THATDOT / PRODUCT / USE CASE  DETAIL
Video Observability for Root Cause Analysis

Video Observability for Root Cause Analysis

Video delivery monitoring is hard. Logs come from multiple sources with high dimensionality and cardinality, at scale. Real-time analysis requires new tools.

Online Video

The Problem

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.

The Solution

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.

Key Value Delivered

  • Identify the QoE impacting issues that matter, in real-time and at scale
  • Graph data modeling eliminates the complexity of deeply nested joins
  • NOC technicians can easily pivot data to understand issue impacts and root causes
  • Automatic handling of out-of-order data arrival
  • Entity resolution between log and event sources
  • Integrates with existing Apache Kafka, AWS Kinesis, data lake, and API event sources.

Check out Quine in Action

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.

Next Steps