Checksum-Based Search Task Distribution for Scalable Data Analysis
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Solution Overview
Problem
Current document search engines face inefficiencies in handling large volumes of data, requiring multiple search engines and federated systems, which can lead to complex query distribution and result aggregation, especially when dealing with ever-increasing data volumes.
Innovation Solution
The method employs a checksum field to uniformly distribute search tasks across agents, allowing for efficient data processing by dividing datasets into ranges for partial trawls, marking items of interest, and combining statistics in parallel, thereby optimizing search task distribution and result processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple search engines and federated systems are used to handle large volumes of data, then data processing capacity is improved, but system complexity and query distribution difficulty increase
Solution Approach 1:
The patent introduces a checksum field as an intermediary mechanism that mediates between the search query and the distributed content indexes. By using the checksum field to generate hash values, the system can uniformly distribute queries across multiple search engines without requiring complex federation logic, thus maintaining high data processing capacity while reducing system complexity
Solution Approach 2:
The patent changes the distribution parameter from date-based to checksum-based. This parameter change enables uniform distribution of search tasks across agents by using hash values of checksum fields, which naturally scatter data across the available search engines without requiring complex distribution algorithms
2Adaptability or versatility
If content distribution is based on ingestion date to allow adding new search engines, then system scalability is improved, but query distribution efficiency deteriorates
Solution Approach 1:
The patent changes the distribution parameter from date-based to checksum-based. This allows new search engines to be added to the system while maintaining uniform query distribution through hash-based routing, rather than relying on date-based partitioning which creates inefficiencies when scaling
3Productivity
If federated search engine distributes queries between multiple search engines, then data processing capacity is improved, but result aggregation complexity increases
Solution Approach 1:
The checksum field acts as an intermediary that simplifies result aggregation. By using hash values of checksum fields to determine query distribution, the system can easily aggregate results by reversing the hash lookup, avoiding complex federation aggregation logic
Data Source
AI summary
According to an example, data analysis control may include receiving a predetermined number of search results for a query, and organizing the predetermined number of search results by checksum values of content of the search results in an ascending or a descending order. A generally final or a generally initial checksum value may be respectively used from the ascending or the descending order of the organized search results to determine a total content number representing an approximate total content of a search related to the query, where the total content number is greater than the predetermined number of search results. Further, the determined total content number may be used to determine a number of sub-tasks needed to complete a full search of the approximate total content.