Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix Scale

What happened

MELT — Metrics, Events, Logs and Traces, and how they are captured across the system. The home screen where you see the list of lists of movies, starts taking about 1.5 seconds longer to render. Netflix is based in Los Gatos, and its products and services include Netflix recommender system and over-the-top media services.

As it so happens, a regression on the application could be related to anywhere in the system — a client app regression from a new version deployment or A/B test rollout, a load shedding in the infrastructure, a GraphQL query responding with bad data because of a service rollout 6 hops downstream, are all suspects at this point. needs insights from across the system, and this involves combing through layers of data in a meticulous manner.

Metrics, events, logs and traces, or MELT, in the industry’s shorthand, are the right raw materials for such an investigation, and each of them answers roughly a quarter of the question. The difficulty is that they do not agree on what they are talking about.