Database IO Pipeline Segmentation for Faster Query 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, leading to inefficiencies in data handling and query execution.
Innovation Solution
A database system architecture that employs parallelized data input, storage, retrieval, and query processing systems, utilizing segment-based indexing and optimized query planning to enhance data management and query execution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware resources are increased to improve processing speed, then query execution speed improves, but system cost and hardware complexity increase
Solution Approach 1:
The patent segments the database system into multiple independent components including compute nodes, storage nodes, and cache nodes. Each segment can be independently scaled and optimized, allowing the system to achieve high processing speeds without proportionally increasing overall hardware complexity. The segmentation enables parallel query execution across multiple nodes.
Solution Approach 2:
The patent introduces an intermediary caching layer between storage and compute operations. This cache layer absorbs some of the processing burden, reducing the direct hardware requirements for query execution while maintaining high speeds. The cache acts as a buffer that decouples storage I/O from compute operations.
2Productivity
If data is stored in a traditional monolithic structure, then storage simplicity is maintained, but query processing speed and efficiency deteriorate
Solution Approach 1:
The storage structure is segmented into multiple independent segments distributed across different nodes. Each segment can be independently accessed and processed, enabling parallel query execution and significantly improving query processing efficiency. The segmented structure allows different query operations to access different segments simultaneously.
Solution Approach 2:
The patent transitions from a single-dimension monolithic storage structure to a multi-dimensional distributed storage architecture. Data is organized across multiple dimensions including spatial distribution across nodes, hierarchical caching layers, and logical segmentation. This dimensional transformation enables efficient query processing while managing structural complexity.
3Speed
If co-processing options are restricted to maintain system simplicity, then system complexity is reduced, but processing speed and capability deteriorate
Solution Approach 1:
The patent implements universal co-processing capabilities where storage nodes can perform compute operations and compute nodes can perform storage operations. This multi-functionality increases processing speed and capability without requiring separate dedicated hardware for each function, thereby controlling overall system complexity.
Solution Approach 2:
The patent merges storage and compute functions into a unified distributed system architecture. By combining these functions at the node level and orchestrating them through a unified control plane, the system achieves high processing speeds while avoiding the complexity of completely separate storage and compute subsystems.
4Productivity
If parallel processing is implemented to improve query execution speed, then processing throughput increases, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments queries into independent sub-queries that can be executed in parallel across different nodes. Each segment operates independently with minimal coordination requirements, achieving high throughput while limiting coordination complexity to simple task distribution and result aggregation.
Solution Approach 2:
The patent implements self-service mechanisms where each node independently manages its own query execution and resource allocation. Nodes autonomously process assigned segments without requiring complex centralized coordination, reducing system coordination complexity while maintaining high parallel processing throughput.
Data Source
AI summary
A store and compute sub-system of a database system, wherein the store and compute sub-system includes pluralities of computing nodes of a plurality of computing device clusters, wherein a set of computing nodes of the pluralities of computing nodes is operable to implement a first input/output (IO) pipeline for a first segment of a plurality of segments of a dataset to support execution of a query, wherein, the first IO pipeline functions to convert long-term storage (LTS) data of the first segment into first query ready raw data. The set of computing nodes is further operable to implement a second IO pipeline for a second segment of the plurality of segments of the dataset to support execution of the query, wherein, the second IO pipeline functions to convert LTS data of the second segment into second query ready raw data.