Metadata-Driven Query Processing for Distributed Data Analysis
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Solution Overview
Problem
The traditional approach to analyzing large data sets becomes impractical and cost-prohibitive as the size of the data grows, requiring excessive memory and infrastructure to keep data in memory and scale efficiently.
Innovation Solution
The method involves generating a data flow graph using metadata documents associated with consistency zones, allowing query nodes to service requests efficiently by distributing data and query processing across nodes, with metadata stored as a series of events, reducing the need to maintain metadata state and minimizing storage requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is loaded into memory for analysis, then analysis speed is improved, but memory cost and infrastructure overhead increase significantly
Solution Approach 1:
The patent segments the data analysis system into multiple query nodes that can independently process different portions of data. Each query node maintains only the metadata it needs in memory, while the actual data remains distributed across storage systems. This segmentation allows the system to achieve parallel processing speed improvements without requiring all data to be loaded into memory simultaneously, thereby reducing overall memory costs.
Solution Approach 2:
The patent introduces a new dimension of metadata-driven query processing that operates independently from the physical data storage layer. By using metadata documents that describe data locations, schemas, and relationships, the system can perform analysis operations without loading the actual data into memory. This dimensional separation allows fast query processing while minimizing memory requirements.
2Productivity
If larger amounts of memory are provisioned to keep data in memory, then data analysis capability is improved, but infrastructure overhead and cost increase
Solution Approach 1:
The patent introduces metadata documents as an intermediary layer between the query processing system and the physical data storage. These metadata documents contain information about data locations, schemas, and relationships, allowing query nodes to process analysis requests without directly accessing or loading the actual data into memory. This intermediary approach enables sophisticated data analysis capabilities while keeping infrastructure overhead minimal, as only lightweight metadata structures need to be maintained in memory rather than large data sets.
3Quantity of substance
If data set size increases, then analytical value is improved, but scaling hardware and software infrastructure becomes difficult and costly
Solution Approach 1:
The patent segments the data infrastructure into distributed query nodes that can independently scale. Each query node processes a portion of the data set using metadata-driven approaches, allowing the system to scale horizontally by adding more nodes rather than vertically by increasing the capacity of single systems. This segmentation enables the system to handle larger data sets while maintaining manageable infrastructure complexity, as each node operates autonomously with its own metadata cache and processing logic.
Solution Approach 2:
The patent uses metadata documents that can be replicated and distributed across multiple query nodes. Instead of duplicating the entire data set across infrastructure components, the system copies only the lightweight metadata structures, which are then used by multiple nodes to access and process the actual data. This copying approach allows the system to scale to handle larger data sets while keeping infrastructure complexity low, as the replicated metadata is minimal in size compared to the actual data.
Data Source
AI summary
In general, the invention relates to a method for servicing query requests in a distributed system. The method includes generating a data flow graph for a data model using a plurality of metadata documents, where each of the metadata documents is associated with a consistency zone, and servicing a query request using the data flow graph and data associated with the data model.