by the utilize of Claude, is a major trend. Expect to see more AI-powered tools that can automatically analyze observability data, identify anomalies, and even suggest solutions. This will free up engineers to focus on more strategic tasks.
Knowledge Graphs as the Fresh Standard
Netflix’s knowledge graph approach is likely to become a standard practice. By representing infrastructure as interconnected entities, organizations can gain a deeper understanding of their systems and improve their ability to respond to incidents.
Shift Towards Proactive Observability
The goal is to move beyond reactive monitoring to proactive observability – predicting and preventing issues before they impact users. This requires sophisticated analytics and machine learning algorithms that can identify patterns and anomalies.
FAQ
What is an ontology in the context of observability?
An ontology is a formal specification of types, properties, and relationships, used to encode knowledge about a system and its components.
What is the MELT layer?
The MELT layer (Metrics, Events, Logs, Traces) is a unified observability layer designed to consolidate data and improve incident resolution time.
What is a Triple?
A Triple is a tuple (Subject | Predicate | Object) that defines one fact in a knowledge graph.
How does Netflix use AI in its observability system?
Netflix uses AI, specifically Claude, to propose code changes and automate parts of the observability workflow.
What are the 12 Operational Namespaces?
These are categories used by Netflix to organize and connect all elements of its infrastructure, including Slack, Alerts, Metrics, Logs, and Incidents.
Did you recognize? The concept of a knowledge graph isn’t new, but its application to large-scale observability, as demonstrated by Netflix, is a significant advancement.
Pro Tip: Start compact when implementing observability solutions. Focus on identifying key metrics and events, and gradually expand your coverage as you gain experience.
Seek to learn more about modern data engineering practices? Explore our other articles on data architecture and observability tools.