Query Node Dataspace Sub-region Association
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Solution Overview
Problem
As data sets grow in size, traditional methods for analyzing data become impractical and cost-prohibitive due to the need for increased memory and infrastructure to keep data in memory, making it difficult to efficiently scale hardware and software for analysis.
Innovation Solution
A method involving query nodes that determine association with a dataspace using distribution keys and node distribution key lists, allowing data to be obtained from persistent storage and associated with sub-regions to service query requests, reducing the need for extensive memory and infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is loaded into memory for analysis, then analysis speed is improved, but memory requirements and infrastructure costs increase
Solution Approach 1:
The patent segments the data storage and processing system into multiple query nodes, each responsible for specific sub-regions of the dataspace. This segmentation allows the system to process large datasets distributed across nodes without requiring all data to be loaded into a single memory space, thus maintaining analysis speed while reducing individual memory requirements.
Solution Approach 2:
The patent introduces a distributed architecture dimension to the traditional single-memory analysis model. By distributing data across multiple nodes in a networked system, the solution transforms the problem from a single-node memory constraint to a multi-node collaborative processing model, enabling scalable analysis without proportionally increasing single-node memory requirements.
2Quantity of substance
If hardware infrastructure is scaled to handle larger data sets, then data storage capacity is improved, but system complexity and scaling difficulty increase
Solution Approach 1:
The system divides the dataspace into multiple sub-regions and assigns them to different query nodes using distribution keys and node distribution key lists. This segmentation allows independent scaling of individual nodes without affecting the entire system, reducing the complexity of scaling operations while increasing overall data storage capacity.
Solution Approach 2:
The patent introduces distribution keys and node distribution key lists as intermediary mechanisms that automatically route queries to the appropriate nodes. These intermediaries abstract the complexity of data distribution, allowing the system to scale capacity without proportionally increasing operational complexity.
3Reliability
If all data is kept in memory to ensure current data, then data freshness is improved, but memory overhead and costs increase
Solution Approach 1:
The patent extracts only the necessary sub-regions of data from persistent storage into memory at each query node, rather than loading all data. The distribution key mechanism ensures that each node loads only the specific sub-regions it needs to service queries, maintaining data freshness for accessed regions while minimizing overall memory overhead.
Solution Approach 2:
Each query node autonomously determines which data sub-regions it needs and loads them from persistent storage as required. This self-service approach ensures that data is kept current in memory only where and when needed, rather than maintaining universal data freshness across the entire system, thus reducing memory overhead while preserving reliability for active queries.
Data Source
AI summary
In general, the invention relates to a method for servicing query requests in distributed systems. The method includes making a first determination by a query node that it is associated with a dataspace, making a second determination that data associated with a first input sub-region is not present on the query node, where the first input sub-region is associated with the dataspace. Based on the first determination and the second determination, the method includes obtaining data for the entire dataspace from persistent storage, associating at least a portion of the obtained data with the first input sub-region to obtain a populated first input sub-region, and servicing a query request using the populated first input sub-region.