Netflix Developing Ontology-Driven Knowledge Graph for Operational Insights

Yet investigating a typical application regression can take around four hours, with much of that time spent reconstructing relationships between systems rather than analysing the underlying data (Source ID: 1). Netflix's solution is to make those relationships explicit through an operational ontology and knowledge graph, creating a foundation for automated root-cause analysis and, eventually, self-healing infrastructure (Source ID: 1). Netflix already collects metrics, events, logs, and traces (MELT) across its applications and infrastructure (Source ID: 1). [1]

However, these signals are spread across systems such as alerting, incident management, deployment tooling, and engineering communication platforms (Source ID: 1). An application regression might originate from a client release, an experiment, infrastructure load shedding, or a service several dependencies downstream (Source ID: 1). Although each system contains useful information, engineers must often connect the evidence manually (Source ID: 1). [1]

Netflix is developing an ontology-driven knowledge graph to accelerate production investigations and provide AI agents with a shared understanding of its operational environment (Source ID: 1). The platform connects information about applications, incidents, deployments, alerts, and infrastructure dependencies, addressing a problem that can leave engineers spending hours piecing together information scattered across different systems (Source ID: 1). The company claims it handles more than 2 billion requests daily and upwards of 38 million real-time logging events every second (Source ID: 1). [1]

Yet investigating a typical application regression can take around four hours, with much of that time spent reconstructing relationships between systems rather than analysing the underlying data (Source ID: 1). An application regression might originate from a client release, an experiment, infrastructure load shedding, or a service several dependencies downstream (Source ID: 1). Although each system contains useful information, engineers must often connect the evidence manually (Source ID: 1). [1]

Netflix identified the missing capability as a consistent way to describe not just individual components, but how they relate to one another (Source ID: 1). Its Observability Ontology provides that common model, defining operational entities, their properties, and the relationships between them (Source ID: 1). It uses established standards including RDF, OWL, SHACL, and SPARQL, while the resulting knowledge graph is stored in QuipuDB, Netflix's distributed RDF database built on Rust's redb storage engine (Source ID: 1). [1]

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Prepared with AI assistance from the cited sources.