Multi-Level Query Operator Flow for Distributed Execution
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Solution Overview
Problem
Existing database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, particularly when handling large volumes of data.
Innovation Solution
A database system architecture that utilizes parallelized data input, storage, retrieval, and query processing across multiple computing devices and nodes, enabling efficient distribution and independent execution of operations to handle massive data scales without coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in traditional database systems with centralized processing, then data consistency is maintained, but processing speed and scalability are limited due to hardware constraints
Solution Approach 1:
The patent divides the database system into multiple autonomous nodes distributed across a network, where each node independently stores and processes data. This segmentation eliminates centralized processing bottlenecks, enabling parallel query execution and significantly improving processing speed while maintaining system functionality through distributed architecture
Solution Approach 2:
The patent transitions from traditional single-dimension centralized processing to multi-dimensional distributed processing across networked nodes. Queries can be executed simultaneously on multiple nodes across different physical locations, adding spatial and temporal dimensions to processing that dramatically accelerate data retrieval and analysis operations
2Productivity
If traditional database storage methods are used, then data integrity is preserved, but execution time increases when handling large volumes of data
Solution Approach 1:
The patent implements pre-computed views and materialized queries that store aggregated results in advance. When queries are executed, the system retrieves pre-computed results from distributed nodes rather than performing full data scans, dramatically reducing query execution time while maintaining data integrity through version control and update propagation mechanisms
Solution Approach 2:
The patent enables continuous query processing across distributed nodes where data is constantly being ingested, processed, and made available for queries. The system maintains continuous synchronization between nodes through efficient replication protocols, ensuring that data is always available and up-to-date without interrupting processing operations, thereby eliminating idle time and maximizing productivity
3Adaptability or versatility
If centralized database processing is used, then coordination is simplified, but co-processing options are restricted and scalability is limited
Solution Approach 1:
The patent implements autonomous nodes that independently execute queries and make local decisions without requiring centralized coordination. Each node maintains its own data, processes queries locally when possible, and autonomously coordinates with other nodes through standardized protocols for distributed transactions. This self-service approach dramatically expands co-processing options and scalability while managing complexity through decentralized control and standardized interfaces
Data Source
AI summary
A database system is operable to execute a query operator execution flow via a plurality of nodes each assigned to participate in a corresponding level of a hierarchical query plan. A first subset of nodes participating in at least one lower level of the hierarchical query plan generate a plurality of first output. A second node participating in at least one upper level of the hierarchical query plan generates second output based on processing the plurality of first output. The second output is segregated into a plurality of second output portions, and the plurality of second output portions are dispersed across the first subset of nodes for processing. The first subset of nodes generates a plurality of third output based on processing corresponding second output portions of the plurality of second output portions. The second node generates fourth output based on processing the plurality of third output.