It really is getting harder to find the source of a problem in modern applications. Why, modern architectural complexity simply makes it combinatorially difficult to consider possible causes, especially for new (unknown-unknown) problems. Traditional Root Cause Analysis methods really aren’t up to the challenge, are time and resource consuming, and are ultimately intractable for dynamic cloud based applications. What is needed is an intelligent, query free method to find the causal-path from symptoms to cause. This quintessential AI problem is addressed by NMLStream’s specialized Causal Graph technology.
DevOps engineers are getting used to wading through a huge amount of metric data and manually manipulating time series. Although this is currently the only way to discover the root cause of problems from applications telemetry, this approach is still highly time and labor intensive. It might take many hours and multiple people to manually check all possible metrics and figure out the root cause.
The complexity of enterprise applications has been increasing steadily due to the adoption of microservice-oriented architectures and cloud technologies. DevOps teams supporting these infrastructures require a smarter way to manage this growing complexity. NMLStream’s Quadrant uses backward chaining technology to reduce complexity through rapid localization and root cause analysis of problems across all monitored metrics. However, knowing what constitutes a problem remains crucial to understanding the health of a complex distributed system.
Quadrant’s data ingestion mechanism is simple and powerful. As a user, you will be able to gain a new level of understanding of your system through Quadrant’s rich features within minutes. It only takes a few steps to start taking advantage of Quadrant’s capabilities. In general, you will need to provide a mechanism for Quadrant to reach out and make queries to each of your telemetry data source. After that, Quadrant will handle the rest.