Query-Aware Data Loading for Fresh, Low-Latency Queries
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Solution Overview
Problem
Current data query systems face challenges in managing query latency and data freshness due to unintegrated loader and cache managers, inefficient data loading, and limited loader scalability, leading to resource wastage and high query latency.
Innovation Solution
A data query method that analyzes query and loading costs to adjust data loading strategies, utilizing a graph structure for data storage and deploying scalable loaders, ensuring data freshness and reducing query latency by pre-loading associated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is loaded in real-time from data source to data destination, then data freshness is improved, but query latency increases
Solution Approach 1:
The system performs preliminary actions by analyzing query patterns and pre-loading associated data into the data destination before actual queries occur. The loader manager proactively identifies data that is likely to be queried based on query pattern analysis, loads this data in advance, and stores it in the data destination, thereby eliminating the need for real-time loading during query operations and reducing query latency while maintaining data freshness
Solution Approach 2:
The system dynamically adjusts the loading strategy based on query patterns and data freshness requirements. The loader manager continuously monitors query patterns, dynamically determines which data to pre-load, and adapts the loading frequency and volume according to changing query demands and data freshness targets, optimizing the balance between data freshness and query latency in real-time
2Adaptability or versatility
If arbitrary data is loaded to data destination without query analysis, then data availability is improved, but resource utilization deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the loader manager continuously analyzes query patterns returned from the query manager, uses this feedback to identify which data is actually being queried, and adjusts the pre-loading strategy accordingly. This feedback loop ensures that only data relevant to actual query patterns is loaded, avoiding waste of resources on arbitrary data while maintaining high data availability for queried items
Solution Approach 2:
The system changes parameters of the loading process based on query pattern analysis. The loader manager adjusts loading frequency, loading volume, and data selection criteria according to the analyzed query patterns, transforming the loading process from a static arbitrary loading approach to a dynamic parameter-adjusted approach that optimizes resource utilization while ensuring data availability
3Device complexity
If loader and cache managers operate independently, then system modularity is improved, but query efficiency deteriorates
Solution Approach 1:
The system merges the loader manager and cache manager into an integrated collaborative system. The loader manager and cache manager communicate and coordinate their operations - the loader manager analyzes query patterns and pre-loads data, while the cache manager manages the cached data and coordinates with the loader manager for refilling and updating. This merging maintains the modular structure of separate components while enabling efficient collaboration to improve query efficiency
Data Source
AI summary
A data query method is provided. The data query method includes: receiving from a client a first query request including a first data identifier; sending back first data indicated by the first data identifier to the client; determining a retrieving strategy based on a query time cost of the first data, a loading time cost of the first data, a data freshness target, and a query latency target, retrieving second data and a data relationship that are associated with the first data according to the retrieving strategy from a data destination, wherein the second data and the data relationship meet the data freshness target, and the data relationship includes a relationship between the second data and the first data; receiving from the client a second query request including a second data identifier; sending back the second data indicated by the second data identifier to the client.