Database IO Pipeline Control for Parallel Segment Query Processing
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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 processing and query execution.
Innovation Solution
A database system architecture that employs parallelized data input, storage, and query processing systems, utilizing segment-based indexing and optimized query plans to enhance data partitioning, storage, and retrieval, allowing for concurrent processing of queries across multiple nodes with independent execution of administrative and configuration operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If parallelized data input and query processing systems are employed, then processing speed and query response time are improved, but device complexity increases
Solution Approach 1:
The database system is divided into multiple independent nodes, each capable of parallel data input and query processing. Data is partitioned into segments distributed across nodes, enabling concurrent processing without centralized bottlenecks. Each node operates independently with local metadata caches, eliminating the need for complex centralized coordination mechanisms.
2Productivity
If segment-based indexing is implemented, then data retrieval efficiency is improved, but manufacturing precision of index structures increases
Solution Approach 1:
The index structure is segmented and distributed across multiple nodes rather than maintained as a single precise global structure. Each node maintains local index segments with locally optimized precision, allowing parallel retrieval operations without requiring complex global index coordination.
Solution Approach 2:
Each node optimizes its local index structures according to its specific data segment characteristics and query workload patterns. This allows each node to achieve high retrieval efficiency for its local data without requiring uniform global index precision across the entire distributed system.
3Productivity
If lock-free parallel execution is enabled, then productivity is improved, but reliability of concurrent operations deteriorates
Solution Approach 1:
The system segments data and operations into independent units that can be processed in parallel without requiring locks. Each node operates on its local data segments independently, eliminating the need for lock coordination mechanisms while maintaining consistency through deterministic local operations and eventual consistency models.
Solution Approach 2:
Each node autonomously manages its own concurrent operations without requiring external locking coordination. The system uses optimistic concurrency control and conflict resolution mechanisms that allow nodes to proceed without locks, resolving conflicts only when necessary, thereby maintaining high throughput while ensuring eventual consistency.
Data Source
AI summary
A store and compute sub-system of a database system, includes a plurality of computing nodes of a plurality of computing device clusters. A first computing node includes memory devices that store, in a long-term storage (LTS) format, a plurality of segments of a dataset. The first computing node further includes processing modules operable to provide first and second input/output (10) pipelines. A pipeline IO controller of the first computing node controls the first IO pipeline to process a first segment to produce first query read raw data and controls the second IO pipeline to process a second segment to produce second query read raw data.