Virtual Data Streams for Cross-Cluster Offset Ordering
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Solution Overview
Problem
Existing data streaming platforms struggle to efficiently monitor and analyze data from cloud environments for security, compliance, and anomaly detection, particularly in complex network scenarios involving multiple compute assets, without compromising performance or security.
Innovation Solution
A data platform that integrates data ingestion, processing, and user interface resources to monitor and analyze data from cloud environments, utilizing agents to collect and report information, and generate polygraphs for anomaly detection, with features like data aggregation and compression to minimize network exposure and optimize data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data streaming platforms monitor and analyze data from cloud environments in real-time, then anomaly detection and compliance monitoring capabilities are improved, but system complexity and computational resources required increase
Solution Approach 1:
The patent introduces a virtual data stream as an intermediary representation that maps to physical data streams in the cloud environment. This virtual layer abstracts the complexity of monitoring physical resources, allowing security and compliance functions to operate on simplified data representations without directly managing the underlying complex infrastructure.
Solution Approach 2:
The system segments monitoring functions by creating separate virtual data streams for different purposes (security monitoring, compliance monitoring, anomaly detection). Each virtual stream can be independently managed and processed, dividing the complex monitoring task into manageable segments that can be handled by specialized processing components.
2Loss of energy
If data aggregation and compression are applied to minimize network exposure, then data transmission efficiency is improved, but data processing complexity increases
Solution Approach 1:
The system performs data aggregation and compression as preliminary actions before data transmission to the cloud environment or between components. By preparing and optimizing data in advance through the virtual data stream layer, the system reduces the burden on network transmission and downstream processing without requiring complex operations at each individual step.
3Reliability
If agents collect and report information from multiple compute assets, then monitoring coverage is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent merges data from multiple compute assets into a unified virtual data stream representation. Instead of transmitting separate data streams from each agent independently, the system combines and aggregates this information at the virtual layer, reducing redundant transmissions and optimizing network bandwidth utilization while maintaining comprehensive monitoring coverage.
Data Source
AI summary
An example method includes receiving, by a data streaming platform from a client, a write request to write data to a data stream; storing, by the data streaming platform and based on the receiving the write request, the data in a particular partition of a plurality of partitions of the data stream and on a particular cluster of a plurality of clusters of the data streaming platform; translating, by the data streaming platform, an actual offset specifying the particular cluster and the particular partition into a virtual offset defining an ordering of the data relative to other data in the data stream stored across the plurality of partitions and the plurality of clusters; and storing, by the data streaming platform, a mapping of the virtual offset to the actual offset.