Real-time video observability that can solve Quality of Experience (QoE) issues while live broadcast events are still playing require the simultaneous monitoring of millions of data points. Video sessions flow across multiple systems including origins, CDNs, manifest services, and players provided by multiple vendors. Relational database approaches to perform this complex log analysis at productions scale run into costs constraints that prohibit comprehensive real-time operations for all but the highest value broadcast events.
Quine streaming graph ingests logs and events from clients, CDNs, origins, etc. in real-time and materializes the data into a graph. The graph data model natively connects chunk QoE metrics with unlimited categorical classifications and calculated metrics to identify "alerts that matter to your audience" and instantly associate them to ASN, Geo, client type, asset names, encoding formats, CDN cache server, origin server, etc. This real-time comprehensive view of the inter-relationships between services allows rapid assessment of root causes while live video streams as still playing.
Want to try it yourself? The CDN Cache Efficiency Recipe demonstrates how to ingest CDN logs to create a graph and keep track of changing conditions that might indicate an emerging problem. This recipe allows you to query for associated ASNs and CDN cache servers to identify potential root cause of poor performance.
Or you can read more about the use case and its specific challenges here.