Clustered Data Routing Architecture for Stream Processing
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Solution Overview
Problem
Existing data stream processing systems face inefficiencies in maintaining data locality, leading to congestion and reduced throughput due to the need to split data streams across multiple processing hosts, which complicates reconciliation and increases processing overhead.
Innovation Solution
The system organizes data channels and processing hosts into clusters, using a cluster management service to route data streams to the same set of processing hosts within a cluster, and dynamically manages processing hosts and channels to maintain data locality and adjust capacity based on load and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data streams are split across multiple processing hosts, then processing capacity is increased, but data locality is lost leading to congestion and reduced throughput
Solution Approach 1:
The system segments processing hosts into multiple clusters, where each cluster is dedicated to processing specific data streams. This segmentation allows the system to maintain data locality within clusters while still providing distributed processing capacity across multiple clusters, resolving the contradiction between processing capacity and data locality maintenance.
Solution Approach 2:
The patent introduces a new dimensional organization by creating clusters as intermediate layers between individual processing hosts and data streams. This cluster dimension enables the system to simultaneously achieve fine-grained data locality (within clusters) and coarse-grained processing capacity (across clusters), effectively adding a organizational dimension that resolves the contradiction.
2Productivity
If data streams are routed to maintain data locality, then throughput is improved, but system complexity increases due to reconciliation requirements
Solution Approach 1:
By segmenting the system into isolated clusters where each cluster processes specific data streams independently, the patent eliminates the need for complex cross-host reconciliation. Each cluster maintains its own data locality without requiring coordination with other clusters, thereby maintaining high throughput while reducing system complexity.
Solution Approach 2:
The patent extracts the reconciliation function entirely by organizing clusters as self-contained processing units. Each cluster independently handles its assigned data streams without requiring reconciliation with other clusters, effectively removing the reconciliation complexity while preserving throughput benefits of data locality.
3Adaptability or versatility
If processing hosts are dynamically managed, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The cluster architecture segments the system into manageable units, where dynamic management operations (creation, deletion, scaling) are performed at the cluster level rather than individual host level. This segmentation simplifies dynamic resource management by providing clear boundaries and isolation between clusters, reducing management complexity while maintaining high resource utilization.
Solution Approach 2:
The patent implements dynamic resource management through the ability to create, delete, and scale clusters based on workload demands. The clustered architecture enables flexible dynamic adjustments at the cluster level without requiring complex coordination between individual processing hosts, thereby improving resource utilization while keeping management complexity manageable.
Data Source
AI summary
Systems and methods are provided for organizing data channels and processing hosts included in a system into clusters. A cluster management service may receive data from a steam of data and may route the data to a cluster associated with the data stream. A data channel routing service included in the cluster may route the data to the set of processing hosts included in the cluster through multiple data channels included in the cluster. In some instances, the data channel routing service may use any of the data channels to send data to the set of processing hosts. Because incoming data may be distributed among multiple data channels, the cluster may experience less congestion. Further, the system may also process the stream of data using the same processing hosts by routing the stream of data to the same cluster, thereby avoiding split processing of the data stream.