Distributed Query System Using Data Flow Graphs for Sub-Region Partitioning
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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 all data in memory, making it difficult to efficiently scale hardware and software for analysis.
Innovation Solution
A distributed system that services query requests by determining user and target sub-regions, identifying input sub-regions using a data flow graph, and distributing data across query nodes, allowing for efficient processing and 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 set into multiple partitions that can be distributed across different computing nodes. Each node processes a subset of the data in parallel, enabling fast analysis without requiring all data to reside in a single memory space. This segmentation allows the system to maintain high processing speed while reducing individual memory requirements.
Solution Approach 2:
The patent introduces a distributed computing dimension by deploying analysis across multiple nodes in a networked architecture. Instead of concentrating all data and processing in one system, the solution distributes data storage and computation across multiple dimensions (spatial distribution across nodes), thereby achieving fast parallel processing without proportionally increasing single-node memory requirements.
2Adaptability or versatility
If larger data sets are analyzed, then analytical capability is improved, but hardware and software infrastructure complexity increases
Solution Approach 1:
The patent creates a universal distributed query processing framework that can handle various types of analytical queries across different data sets. The system uses standardized data models, query languages, and processing mechanisms that work consistently regardless of data size or type, thereby improving analytical capability without proportionally increasing infrastructure complexity.
Solution Approach 2:
The patent introduces intermediary components such as query optimizers, data catalogs, and coordination services that manage the complexity of distributed processing. These intermediaries abstract the underlying infrastructure complexity from users while enabling the system to scale to larger data sets, thereby improving analytical capability without directly increasing perceived infrastructure complexity.
3Reliability
If more memory is provisioned to keep data current, then data freshness is improved, but overhead and costs increase
Solution Approach 1:
The patent extracts the requirement to maintain all data in memory by separating hot data (frequently accessed) from cold data (infrequently accessed). Only essential data subsets are kept in memory on each node, while other data resides in distributed storage. This extraction allows the system to maintain data freshness for accessed data without the overhead of keeping entire data sets in memory.
Solution Approach 2:
The patent implements dynamic data loading and caching mechanisms where data is loaded into memory based on access patterns and query requirements. The system dynamically adjusts which data subsets are kept in memory versus stored on disk, optimizing the balance between data freshness and memory overhead based on actual usage rather than static provisioning.
Data Source
AI summary
In general, the invention relates to a method for servicing query requests. The method includes determining, by a first query node and in response to a first query request, a user data sub-region and a target sub-region associated with the first query request. The further includes identifying a first input sub-region of the target sub-region using a data flow graph, identifying a second query node associated with the first input sub-region, and issuing a second query request to the second query node to obtain data associated with the first input sub-region. The method further includes receiving, in response to the second query request, a second query result from the second query node, generating a first query result for the target sub-region using at least the second query result, and providing the first query result to an entity associated with the issuance of the first query request.