Real-time interactive statistical query optimization method in big data environment

Through end-to-end collaborative optimization design, the problems of high latency, resource scheduling imbalance and poor dynamic adaptation capability of real-time interactive statistical queries in big data environment have been solved. It has achieved query optimization with low latency, high concurrency, dynamic adaptation and low overhead, and improved query performance and system stability.

CN122173547APending Publication Date: 2026-06-09梁龙龙
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
梁龙龙
Filing Date
2026-01-30
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies for real-time interactive statistical queries in big data environments suffer from high latency, unbalanced resource scheduling, poor dynamic adaptation capabilities, high system complexity, and insufficient generalization due to reliance on AI algorithms, making it difficult to balance query efficiency and system stability.

Method used

The system employs a full-link collaborative optimization design, including adaptive access and preprocessing of heterogeneous data sources, dynamic priority sharding storage and index adaptation, query request parsing and priority classification, proactive load prediction and resource pre-allocation, intelligent query routing and operator pushdown optimization, dynamic scheduling and parallel optimization of computing tasks, query result caching and incremental updates, anomaly monitoring and adaptive fault tolerance, collaborative optimization of data updates and queries, and dynamic iteration of optimization strategies. This enables proactive load prediction, dynamic index adaptation, intelligent query routing, and elastic resource scheduling, reducing system overhead and improving query response speed and concurrent processing capabilities.

Benefits of technology

Significantly reduces query latency, improves resource utilization, ensures the accuracy and consistency of query results, adapts to complex scenarios, reduces operation and maintenance costs, and extends system lifecycle.

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Abstract

The application discloses a real-time interactive statistical query optimization method in a big data environment and relates to the technical field of big data processing, and aims to solve the defects of the prior art, such as dependence on an AI algorithm, scheduling lag and single-link optimization. The method does not need to be supported by an AI algorithm, and through ten core links of full-link collaborative optimization, such as adaptive access and preprocessing of heterogeneous data sources, dynamic priority slice storage and index adaptation, active load prediction, intelligent routing, operator pushdown and strategy dynamic iteration are realized. The method can be adapted to PB-level heterogeneous data scenes in multiple industries such as finance, the Internet and government affairs, effectively avoids load imbalance, reduces cross-node data transmission and repeated calculation, guarantees low-latency query, high concurrency and result accuracy, simultaneously reduces system complexity and operation and maintenance cost, and has wide practical application value.
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